diff --git a/Data/dfindicators - Copy.xlsx b/Data/dfindicators - Copy.xlsx
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diff --git a/Demo overall graphs.ipynb b/Demo overall graphs.ipynb
deleted file mode 100644
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--- a/Demo overall graphs.ipynb	
+++ /dev/null
@@ -1,102914 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Graphs for demo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import seaborn as sns\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Mortality-pollution</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
-       "      <th>Continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>347</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>12.9725</td>\n",
-       "      <td>10.224579</td>\n",
-       "      <td>2.251034</td>\n",
-       "      <td>31.338276</td>\n",
-       "      <td>66.412414</td>\n",
-       "      <td>4.922992e+10</td>\n",
-       "      <td>4.045759e+11</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3090.866667</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.478270e+06</td>\n",
-       "      <td>7.866111</td>\n",
-       "      <td>2.608368</td>\n",
-       "      <td>1.667000e+09</td>\n",
-       "      <td>13.30</td>\n",
-       "      <td>74.552174</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>348</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>12.9725</td>\n",
-       "      <td>10.224579</td>\n",
-       "      <td>3.480000</td>\n",
-       "      <td>37.720001</td>\n",
-       "      <td>58.790001</td>\n",
-       "      <td>4.842279e+10</td>\n",
-       "      <td>4.422840e+11</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5404.000000</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.478270e+06</td>\n",
-       "      <td>7.866111</td>\n",
-       "      <td>2.608368</td>\n",
-       "      <td>2.088000e+09</td>\n",
-       "      <td>13.30</td>\n",
-       "      <td>74.552174</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>349</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>12.9725</td>\n",
-       "      <td>10.224579</td>\n",
-       "      <td>3.400000</td>\n",
-       "      <td>37.369999</td>\n",
-       "      <td>59.230000</td>\n",
-       "      <td>5.465244e+10</td>\n",
-       "      <td>4.730958e+11</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5019.000000</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>2.628459e+06</td>\n",
-       "      <td>7.866111</td>\n",
-       "      <td>2.608368</td>\n",
-       "      <td>2.338000e+09</td>\n",
-       "      <td>13.30</td>\n",
-       "      <td>74.552174</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>350</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1993</td>\n",
-       "      <td>12.9725</td>\n",
-       "      <td>9.582970</td>\n",
-       "      <td>3.350000</td>\n",
-       "      <td>36.740002</td>\n",
-       "      <td>59.919998</td>\n",
-       "      <td>5.254376e+10</td>\n",
-       "      <td>4.207571e+11</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4679.000000</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.478270e+06</td>\n",
-       "      <td>7.866111</td>\n",
-       "      <td>2.608368</td>\n",
-       "      <td>2.642000e+09</td>\n",
-       "      <td>13.30</td>\n",
-       "      <td>74.552174</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>351</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1994</td>\n",
-       "      <td>12.9725</td>\n",
-       "      <td>9.395570</td>\n",
-       "      <td>3.260000</td>\n",
-       "      <td>36.419998</td>\n",
-       "      <td>60.320000</td>\n",
-       "      <td>5.468545e+10</td>\n",
-       "      <td>4.655068e+11</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4388.000000</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.478270e+06</td>\n",
-       "      <td>7.866111</td>\n",
-       "      <td>2.608368</td>\n",
-       "      <td>3.503000e+09</td>\n",
-       "      <td>13.30</td>\n",
-       "      <td>74.552174</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1472</th>\n",
-       "      <td>247</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>5.8200</td>\n",
-       "      <td>12.269560</td>\n",
-       "      <td>27.700001</td>\n",
-       "      <td>28.799999</td>\n",
-       "      <td>43.500000</td>\n",
-       "      <td>2.071921e+11</td>\n",
-       "      <td>2.199968e+12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>90.167660</td>\n",
-       "      <td>131852.000000</td>\n",
-       "      <td>112.7</td>\n",
-       "      <td>-1.433525e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.100330</td>\n",
-       "      <td>1.431141e+10</td>\n",
-       "      <td>8.20</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1473</th>\n",
-       "      <td>248</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>5.8200</td>\n",
-       "      <td>12.155300</td>\n",
-       "      <td>26.980000</td>\n",
-       "      <td>28.110001</td>\n",
-       "      <td>44.910000</td>\n",
-       "      <td>2.113642e+11</td>\n",
-       "      <td>2.424200e+12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>90.167660</td>\n",
-       "      <td>120075.000000</td>\n",
-       "      <td>112.7</td>\n",
-       "      <td>-1.741996e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.116030</td>\n",
-       "      <td>1.431141e+10</td>\n",
-       "      <td>8.10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1474</th>\n",
-       "      <td>249</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>7.0500</td>\n",
-       "      <td>11.450690</td>\n",
-       "      <td>26.070000</td>\n",
-       "      <td>28.320000</td>\n",
-       "      <td>45.610001</td>\n",
-       "      <td>2.318095e+11</td>\n",
-       "      <td>2.655592e+12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>96.840889</td>\n",
-       "      <td>109028.000000</td>\n",
-       "      <td>112.7</td>\n",
-       "      <td>-1.433525e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.140580</td>\n",
-       "      <td>1.431141e+10</td>\n",
-       "      <td>8.10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1475</th>\n",
-       "      <td>250</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>5.8200</td>\n",
-       "      <td>13.037553</td>\n",
-       "      <td>25.330000</td>\n",
-       "      <td>27.420000</td>\n",
-       "      <td>47.250000</td>\n",
-       "      <td>2.427723e+11</td>\n",
-       "      <td>2.628935e+12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>90.167660</td>\n",
-       "      <td>98805.000000</td>\n",
-       "      <td>112.7</td>\n",
-       "      <td>-1.433525e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.427239</td>\n",
-       "      <td>1.431141e+10</td>\n",
-       "      <td>8.10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1476</th>\n",
-       "      <td>251</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>5.8200</td>\n",
-       "      <td>13.037553</td>\n",
-       "      <td>42.551724</td>\n",
-       "      <td>25.390690</td>\n",
-       "      <td>32.057586</td>\n",
-       "      <td>2.327021e+11</td>\n",
-       "      <td>2.723250e+12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>90.167660</td>\n",
-       "      <td>89463.000000</td>\n",
-       "      <td>112.7</td>\n",
-       "      <td>-1.433525e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.427239</td>\n",
-       "      <td>1.431141e+10</td>\n",
-       "      <td>10.51</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1477 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Unnamed: 0 Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "0            347     DEU  1990             12.9725       10.224579   \n",
-       "1            348     DEU  1991             12.9725       10.224579   \n",
-       "2            349     DEU  1992             12.9725       10.224579   \n",
-       "3            350     DEU  1993             12.9725        9.582970   \n",
-       "4            351     DEU  1994             12.9725        9.395570   \n",
-       "...          ...     ...   ...                 ...             ...   \n",
-       "1472         247     CHN  2016              5.8200       12.269560   \n",
-       "1473         248     CHN  2017              5.8200       12.155300   \n",
-       "1474         249     CHN  2018              7.0500       11.450690   \n",
-       "1475         250     CHN  2019              5.8200       13.037553   \n",
-       "1476         251     CHN  2020              5.8200       13.037553   \n",
-       "\n",
-       "      Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "0                   2.251034            31.338276            66.412414   \n",
-       "1                   3.480000            37.720001            58.790001   \n",
-       "2                   3.400000            37.369999            59.230000   \n",
-       "3                   3.350000            36.740002            59.919998   \n",
-       "4                   3.260000            36.419998            60.320000   \n",
-       "...                      ...                  ...                  ...   \n",
-       "1472               27.700001            28.799999            43.500000   \n",
-       "1473               26.980000            28.110001            44.910000   \n",
-       "1474               26.070000            28.320000            45.610001   \n",
-       "1475               25.330000            27.420000            47.250000   \n",
-       "1476               42.551724            25.390690            32.057586   \n",
-       "\n",
-       "      Exports-Commercial services   Exports-G&S  ...  Literacy rate  \\\n",
-       "0                    4.922992e+10  4.045759e+11  ...            NaN   \n",
-       "1                    4.842279e+10  4.422840e+11  ...            NaN   \n",
-       "2                    5.465244e+10  4.730958e+11  ...            NaN   \n",
-       "3                    5.254376e+10  4.207571e+11  ...            NaN   \n",
-       "4                    5.468545e+10  4.655068e+11  ...            NaN   \n",
-       "...                           ...           ...  ...            ...   \n",
-       "1472                 2.071921e+11  2.199968e+12  ...      90.167660   \n",
-       "1473                 2.113642e+11  2.424200e+12  ...      90.167660   \n",
-       "1474                 2.318095e+11  2.655592e+12  ...      96.840889   \n",
-       "1475                 2.427723e+11  2.628935e+12  ...      90.167660   \n",
-       "1476                 2.327021e+11  2.723250e+12  ...      90.167660   \n",
-       "\n",
-       "      Mortality-infants  Mortality-pollution  Net migration     Ninis  \\\n",
-       "0           3090.866667                 16.0   1.478270e+06  7.866111   \n",
-       "1           5404.000000                 16.0   1.478270e+06  7.866111   \n",
-       "2           5019.000000                 16.0   2.628459e+06  7.866111   \n",
-       "3           4679.000000                 16.0   1.478270e+06  7.866111   \n",
-       "4           4388.000000                 16.0   1.478270e+06  7.866111   \n",
-       "...                 ...                  ...            ...       ...   \n",
-       "1472      131852.000000                112.7  -1.433525e+06       NaN   \n",
-       "1473      120075.000000                112.7  -1.741996e+06       NaN   \n",
-       "1474      109028.000000                112.7  -1.433525e+06       NaN   \n",
-       "1475       98805.000000                112.7  -1.433525e+06       NaN   \n",
-       "1476       89463.000000                112.7  -1.433525e+06       NaN   \n",
-       "\n",
-       "      R&D GExp  Renewable electricity  Suicide  Workers high education  \\\n",
-       "0     2.608368           1.667000e+09    13.30               74.552174   \n",
-       "1     2.608368           2.088000e+09    13.30               74.552174   \n",
-       "2     2.608368           2.338000e+09    13.30               74.552174   \n",
-       "3     2.608368           2.642000e+09    13.30               74.552174   \n",
-       "4     2.608368           3.503000e+09    13.30               74.552174   \n",
-       "...        ...                    ...      ...                     ...   \n",
-       "1472  2.100330           1.431141e+10     8.20                     NaN   \n",
-       "1473  2.116030           1.431141e+10     8.10                     NaN   \n",
-       "1474  2.140580           1.431141e+10     8.10                     NaN   \n",
-       "1475  1.427239           1.431141e+10     8.10                     NaN   \n",
-       "1476  1.427239           1.431141e+10    10.51                     NaN   \n",
-       "\n",
-       "      Continent  \n",
-       "0        Europe  \n",
-       "1        Europe  \n",
-       "2        Europe  \n",
-       "3        Europe  \n",
-       "4        Europe  \n",
-       "...         ...  \n",
-       "1472       Pair  \n",
-       "1473       Pair  \n",
-       "1474       Pair  \n",
-       "1475       Pair  \n",
-       "1476       Pair  \n",
-       "\n",
-       "[1477 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df= pd.DataFrame(pd.read_csv(os.getcwd()+ \"/Data/\"+\"GoldenDataFrame.csv\"))\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following cell we are going to define all the variables that we have and want to compare with the GDP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "variables=['Gender equality','Exports-Commercial services','Renewable electricity','Employment-agriculture','Employment-industry','Employment-services','Exports-G&S','Fertility rate','Foreign investment','Education GExp','Workers high education','Literacy rate','Mortality-pollution','Net migration','Mortality-infants','Health services use','R&D GExp','Ninis','Suicide','International taxes','Alcohol per capita']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " Moreover,we are going to provide a scatterplot of all posible variations of the data to see if there are any relationships between them as well as histograms."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  background-color: #7699f6;\n",
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-       "  background-color: #3b4cc0;\n",
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-       "  background-color: #94b6ff;\n",
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-       "  background-color: #dddcdc;\n",
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-       "  background-color: #f7ac8e;\n",
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-       "  background-color: #e6d7cf;\n",
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-       "  background-color: #e2dad5;\n",
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-       "  background-color: #7597f6;\n",
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-       "  background-color: #f3c8b2;\n",
-       "  color: #000000;\n",
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-       "  background-color: #f7b497;\n",
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-       "  background-color: #88abfd;\n",
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-       "  background-color: #98b9ff;\n",
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-       "  background-color: #e5d8d1;\n",
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-       "  background-color: #c0d4f5;\n",
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-       "  background-color: #f5c0a7;\n",
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-       "  background-color: #b2ccfb;\n",
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-       "  background-color: #84a7fc;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_54ed9_row3_col4, #T_54ed9_row18_col20 {\n",
-       "  background-color: #f2cab5;\n",
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-       "  background-color: #516ddb;\n",
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-       "  background-color: #6282ea;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_54ed9_row3_col9, #T_54ed9_row13_col2 {\n",
-       "  background-color: #eed0c0;\n",
-       "  color: #000000;\n",
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-       "  background-color: #4e68d8;\n",
-       "  color: #f1f1f1;\n",
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-       "  background-color: #adc9fd;\n",
-       "  color: #000000;\n",
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-       "  background-color: #f1cdba;\n",
-       "  color: #000000;\n",
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-       "  background-color: #8badfd;\n",
-       "  color: #000000;\n",
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-       "  background-color: #4b64d5;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_54ed9_row4_col2, #T_54ed9_row5_col16, #T_54ed9_row5_col17, #T_54ed9_row14_col21, #T_54ed9_row15_col4, #T_54ed9_row17_col6, #T_54ed9_row19_col1 {\n",
-       "  background-color: #6e90f2;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_54ed9_row4_col8, #T_54ed9_row9_col5 {\n",
-       "  background-color: #4961d2;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_54ed9_row4_col9 {\n",
-       "  background-color: #f4987a;\n",
-       "  color: #000000;\n",
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-       "  background-color: #e1dad6;\n",
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-       "#T_54ed9_row4_col13, #T_54ed9_row19_col10 {\n",
-       "  background-color: #3c4ec2;\n",
-       "  color: #f1f1f1;\n",
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-       "  background-color: #536edd;\n",
-       "  color: #f1f1f1;\n",
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-       "  background-color: #e67259;\n",
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-       "  background-color: #465ecf;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_54ed9_row4_col22, #T_54ed9_row12_col14 {\n",
-       "  background-color: #5673e0;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_54ed9_row4_col23, #T_54ed9_row23_col1 {\n",
-       "  background-color: #6a8bef;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_54ed9_row5_col0 {\n",
-       "  background-color: #5f7fe8;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row5_col2, #T_54ed9_row8_col5, #T_54ed9_row11_col15 {\n",
-       "  background-color: #dfdbd9;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row5_col3, #T_54ed9_row9_col12 {\n",
-       "  background-color: #3f53c6;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row5_col4, #T_54ed9_row12_col21 {\n",
-       "  background-color: #688aef;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row5_col6, #T_54ed9_row20_col2, #T_54ed9_row20_col13 {\n",
-       "  background-color: #f7b89c;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row5_col7, #T_54ed9_row6_col1, #T_54ed9_row18_col0 {\n",
-       "  background-color: #abc8fd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row5_col10, #T_54ed9_row8_col0, #T_54ed9_row14_col11, #T_54ed9_row16_col14, #T_54ed9_row21_col23, #T_54ed9_row23_col11 {\n",
-       "  background-color: #6f92f3;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row5_col13, #T_54ed9_row11_col21, #T_54ed9_row21_col11 {\n",
-       "  background-color: #ea7b60;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row5_col15, #T_54ed9_row6_col15, #T_54ed9_row18_col6, #T_54ed9_row20_col6 {\n",
-       "  background-color: #f5a081;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row5_col20, #T_54ed9_row12_col5, #T_54ed9_row14_col17 {\n",
-       "  background-color: #dbdcde;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row6_col0, #T_54ed9_row12_col1, #T_54ed9_row14_col0 {\n",
-       "  background-color: #7093f3;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row6_col2, #T_54ed9_row8_col20 {\n",
-       "  background-color: #f7bca1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row6_col3, #T_54ed9_row6_col17, #T_54ed9_row10_col14, #T_54ed9_row15_col16 {\n",
-       "  background-color: #5d7ce6;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row6_col8, #T_54ed9_row22_col18 {\n",
-       "  background-color: #d3dbe7;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row6_col9, #T_54ed9_row8_col19, #T_54ed9_row16_col21 {\n",
-       "  background-color: #779af7;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row6_col10, #T_54ed9_row8_col17, #T_54ed9_row16_col12, #T_54ed9_row22_col11, #T_54ed9_row22_col16 {\n",
-       "  background-color: #a9c6fd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row6_col11 {\n",
-       "  background-color: #b5cdfa;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row6_col14, #T_54ed9_row15_col17, #T_54ed9_row17_col5 {\n",
-       "  background-color: #506bda;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row6_col18 {\n",
-       "  background-color: #f6bfa6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row6_col20 {\n",
-       "  background-color: #f7b99e;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row6_col22, #T_54ed9_row21_col17, #T_54ed9_row23_col17 {\n",
-       "  background-color: #b3cdfb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row7_col3, #T_54ed9_row9_col23, #T_54ed9_row13_col4 {\n",
-       "  background-color: #4c66d6;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row7_col5, #T_54ed9_row10_col20, #T_54ed9_row19_col3 {\n",
-       "  background-color: #d5dbe5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row7_col6, #T_54ed9_row7_col20, #T_54ed9_row15_col2 {\n",
-       "  background-color: #f7ba9f;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row7_col8, #T_54ed9_row8_col7 {\n",
-       "  background-color: #d55042;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row7_col9, #T_54ed9_row8_col16, #T_54ed9_row9_col6, #T_54ed9_row23_col22 {\n",
-       "  background-color: #90b2fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row7_col11 {\n",
-       "  background-color: #d85646;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row7_col13, #T_54ed9_row11_col6, #T_54ed9_row12_col4, #T_54ed9_row18_col8, #T_54ed9_row20_col7 {\n",
-       "  background-color: #f2cbb7;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row7_col14, #T_54ed9_row9_col14, #T_54ed9_row9_col20, #T_54ed9_row16_col1 {\n",
-       "  background-color: #5977e3;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row7_col15, #T_54ed9_row20_col15 {\n",
-       "  background-color: #e9d5cb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row7_col18, #T_54ed9_row11_col18, #T_54ed9_row19_col17 {\n",
-       "  background-color: #f6a283;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row7_col19, #T_54ed9_row20_col9, #T_54ed9_row20_col17 {\n",
-       "  background-color: #80a3fa;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row7_col21 {\n",
-       "  background-color: #ec8165;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row7_col22, #T_54ed9_row13_col7 {\n",
-       "  background-color: #cedaeb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row8_col2 {\n",
-       "  background-color: #f3c7b1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row8_col6 {\n",
-       "  background-color: #f4c5ad;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row8_col10 {\n",
-       "  background-color: #7396f5;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row8_col11 {\n",
-       "  background-color: #d75445;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row8_col13, #T_54ed9_row11_col20, #T_54ed9_row18_col2 {\n",
-       "  background-color: #f1ccb8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row8_col14, #T_54ed9_row21_col3 {\n",
-       "  background-color: #6485ec;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row8_col18 {\n",
-       "  background-color: #f5c4ac;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row8_col21 {\n",
-       "  background-color: #f18d6f;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row9_col0, #T_54ed9_row10_col22, #T_54ed9_row14_col12, #T_54ed9_row15_col22 {\n",
-       "  background-color: #aec9fc;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row9_col2, #T_54ed9_row10_col1, #T_54ed9_row20_col0 {\n",
-       "  background-color: #6687ed;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row9_col4 {\n",
-       "  background-color: #f18f71;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row9_col7, #T_54ed9_row13_col16 {\n",
-       "  background-color: #3e51c5;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row9_col8, #T_54ed9_row9_col22 {\n",
-       "  background-color: #4055c8;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row9_col10, #T_54ed9_row11_col3, #T_54ed9_row12_col23, #T_54ed9_row13_col19 {\n",
-       "  background-color: #6384eb;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row9_col17 {\n",
-       "  background-color: #e26952;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row9_col19 {\n",
-       "  background-color: #f59c7d;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row9_col21, #T_54ed9_row19_col11, #T_54ed9_row19_col21 {\n",
-       "  background-color: #3d50c3;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row10_col2, #T_54ed9_row14_col15 {\n",
-       "  background-color: #dcdddd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row10_col3, #T_54ed9_row11_col10, #T_54ed9_row23_col21 {\n",
-       "  background-color: #6b8df0;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row10_col12, #T_54ed9_row19_col6, #T_54ed9_row21_col1, #T_54ed9_row22_col21 {\n",
-       "  background-color: #9dbdff;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row10_col16 {\n",
-       "  background-color: #799cf8;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row10_col17, #T_54ed9_row11_col12 {\n",
-       "  background-color: #b9d0f9;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row10_col19, #T_54ed9_row16_col15, #T_54ed9_row16_col22 {\n",
-       "  background-color: #9fbfff;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row10_col21 {\n",
-       "  background-color: #a7c5fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row11_col2, #T_54ed9_row11_col13, #T_54ed9_row18_col15 {\n",
-       "  background-color: #ead5c9;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row11_col4, #T_54ed9_row16_col2, #T_54ed9_row20_col12 {\n",
-       "  background-color: #b6cefa;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row11_col5, #T_54ed9_row15_col18, #T_54ed9_row23_col4 {\n",
-       "  background-color: #d2dbe8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row11_col7 {\n",
-       "  background-color: #d65244;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row11_col8 {\n",
-       "  background-color: #d44e41;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row11_col16, #T_54ed9_row11_col19, #T_54ed9_row17_col0, #T_54ed9_row21_col19, #T_54ed9_row23_col3 {\n",
-       "  background-color: #97b8ff;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row11_col23, #T_54ed9_row12_col11, #T_54ed9_row22_col1 {\n",
-       "  background-color: #7295f4;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row12_col6 {\n",
-       "  background-color: #a6c4fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row12_col9, #T_54ed9_row18_col3 {\n",
-       "  background-color: #5a78e4;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row12_col10, #T_54ed9_row12_col19, #T_54ed9_row17_col10, #T_54ed9_row17_col21, #T_54ed9_row18_col16 {\n",
-       "  background-color: #455cce;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row12_col15 {\n",
-       "  background-color: #f08b6e;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row12_col17, #T_54ed9_row13_col0, #T_54ed9_row16_col0, #T_54ed9_row23_col10 {\n",
-       "  background-color: #6788ee;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row12_col22 {\n",
-       "  background-color: #8db0fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row13_col5, #T_54ed9_row21_col8 {\n",
-       "  background-color: #ee8468;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row13_col6, #T_54ed9_row17_col4 {\n",
-       "  background-color: #e46e56;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row13_col9, #T_54ed9_row17_col12, #T_54ed9_row20_col3 {\n",
-       "  background-color: #4358cb;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row13_col15 {\n",
-       "  background-color: #e16751;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row13_col18 {\n",
-       "  background-color: #ead4c8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row13_col20, #T_54ed9_row20_col8, #T_54ed9_row21_col6 {\n",
-       "  background-color: #f2c9b4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row14_col1, #T_54ed9_row20_col23, #T_54ed9_row23_col8 {\n",
-       "  background-color: #93b5fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row14_col7, #T_54ed9_row15_col1, #T_54ed9_row20_col16, #T_54ed9_row22_col3, #T_54ed9_row23_col14 {\n",
-       "  background-color: #7ea1fa;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row14_col8, #T_54ed9_row14_col23, #T_54ed9_row16_col13, #T_54ed9_row18_col17, #T_54ed9_row22_col10 {\n",
-       "  background-color: #8fb1fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row14_col16, #T_54ed9_row16_col5 {\n",
-       "  background-color: #9ebeff;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row14_col19 {\n",
-       "  background-color: #d7dce3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row15_col0, #T_54ed9_row17_col20, #T_54ed9_row17_col22 {\n",
-       "  background-color: #4f69d9;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row15_col3 {\n",
-       "  background-color: #5e7de7;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row15_col5 {\n",
-       "  background-color: #f7a688;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row15_col6, #T_54ed9_row19_col9 {\n",
-       "  background-color: #f39778;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row15_col7, #T_54ed9_row15_col8 {\n",
-       "  background-color: #bfd3f6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row15_col12 {\n",
-       "  background-color: #f39475;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row15_col13 {\n",
-       "  background-color: #df634e;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row16_col4, #T_54ed9_row18_col11 {\n",
-       "  background-color: #f7b599;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row16_col10, #T_54ed9_row17_col23 {\n",
-       "  background-color: #4a63d3;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row16_col17 {\n",
-       "  background-color: #f7b093;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row17_col8 {\n",
-       "  background-color: #5875e1;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row17_col9 {\n",
-       "  background-color: #e36b54;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row17_col19 {\n",
-       "  background-color: #f7aa8c;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row18_col13 {\n",
-       "  background-color: #f6bea4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row18_col21 {\n",
-       "  background-color: #f7b79b;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row18_col22, #T_54ed9_row23_col20 {\n",
-       "  background-color: #c4d5f3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row19_col4, #T_54ed9_row22_col20 {\n",
-       "  background-color: #f7b194;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row19_col16 {\n",
-       "  background-color: #ccd9ed;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row19_col23 {\n",
-       "  background-color: #4257c9;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row20_col5, #T_54ed9_row23_col2 {\n",
-       "  background-color: #e3d9d3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row20_col18, #T_54ed9_row21_col20 {\n",
-       "  background-color: #efcfbf;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row20_col21 {\n",
-       "  background-color: #d4dbe6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row21_col2 {\n",
-       "  background-color: #ecd3c5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row21_col5 {\n",
-       "  background-color: #cbd8ee;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row21_col7 {\n",
-       "  background-color: #e97a5f;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_54ed9_row21_col13 {\n",
-       "  background-color: #e7d7ce;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row21_col18 {\n",
-       "  background-color: #f6a385;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row22_col2 {\n",
-       "  background-color: #f7a889;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row22_col6 {\n",
-       "  background-color: #ebd3c6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row22_col12 {\n",
-       "  background-color: #bad0f8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_54ed9_row22_col13 {\n",
-       "  background-color: #e4d9d2;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_54ed9\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_54ed9_level0_col0\" class=\"col_heading level0 col0\" >Unnamed: 0</th>\n",
-       "      <th id=\"T_54ed9_level0_col1\" class=\"col_heading level0 col1\" >Year</th>\n",
-       "      <th id=\"T_54ed9_level0_col2\" class=\"col_heading level0 col2\" >Alcohol per capita</th>\n",
-       "      <th id=\"T_54ed9_level0_col3\" class=\"col_heading level0 col3\" >Education GExp</th>\n",
-       "      <th id=\"T_54ed9_level0_col4\" class=\"col_heading level0 col4\" >Employment-agriculture</th>\n",
-       "      <th id=\"T_54ed9_level0_col5\" class=\"col_heading level0 col5\" >Employment-industry</th>\n",
-       "      <th id=\"T_54ed9_level0_col6\" class=\"col_heading level0 col6\" >Employment-services</th>\n",
-       "      <th id=\"T_54ed9_level0_col7\" class=\"col_heading level0 col7\" >Exports-Commercial services</th>\n",
-       "      <th id=\"T_54ed9_level0_col8\" class=\"col_heading level0 col8\" >Exports-G&S</th>\n",
-       "      <th id=\"T_54ed9_level0_col9\" class=\"col_heading level0 col9\" >Fertility rate</th>\n",
-       "      <th id=\"T_54ed9_level0_col10\" class=\"col_heading level0 col10\" >Foreign investment</th>\n",
-       "      <th id=\"T_54ed9_level0_col11\" class=\"col_heading level0 col11\" >GDP</th>\n",
-       "      <th id=\"T_54ed9_level0_col12\" class=\"col_heading level0 col12\" >Gender equality</th>\n",
-       "      <th id=\"T_54ed9_level0_col13\" class=\"col_heading level0 col13\" >Health services use</th>\n",
-       "      <th id=\"T_54ed9_level0_col14\" class=\"col_heading level0 col14\" >International taxes</th>\n",
-       "      <th id=\"T_54ed9_level0_col15\" class=\"col_heading level0 col15\" >Literacy rate</th>\n",
-       "      <th id=\"T_54ed9_level0_col16\" class=\"col_heading level0 col16\" >Mortality-infants</th>\n",
-       "      <th id=\"T_54ed9_level0_col17\" class=\"col_heading level0 col17\" >Mortality-pollution</th>\n",
-       "      <th id=\"T_54ed9_level0_col18\" class=\"col_heading level0 col18\" >Net migration</th>\n",
-       "      <th id=\"T_54ed9_level0_col19\" class=\"col_heading level0 col19\" >Ninis</th>\n",
-       "      <th id=\"T_54ed9_level0_col20\" class=\"col_heading level0 col20\" >R&D GExp</th>\n",
-       "      <th id=\"T_54ed9_level0_col21\" class=\"col_heading level0 col21\" >Renewable electricity</th>\n",
-       "      <th id=\"T_54ed9_level0_col22\" class=\"col_heading level0 col22\" >Suicide</th>\n",
-       "      <th id=\"T_54ed9_level0_col23\" class=\"col_heading level0 col23\" >Workers high education</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row0\" class=\"row_heading level0 row0\" >Unnamed: 0</th>\n",
-       "      <td id=\"T_54ed9_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row0_col1\" class=\"data row0 col1\" >0.019877</td>\n",
-       "      <td id=\"T_54ed9_row0_col2\" class=\"data row0 col2\" >-0.117914</td>\n",
-       "      <td id=\"T_54ed9_row0_col3\" class=\"data row0 col3\" >0.208743</td>\n",
-       "      <td id=\"T_54ed9_row0_col4\" class=\"data row0 col4\" >0.043612</td>\n",
-       "      <td id=\"T_54ed9_row0_col5\" class=\"data row0 col5\" >-0.078586</td>\n",
-       "      <td id=\"T_54ed9_row0_col6\" class=\"data row0 col6\" >-0.019212</td>\n",
-       "      <td id=\"T_54ed9_row0_col7\" class=\"data row0 col7\" >0.025018</td>\n",
-       "      <td id=\"T_54ed9_row0_col8\" class=\"data row0 col8\" >-0.020856</td>\n",
-       "      <td id=\"T_54ed9_row0_col9\" class=\"data row0 col9\" >0.197864</td>\n",
-       "      <td id=\"T_54ed9_row0_col10\" class=\"data row0 col10\" >0.079150</td>\n",
-       "      <td id=\"T_54ed9_row0_col11\" class=\"data row0 col11\" >0.070815</td>\n",
-       "      <td id=\"T_54ed9_row0_col12\" class=\"data row0 col12\" >-0.226796</td>\n",
-       "      <td id=\"T_54ed9_row0_col13\" class=\"data row0 col13\" >-0.053614</td>\n",
-       "      <td id=\"T_54ed9_row0_col14\" class=\"data row0 col14\" >-0.018592</td>\n",
-       "      <td id=\"T_54ed9_row0_col15\" class=\"data row0 col15\" >-0.144043</td>\n",
-       "      <td id=\"T_54ed9_row0_col16\" class=\"data row0 col16\" >-0.050134</td>\n",
-       "      <td id=\"T_54ed9_row0_col17\" class=\"data row0 col17\" >0.113730</td>\n",
-       "      <td id=\"T_54ed9_row0_col18\" class=\"data row0 col18\" >0.188462</td>\n",
-       "      <td id=\"T_54ed9_row0_col19\" class=\"data row0 col19\" >0.129553</td>\n",
-       "      <td id=\"T_54ed9_row0_col20\" class=\"data row0 col20\" >-0.059008</td>\n",
-       "      <td id=\"T_54ed9_row0_col21\" class=\"data row0 col21\" >0.062481</td>\n",
-       "      <td id=\"T_54ed9_row0_col22\" class=\"data row0 col22\" >0.048131</td>\n",
-       "      <td id=\"T_54ed9_row0_col23\" class=\"data row0 col23\" >0.038948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row1\" class=\"row_heading level0 row1\" >Year</th>\n",
-       "      <td id=\"T_54ed9_row1_col0\" class=\"data row1 col0\" >0.019877</td>\n",
-       "      <td id=\"T_54ed9_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row1_col2\" class=\"data row1 col2\" >0.003568</td>\n",
-       "      <td id=\"T_54ed9_row1_col3\" class=\"data row1 col3\" >0.003605</td>\n",
-       "      <td id=\"T_54ed9_row1_col4\" class=\"data row1 col4\" >-0.154998</td>\n",
-       "      <td id=\"T_54ed9_row1_col5\" class=\"data row1 col5\" >0.017919</td>\n",
-       "      <td id=\"T_54ed9_row1_col6\" class=\"data row1 col6\" >0.192225</td>\n",
-       "      <td id=\"T_54ed9_row1_col7\" class=\"data row1 col7\" >0.233563</td>\n",
-       "      <td id=\"T_54ed9_row1_col8\" class=\"data row1 col8\" >0.256772</td>\n",
-       "      <td id=\"T_54ed9_row1_col9\" class=\"data row1 col9\" >-0.222668</td>\n",
-       "      <td id=\"T_54ed9_row1_col10\" class=\"data row1 col10\" >-0.051398</td>\n",
-       "      <td id=\"T_54ed9_row1_col11\" class=\"data row1 col11\" >0.157381</td>\n",
-       "      <td id=\"T_54ed9_row1_col12\" class=\"data row1 col12\" >-0.015098</td>\n",
-       "      <td id=\"T_54ed9_row1_col13\" class=\"data row1 col13\" >0.064057</td>\n",
-       "      <td id=\"T_54ed9_row1_col14\" class=\"data row1 col14\" >0.103394</td>\n",
-       "      <td id=\"T_54ed9_row1_col15\" class=\"data row1 col15\" >0.031291</td>\n",
-       "      <td id=\"T_54ed9_row1_col16\" class=\"data row1 col16\" >-0.098830</td>\n",
-       "      <td id=\"T_54ed9_row1_col17\" class=\"data row1 col17\" >0.002666</td>\n",
-       "      <td id=\"T_54ed9_row1_col18\" class=\"data row1 col18\" >0.006420</td>\n",
-       "      <td id=\"T_54ed9_row1_col19\" class=\"data row1 col19\" >-0.024328</td>\n",
-       "      <td id=\"T_54ed9_row1_col20\" class=\"data row1 col20\" >0.050859</td>\n",
-       "      <td id=\"T_54ed9_row1_col21\" class=\"data row1 col21\" >0.136225</td>\n",
-       "      <td id=\"T_54ed9_row1_col22\" class=\"data row1 col22\" >-0.011579</td>\n",
-       "      <td id=\"T_54ed9_row1_col23\" class=\"data row1 col23\" >-0.040452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row2\" class=\"row_heading level0 row2\" >Alcohol per capita</th>\n",
-       "      <td id=\"T_54ed9_row2_col0\" class=\"data row2 col0\" >-0.117914</td>\n",
-       "      <td id=\"T_54ed9_row2_col1\" class=\"data row2 col1\" >0.003568</td>\n",
-       "      <td id=\"T_54ed9_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row2_col3\" class=\"data row2 col3\" >-0.300681</td>\n",
-       "      <td id=\"T_54ed9_row2_col4\" class=\"data row2 col4\" >-0.407674</td>\n",
-       "      <td id=\"T_54ed9_row2_col5\" class=\"data row2 col5\" >0.176332</td>\n",
-       "      <td id=\"T_54ed9_row2_col6\" class=\"data row2 col6\" >0.419162</td>\n",
-       "      <td id=\"T_54ed9_row2_col7\" class=\"data row2 col7\" >0.389629</td>\n",
-       "      <td id=\"T_54ed9_row2_col8\" class=\"data row2 col8\" >0.352551</td>\n",
-       "      <td id=\"T_54ed9_row2_col9\" class=\"data row2 col9\" >-0.443234</td>\n",
-       "      <td id=\"T_54ed9_row2_col10\" class=\"data row2 col10\" >0.160651</td>\n",
-       "      <td id=\"T_54ed9_row2_col11\" class=\"data row2 col11\" >0.247933</td>\n",
-       "      <td id=\"T_54ed9_row2_col12\" class=\"data row2 col12\" >0.006225</td>\n",
-       "      <td id=\"T_54ed9_row2_col13\" class=\"data row2 col13\" >0.291795</td>\n",
-       "      <td id=\"T_54ed9_row2_col14\" class=\"data row2 col14\" >-0.163526</td>\n",
-       "      <td id=\"T_54ed9_row2_col15\" class=\"data row2 col15\" >0.427703</td>\n",
-       "      <td id=\"T_54ed9_row2_col16\" class=\"data row2 col16\" >-0.055892</td>\n",
-       "      <td id=\"T_54ed9_row2_col17\" class=\"data row2 col17\" >-0.308952</td>\n",
-       "      <td id=\"T_54ed9_row2_col18\" class=\"data row2 col18\" >0.319085</td>\n",
-       "      <td id=\"T_54ed9_row2_col19\" class=\"data row2 col19\" >-0.676723</td>\n",
-       "      <td id=\"T_54ed9_row2_col20\" class=\"data row2 col20\" >0.438434</td>\n",
-       "      <td id=\"T_54ed9_row2_col21\" class=\"data row2 col21\" >0.271002</td>\n",
-       "      <td id=\"T_54ed9_row2_col22\" class=\"data row2 col22\" >0.518274</td>\n",
-       "      <td id=\"T_54ed9_row2_col23\" class=\"data row2 col23\" >0.207073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row3\" class=\"row_heading level0 row3\" >Education GExp</th>\n",
-       "      <td id=\"T_54ed9_row3_col0\" class=\"data row3 col0\" >0.208743</td>\n",
-       "      <td id=\"T_54ed9_row3_col1\" class=\"data row3 col1\" >0.003605</td>\n",
-       "      <td id=\"T_54ed9_row3_col2\" class=\"data row3 col2\" >-0.300681</td>\n",
-       "      <td id=\"T_54ed9_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row3_col4\" class=\"data row3 col4\" >0.230819</td>\n",
-       "      <td id=\"T_54ed9_row3_col5\" class=\"data row3 col5\" >-0.276716</td>\n",
-       "      <td id=\"T_54ed9_row3_col6\" class=\"data row3 col6\" >-0.156344</td>\n",
-       "      <td id=\"T_54ed9_row3_col7\" class=\"data row3 col7\" >-0.221611</td>\n",
-       "      <td id=\"T_54ed9_row3_col8\" class=\"data row3 col8\" >-0.184731</td>\n",
-       "      <td id=\"T_54ed9_row3_col9\" class=\"data row3 col9\" >0.248518</td>\n",
-       "      <td id=\"T_54ed9_row3_col10\" class=\"data row3 col10\" >-0.102232</td>\n",
-       "      <td id=\"T_54ed9_row3_col11\" class=\"data row3 col11\" >-0.129283</td>\n",
-       "      <td id=\"T_54ed9_row3_col12\" class=\"data row3 col12\" >-0.065926</td>\n",
-       "      <td id=\"T_54ed9_row3_col13\" class=\"data row3 col13\" >-0.248386</td>\n",
-       "      <td id=\"T_54ed9_row3_col14\" class=\"data row3 col14\" >0.100123</td>\n",
-       "      <td id=\"T_54ed9_row3_col15\" class=\"data row3 col15\" >-0.150013</td>\n",
-       "      <td id=\"T_54ed9_row3_col16\" class=\"data row3 col16\" >-0.066730</td>\n",
-       "      <td id=\"T_54ed9_row3_col17\" class=\"data row3 col17\" >0.155575</td>\n",
-       "      <td id=\"T_54ed9_row3_col18\" class=\"data row3 col18\" >-0.164040</td>\n",
-       "      <td id=\"T_54ed9_row3_col19\" class=\"data row3 col19\" >0.312791</td>\n",
-       "      <td id=\"T_54ed9_row3_col20\" class=\"data row3 col20\" >-0.264417</td>\n",
-       "      <td id=\"T_54ed9_row3_col21\" class=\"data row3 col21\" >-0.125841</td>\n",
-       "      <td id=\"T_54ed9_row3_col22\" class=\"data row3 col22\" >-0.029935</td>\n",
-       "      <td id=\"T_54ed9_row3_col23\" class=\"data row3 col23\" >0.065043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row4\" class=\"row_heading level0 row4\" >Employment-agriculture</th>\n",
-       "      <td id=\"T_54ed9_row4_col0\" class=\"data row4 col0\" >0.043612</td>\n",
-       "      <td id=\"T_54ed9_row4_col1\" class=\"data row4 col1\" >-0.154998</td>\n",
-       "      <td id=\"T_54ed9_row4_col2\" class=\"data row4 col2\" >-0.407674</td>\n",
-       "      <td id=\"T_54ed9_row4_col3\" class=\"data row4 col3\" >0.230819</td>\n",
-       "      <td id=\"T_54ed9_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row4_col5\" class=\"data row4 col5\" >-0.655428</td>\n",
-       "      <td id=\"T_54ed9_row4_col6\" class=\"data row4 col6\" >-0.936963</td>\n",
-       "      <td id=\"T_54ed9_row4_col7\" class=\"data row4 col7\" >-0.322845</td>\n",
-       "      <td id=\"T_54ed9_row4_col8\" class=\"data row4 col8\" >-0.289772</td>\n",
-       "      <td id=\"T_54ed9_row4_col9\" class=\"data row4 col9\" >0.564017</td>\n",
-       "      <td id=\"T_54ed9_row4_col10\" class=\"data row4 col10\" >-0.178304</td>\n",
-       "      <td id=\"T_54ed9_row4_col11\" class=\"data row4 col11\" >-0.220006</td>\n",
-       "      <td id=\"T_54ed9_row4_col12\" class=\"data row4 col12\" >0.222188</td>\n",
-       "      <td id=\"T_54ed9_row4_col13\" class=\"data row4 col13\" >-0.817140</td>\n",
-       "      <td id=\"T_54ed9_row4_col14\" class=\"data row4 col14\" >0.066569</td>\n",
-       "      <td id=\"T_54ed9_row4_col15\" class=\"data row4 col15\" >-0.622953</td>\n",
-       "      <td id=\"T_54ed9_row4_col16\" class=\"data row4 col16\" >0.368582</td>\n",
-       "      <td id=\"T_54ed9_row4_col17\" class=\"data row4 col17\" >0.704718</td>\n",
-       "      <td id=\"T_54ed9_row4_col18\" class=\"data row4 col18\" >-0.459063</td>\n",
-       "      <td id=\"T_54ed9_row4_col19\" class=\"data row4 col19\" >0.393902</td>\n",
-       "      <td id=\"T_54ed9_row4_col20\" class=\"data row4 col20\" >-0.482398</td>\n",
-       "      <td id=\"T_54ed9_row4_col21\" class=\"data row4 col21\" >-0.217153</td>\n",
-       "      <td id=\"T_54ed9_row4_col22\" class=\"data row4 col22\" >-0.213016</td>\n",
-       "      <td id=\"T_54ed9_row4_col23\" class=\"data row4 col23\" >-0.049262</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row5\" class=\"row_heading level0 row5\" >Employment-industry</th>\n",
-       "      <td id=\"T_54ed9_row5_col0\" class=\"data row5 col0\" >-0.078586</td>\n",
-       "      <td id=\"T_54ed9_row5_col1\" class=\"data row5 col1\" >0.017919</td>\n",
-       "      <td id=\"T_54ed9_row5_col2\" class=\"data row5 col2\" >0.176332</td>\n",
-       "      <td id=\"T_54ed9_row5_col3\" class=\"data row5 col3\" >-0.276716</td>\n",
-       "      <td id=\"T_54ed9_row5_col4\" class=\"data row5 col4\" >-0.655428</td>\n",
-       "      <td id=\"T_54ed9_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row5_col6\" class=\"data row5 col6\" >0.354743</td>\n",
-       "      <td id=\"T_54ed9_row5_col7\" class=\"data row5 col7\" >0.123608</td>\n",
-       "      <td id=\"T_54ed9_row5_col8\" class=\"data row5 col8\" >0.187650</td>\n",
-       "      <td id=\"T_54ed9_row5_col9\" class=\"data row5 col9\" >-0.577826</td>\n",
-       "      <td id=\"T_54ed9_row5_col10\" class=\"data row5 col10\" >0.017805</td>\n",
-       "      <td id=\"T_54ed9_row5_col11\" class=\"data row5 col11\" >0.102391</td>\n",
-       "      <td id=\"T_54ed9_row5_col12\" class=\"data row5 col12\" >0.161984</td>\n",
-       "      <td id=\"T_54ed9_row5_col13\" class=\"data row5 col13\" >0.667184</td>\n",
-       "      <td id=\"T_54ed9_row5_col14\" class=\"data row5 col14\" >-0.014293</td>\n",
-       "      <td id=\"T_54ed9_row5_col15\" class=\"data row5 col15\" >0.529376</td>\n",
-       "      <td id=\"T_54ed9_row5_col16\" class=\"data row5 col16\" >-0.158150</td>\n",
-       "      <td id=\"T_54ed9_row5_col17\" class=\"data row5 col17\" >-0.534884</td>\n",
-       "      <td id=\"T_54ed9_row5_col18\" class=\"data row5 col18\" >0.199263</td>\n",
-       "      <td id=\"T_54ed9_row5_col19\" class=\"data row5 col19\" >-0.298690</td>\n",
-       "      <td id=\"T_54ed9_row5_col20\" class=\"data row5 col20\" >0.216375</td>\n",
-       "      <td id=\"T_54ed9_row5_col21\" class=\"data row5 col21\" >0.064149</td>\n",
-       "      <td id=\"T_54ed9_row5_col22\" class=\"data row5 col22\" >0.232889</td>\n",
-       "      <td id=\"T_54ed9_row5_col23\" class=\"data row5 col23\" >0.042099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row6\" class=\"row_heading level0 row6\" >Employment-services</th>\n",
-       "      <td id=\"T_54ed9_row6_col0\" class=\"data row6 col0\" >-0.019212</td>\n",
-       "      <td id=\"T_54ed9_row6_col1\" class=\"data row6 col1\" >0.192225</td>\n",
-       "      <td id=\"T_54ed9_row6_col2\" class=\"data row6 col2\" >0.419162</td>\n",
-       "      <td id=\"T_54ed9_row6_col3\" class=\"data row6 col3\" >-0.156344</td>\n",
-       "      <td id=\"T_54ed9_row6_col4\" class=\"data row6 col4\" >-0.936963</td>\n",
-       "      <td id=\"T_54ed9_row6_col5\" class=\"data row6 col5\" >0.354743</td>\n",
-       "      <td id=\"T_54ed9_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row6_col7\" class=\"data row6 col7\" >0.341324</td>\n",
-       "      <td id=\"T_54ed9_row6_col8\" class=\"data row6 col8\" >0.270584</td>\n",
-       "      <td id=\"T_54ed9_row6_col9\" class=\"data row6 col9\" >-0.432588</td>\n",
-       "      <td id=\"T_54ed9_row6_col10\" class=\"data row6 col10\" >0.210286</td>\n",
-       "      <td id=\"T_54ed9_row6_col11\" class=\"data row6 col11\" >0.225371</td>\n",
-       "      <td id=\"T_54ed9_row6_col12\" class=\"data row6 col12\" >-0.313698</td>\n",
-       "      <td id=\"T_54ed9_row6_col13\" class=\"data row6 col13\" >0.702957</td>\n",
-       "      <td id=\"T_54ed9_row6_col14\" class=\"data row6 col14\" >-0.078479</td>\n",
-       "      <td id=\"T_54ed9_row6_col15\" class=\"data row6 col15\" >0.528697</td>\n",
-       "      <td id=\"T_54ed9_row6_col16\" class=\"data row6 col16\" >-0.384806</td>\n",
-       "      <td id=\"T_54ed9_row6_col17\" class=\"data row6 col17\" >-0.624623</td>\n",
-       "      <td id=\"T_54ed9_row6_col18\" class=\"data row6 col18\" >0.478032</td>\n",
-       "      <td id=\"T_54ed9_row6_col19\" class=\"data row6 col19\" >-0.365326</td>\n",
-       "      <td id=\"T_54ed9_row6_col20\" class=\"data row6 col20\" >0.479046</td>\n",
-       "      <td id=\"T_54ed9_row6_col21\" class=\"data row6 col21\" >0.237553</td>\n",
-       "      <td id=\"T_54ed9_row6_col22\" class=\"data row6 col22\" >0.148463</td>\n",
-       "      <td id=\"T_54ed9_row6_col23\" class=\"data row6 col23\" >0.043815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row7\" class=\"row_heading level0 row7\" >Exports-Commercial services</th>\n",
-       "      <td id=\"T_54ed9_row7_col0\" class=\"data row7 col0\" >0.025018</td>\n",
-       "      <td id=\"T_54ed9_row7_col1\" class=\"data row7 col1\" >0.233563</td>\n",
-       "      <td id=\"T_54ed9_row7_col2\" class=\"data row7 col2\" >0.389629</td>\n",
-       "      <td id=\"T_54ed9_row7_col3\" class=\"data row7 col3\" >-0.221611</td>\n",
-       "      <td id=\"T_54ed9_row7_col4\" class=\"data row7 col4\" >-0.322845</td>\n",
-       "      <td id=\"T_54ed9_row7_col5\" class=\"data row7 col5\" >0.123608</td>\n",
-       "      <td id=\"T_54ed9_row7_col6\" class=\"data row7 col6\" >0.341324</td>\n",
-       "      <td id=\"T_54ed9_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row7_col8\" class=\"data row7 col8\" >0.871731</td>\n",
-       "      <td id=\"T_54ed9_row7_col9\" class=\"data row7 col9\" >-0.305654</td>\n",
-       "      <td id=\"T_54ed9_row7_col10\" class=\"data row7 col10\" >0.141709</td>\n",
-       "      <td id=\"T_54ed9_row7_col11\" class=\"data row7 col11\" >0.869489</td>\n",
-       "      <td id=\"T_54ed9_row7_col12\" class=\"data row7 col12\" >0.006302</td>\n",
-       "      <td id=\"T_54ed9_row7_col13\" class=\"data row7 col13\" >0.270483</td>\n",
-       "      <td id=\"T_54ed9_row7_col14\" class=\"data row7 col14\" >-0.045417</td>\n",
-       "      <td id=\"T_54ed9_row7_col15\" class=\"data row7 col15\" >0.202589</td>\n",
-       "      <td id=\"T_54ed9_row7_col16\" class=\"data row7 col16\" >-0.020433</td>\n",
-       "      <td id=\"T_54ed9_row7_col17\" class=\"data row7 col17\" >-0.267740</td>\n",
-       "      <td id=\"T_54ed9_row7_col18\" class=\"data row7 col18\" >0.602334</td>\n",
-       "      <td id=\"T_54ed9_row7_col19\" class=\"data row7 col19\" >-0.321658</td>\n",
-       "      <td id=\"T_54ed9_row7_col20\" class=\"data row7 col20\" >0.470513</td>\n",
-       "      <td id=\"T_54ed9_row7_col21\" class=\"data row7 col21\" >0.766557</td>\n",
-       "      <td id=\"T_54ed9_row7_col22\" class=\"data row7 col22\" >0.263894</td>\n",
-       "      <td id=\"T_54ed9_row7_col23\" class=\"data row7 col23\" >0.007372</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row8\" class=\"row_heading level0 row8\" >Exports-G&S</th>\n",
-       "      <td id=\"T_54ed9_row8_col0\" class=\"data row8 col0\" >-0.020856</td>\n",
-       "      <td id=\"T_54ed9_row8_col1\" class=\"data row8 col1\" >0.256772</td>\n",
-       "      <td id=\"T_54ed9_row8_col2\" class=\"data row8 col2\" >0.352551</td>\n",
-       "      <td id=\"T_54ed9_row8_col3\" class=\"data row8 col3\" >-0.184731</td>\n",
-       "      <td id=\"T_54ed9_row8_col4\" class=\"data row8 col4\" >-0.289772</td>\n",
-       "      <td id=\"T_54ed9_row8_col5\" class=\"data row8 col5\" >0.187650</td>\n",
-       "      <td id=\"T_54ed9_row8_col6\" class=\"data row8 col6\" >0.270584</td>\n",
-       "      <td id=\"T_54ed9_row8_col7\" class=\"data row8 col7\" >0.871731</td>\n",
-       "      <td id=\"T_54ed9_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row8_col9\" class=\"data row8 col9\" >-0.326182</td>\n",
-       "      <td id=\"T_54ed9_row8_col10\" class=\"data row8 col10\" >0.031704</td>\n",
-       "      <td id=\"T_54ed9_row8_col11\" class=\"data row8 col11\" >0.875233</td>\n",
-       "      <td id=\"T_54ed9_row8_col12\" class=\"data row8 col12\" >0.070311</td>\n",
-       "      <td id=\"T_54ed9_row8_col13\" class=\"data row8 col13\" >0.258878</td>\n",
-       "      <td id=\"T_54ed9_row8_col14\" class=\"data row8 col14\" >-0.008011</td>\n",
-       "      <td id=\"T_54ed9_row8_col15\" class=\"data row8 col15\" >0.181418</td>\n",
-       "      <td id=\"T_54ed9_row8_col16\" class=\"data row8 col16\" >-0.024627</td>\n",
-       "      <td id=\"T_54ed9_row8_col17\" class=\"data row8 col17\" >-0.225547</td>\n",
-       "      <td id=\"T_54ed9_row8_col18\" class=\"data row8 col18\" >0.455150</td>\n",
-       "      <td id=\"T_54ed9_row8_col19\" class=\"data row8 col19\" >-0.357653</td>\n",
-       "      <td id=\"T_54ed9_row8_col20\" class=\"data row8 col20\" >0.468986</td>\n",
-       "      <td id=\"T_54ed9_row8_col21\" class=\"data row8 col21\" >0.729153</td>\n",
-       "      <td id=\"T_54ed9_row8_col22\" class=\"data row8 col22\" >0.254999</td>\n",
-       "      <td id=\"T_54ed9_row8_col23\" class=\"data row8 col23\" >0.007270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row9\" class=\"row_heading level0 row9\" >Fertility rate</th>\n",
-       "      <td id=\"T_54ed9_row9_col0\" class=\"data row9 col0\" >0.197864</td>\n",
-       "      <td id=\"T_54ed9_row9_col1\" class=\"data row9 col1\" >-0.222668</td>\n",
-       "      <td id=\"T_54ed9_row9_col2\" class=\"data row9 col2\" >-0.443234</td>\n",
-       "      <td id=\"T_54ed9_row9_col3\" class=\"data row9 col3\" >0.248518</td>\n",
-       "      <td id=\"T_54ed9_row9_col4\" class=\"data row9 col4\" >0.564017</td>\n",
-       "      <td id=\"T_54ed9_row9_col5\" class=\"data row9 col5\" >-0.577826</td>\n",
-       "      <td id=\"T_54ed9_row9_col6\" class=\"data row9 col6\" >-0.432588</td>\n",
-       "      <td id=\"T_54ed9_row9_col7\" class=\"data row9 col7\" >-0.305654</td>\n",
-       "      <td id=\"T_54ed9_row9_col8\" class=\"data row9 col8\" >-0.326182</td>\n",
-       "      <td id=\"T_54ed9_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row9_col10\" class=\"data row9 col10\" >-0.022310</td>\n",
-       "      <td id=\"T_54ed9_row9_col11\" class=\"data row9 col11\" >-0.221499</td>\n",
-       "      <td id=\"T_54ed9_row9_col12\" class=\"data row9 col12\" >-0.585553</td>\n",
-       "      <td id=\"T_54ed9_row9_col13\" class=\"data row9 col13\" >-0.711077</td>\n",
-       "      <td id=\"T_54ed9_row9_col14\" class=\"data row9 col14\" >-0.049193</td>\n",
-       "      <td id=\"T_54ed9_row9_col15\" class=\"data row9 col15\" >-0.765607</td>\n",
-       "      <td id=\"T_54ed9_row9_col16\" class=\"data row9 col16\" >0.154550</td>\n",
-       "      <td id=\"T_54ed9_row9_col17\" class=\"data row9 col17\" >0.741216</td>\n",
-       "      <td id=\"T_54ed9_row9_col18\" class=\"data row9 col18\" >-0.161322</td>\n",
-       "      <td id=\"T_54ed9_row9_col19\" class=\"data row9 col19\" >0.571657</td>\n",
-       "      <td id=\"T_54ed9_row9_col20\" class=\"data row9 col20\" >-0.391923</td>\n",
-       "      <td id=\"T_54ed9_row9_col21\" class=\"data row9 col21\" >-0.204778</td>\n",
-       "      <td id=\"T_54ed9_row9_col22\" class=\"data row9 col22\" >-0.303122</td>\n",
-       "      <td id=\"T_54ed9_row9_col23\" class=\"data row9 col23\" >-0.160760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row10\" class=\"row_heading level0 row10\" >Foreign investment</th>\n",
-       "      <td id=\"T_54ed9_row10_col0\" class=\"data row10 col0\" >0.079150</td>\n",
-       "      <td id=\"T_54ed9_row10_col1\" class=\"data row10 col1\" >-0.051398</td>\n",
-       "      <td id=\"T_54ed9_row10_col2\" class=\"data row10 col2\" >0.160651</td>\n",
-       "      <td id=\"T_54ed9_row10_col3\" class=\"data row10 col3\" >-0.102232</td>\n",
-       "      <td id=\"T_54ed9_row10_col4\" class=\"data row10 col4\" >-0.178304</td>\n",
-       "      <td id=\"T_54ed9_row10_col5\" class=\"data row10 col5\" >0.017805</td>\n",
-       "      <td id=\"T_54ed9_row10_col6\" class=\"data row10 col6\" >0.210286</td>\n",
-       "      <td id=\"T_54ed9_row10_col7\" class=\"data row10 col7\" >0.141709</td>\n",
-       "      <td id=\"T_54ed9_row10_col8\" class=\"data row10 col8\" >0.031704</td>\n",
-       "      <td id=\"T_54ed9_row10_col9\" class=\"data row10 col9\" >-0.022310</td>\n",
-       "      <td id=\"T_54ed9_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row10_col11\" class=\"data row10 col11\" >0.004838</td>\n",
-       "      <td id=\"T_54ed9_row10_col12\" class=\"data row10 col12\" >-0.136716</td>\n",
-       "      <td id=\"T_54ed9_row10_col13\" class=\"data row10 col13\" >0.111193</td>\n",
-       "      <td id=\"T_54ed9_row10_col14\" class=\"data row10 col14\" >-0.034543</td>\n",
-       "      <td id=\"T_54ed9_row10_col15\" class=\"data row10 col15\" >-0.088726</td>\n",
-       "      <td id=\"T_54ed9_row10_col16\" class=\"data row10 col16\" >-0.114495</td>\n",
-       "      <td id=\"T_54ed9_row10_col17\" class=\"data row10 col17\" >-0.135496</td>\n",
-       "      <td id=\"T_54ed9_row10_col18\" class=\"data row10 col18\" >0.296592</td>\n",
-       "      <td id=\"T_54ed9_row10_col19\" class=\"data row10 col19\" >-0.170143</td>\n",
-       "      <td id=\"T_54ed9_row10_col20\" class=\"data row10 col20\" >0.183884</td>\n",
-       "      <td id=\"T_54ed9_row10_col21\" class=\"data row10 col21\" >0.181001</td>\n",
-       "      <td id=\"T_54ed9_row10_col22\" class=\"data row10 col22\" >0.124718</td>\n",
-       "      <td id=\"T_54ed9_row10_col23\" class=\"data row10 col23\" >-0.009456</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row11\" class=\"row_heading level0 row11\" >GDP</th>\n",
-       "      <td id=\"T_54ed9_row11_col0\" class=\"data row11 col0\" >0.070815</td>\n",
-       "      <td id=\"T_54ed9_row11_col1\" class=\"data row11 col1\" >0.157381</td>\n",
-       "      <td id=\"T_54ed9_row11_col2\" class=\"data row11 col2\" >0.247933</td>\n",
-       "      <td id=\"T_54ed9_row11_col3\" class=\"data row11 col3\" >-0.129283</td>\n",
-       "      <td id=\"T_54ed9_row11_col4\" class=\"data row11 col4\" >-0.220006</td>\n",
-       "      <td id=\"T_54ed9_row11_col5\" class=\"data row11 col5\" >0.102391</td>\n",
-       "      <td id=\"T_54ed9_row11_col6\" class=\"data row11 col6\" >0.225371</td>\n",
-       "      <td id=\"T_54ed9_row11_col7\" class=\"data row11 col7\" >0.869489</td>\n",
-       "      <td id=\"T_54ed9_row11_col8\" class=\"data row11 col8\" >0.875233</td>\n",
-       "      <td id=\"T_54ed9_row11_col9\" class=\"data row11 col9\" >-0.221499</td>\n",
-       "      <td id=\"T_54ed9_row11_col10\" class=\"data row11 col10\" >0.004838</td>\n",
-       "      <td id=\"T_54ed9_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row11_col12\" class=\"data row11 col12\" >-0.009484</td>\n",
-       "      <td id=\"T_54ed9_row11_col13\" class=\"data row11 col13\" >0.185386</td>\n",
-       "      <td id=\"T_54ed9_row11_col14\" class=\"data row11 col14\" >-0.017806</td>\n",
-       "      <td id=\"T_54ed9_row11_col15\" class=\"data row11 col15\" >0.133591</td>\n",
-       "      <td id=\"T_54ed9_row11_col16\" class=\"data row11 col16\" >0.004512</td>\n",
-       "      <td id=\"T_54ed9_row11_col17\" class=\"data row11 col17\" >-0.171565</td>\n",
-       "      <td id=\"T_54ed9_row11_col18\" class=\"data row11 col18\" >0.601395</td>\n",
-       "      <td id=\"T_54ed9_row11_col19\" class=\"data row11 col19\" >-0.208874</td>\n",
-       "      <td id=\"T_54ed9_row11_col20\" class=\"data row11 col20\" >0.374566</td>\n",
-       "      <td id=\"T_54ed9_row11_col21\" class=\"data row11 col21\" >0.780299</td>\n",
-       "      <td id=\"T_54ed9_row11_col22\" class=\"data row11 col22\" >0.183871</td>\n",
-       "      <td id=\"T_54ed9_row11_col23\" class=\"data row11 col23\" >-0.018627</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row12\" class=\"row_heading level0 row12\" >Gender equality</th>\n",
-       "      <td id=\"T_54ed9_row12_col0\" class=\"data row12 col0\" >-0.226796</td>\n",
-       "      <td id=\"T_54ed9_row12_col1\" class=\"data row12 col1\" >-0.015098</td>\n",
-       "      <td id=\"T_54ed9_row12_col2\" class=\"data row12 col2\" >0.006225</td>\n",
-       "      <td id=\"T_54ed9_row12_col3\" class=\"data row12 col3\" >-0.065926</td>\n",
-       "      <td id=\"T_54ed9_row12_col4\" class=\"data row12 col4\" >0.222188</td>\n",
-       "      <td id=\"T_54ed9_row12_col5\" class=\"data row12 col5\" >0.161984</td>\n",
-       "      <td id=\"T_54ed9_row12_col6\" class=\"data row12 col6\" >-0.313698</td>\n",
-       "      <td id=\"T_54ed9_row12_col7\" class=\"data row12 col7\" >0.006302</td>\n",
-       "      <td id=\"T_54ed9_row12_col8\" class=\"data row12 col8\" >0.070311</td>\n",
-       "      <td id=\"T_54ed9_row12_col9\" class=\"data row12 col9\" >-0.585553</td>\n",
-       "      <td id=\"T_54ed9_row12_col10\" class=\"data row12 col10\" >-0.136716</td>\n",
-       "      <td id=\"T_54ed9_row12_col11\" class=\"data row12 col11\" >-0.009484</td>\n",
-       "      <td id=\"T_54ed9_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row12_col13\" class=\"data row12 col13\" >0.251628</td>\n",
-       "      <td id=\"T_54ed9_row12_col14\" class=\"data row12 col14\" >-0.058898</td>\n",
-       "      <td id=\"T_54ed9_row12_col15\" class=\"data row12 col15\" >0.615286</td>\n",
-       "      <td id=\"T_54ed9_row12_col16\" class=\"data row12 col16\" >-0.082477</td>\n",
-       "      <td id=\"T_54ed9_row12_col17\" class=\"data row12 col17\" >-0.569214</td>\n",
-       "      <td id=\"T_54ed9_row12_col18\" class=\"data row12 col18\" >-0.113754</td>\n",
-       "      <td id=\"T_54ed9_row12_col19\" class=\"data row12 col19\" >-0.615597</td>\n",
-       "      <td id=\"T_54ed9_row12_col20\" class=\"data row12 col20\" >-0.016683</td>\n",
-       "      <td id=\"T_54ed9_row12_col21\" class=\"data row12 col21\" >-0.040664</td>\n",
-       "      <td id=\"T_54ed9_row12_col22\" class=\"data row12 col22\" >-0.000291</td>\n",
-       "      <td id=\"T_54ed9_row12_col23\" class=\"data row12 col23\" >-0.073308</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row13\" class=\"row_heading level0 row13\" >Health services use</th>\n",
-       "      <td id=\"T_54ed9_row13_col0\" class=\"data row13 col0\" >-0.053614</td>\n",
-       "      <td id=\"T_54ed9_row13_col1\" class=\"data row13 col1\" >0.064057</td>\n",
-       "      <td id=\"T_54ed9_row13_col2\" class=\"data row13 col2\" >0.291795</td>\n",
-       "      <td id=\"T_54ed9_row13_col3\" class=\"data row13 col3\" >-0.248386</td>\n",
-       "      <td id=\"T_54ed9_row13_col4\" class=\"data row13 col4\" >-0.817140</td>\n",
-       "      <td id=\"T_54ed9_row13_col5\" class=\"data row13 col5\" >0.667184</td>\n",
-       "      <td id=\"T_54ed9_row13_col6\" class=\"data row13 col6\" >0.702957</td>\n",
-       "      <td id=\"T_54ed9_row13_col7\" class=\"data row13 col7\" >0.270483</td>\n",
-       "      <td id=\"T_54ed9_row13_col8\" class=\"data row13 col8\" >0.258878</td>\n",
-       "      <td id=\"T_54ed9_row13_col9\" class=\"data row13 col9\" >-0.711077</td>\n",
-       "      <td id=\"T_54ed9_row13_col10\" class=\"data row13 col10\" >0.111193</td>\n",
-       "      <td id=\"T_54ed9_row13_col11\" class=\"data row13 col11\" >0.185386</td>\n",
-       "      <td id=\"T_54ed9_row13_col12\" class=\"data row13 col12\" >0.251628</td>\n",
-       "      <td id=\"T_54ed9_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row13_col14\" class=\"data row13 col14\" >-0.060750</td>\n",
-       "      <td id=\"T_54ed9_row13_col15\" class=\"data row13 col15\" >0.757966</td>\n",
-       "      <td id=\"T_54ed9_row13_col16\" class=\"data row13 col16\" >-0.363231</td>\n",
-       "      <td id=\"T_54ed9_row13_col17\" class=\"data row13 col17\" >-0.828212</td>\n",
-       "      <td id=\"T_54ed9_row13_col18\" class=\"data row13 col18\" >0.355327</td>\n",
-       "      <td id=\"T_54ed9_row13_col19\" class=\"data row13 col19\" >-0.459800</td>\n",
-       "      <td id=\"T_54ed9_row13_col20\" class=\"data row13 col20\" >0.391076</td>\n",
-       "      <td id=\"T_54ed9_row13_col21\" class=\"data row13 col21\" >0.157900</td>\n",
-       "      <td id=\"T_54ed9_row13_col22\" class=\"data row13 col22\" >0.141793</td>\n",
-       "      <td id=\"T_54ed9_row13_col23\" class=\"data row13 col23\" >0.052584</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row14\" class=\"row_heading level0 row14\" >International taxes</th>\n",
-       "      <td id=\"T_54ed9_row14_col0\" class=\"data row14 col0\" >-0.018592</td>\n",
-       "      <td id=\"T_54ed9_row14_col1\" class=\"data row14 col1\" >0.103394</td>\n",
-       "      <td id=\"T_54ed9_row14_col2\" class=\"data row14 col2\" >-0.163526</td>\n",
-       "      <td id=\"T_54ed9_row14_col3\" class=\"data row14 col3\" >0.100123</td>\n",
-       "      <td id=\"T_54ed9_row14_col4\" class=\"data row14 col4\" >0.066569</td>\n",
-       "      <td id=\"T_54ed9_row14_col5\" class=\"data row14 col5\" >-0.014293</td>\n",
-       "      <td id=\"T_54ed9_row14_col6\" class=\"data row14 col6\" >-0.078479</td>\n",
-       "      <td id=\"T_54ed9_row14_col7\" class=\"data row14 col7\" >-0.045417</td>\n",
-       "      <td id=\"T_54ed9_row14_col8\" class=\"data row14 col8\" >-0.008011</td>\n",
-       "      <td id=\"T_54ed9_row14_col9\" class=\"data row14 col9\" >-0.049193</td>\n",
-       "      <td id=\"T_54ed9_row14_col10\" class=\"data row14 col10\" >-0.034543</td>\n",
-       "      <td id=\"T_54ed9_row14_col11\" class=\"data row14 col11\" >-0.017806</td>\n",
-       "      <td id=\"T_54ed9_row14_col12\" class=\"data row14 col12\" >-0.058898</td>\n",
-       "      <td id=\"T_54ed9_row14_col13\" class=\"data row14 col13\" >-0.060750</td>\n",
-       "      <td id=\"T_54ed9_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row14_col15\" class=\"data row14 col15\" >0.116938</td>\n",
-       "      <td id=\"T_54ed9_row14_col16\" class=\"data row14 col16\" >0.031007</td>\n",
-       "      <td id=\"T_54ed9_row14_col17\" class=\"data row14 col17\" >0.072819</td>\n",
-       "      <td id=\"T_54ed9_row14_col18\" class=\"data row14 col18\" >-0.098474</td>\n",
-       "      <td id=\"T_54ed9_row14_col19\" class=\"data row14 col19\" >0.124670</td>\n",
-       "      <td id=\"T_54ed9_row14_col20\" class=\"data row14 col20\" >-0.061600</td>\n",
-       "      <td id=\"T_54ed9_row14_col21\" class=\"data row14 col21\" >-0.020631</td>\n",
-       "      <td id=\"T_54ed9_row14_col22\" class=\"data row14 col22\" >-0.038910</td>\n",
-       "      <td id=\"T_54ed9_row14_col23\" class=\"data row14 col23\" >0.080421</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row15\" class=\"row_heading level0 row15\" >Literacy rate</th>\n",
-       "      <td id=\"T_54ed9_row15_col0\" class=\"data row15 col0\" >-0.144043</td>\n",
-       "      <td id=\"T_54ed9_row15_col1\" class=\"data row15 col1\" >0.031291</td>\n",
-       "      <td id=\"T_54ed9_row15_col2\" class=\"data row15 col2\" >0.427703</td>\n",
-       "      <td id=\"T_54ed9_row15_col3\" class=\"data row15 col3\" >-0.150013</td>\n",
-       "      <td id=\"T_54ed9_row15_col4\" class=\"data row15 col4\" >-0.622953</td>\n",
-       "      <td id=\"T_54ed9_row15_col5\" class=\"data row15 col5\" >0.529376</td>\n",
-       "      <td id=\"T_54ed9_row15_col6\" class=\"data row15 col6\" >0.528697</td>\n",
-       "      <td id=\"T_54ed9_row15_col7\" class=\"data row15 col7\" >0.202589</td>\n",
-       "      <td id=\"T_54ed9_row15_col8\" class=\"data row15 col8\" >0.181418</td>\n",
-       "      <td id=\"T_54ed9_row15_col9\" class=\"data row15 col9\" >-0.765607</td>\n",
-       "      <td id=\"T_54ed9_row15_col10\" class=\"data row15 col10\" >-0.088726</td>\n",
-       "      <td id=\"T_54ed9_row15_col11\" class=\"data row15 col11\" >0.133591</td>\n",
-       "      <td id=\"T_54ed9_row15_col12\" class=\"data row15 col12\" >0.615286</td>\n",
-       "      <td id=\"T_54ed9_row15_col13\" class=\"data row15 col13\" >0.757966</td>\n",
-       "      <td id=\"T_54ed9_row15_col14\" class=\"data row15 col14\" >0.116938</td>\n",
-       "      <td id=\"T_54ed9_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row15_col16\" class=\"data row15 col16\" >-0.232806</td>\n",
-       "      <td id=\"T_54ed9_row15_col17\" class=\"data row15 col17\" >-0.694267</td>\n",
-       "      <td id=\"T_54ed9_row15_col18\" class=\"data row15 col18\" >0.212623</td>\n",
-       "      <td id=\"T_54ed9_row15_col19\" class=\"data row15 col19\" >-0.539413</td>\n",
-       "      <td id=\"T_54ed9_row15_col20\" class=\"data row15 col20\" >0.201243</td>\n",
-       "      <td id=\"T_54ed9_row15_col21\" class=\"data row15 col21\" >0.126159</td>\n",
-       "      <td id=\"T_54ed9_row15_col22\" class=\"data row15 col22\" >0.126309</td>\n",
-       "      <td id=\"T_54ed9_row15_col23\" class=\"data row15 col23\" >0.196954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row16\" class=\"row_heading level0 row16\" >Mortality-infants</th>\n",
-       "      <td id=\"T_54ed9_row16_col0\" class=\"data row16 col0\" >-0.050134</td>\n",
-       "      <td id=\"T_54ed9_row16_col1\" class=\"data row16 col1\" >-0.098830</td>\n",
-       "      <td id=\"T_54ed9_row16_col2\" class=\"data row16 col2\" >-0.055892</td>\n",
-       "      <td id=\"T_54ed9_row16_col3\" class=\"data row16 col3\" >-0.066730</td>\n",
-       "      <td id=\"T_54ed9_row16_col4\" class=\"data row16 col4\" >0.368582</td>\n",
-       "      <td id=\"T_54ed9_row16_col5\" class=\"data row16 col5\" >-0.158150</td>\n",
-       "      <td id=\"T_54ed9_row16_col6\" class=\"data row16 col6\" >-0.384806</td>\n",
-       "      <td id=\"T_54ed9_row16_col7\" class=\"data row16 col7\" >-0.020433</td>\n",
-       "      <td id=\"T_54ed9_row16_col8\" class=\"data row16 col8\" >-0.024627</td>\n",
-       "      <td id=\"T_54ed9_row16_col9\" class=\"data row16 col9\" >0.154550</td>\n",
-       "      <td id=\"T_54ed9_row16_col10\" class=\"data row16 col10\" >-0.114495</td>\n",
-       "      <td id=\"T_54ed9_row16_col11\" class=\"data row16 col11\" >0.004512</td>\n",
-       "      <td id=\"T_54ed9_row16_col12\" class=\"data row16 col12\" >-0.082477</td>\n",
-       "      <td id=\"T_54ed9_row16_col13\" class=\"data row16 col13\" >-0.363231</td>\n",
-       "      <td id=\"T_54ed9_row16_col14\" class=\"data row16 col14\" >0.031007</td>\n",
-       "      <td id=\"T_54ed9_row16_col15\" class=\"data row16 col15\" >-0.232806</td>\n",
-       "      <td id=\"T_54ed9_row16_col16\" class=\"data row16 col16\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row16_col17\" class=\"data row16 col17\" >0.434229</td>\n",
-       "      <td id=\"T_54ed9_row16_col18\" class=\"data row16 col18\" >-0.332947</td>\n",
-       "      <td id=\"T_54ed9_row16_col19\" class=\"data row16 col19\" >0.223247</td>\n",
-       "      <td id=\"T_54ed9_row16_col20\" class=\"data row16 col20\" >-0.093491</td>\n",
-       "      <td id=\"T_54ed9_row16_col21\" class=\"data row16 col21\" >0.011425</td>\n",
-       "      <td id=\"T_54ed9_row16_col22\" class=\"data row16 col22\" >0.071272</td>\n",
-       "      <td id=\"T_54ed9_row16_col23\" class=\"data row16 col23\" >-0.235700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row17\" class=\"row_heading level0 row17\" >Mortality-pollution</th>\n",
-       "      <td id=\"T_54ed9_row17_col0\" class=\"data row17 col0\" >0.113730</td>\n",
-       "      <td id=\"T_54ed9_row17_col1\" class=\"data row17 col1\" >0.002666</td>\n",
-       "      <td id=\"T_54ed9_row17_col2\" class=\"data row17 col2\" >-0.308952</td>\n",
-       "      <td id=\"T_54ed9_row17_col3\" class=\"data row17 col3\" >0.155575</td>\n",
-       "      <td id=\"T_54ed9_row17_col4\" class=\"data row17 col4\" >0.704718</td>\n",
-       "      <td id=\"T_54ed9_row17_col5\" class=\"data row17 col5\" >-0.534884</td>\n",
-       "      <td id=\"T_54ed9_row17_col6\" class=\"data row17 col6\" >-0.624623</td>\n",
-       "      <td id=\"T_54ed9_row17_col7\" class=\"data row17 col7\" >-0.267740</td>\n",
-       "      <td id=\"T_54ed9_row17_col8\" class=\"data row17 col8\" >-0.225547</td>\n",
-       "      <td id=\"T_54ed9_row17_col9\" class=\"data row17 col9\" >0.741216</td>\n",
-       "      <td id=\"T_54ed9_row17_col10\" class=\"data row17 col10\" >-0.135496</td>\n",
-       "      <td id=\"T_54ed9_row17_col11\" class=\"data row17 col11\" >-0.171565</td>\n",
-       "      <td id=\"T_54ed9_row17_col12\" class=\"data row17 col12\" >-0.569214</td>\n",
-       "      <td id=\"T_54ed9_row17_col13\" class=\"data row17 col13\" >-0.828212</td>\n",
-       "      <td id=\"T_54ed9_row17_col14\" class=\"data row17 col14\" >0.072819</td>\n",
-       "      <td id=\"T_54ed9_row17_col15\" class=\"data row17 col15\" >-0.694267</td>\n",
-       "      <td id=\"T_54ed9_row17_col16\" class=\"data row17 col16\" >0.434229</td>\n",
-       "      <td id=\"T_54ed9_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row17_col18\" class=\"data row17 col18\" >-0.362484</td>\n",
-       "      <td id=\"T_54ed9_row17_col19\" class=\"data row17 col19\" >0.502968</td>\n",
-       "      <td id=\"T_54ed9_row17_col20\" class=\"data row17 col20\" >-0.441051</td>\n",
-       "      <td id=\"T_54ed9_row17_col21\" class=\"data row17 col21\" >-0.170879</td>\n",
-       "      <td id=\"T_54ed9_row17_col22\" class=\"data row17 col22\" >-0.242762</td>\n",
-       "      <td id=\"T_54ed9_row17_col23\" class=\"data row17 col23\" >-0.169108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row18\" class=\"row_heading level0 row18\" >Net migration</th>\n",
-       "      <td id=\"T_54ed9_row18_col0\" class=\"data row18 col0\" >0.188462</td>\n",
-       "      <td id=\"T_54ed9_row18_col1\" class=\"data row18 col1\" >0.006420</td>\n",
-       "      <td id=\"T_54ed9_row18_col2\" class=\"data row18 col2\" >0.319085</td>\n",
-       "      <td id=\"T_54ed9_row18_col3\" class=\"data row18 col3\" >-0.164040</td>\n",
-       "      <td id=\"T_54ed9_row18_col4\" class=\"data row18 col4\" >-0.459063</td>\n",
-       "      <td id=\"T_54ed9_row18_col5\" class=\"data row18 col5\" >0.199263</td>\n",
-       "      <td id=\"T_54ed9_row18_col6\" class=\"data row18 col6\" >0.478032</td>\n",
-       "      <td id=\"T_54ed9_row18_col7\" class=\"data row18 col7\" >0.602334</td>\n",
-       "      <td id=\"T_54ed9_row18_col8\" class=\"data row18 col8\" >0.455150</td>\n",
-       "      <td id=\"T_54ed9_row18_col9\" class=\"data row18 col9\" >-0.161322</td>\n",
-       "      <td id=\"T_54ed9_row18_col10\" class=\"data row18 col10\" >0.296592</td>\n",
-       "      <td id=\"T_54ed9_row18_col11\" class=\"data row18 col11\" >0.601395</td>\n",
-       "      <td id=\"T_54ed9_row18_col12\" class=\"data row18 col12\" >-0.113754</td>\n",
-       "      <td id=\"T_54ed9_row18_col13\" class=\"data row18 col13\" >0.355327</td>\n",
-       "      <td id=\"T_54ed9_row18_col14\" class=\"data row18 col14\" >-0.098474</td>\n",
-       "      <td id=\"T_54ed9_row18_col15\" class=\"data row18 col15\" >0.212623</td>\n",
-       "      <td id=\"T_54ed9_row18_col16\" class=\"data row18 col16\" >-0.332947</td>\n",
-       "      <td id=\"T_54ed9_row18_col17\" class=\"data row18 col17\" >-0.362484</td>\n",
-       "      <td id=\"T_54ed9_row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row18_col19\" class=\"data row18 col19\" >-0.280819</td>\n",
-       "      <td id=\"T_54ed9_row18_col20\" class=\"data row18 col20\" >0.386082</td>\n",
-       "      <td id=\"T_54ed9_row18_col21\" class=\"data row18 col21\" >0.600175</td>\n",
-       "      <td id=\"T_54ed9_row18_col22\" class=\"data row18 col22\" >0.217793</td>\n",
-       "      <td id=\"T_54ed9_row18_col23\" class=\"data row18 col23\" >0.114323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row19\" class=\"row_heading level0 row19\" >Ninis</th>\n",
-       "      <td id=\"T_54ed9_row19_col0\" class=\"data row19 col0\" >0.129553</td>\n",
-       "      <td id=\"T_54ed9_row19_col1\" class=\"data row19 col1\" >-0.024328</td>\n",
-       "      <td id=\"T_54ed9_row19_col2\" class=\"data row19 col2\" >-0.676723</td>\n",
-       "      <td id=\"T_54ed9_row19_col3\" class=\"data row19 col3\" >0.312791</td>\n",
-       "      <td id=\"T_54ed9_row19_col4\" class=\"data row19 col4\" >0.393902</td>\n",
-       "      <td id=\"T_54ed9_row19_col5\" class=\"data row19 col5\" >-0.298690</td>\n",
-       "      <td id=\"T_54ed9_row19_col6\" class=\"data row19 col6\" >-0.365326</td>\n",
-       "      <td id=\"T_54ed9_row19_col7\" class=\"data row19 col7\" >-0.321658</td>\n",
-       "      <td id=\"T_54ed9_row19_col8\" class=\"data row19 col8\" >-0.357653</td>\n",
-       "      <td id=\"T_54ed9_row19_col9\" class=\"data row19 col9\" >0.571657</td>\n",
-       "      <td id=\"T_54ed9_row19_col10\" class=\"data row19 col10\" >-0.170143</td>\n",
-       "      <td id=\"T_54ed9_row19_col11\" class=\"data row19 col11\" >-0.208874</td>\n",
-       "      <td id=\"T_54ed9_row19_col12\" class=\"data row19 col12\" >-0.615597</td>\n",
-       "      <td id=\"T_54ed9_row19_col13\" class=\"data row19 col13\" >-0.459800</td>\n",
-       "      <td id=\"T_54ed9_row19_col14\" class=\"data row19 col14\" >0.124670</td>\n",
-       "      <td id=\"T_54ed9_row19_col15\" class=\"data row19 col15\" >-0.539413</td>\n",
-       "      <td id=\"T_54ed9_row19_col16\" class=\"data row19 col16\" >0.223247</td>\n",
-       "      <td id=\"T_54ed9_row19_col17\" class=\"data row19 col17\" >0.502968</td>\n",
-       "      <td id=\"T_54ed9_row19_col18\" class=\"data row19 col18\" >-0.280819</td>\n",
-       "      <td id=\"T_54ed9_row19_col19\" class=\"data row19 col19\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row19_col20\" class=\"data row19 col20\" >-0.543981</td>\n",
-       "      <td id=\"T_54ed9_row19_col21\" class=\"data row19 col21\" >-0.207203</td>\n",
-       "      <td id=\"T_54ed9_row19_col22\" class=\"data row19 col22\" >-0.333862</td>\n",
-       "      <td id=\"T_54ed9_row19_col23\" class=\"data row19 col23\" >-0.202677</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row20\" class=\"row_heading level0 row20\" >R&D GExp</th>\n",
-       "      <td id=\"T_54ed9_row20_col0\" class=\"data row20 col0\" >-0.059008</td>\n",
-       "      <td id=\"T_54ed9_row20_col1\" class=\"data row20 col1\" >0.050859</td>\n",
-       "      <td id=\"T_54ed9_row20_col2\" class=\"data row20 col2\" >0.438434</td>\n",
-       "      <td id=\"T_54ed9_row20_col3\" class=\"data row20 col3\" >-0.264417</td>\n",
-       "      <td id=\"T_54ed9_row20_col4\" class=\"data row20 col4\" >-0.482398</td>\n",
-       "      <td id=\"T_54ed9_row20_col5\" class=\"data row20 col5\" >0.216375</td>\n",
-       "      <td id=\"T_54ed9_row20_col6\" class=\"data row20 col6\" >0.479046</td>\n",
-       "      <td id=\"T_54ed9_row20_col7\" class=\"data row20 col7\" >0.470513</td>\n",
-       "      <td id=\"T_54ed9_row20_col8\" class=\"data row20 col8\" >0.468986</td>\n",
-       "      <td id=\"T_54ed9_row20_col9\" class=\"data row20 col9\" >-0.391923</td>\n",
-       "      <td id=\"T_54ed9_row20_col10\" class=\"data row20 col10\" >0.183884</td>\n",
-       "      <td id=\"T_54ed9_row20_col11\" class=\"data row20 col11\" >0.374566</td>\n",
-       "      <td id=\"T_54ed9_row20_col12\" class=\"data row20 col12\" >-0.016683</td>\n",
-       "      <td id=\"T_54ed9_row20_col13\" class=\"data row20 col13\" >0.391076</td>\n",
-       "      <td id=\"T_54ed9_row20_col14\" class=\"data row20 col14\" >-0.061600</td>\n",
-       "      <td id=\"T_54ed9_row20_col15\" class=\"data row20 col15\" >0.201243</td>\n",
-       "      <td id=\"T_54ed9_row20_col16\" class=\"data row20 col16\" >-0.093491</td>\n",
-       "      <td id=\"T_54ed9_row20_col17\" class=\"data row20 col17\" >-0.441051</td>\n",
-       "      <td id=\"T_54ed9_row20_col18\" class=\"data row20 col18\" >0.386082</td>\n",
-       "      <td id=\"T_54ed9_row20_col19\" class=\"data row20 col19\" >-0.543981</td>\n",
-       "      <td id=\"T_54ed9_row20_col20\" class=\"data row20 col20\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row20_col21\" class=\"data row20 col21\" >0.351413</td>\n",
-       "      <td id=\"T_54ed9_row20_col22\" class=\"data row20 col22\" >0.513851</td>\n",
-       "      <td id=\"T_54ed9_row20_col23\" class=\"data row20 col23\" >0.093306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row21\" class=\"row_heading level0 row21\" >Renewable electricity</th>\n",
-       "      <td id=\"T_54ed9_row21_col0\" class=\"data row21 col0\" >0.062481</td>\n",
-       "      <td id=\"T_54ed9_row21_col1\" class=\"data row21 col1\" >0.136225</td>\n",
-       "      <td id=\"T_54ed9_row21_col2\" class=\"data row21 col2\" >0.271002</td>\n",
-       "      <td id=\"T_54ed9_row21_col3\" class=\"data row21 col3\" >-0.125841</td>\n",
-       "      <td id=\"T_54ed9_row21_col4\" class=\"data row21 col4\" >-0.217153</td>\n",
-       "      <td id=\"T_54ed9_row21_col5\" class=\"data row21 col5\" >0.064149</td>\n",
-       "      <td id=\"T_54ed9_row21_col6\" class=\"data row21 col6\" >0.237553</td>\n",
-       "      <td id=\"T_54ed9_row21_col7\" class=\"data row21 col7\" >0.766557</td>\n",
-       "      <td id=\"T_54ed9_row21_col8\" class=\"data row21 col8\" >0.729153</td>\n",
-       "      <td id=\"T_54ed9_row21_col9\" class=\"data row21 col9\" >-0.204778</td>\n",
-       "      <td id=\"T_54ed9_row21_col10\" class=\"data row21 col10\" >0.181001</td>\n",
-       "      <td id=\"T_54ed9_row21_col11\" class=\"data row21 col11\" >0.780299</td>\n",
-       "      <td id=\"T_54ed9_row21_col12\" class=\"data row21 col12\" >-0.040664</td>\n",
-       "      <td id=\"T_54ed9_row21_col13\" class=\"data row21 col13\" >0.157900</td>\n",
-       "      <td id=\"T_54ed9_row21_col14\" class=\"data row21 col14\" >-0.020631</td>\n",
-       "      <td id=\"T_54ed9_row21_col15\" class=\"data row21 col15\" >0.126159</td>\n",
-       "      <td id=\"T_54ed9_row21_col16\" class=\"data row21 col16\" >0.011425</td>\n",
-       "      <td id=\"T_54ed9_row21_col17\" class=\"data row21 col17\" >-0.170879</td>\n",
-       "      <td id=\"T_54ed9_row21_col18\" class=\"data row21 col18\" >0.600175</td>\n",
-       "      <td id=\"T_54ed9_row21_col19\" class=\"data row21 col19\" >-0.207203</td>\n",
-       "      <td id=\"T_54ed9_row21_col20\" class=\"data row21 col20\" >0.351413</td>\n",
-       "      <td id=\"T_54ed9_row21_col21\" class=\"data row21 col21\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row21_col22\" class=\"data row21 col22\" >0.143960</td>\n",
-       "      <td id=\"T_54ed9_row21_col23\" class=\"data row21 col23\" >-0.030940</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row22\" class=\"row_heading level0 row22\" >Suicide</th>\n",
-       "      <td id=\"T_54ed9_row22_col0\" class=\"data row22 col0\" >0.048131</td>\n",
-       "      <td id=\"T_54ed9_row22_col1\" class=\"data row22 col1\" >-0.011579</td>\n",
-       "      <td id=\"T_54ed9_row22_col2\" class=\"data row22 col2\" >0.518274</td>\n",
-       "      <td id=\"T_54ed9_row22_col3\" class=\"data row22 col3\" >-0.029935</td>\n",
-       "      <td id=\"T_54ed9_row22_col4\" class=\"data row22 col4\" >-0.213016</td>\n",
-       "      <td id=\"T_54ed9_row22_col5\" class=\"data row22 col5\" >0.232889</td>\n",
-       "      <td id=\"T_54ed9_row22_col6\" class=\"data row22 col6\" >0.148463</td>\n",
-       "      <td id=\"T_54ed9_row22_col7\" class=\"data row22 col7\" >0.263894</td>\n",
-       "      <td id=\"T_54ed9_row22_col8\" class=\"data row22 col8\" >0.254999</td>\n",
-       "      <td id=\"T_54ed9_row22_col9\" class=\"data row22 col9\" >-0.303122</td>\n",
-       "      <td id=\"T_54ed9_row22_col10\" class=\"data row22 col10\" >0.124718</td>\n",
-       "      <td id=\"T_54ed9_row22_col11\" class=\"data row22 col11\" >0.183871</td>\n",
-       "      <td id=\"T_54ed9_row22_col12\" class=\"data row22 col12\" >-0.000291</td>\n",
-       "      <td id=\"T_54ed9_row22_col13\" class=\"data row22 col13\" >0.141793</td>\n",
-       "      <td id=\"T_54ed9_row22_col14\" class=\"data row22 col14\" >-0.038910</td>\n",
-       "      <td id=\"T_54ed9_row22_col15\" class=\"data row22 col15\" >0.126309</td>\n",
-       "      <td id=\"T_54ed9_row22_col16\" class=\"data row22 col16\" >0.071272</td>\n",
-       "      <td id=\"T_54ed9_row22_col17\" class=\"data row22 col17\" >-0.242762</td>\n",
-       "      <td id=\"T_54ed9_row22_col18\" class=\"data row22 col18\" >0.217793</td>\n",
-       "      <td id=\"T_54ed9_row22_col19\" class=\"data row22 col19\" >-0.333862</td>\n",
-       "      <td id=\"T_54ed9_row22_col20\" class=\"data row22 col20\" >0.513851</td>\n",
-       "      <td id=\"T_54ed9_row22_col21\" class=\"data row22 col21\" >0.143960</td>\n",
-       "      <td id=\"T_54ed9_row22_col22\" class=\"data row22 col22\" >1.000000</td>\n",
-       "      <td id=\"T_54ed9_row22_col23\" class=\"data row22 col23\" >0.012819</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54ed9_level0_row23\" class=\"row_heading level0 row23\" >Workers high education</th>\n",
-       "      <td id=\"T_54ed9_row23_col0\" class=\"data row23 col0\" >0.038948</td>\n",
-       "      <td id=\"T_54ed9_row23_col1\" class=\"data row23 col1\" >-0.040452</td>\n",
-       "      <td id=\"T_54ed9_row23_col2\" class=\"data row23 col2\" >0.207073</td>\n",
-       "      <td id=\"T_54ed9_row23_col3\" class=\"data row23 col3\" >0.065043</td>\n",
-       "      <td id=\"T_54ed9_row23_col4\" class=\"data row23 col4\" >-0.049262</td>\n",
-       "      <td id=\"T_54ed9_row23_col5\" class=\"data row23 col5\" >0.042099</td>\n",
-       "      <td id=\"T_54ed9_row23_col6\" class=\"data row23 col6\" >0.043815</td>\n",
-       "      <td id=\"T_54ed9_row23_col7\" class=\"data row23 col7\" >0.007372</td>\n",
-       "      <td id=\"T_54ed9_row23_col8\" class=\"data row23 col8\" >0.007270</td>\n",
-       "      <td id=\"T_54ed9_row23_col9\" class=\"data row23 col9\" >-0.160760</td>\n",
-       "      <td id=\"T_54ed9_row23_col10\" class=\"data row23 col10\" >-0.009456</td>\n",
-       "      <td id=\"T_54ed9_row23_col11\" class=\"data row23 col11\" >-0.018627</td>\n",
-       "      <td id=\"T_54ed9_row23_col12\" class=\"data row23 col12\" >-0.073308</td>\n",
-       "      <td id=\"T_54ed9_row23_col13\" class=\"data row23 col13\" >0.052584</td>\n",
-       "      <td id=\"T_54ed9_row23_col14\" class=\"data row23 col14\" >0.080421</td>\n",
-       "      <td id=\"T_54ed9_row23_col15\" class=\"data row23 col15\" >0.196954</td>\n",
-       "      <td id=\"T_54ed9_row23_col16\" class=\"data row23 col16\" >-0.235700</td>\n",
-       "      <td id=\"T_54ed9_row23_col17\" class=\"data row23 col17\" >-0.169108</td>\n",
-       "      <td id=\"T_54ed9_row23_col18\" class=\"data row23 col18\" >0.114323</td>\n",
-       "      <td id=\"T_54ed9_row23_col19\" class=\"data row23 col19\" >-0.202677</td>\n",
-       "      <td id=\"T_54ed9_row23_col20\" class=\"data row23 col20\" >0.093306</td>\n",
-       "      <td id=\"T_54ed9_row23_col21\" class=\"data row23 col21\" >-0.030940</td>\n",
-       "      <td id=\"T_54ed9_row23_col22\" class=\"data row23 col22\" >0.012819</td>\n",
-       "      <td id=\"T_54ed9_row23_col23\" class=\"data row23 col23\" >1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x284c75f4400>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corr = df.corr()\n",
-    "corr.style.background_gradient(cmap='coolwarm')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x284c8778430>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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6dIylSD2n4FMsvt7JzIK27ok0NZGZgvfVtWGKhsWbN9aRKhiMJfO84+wGnuya5H8f6sKlqyyvDi72tMWrTMY0sc6rxrGmFDQFrcxD4G1L6UTOD+LVp70yyFUrq7hpez9fufcoiZzBWzbV43VpqGUe/G9filbmAU3BvbYMzyV1qA5tbvnjPduNJ/Ok83K8CHEyMkWDnon0gvabe2OE3rEMvdY3c3+8NIJyTRO638H3H+/lvx/oYCSe44b1NaiqchpmLrxlBRQNkH8mFEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHOGPoir+9TwO2KojwEzFXo27b95UWexyvOknL/grbLllVQN1tgvLMvzidv2sXHL23lgxc0M5ku8I+37mdZVZC6Eu+CZU83K1Ugu38C37pybCB/dArPshLwOk731MQZyjYs0ttHmb65E2xQQ04ib16Ko8KLHnLxTN8Ub/nWU5iWzUVtZXSMp3i6Z5L2ygCbm6PUzx4H48k8n/rFHnKGhUNTSeUNUnmDrz/QwevWVnNBaynr6iNE/ceCG8JeJx+8oJlrVldhWjY1YQ/be6cA2N0/zTktUZ7ojM2bb2PpwmNWiJeboiloETeBxhCFoRSJu3qIvLEVPew+qfF6JlJ87KfP8N5zm3nf+U2kcwb/cvtBPnPNMr77WC8AV62spNQvb44/ndbVRRa0nddaOndeeuP6Wj7zm720VwT44AUtqAoMTWX45js3UOp3sqI6hKJI4a14aWwUYhMZqhMFAufVYMTzTO8cpez8mtM9NSFOOU1VqA67edOGWtorAqTyBj/b1seewTi/+tA5RBM5nLV+9OVRcp3TpA5P4Sn1ougzGaAt5X5UBazn1NyvrQtTHfacpi0S4pWtJuThwvYyeh7vndf+xrZyph8ZxBN242mJUBhL0zmcoGsszRdfvwLbhrsPjKJJAMnpJyEkQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcQZY7FDSP4JSAFuwLnI635F29AQ5nPXLecr9xwhkTO4oLWUP7mgGac+80bt8WSeqUyRv7/lwLzlRhO5My6ExLZt0ttGKPalKPal5tpzy6bxb5bCO3F8xfHsXAAJgBUvMPnDA5T/+XoAHjkyjmnZNES9tJT7+dJdh+eWXVkT5Nvv3EhlyM3B4QQPHZ3g9Wtr2N47Odcnni3ygyd6iXgdXLa8csH6FUWhNnLsWFpbF+K/3rKOf7rtAO85twnDtHi6Z4qI18HnX7eSZZWBl+mXEOL4bMMi+fAA2V3j89rzPQn0tScXQjKcyDGeKvL/nnM8AWQLJgAXt5fxoQtbcMwWVYvTY119iL+7Zhn/fs8RskWTa1dXsbmphN/uHKQy6MbjnPn7OTya5PBocm65y5ZXMjydZWVN+DTNXLya6IZN2dNjFAfTFLsTAGiAe2UpNJzeuQnxchiazvFf93csaPdnTazbeskWred0TpFZEsZXM3N/uKI6yFfevJbP33yAWLrAmtoQn75qKS3HCZ4UQrwwn9vBH22oYzyZ5859Izh1lY9cvIRI0iBzcIrsc/o25EwSep4/++muubb3nddEQ9S36PMWx9gSQiKEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCnDEWO4SkxLbtyxd5na8KIY+TLc0lrHjnWVi2TdCt01h6rEgm4nPg0BSK5rHCjbYKP36ndjqm+wfZhkW+O76gvdCfhM1Vp2FG4pXAShXmAkieZRctrHQRyjw4tJki+6tWVvL9570Be99ggkMjCSpDbuK5IgDbeyc5v7WUn27rn9d3fX3JH5zHcDzLQ4fHuWPfMOe2lPL9927C79J5y6Z6RpM5/C4dTVEYSeSoDLpxOc68Y1C8Oll5g0JvYkG7MZY56THdmkZbhZ8jo8cCoxyaQtTn5Cfv34xbVyn1u056fHFqBD1O3nteE+e3llIwbVyawh37R/i7a5fTWu6nYFp4nRqZ2fAYgJqwh6Jp4XdLJpw4NaIODUYWnm/0jHEaZiPEy6884DruuVXLmdjPDSABsMBOFeY+OjSV85aU8t9vW088W0RTIeRxzF/EshmazqIoUB32oCjKy7o9QryS5A2TkXgOt0OjIjgTthf2OPjjDXXcsK4Gt0OjscSLsn9qwbLKSIZVa4Lce3QCgOZSH+HnHX9CCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCvZYsdQnKvoiiX27Z99yKv9xVv/1CcxzpifPexbsaSeS5sK+NPLmjmnJZSAFRF4a+vXMq3H+lmJJFjRXWQ92xppOQMLA5XHRqelaUUh9Lz2l1LwqdnQuIVQQu7QFfBOFbUqXp1tNBMAf35rWX8531HcWgqOcNcsHx+thi0pdSHrioMTGXxOnWuWFHBPQdG8Tg0/uLyNtbVh3/vHPKGyVfv6+Cn2/oAeOjIBD99up+fvH8zQY8Dt0PjgUNjfO7mfYwl81y1spK/uqKdplJ5q714+akeB57lUVKPDc1rd9YGTnpMr0vjvec28cMne9k/lKAy6Ob95zfh0ODt33mKrcsr+eOzarl0WcVLnb54CVJ5g9t2D/Evdx4ilTO4ZnUVYa+TL99zFIAfv38Tf3VFO995tJuBqSxtFX7ed14zdx8Y5QPnN5/m2YtXi4yqEF4Sxjj8vILvyJl3LyrEqbCk3M/fXbucrz/QMXdufevmeiYVmzKvjvWcAB7FoeIq8cx9tm2bm54e4F/vPDTXFvE6+NWfbqG5zM9YMsePnujlfx/uQlcV/vzSVv7orFpKfHI8CdEbS/Nf93fwm52DRLwO/u7a5Zy/pJTP37Kfew6OzfVbUxfmR5cuXbC81hrmwPRMwN7yqiAfvriF2hLvos1fCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDiTLfYISQfAT6lKEoBKM622bZtBxd5Hq843RMZ/vXOQ9j2zOcHD4+jqyrLq4KEvE6iXieDeoY3rq9BVRV0VaXE56R89q3AZxrvmjIK/UlyBydBAd/mKlzNodM9LXEG00s9RN+2lKlfHMHKGKh+ByVvWYoemdnHV9WG+MWHzmFn3zRXrqjkjn0jc8sG3TqtFTNBIEsrg3zvPRv5+5v3873He/jwRS18+KIllPic1Eb+8Fvm+2IZfv5039zn2oiHrcsr6ImlCXudHBlN8qEf78C2QVMVHJrKE50xckWLljIfTl17mX4dIUBRFXxnV1EcTpPvioMK/vNrcTac/CW2tsTLvoE4l7SXc8nScgB8Tp1c0eYjFy/hzn0j3LpniDW1IUoDZ+b15rXg0FCComXzV5e3M5bMc2A4QcBlUR1yMxTP8R/3HOVjl7Zy/ZpqNFVB1xSmM3kcmsLSqpMPqRHiuXKKTe6cSlzxPOZIBjQF5yV1HLENSkeTtFbIviZeXfxuBxubIrw730A8a5ArmLSU+Tk0lcW6pIaKB4ewUkVUr4779S0cyucJT0JdiZfheI7/uv/ovPGmMkUODidoLvPzwKExvnp/BwB54F/uOERdiZerV1Wdhi0V4sxhWjbfe7yHX+4YAGAiVeBjP9vF996zkXsOjhF067xxQy0+l07RsBjzaYQuqSP30ACYNmq1j+S6Uhq64c9rgpR4XayuCeHQ1NO8ZWLuH7uEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCHHaLWoIiW3bUn14ksaT+QU1GfcfGiWWLhBL57nn0ChlAReKouBzalSGPKyuC5+WuZ4IPeqh5MZ2jMkcqAqOUjeKBDSIP0BRFDzLojj+bB1mqogWcKKH578NfnVtmNW1YS5ZWk5rhZ/f7hxiRXWQP72oheaymRASVVU4r7WMX35oC5mCQWnAhet5+17PRJpDIwlURWFZVZC657wZ+9nD8JzmKGvqQvzwiV6+9UgXH7mohfNby3jzxjpu3T3Mn17Uws27h/jdriE0VeFDFzbzgfObCXudL+vvJF7bHGVeSt6xDHMyh6Kr6FEPin7yRZV9sQyT2SIepwbYqIqCjc3Hfr6LomnxjrMb6J5IE88alMoVftEVDIt7D47yZFeMiWSe22fDlzY3lbC6JsTm5ii/2TnIZLrA6toQJT4HewYTPNYxQXOZj09ubV9w/hPiZLl0lUPFHI4LK6lVNHCo3Dme4Is/2MeScj9ff+s62isld1C8uiwpC1Ab9nBkNMUjR8Z5sitGU9THHsVE3xylyeOiotLHJ+47zFPdU4Q8Dv7zzWtZWnn8i2bBtDBNi5u2Dyz47o69wxJCIl7zxpM5fvXMwuOjczxNxOvgzy5p5esPdBBLF/A5NdorA1x5fjXpxgB9oymenk4Tmkhw7eoqSnxOKoJuCSA5U0gIiRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghxxljUEBIARVGuBy6Y/figbdu3LvYcXolKAwuDC6pCHkJuBw8eHefrD3SiqQo3nlWLU/PQMZbkujXVp2GmJ05166h+B4pDlQASgVU0oWiheh3H/z5vggJ6xI0ecf/BsepKvHxyazvvPbcJj1ObV2RfMEx0VSXicxLxLTyuDg4leOu3n2QqUwSgOuTm++/dRGtFgLqIhxvPquNnT/dzXmspX7rrMACf3NrGM71TfP3BTiqDbv7+umXcc3CMQyNJYOaN3V9/oJNNjVEubC87qd9HiBdiGyZW3kL16iilHlCVlxRAEs8U+MlTvTzdM0WmYPKmDTWMJXL86M7Dc32++1gPX3z9CkoDrj8wkni5HB5J8MmbdvGxS1v5wRO9c+1PdU+yri5M0bQAeN95TYS8TkJeJ0sqAlyxvIISv/ydiVOrvsTH4eEE/oCbkN/Fo10x/u2+owB0jKX497uO8J9vWTcbaiTEq0c6Z7Knf4q8YfHtR7pxairvO6+J162pxq8ofOzXe9jWM8UFzVEag26+eNtBvvPOs/jIJUv4/55zTQ17HYzE8wzFsyyvCrCjd2reetoqJO1LCK9Tp6HEy97BxLz2kFvnwxct4b8fnAkgAagIutnbEWNlZYCuTI4R06A/nacnlcfj1LiwvVwCSM4kEkIihBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQpwxFjWERFGUfwU2Aj+ebfqYoijn2bb9N4s5j1ca27ZRbTinpYQnOicBUBX46yvbificHBhKEHTrfOzSVnYNxNl/aJQL28oYmMzQUOo7zbM/vuJ4htzhKTLbR1DcOoELanG3R1CkCOg1x7ZtCr0JEvf1YcRy+M6qwLu+HD08EzRiZorkDk+RemQAxaURuLgOd3N4XriCYVrEs0UCbh3ncwJHwt5jISMTyTz3HRrlp9v6aSr18a4tjaytCy+Yz8+3988FkAAMxXM80RVjIlXge493s6Y2zBdet4KBySwAm5tK2NE7xUNHxgEYjufY1R9nW/fkgrGPjiUlhES8LPL9SZIP9KF6HTjKvWSeGUXxOQheXI+rKYSiKS9qvNFEjjv2DbNnIE5TqY9zl5TSGPXwzYe7FvQ1LZsXN7o4VbpjaepLvBwcTi747pGOCa5cUcEFb1zF/YfGuKCtDMOy+e3OQe47NMoFrWW8aUMtzWX+0zBz8WqUK5qcXRrA3Zkgt7OPCwMOnn7zRj75VCf3HJngoaPjTGUKeJye0z1VIU6ZkXiOW/cMcfPuIRpKvPzl5e185d4jRE3wPzOOuT/Gv5YF8byjlfxTI2i9Gd7ZVoWaKnLligq8To17D4zSEPVxQWsZn/3tXtwOlTdvrOfm3cPEszP3pOVBF5evrDzNWyvE6Rf0OPjM1ct453e3UTRnQitW14Y4NJJkQ0OEidRMAMm/XNzK5oSFP1HA058h9Mw4dsHiovWlHPVr/Hr/ML/YPsjbz2ng4vYySnwSznbaSQiJEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCHHGWNQQEuBqYK1t2xaAoijfB3YCEkLyBxRNi+mcQU3Yy6evKqdoWgCYto2qKpQHXPzlFe186c7DJPMGALsH4tjAJy5rQ1XPrPJw27TIHZwkfnv3XFus9wCl71mJuy1yGmcmTofiSJrxb+8FY6boKHF3Lyjg21hJcTSNMZFj+jcdc/1jPfsp+8BqXM0hADrHUnzv8W7uOzjGWU0lfOjCZpZXheb6Hx5JsKN3ioGpLP/9YCcAu/qnuWv/CL/58BbaK4NzfU3TYs9AfN78XLqK36Xz1m8/iW3DXftHCXscfPrqpQCc1VjCfz/YMW+ZzvE0q2vDc8Ekz2qInpmhQOKVrTieYeLbe0FV8J9TNe/cOtG1l7IPrcHVEPwDI8xnWTY/frKXr94/s1/vHojz6NEJ/vbaZayrj/C2zQ38xS92k8qbAPhdOtmiQdDjOLUbJl5Qic+JU1NpKV8YJLKyOohlw5fvOcpbNtUykcrz+VsOsKt/GoB9gwkePDLON962nno5N4lTIJUrEjw0Ter+fgCMMcj3JPjSW5ey9sgEa+vCBN2L/eghxMvHMC2+82gX33pk5rq7ZyDOw0cn+O7bN1D/1Dj5/aMAqONZij0JghfWkjg8hfupUTwW3FKu8y93HWZFdZCHj45z295h3ra5gaNjKd5zbhO/+fAWDg4n0FSFZVVBuY8UYtbmpii/+8i5HB1N4XfrlAVcfOPBTsqDLgIunfMbSzivN4vSlcBzfQvx5zxL6relqb68jgcOj5MrWuzsn+bz1y/nXVuaTuMWCQDbkhASIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOJMcToqAcPA5OyfQ3+gn5jl1DUODMX55Y6Bee3ntES5dnU1pQEnBcOeCyB51rce6eLNG+uoiXgXc7ovyJjOkd4+Or/Rhnx3XEJIXoOKI5m5ABJnXQDPiii2bTN9Sxd2zsBMFecvYEP28CSu5hDTmQJ/+Yvd7Jwtqr951xDbumP8+k/PpTrsoXMsxVu/9RSvW1u94PjJFEwODifmhZAYts0N62von8pw+fIKllUFsG3Y1j0592Lm8oCLGzfWEXDprKsPM5UuUBFwM5LIzY3zRFeMH71vE/uH4nNv475+TTVrauWUJ0694mgGO2/iWV1Kdu/E/C9tyHdMvagQkqF4lv99uGteWzJvMDSd4z/v66Ctws8XXreST960m2tWVaEoiry4/DRZXhVkVW2ImrCbNbUhds+GKDVEvWxoKOH/Hu9mJJGjvTLIQ0fG5wJInnVgKMEzfdMSQiJOCUfGJLttZH6jaaNO5lhbF+IzVy/D75awIvHqMTid5XuP98xri2eLhAs25oHYvHYrY6D6HISubiL1+BDZHWO4Lqwgb1g80zc91y/o1ufuF5vL/DSXLQyZEuK1TlUVlleHWF49c6xMpQuc1VTC/zzYyUcuXkJ1EZT7hghcUocxllmwfPTgNOc1Rbl3NjDy6w90cs2qakoDrkXdDjGfWSwgdwlCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxJlhsUNI/gXYqSjKA4ACXAB8epHn8IrkcmgL2zQVBWgrD7BntvD4uZyaSs4wF2F2L5KioroWbo/iUE/DZMTpYmYNCv1J7NxMeI7i0nAvKyF+Zw/BrQ1kd4/jagkdd19R3TNtfZOZuQCSZ43E83RNpKgOe9g7OE0sXaBg2jj1hfuXph5r2z8U578f6GBjYwnvOLuBHz7Ry30Hx3jLpjpqIx4AQh4HH7igmX+/+zC5osWNG+vY1BRhY1OEv7hpN8++vPnC1lLaKgL89iPn0j2RxuvUWFIeIOSRsipx6qmz506txI0Ryx2nw4s7t2qKglNTyRvWvHZFmfn/kdEUAJ/Y2kYsmcMwLaYzRSpDnhc/efGSpHImQbcDr1NnTW2Yi5eWY9uQzBXRNYWDw0mqQm6e7pki4D7+Ld9wPEsslSfql8Jb8dKoKthODZgfHqboKv/4+pWsqgmflnkJ8XJRFQVdVSma85+3cpaFT1PmQvaeZWcM8j1x/OdWk7i/n6y18Dkt4HawqankZZ23EK82EZ+TllIf48k8P3yyl29cvRxXSwgt4sLKGAv6K26NTPHY8efU1bn7XHH62Kb1wp2EEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCLEoFjX1wbbtnwJnA7+e/e8c27Z/tphzeKVaWR3CoR2rjFEUuHp1FZZlc8feYaJ+J+XPe3Pv2zY3EHAtds7MCbBsPGvKZmJoZikuDb1MCthfS7K7x4h9dx/GZA6txI1neZTMjlHgWAFSvjuOe1nJ/H3FqeFunynOdOkq6nEKxtz6TEhJYbb48859w7x5U/28PlGfkxXVQQAGpjK867vbuO/QGFOZIl++5wjjqTwjiRz/ce9RygMu3A6Vd57TwDce7CRXnJnfz5/u5+M/301rmZ/ffuRcvnLjGr73no186Y/WUB50Uxvxcn5rGRsaSiSARLxsHJU+9BofqkfDu7583neKW0OLvLhwiaqwh09sbZvXVh5wkS8eKwy0bBsFyJs2bqfGgeHESc9fnLyHjozxvw938fGf7cLj1FCAkEenPOBmcDqLz6nREPWSzhvs7o9zQWvpvOW3tEQZS+ZxahICJl46BfBtrpzXpvodaF6dJzpjmJZ9/AWFeIWqjXj40IXN89qqQ262TaXwnVs9r12v8GLE82hBF4XeBMHL6zmrPjKvT1XIzXA8Qzw7P8hHCPHC+qcyXNhWxuB0lp7pLN71FZhTefSwC8X5nPscBZwbynmyZ3Ku6YMXNEsY2xnANOXcJ4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKcKRYloUJRlCuAgG3bv7Rtexi4ebb9bYqijNm2fc9izOOVKlso8kTnOJ+6cimHhhMUTZtVtSH29E9zbkuU+w+Ps64+zDvObiCWLjCayLG8Okhl0EV58MwL9rAtCzORJ3RlE8ZEFsWpovodILWprxnGdI74nT0ApB4bInBBDXq5l1znNACKpoKmgGmTfmqE0JWNGNN5tJALd1sEZ7UfgMZSH+89t4lvP9o9N/bW5eUsKZ/5fkVNEJeuMpEqsKtvmr+8vJ3O8SRLygNctqyc5rKZfp1jKSZSBa5bXcXjnRNzY7l0lWtXV6GpCv/z9g2MJXLE0oUF2zMUz3H5ikpW14YBmEwXGJzKUB5w49CluF+8vLSQi9K3LaMYy1LoSxK6qhFjIjdzbvU5sE+i8P+G9TXURjzcc2CUqN+Jx6Hx3w92AlDmd1Hqd/FEZ4w79o3QWu6Xt8efJk/PFtAWTItf7hjgqlVVuHSVZZUBbtoxwKeubCeWLlAb9vKpX+3hbZvr+fBFLXSOp1hfH2EkkeP81lICEpIkTgHFqWEXTUJXNlKcyKJ6dFSXhm3ZFIsmk+k8ZQH36Z6mEKeMoihcv7YGh6ayZzBOdchNwO3gkY4YV25qJHR5A8ZEFi3oxDZtrGSBwlAavcSNMZKh0uHnE1vb2D8YpyrsocTr4FuPdLF1eeULr1wIMSdbmAlbq4t4+PBFLaxxOLAKJsXhNLZhEbi4HitZwDYs9KibcZfKWzc3MJUusKI6yNq68OneBAFYpnm6pyCEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiFmLEkICfB647jjt9wG/ASSE5A9QFZU1dRH+7nf7qSvx4FBVbt49xH/88Ro8DpUbz6qjfzJDa4Wfrn0j2EDAqXNRW/npnvrxqQqOCh/ZAzEK/UnQFHzrK1B8i7U7itPNNm3sgjXzwbJJPjiAGnbh31JN4s4e0ttmgkdSjw5hTGTJHpoi8oYlOMq988Zx6Rp/elELZzdH2TcUp7U8wIaGCGGvE4DlVUF++oHNfPuRbnonM5T6nbxt8wocqsLhsSQPHR4j7HWSLZh8YmsblmXPZeH4XTp/cXkbP3yil7aKAH/z67380YY6qkNuhuK5uTmoCtREZsJ+DNPi0Y4JPn/LfgancrxhfTUfvmgJDVHfy/6bitc2vcSD6nFgZUxy+ybI9yUA8K0tR/O++ICJsNfJ5SsquXxFJV3jKX63a4imUh8rq0NcuaqSP//pTsJeJx+/rI3mUh+hk1iHeOnOW1LGzbuHOXdJlHX1EX76VB8Fw+KPzqrlTRtq+c6j3TzaEeN/376BH753E994uAvLsnjnOY1kCwbr6yOsqQud7s0QrxIOn45e5iW3d4JCfxIb8K2vwNYUDFshnilKCIl41Wkq9XFRexnZgsETXZO0VwbY2FTCM9kCG50K6mAKc18OV0sYR4UXY9c4vg3lxO/swVOzhO8+2kVVyMOO3ili6QJv3VSPzynPREK8GKqqsL4hwud+t4+/uXIpxbxJYTCJlS7iWREle2ASYzyDFnJBS4gdqSy9sTR1ES/rGyKsqJZ7oTOBLSEkQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcQZY7EqnLy2bY8/v9G27RFFUaQ6/3nMTBEra6D5HKhuHZdDozHq49NXLeWu/aOAzaevXkpTqQ9VUQh4dL5w6wHed14zm5tLcDk0Ai6dsuCZWeiph1zkMzFczSFcDQHQVBSnih5yne6piUWih134NleSfmJ4rs3OFHG3hdF8raSeGKIwkKLkbUtR3Tpq0Inmnn+6OjKaZN9gHNuGlTVBLltesWA9iqKwvqGEr9aGKZgWXqdOPFPk3+45wvce7+Fz1y7nK/fu5K8ub+dXOwbom8zw2WuWcd/BMf74rFq+dn8HLl2lYzxF0bT57a5B/uqKdv77wU7Gk3k8Do1/esNKWsv9WEWTyeEU2/aPMp4sUDAtfv70AJYF//SGlTh17WX/XcWZzUwXsXIGmt+J6jr1+4Pq0VFUcLWEcNYHAFBcOqr3pV3qG6M+nJrKxy5Zwr7hOO///nYAEjmDf73jIN9651msq4uQL5rsG4pzeCRJxOdkdU2Imoj3BUYXL8X5raXcsL6GljI/X7rr8Fz7dx/rIex1sqEhwoVt5ZT6nWxoLGFzcwmxVIGpyQwOpwN0FdOy/8AahDhxEZ+buCuNsyUycw5SFRSnRk86z3/ed4RYKsfnrluBrqkveV22ZWPG86CAHj4z73fFa4dlQ8TnZE1diDv2jjCazPH561ewO+Sk5ep6qtAo7I+R74oTurKR7L4YaAruKh9fedNq7JRBfyJHzLaI+p1MZvK04D/dmyXEKWGmC1g5Ey3gRHW+PM9Dg5NZgm6dr755Hf929xGq19Wx6mCMwAW1GBM59Aovvk2VZCJOfj04xXA8yzvPqWdLcxk+t4T+nCkswzjdUxBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxKzFqrhwK4qi27Y9r6pAURQH4FmkObwi5LvjTP32KMZoFldLiNC1zaQDDn65o58V1SGuX1OFQ1eYShfpHE8R9jp4rCPGhy9awtfu7yCVn/mJ/S6dH7/fxZq6yGneooVUh4a7rYTM3gnMeB7FreNdEcVRKsXqrwXFkTS5jmmc9QFUr4Ps7nH0ci+Bi2pxVgdwVgfwrClDURUU/fiFyvsG47zlm0+SfM7+/pMPbGZ1bXhmHabF4ZEkPbE0UZ+TZVVBwl4nAAeGE3zv8R7esK6a8WQORVGYSBfom8wA8N8PdPDhi1uoCLr57mM91JV45or0MwWTf7/7CG/aUEtdxMNF7WU0l/kxJrJM3t1LYd8E7wi6uPqSpXxsWxedsQy/3jnIn1/aSl2J7N+vZfnOaaZ+24ExnsW1JEz42mYclac+g8vZECR3YJLieBZUG097FGfNSytknsoU+OGTvXzm6qV88+Hued9ZNvRNZphKFXiyJ8ZHfrJz7rvVtSG+8fYNVIflMv9yqQp7+KfXr+Rvf7tvwXf3HhzlS29cxb2HxrhpRz+ra8MksgZTHVOEHhnCHs1CrZ/JrfXUtEdPw+zFq03eMNmez7PWoaCM5bEKJlpTkGeyBSwbfvnMIH960RKqXuI5wUjkST81QvKhARRNIbi1Ae/6cjSv4xRtiRAvTrZg8o+3HeSqlZX80Vl1LCn38ZV7jtI5kQbAqSvc8q7NlNf5ST86hBp0Enn7MhwunRV7pinsmWCp34l6eT3fHprk1t3D/PMNq2gu86OpymneOiFOjm3b5Dunmf5tB8ZEDldbmNA1zTgrTu39r2Fa7B2K86W7DvOpK9rpHE/x7QPDfPPGdvLbRsC0cJ9VyW8m4/ztL44C8OaNddx7YIy1dREJITmDWEbhdE9BCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCzXvqryE/Mr4FvKYoyV3Ey++dvzH4ngOJ4hqlbOvCuKiNwSR2uphDTd3RRTBd5omuSf77jEF++5whfvPUQ/3b3EbpjGRyawqqaIEdGk3MBJACpvMHNu4dO49b8frZhkX5mjMz2Uey8RbE3QfzOHsyUFJ28WpnJApk946R3jDL+zT3Eb+1i6udHSD89QviGJUTf2o6rPjjXX3VqvzeABOB3uwbnAkhgZn//1Y6Buc/3Hxzl+q89ykd/spO3fOsp/vn2g0xnZvavyUyeT1+1FFDYNTDNF163gobosYCQqUyRf7/7CANTGXRVoX8yy9LKIM/WgKbyBt97vIf6qI/mqA9jIkv8zh5yeyfABj3qpt5Q+NGF7fzjhUtoKfPhcbw8b/0WrwzFsQzTd3bjXVtO4JI6nPUBpm7twsoU5/WzDYviWJrCUAozd3JvAi8OpYnf1YOdMTBjeSZ/eRRjPPuS5u916iytCrBnIE5F0L3g+6DbwXg6zz/ccmBe+56BOAeGEi9p3eKFKUBtZGGoQ0uZj7HRFPVF2BzxkyuaZMczlDw1SmD2PiPQXoJ6fz9TY+nFn7h41emPZQhmTHK39WDF85AxSN/czaUlAQCqQm7czpd+PcwdmiR5Xx8YFnbeJH5rF/mu+EmNZWaK5Hvi5DqmMKbzL3lu4rWprsTDlpYot+8d4T/vO0rXeHougASgYNj0DifIPzKE96wKPO0lmKMZCl0zASTYYCULGL/u4M21JQwncuwfSvDI0fHTuFXitcy2bIpjabKHpygMpbCL1oseoziQYuJ7+zEmcgDkj0wz9aujmNniCyz54jzWMcFoIsfAVJYjYymcusKXzm4i+9NDKKaN5neSvruXav1YUNXPt/dzUXs5fpeEV51JLPPknn+EEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCHHqLdZrX/8W+CLQqyhK72xbPfAd4O8WaQ5nPGM6j2dplMT9fWABmkLw0nrIGVyytJyfPd1P4jmF6csqg4S9TjY0RHjg8MIite6JzCLO/sQVJzKkHhkEyyY7fWzexaE0WpvzNM5MvBxswyL58ACpJ4YJnFuNlTm2D1uJAvFbuyj94Gq0F3E26o0t3Le7Z4s9h+NZPvObfaiKQmPUy0Qqz03bB7hhXQ1Lq4KU+lx89jf7mM4U+dy1y/nsb/Zx7eoq2isCHB5NHpubZfPXVy7ln24/yM+29fGZq5fxRFeMfNHkPec2cU51iMQD/WDZ5A7EAHAvK0FxaiTumTnNXeLRueLGFZQGXCfz04lXCTOex9UQJHFvL9iArhK6vAEjnsfpnSl+NNNFko8MkHp45tzoWhIm/PolOEoXhkv8PlbeIHFvL3bWILtvYq49e3QKR+XJv3Xe49T42KWtfPAH2/n4ZW387e/2Ydsz362qCVJf4kHXYDK9MEjqueFY4uXRMZ5mS0uUHz/Vx8RsmFfY6+Cz6xvI/6YDezKP4tJQSwJ4nCqOlgiJ+/pm9kVNIXhZ/UwIWPnJ7yNCADh0hcaRHPmsQe7A5Fy7a2+M1dVBPr61jYj3pd3n2aZF5unRBe3ZPeO428KozhO/mTCmc0z9roP8wSkAtLCT6LtW4Kzyv6Q5iteeypCHP7u4ldbyALv7p3Hq88N2HJpCq6Hgbo+Q3TVOcTAFgF7mIXhpPYl7+2Y62lA2VeB7W5fzo/4J7t4/ys0fPU/uI8Wiyx2aJPaTg2DYoEDommb8mytRTiBY0bZscocnKQymZpZ/jmJfEnM6j+Y5NeEfo4kcf/WLPfzddcsBuHPfCF96/Ur8e2IULOYFVC1LWZQFXIwn89g2uHQVzykIxhKnjl04tQE1QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQ4eYsSQmLbtgH8jaIonweWzDZ32LadXYz1v1IoCiQf7J8JIAEwbZIP9BNuXsnr1tYwksjx4OFxPA6ND13YTGXQRdd4mlt3D/H6tTU8cnRi3nhv2lC7+BtxAqysAZZ9/HbxqlMcy5B6bBDFoWGbC9+ibeVMMBfuD8czlS6wfzDOpcvKufvA/CLkP95YB0AyZ3BWYwkrqoMcHknSUu6nvSJA3rB427ef4prVVUxnimxuKuH+Q2Ok8gY/397Phy5o4bzWUganMmxdUcGFLaX4XDpnNUYYmMpSHfbwlo31uBwquqaSPTxJ8t4+fJsq0QJOzEQBZ12AxN29c3Oyswb2Pf1YDWFUz2JlPokzUerRoWMfDIvkQwO4WsNzTYXeOKkHB+Y+5zumSW8bJnRlE4qqnNA6rLyJnTMXtmde2rk1UzBI5Qz+7JJWPA6NH753E53jaRyaQsDtYGAqS23EyxvX13DT9mPboKsKS8qlmP/lljdM9g7G+Y8b1zIwlcW0bDZXBin+shN7Mg+AnTeZ+vlhou9dSeyh/pkAEpi9zxgg+v6Vp28DxKtGRdBD6jjnGztt8MXXrcD/EgNIAFAV9Cofhf7k/Gafg+JwBke1D/UEiuQB8r2JuQASAHO6QOrRQSJvaEXR1Zc+V/GasqmphKjfyYXtpZimjaoce9xRFYWwU4OsNRdAAmCMZzHjBbSwC3N65nytKAreO3u5cGs1v9s1RCpvSAiJWFTGZI7JXxw5FiBiQ/y2LlxNIZw1x7+vs4omiq6iKArGRJbYjw8SvLh+QT/FpaG6Tl3wRzJnMJbKoyo2/3Ddcv7j3qM0uJyQXXg/rOctPLPXh9qwhxU1oVM2D3FqWJaEkAghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQZ4pFrYqfDR3ZC6AoyjeBDy7m+s90Vrp4LIBkll20IGfwX48cYUNDhDduqMGy4Gfb+lhbF8Zta3zr0R7+88a1fOSiFm7aMYAC3HhWHU2l3oXrsGzi41nMokkg6sZ1it5C/GIobg1XS5h85/Rcm+p3oIZOQXGqOGNYBZP80SmMyRxYM0Xwmt8JKvP288D5NWi+F94P0wWDr953lP97vIeL2sr4yMVL+MX2fmzgzy9dwnlLSgGoCLioDrn58j1H5pZtjHp52+YG9g8l2Lq8AoCqkIftvZMA2Db8z0OdhDwOvnT9Cs53u0n+/Cj5Wj/tdQFa8wp6HhyWjabNFCYXh9IAZPaME7y4jvidPdjGwpCV4mAKK1uUEJJXmdR0nkyigDfgwB9x/8G+Vn5hUb6VLmI/J4yp0J9a0Ce7d4LARXVo3hM7T6sODc/aMpL39h1rVMBZ6Tuh5Y/Htm1+s3OQz/5m31zbhW1lNJV6uX3vCGPJPH9/3XJK/S4+evESvA6dXz4zQF2Jl89evYzlVcGTXrc4MVUhNz8YTPDF2w5RX+LFoan4VlWzceJ5OW82WKnCsQCSZ5sLJspxgsGEeLF0VcHdHiG3d34onntZCV+9v4NPXtH+ktehKAq+9eVk94zPhS6pAQea30mhP0mhP4nq0XA1h9Ff4NxsDGcWtOW7E1gFE01CSMSLpKoKrRUBuifSPNUd4x9ft5LvPtbDeDLHFSsrcdQEyHUOLliuOJJGL/VgTudx1PgxEwWsRIFNThe/O6+NMlX2RbG4zEwR+/nhoDaYyTwwP4TEiGXJ7BnHTORxVPnBtFG9Ohg2hcEU7qUl5A5NzvUPX9eMXuI5ZXOtCLp45zn1DEzlePDwGP/4+hWUuHXcS0sWhFXpS0JM7O7knJYo7zi7gaBbns3ONLYpobRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxJnidFZenHUa131GUn1O0FV4TpCB4tGxfA4e75zk8c7Jef2vWV3N2roQ162u5I79IzzdPcnlK2YCFn6yrQ+PU2NZ1bE3/OYzRfY/MsS2W7oxDYvqtjAXvbWdyEsoUD8ZqlvHvTSCVuKi0BVHr/Dhag2jnkAQhXjlKPQkiP3wIK4lYVxtEfJHpkhtGyF0dTPZfRNYGQP/udV4VkRPaLyO0RT/93gPAA8eGWfPYJwrV1TwjrMbWVY9E3RgWjZ9Uxl+sq1v3rI9sQyZwkxRUypvUBOeCSA5v7WUn27rn+vn0BTO0pzEvn8AR60fK2sw+eNDwMyxGLqmEWd9CGMkjeKcKQq1cybp7aMEtzagHafg2dUSQj3BEAnxyjBweIp7/28/6ekC3qCTy96zjLplv38/1gIuUJgX/qCFXPP2C718YWiUo8qHlS2eeAiJR0cv8xC4pI7s/hiqW8ezMorqP/lLfd9khn+67eC8toeOjLOhoY2xZB6AmrCHXNGkPurjb69dxp9c1IzXqRHySLDUYqgOe9nSEuV3u4bom5wJVRjMFdnsd2Cl5r9RXnHroCtgHNsZVa+OJYW44hTomkhT7nMQurKRzL4JsGw8K0tRgk4cukp16NQUnjvKvQQurcfOmSjKTGBf4oE+Ije0MvXLo/jPq6EwliG8tRHlD4SJ6DX+BW2u9giqHA/iJaiPejk8muQfbzvA5csrKfGV8vCRcX4advPmlhC5g/Of51ztERRdwdUUwoznST81jOLSKI5kiLg08o8M4r62GUWTMBKxODS/EzXgwEo+5x5CVVD88+/rrJzB9K1dWBkDPepm+tcdqH4H4de1gAK5AzE8q0oJbm3ANi1czSGcDac2nC7gdvCGtTV8/cFOnuia5ImuSb51/Uo2Fm2Cl9WT3R9D0VU8q0sZ0GzevLGOQyNJPvKTZ7j1o+exoib0wisRi8YuFl+4kxBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghFsXprGYaO9GOiqLUKYrygKIoBxVF2a8oysdezomdLrYO4WubUFwaMFMYHHnDEiyvypLyhYWSIY9OPGvwx2fVMRrPEUsX+Om2fn66rZ9YukB3LD2v/2hPggOPDbF2ax1nXd2IP+xi1719mKa1YOwXMpbIMTydxbbtF+78PFa8gJU2UH0O3MtK0Cs92FkDOy1FJ68mmX0TAOQ7pnFUePGdXYXqVDEmsoRvWELZh9fg31yF5j+xoIJUfv6bkSfTBX6yrZ9M0QRgOlPgvx/s4J4DoxSPs087dZWw18HgZJb3n9/EWzbVc+nSct6+uY6r2sv47vUrufOKFZjbRgDwLC0hs310ZtmmEIFzq8nsmSD18ACTPzmEMZLB1TxTuGaMZ0k9OkjCMHGcVw2aAoAWdRO6plkKml9F4hNZ7v/+AVrWlbPhqkaWnFXO/T84xPRY5rj9s6kChmUSvKIRxTFzyVX9M0X6tmHO9XPWBXA2BOY+q34HrqYQhZ7kgjF/HytjkD86jeJUcbVFcNYHQFEojGQoGhZD01km04UXtb2ZgkmmYC5oL5oWYa+Dz1+/grFkjtRsyI+uqVSFPBJAssgM0+JPL2rBpavoqkLMtlGvbpw7FwH4NlaS3jFK8LKGefcZ4TcsITWdx7ZsrLxxUtd1IQCKhkWiLw66gqs5hKs5DKpC/uAkHzi7EccfCAR5MVSvA1etn9QjAyTu7SP50ADu9hLy3XFK3tJO+qlhVBRyHdMYUzmsonXc/Trr1tDWlM2ERAFajY9MtR/rBY6BbLLAWG+C6bGMHC+vQSPxLCPx7IL2+ESWsd4EtR4XdREPuaLFzbuH+N7jPXRNpKnxulEDTjyrSueWcS0J42wMYiWLJO7pJb1tBFQIX9uM6tIojmfIHp3GmMov5iaK15BEbGa/TU0f28fUgAPnlU1zAaGKU0O7pI5YuohVtLAKJoX+JNkDMXIHJ3G3hsk8M4Z7WQn+C2spjqTxn1cDCmT3TpC4rxc96sHVEkZ1aKd0/k90TrCtZ5KG6LFQ1UDahKKJmTdxNoVwLY2AbXN0NMV3H+vh8c4Ytg3J3My9q2nZDE5liKXkODvdLNt44U5CCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhFgUp7My/mpFUYK2bSdOoK8B/IVt288oihIAdiiKco9t2wde5jkuKiuWJ3d4isgbW7GLJoquknion9AbW3n/eU184dYDc8XgV66spDrkoabEwx17hrlmdRU7+6fnjXf58op5n9PTeZrWlLLr3n7MokW0xs/ScyrJJYv4wq4TmuN0psBte4b5yr1HyRQM3r2lkbdurqcm4j3h7VR0leSD/WhhF44qH8Z4FmMiS/lH157wGOLMVZzIkjs0iaIeK35PPTKIGnTiO7uKwAW1qCdRiNwQ9RLxOpjKFOe11Zd4AHi6Z4rvPdbD+89v4vVra/j1zsG5fhGvg9U1Id68sY4nOmOscjqpH8/gSKU5u7IU018ku20CU1fRZgvubMsGG1DA3RYhcVcPgUvqSD7YD0D66ZGZt3tfVo8VcNIznKH7kWGaVkVxbqlBAzKmjdOG6En8juLMlI0XWH1pHTvv6iOTKOAJOFh/eQPp6Tzh8vnnwXymyJO/7WTT6lLSTwzj3zITUGPlTKZu7qD0PSvn+qpuDUeVD3drBNsGu2CSuLuXwGX1Jz45XcEYy5DdPY6rOYSVNyk8Oojvmia+cOt+fv70ANURN/9w3QrOW1KKrr3wcVgVdLG+PswzfdNzbR6HxorqIG/dVM93Hu3io5csIexxnPg8xSk1Es9SFXajqSqfu3Y5yuypdyDigDc00W6p2MkiuSNTFHoTFHsT+M+vwVnjJ985jTGRg74Eqcks6adG8CwrwXtWBY7SE7+uCwFQHXKTAOK3d+NqDIGukn8yjqM+gKop+Fyn7rFDi7jxX1ALlo2iqRR6E2SeHkWPevCsjIKuUOhJkN0/AW4NV5UfLezC1RBEmT33DfQk6epLsey8WlQFBofSdN90lNdV+imtWxj+BzDen+Se7+xnaiSDw6Vx3h+30rqpAscpLqwXZ55YKs8vtg/wPw91AvAnFzbzhrU1lAdcdO8a54EfHaaQNQhE3VzwzqWEvTrTmWMF9c1BL8lHh9ADTsKvb8HKmxTHsmT3TlAcTBHc2oBtWiiqQq4vgepUUVwaWsCBmTOQq6w4lWzLpnffBPd+/yD5tIEvHtmwMgABAABJREFU7GTre1dQ0xYhPpbl7lu6aF0RJejRyRk200Npzqr2M3nTYVxNIRJ3dM+cg5XZZyZdxVnjx84YJB8ewFHhJXjZ7D6tqWhhF4qivPDEXoTeWJoP/mAH79zSiMep0V4R4PBoEtWhkrqvfyZMpTZAvjOHEcuSPq98btnygIuGqJejo0l++GQvN23vp8Tr5FNXLuWqlZW45Jy+aGzbfjYLDNuQEBIhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ4kyxqCEkiqL8BPgQYAI7gJCiKF+2bftLf2g527aHgeHZPycVRTkI1ACvqhAS27DIHZwkd3DyWKOmYJk2ecPkLy5vYzpTxOvUqAi4yBkmbl3jlj3D1Ea8vHtLI7/cMYBDU/j4ZW1sbiqZN77u1Nh1T//c59hgir4DMdo2zQ8r+UOe6Irx2d/um/v89Qc7CXoc/MmFLSc8huLTca+Kktsbw5x967JndSlq6MSCUMSZy8wUmfzFEYq9CUJXNqI4NezZ4BwrXcTdHDqpABKA2oiX771nI1+49SA7+6bY0lLKZ65eSlnADUB/LM3bzq7nW490c/2aKv7skiU8eHiMt25uwONQefjoBP/7cBf/fFEr1Xf2UzQtXOfWMP27ToKX1WOMZEAB79VN5I5MYWWMmYI5XaU4nJqZhKKAdWxO2b0TZPeCfVk9j93Zy9mva+axX3XOm3d/T4Ir/2QVTpcUs70aqLrC9tt6yM8WFmeTRbbd2s3rPr52Qd/J4TQHHh1m0/ISzHie5EMD8763i+bcnzW/Ey3gJHFP37w+rvrgic/NqeE7t4apnx0id3hqps2rY0Y9/PDW3QD0TGR43/e385sPb2F1bfgFx+yOZdi6vAKfS+fxzhhLyvx8/LJWRuI5on4n/3rDanpjaaYzRTzO05lr9to1OJ0jW7D4m1/vnWtbXx+hOuzmtr3D7Lh2LZl7eue+s5JF0o8P4XxTG2aySKE3SaE/iavGjzGaITmaIdcdp+xdK1C9UvYuTlzE78LREiH+5Cj5rvhMowLuTRVM5IqkckX87lOzT9mmTfL+frDs+e1FC73Ci6KppJ8awZjIEriwlulbuvCfU4WiKLiaQgAEom6GuxMMdx/LQ6xuCzMxkDxuCEk+U+TBHx9iaiQDQDFv8sAPD1FS7aNydkzx6vXA4XH+9c5Dc5//vzsPE/U6ubQ6wt3fOTATxAAkYzke/uEhvvbmNXzhnsN0jqc5tyWKEwXP5iqSvzhMbu8EAI66AHqph+JAiuJAam5sV0sY07Dwn1cNqoIxmYXawOJusHhVmxrNcOc392MaMw826ekCd31rH3/06Y0kJnP4Ix623Tdz3+p0a9z43uVM/+IIAHrQiV20yB+dwrOqDDtn4moLk++K42wMgmVTHEpTHErPrS/67uWnfBsGprIk8wY/f7qPD1+0hAvbS7lqVSVNNQHsugCF/uTcv6v4LqrlmUQcVYENDSV87tpllAdcfPvRbn7wxMw90lA8x8d/vouobxPnt5Wd8vmKFyYhJEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEmePk0gBO3nLbthPA64HbgXrgHS9mAEVRGoF1wFOnenKnk23baEEnint+UIF3bRn4HPx/dx7mya5JbGA4nuMLtx0k4HZwYCjBM33T3Lx7iHsPjvKWTXW8bm0N7ZV+gh7nvLEyicKC9Q4emiafPfFij0eOjC9o+9UzAySzxRMew5zMo2oqwa0N+DZXEtzaAKqClcif8BjizFQczVDsnSnmTTw0QODiWvwX1BC8opGyD63B+SICFY5nTV2E771nI4986mL+950bWF59rOg36nfx1fs6mEwX+N7jvfzkqT4+cVkb33mkm72DCX7y1Ey4Q7umzwSitEXI7pnZn+1na5htSD85TOiqJmzTInRNE1qVb64Qv9CbwN0emTcnxe/AUe5Fd6gUCxbP139gkmxy4bEnXpmKeXMugOS5bYWcuaDvs/20gBM1OP987G6PgPN55/sNFfgvqkVxamhhFyVvXYqjdmEh/O9jZoukd4wQurIR/5ZqAhfW4t9SzVT3FEH3sYAQ07LpGEv9gZGOeap7kv9352EKhsWHLmymtcLPV+49wjN90/zjrQf5h1v2E/W7CLglgOR0KfU7ebwzNq/tnJYot+8dxrZh2q/B88KfvOvLsQ0LRVUo9CeB55wHgWJvkuJE9mWfu3j1yQ4kCZxXM3P+Obea0BWN5LePUqJrHBlNnrL16GEXvk2V89pm7qFtHBU+Cr0JjLHMzI6tgJ01QFHI7j12H1veEKCmPTz32eXVaV5TxsDhSY4nkygw1rNwGxLjcqy8Ftyye2hB2217h4lPZOcCSJ6VnMjR05+krSLAn1zQTM6wGBhJYRYMQpc34j935hrtWVqC8zjXeXdrGCOWBYeGOZXHmspjmwvvMYU4WanJ3FwAybOyySLp6TzToxnqV5bg8s3c27WsikIsi501UJRj9wuFviSKrqB4dXwbK1CDTgo9CdxL5wehqrPPSqdaic+JqsBEqsC/3X2YeNbAoSno/WkcNX4Cl9bP/FvD5Q1YmSLLoj4+dGELn9zayqraMD2xNDfvWnhc7xmMn/K5ij/gOTeglikhJEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEmWKxq4YdiqI4mAkh+Zpt20VFUU54YUVR/MCvgI/Phpk8//sPAh8EqK+vPyUTXlS6SujKRnJHpzEnc7iWhHE2h3CHXXz+dSv5q1/u5p4DowB87NJWWsv9PNU9UyhZFXLzlk31pPMGHqfC8erUPAHngrZorQ/vcdp/n4qAm3dtaaTE68C0bTRFoWM8hUN/UX+PZHaNgwqqW8fKGmBD4ILaEx7j1eaVvu+a2SK5g5NYqWNhNHbWIHFXL4pTpfQDq3DVndwb3G3bZt9Qgn2DcZy6ypraEEvK54+VzhUXFIzF0gUOjSTpGE+xubkEt1PD7dAIe2YCRWzDQnHMFOZbmSJa1I0Zy2HEcsRv78Z7dhXu9hLc7SUUR9Jk94yT75jGf14NvhI3he44dpmXVIWXx392hLVb6wmVexbMP1rjw+V5dQc0vNL33xfDE3Ci6eq8wk1VVfAGF55HQ2UedKeKrSkEt9aT74hjjKZxNoZwNgbn9r9n6WE3gUvq8J1VgerW0fwnfm4GQFGwUkXid/SgeHQwLOyiheviWj588RLSeQNdU+mfzBCcPQ5ecHsdGmc3l3B+axmJbJG2igANUS9HRmdCTI6MppjKFPG7T2y8M82rYd+Neh24nhcyEs8UWFMb4tJllTyeyfH6G9vIbR/FTBZwt5WACtmDMbI7ZwIZnA1BjNH0vDEUbbFz6sSLcabuu4qikLinZ+b8pinYORO90ovboTFZWBjWdNLr0VT859WgenVyByfRSty4GoMYqQKJu3soDs7sz55VZeSPTs/ODezn7NeBqIfVF9dR0xrBsmxsy+apW7q44MY2bMtGUeff27p8OoFSN8mJ3Lx2X8h1yrbrteJM3X//kLqShfd41WEPnuNc/90+BznF5ra9w7zrnEbObYlSEvVQiOXI3Ns3E5hj2thFC2dLkJJ3LCf16ACYNu7lUbIHJ/GfX8P0r46CZeNqDQMn/qwlXj6vxH33eDxB57xAEQCHS8PtcxAq83D3t/ez6qJaVE2hrMILs4GjVsZADzlBU8C0yTwzBrpC9O3L8W+pZuLbe/FtrMS3pZpC1zSO2gD+c6vRj3P8vFTNpT4+fdUy/un2g2QKJj9/up9/vWElalYh+eQwqAqqW8PKGuhVPlavrONgLM3QdI7eWBqnrlERcjGemh+EGva+Mu9pXw1sS4I7hRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQogzxWJXxv8v0APsBh5WFKUBOKFXzc6Gl/wK+LFt278+Xh/btr8JfBPgrLPOso/X50ylKAooM28Kdi8vARRUl4ri1lBVlaWVAf7huhWMp/KU+JycVR/B49TRVYWrVlaysbGEf7njIEVzZrNL/X389ANn01pxLKzBG3TQsr6Mzmdmi47dGhuvbcb1Igptzm2N8te/2kfn+EwButuh8o23b8DtOPFdSfE7cNT4KQ6msDIzb7t1r4iiHaeI77XilbzvAuQ7ppm66Qju5VFcLSHynccOa2djCL305AvPdvRO8dZvPUVhNlmnxOfkpx/YTHtlEID9Q3H+5Y6DrKgKzVtOVcCaray7fe8wX712JRVDGcoKNumgk9yRKUJbG4gPpUk/NUzwknrMRIHiSBrP6jI8y6OoTg0AV32Qsg+tJt+XJD2VJ+1wMNkcxhVwsu22HgoZg9HxDEqDl/qzyujbPnOMOVwa697QjNv36i5me6Xvvy+G7lRZf2U922/rmSncVGDdFfXozoXFwZFKH9d+dA2xyRwRl46rPYKrNTxTnK8oOCLuef3zvQmSD/RhjGfxbqzEu7YMPexeMO7vo7l1/Fuqmf7lUezszLlVcahEGkN86ftPYlozfzUbGyM0Rk/sjfRbWqIMTWf50l2H59rOby2l1HfsfF0wTl2wwGJ7pe+7O3qn+OET3VzQWo5LV8nPhuM8cGiUv7pyKZ+8aTeWDXc3RvjcOc1UKCpkTRSHipU2cNYHcDWFUINO4rd0zY3rWVf2ks7b4uV3Ju67ecPEVeUj71SxCxbM5pJ5zq7i/u4Y5zRFT+n6HKUe/FuqcbVFMKfzaD4HTn8YxQZF13A2BLBzJtn+JFqJe6YQvcxL9kAM15IwqlMjUuWl78Akh54YRtUUVpxXTeeuMcobg5RU+eatzxtwcfHbl3L71/dgFGeOtVUX1xKt85/S7XotOBP33xdy3epqbt41RCI3c30NenSuXlVFWY2fs65pZPttPQComkLjVXWMB1XedU4jT3TG6JpIsW7rSmrrAmS8+tzzD4CrKYyjzk/o2maMWBYrXsC3uZJc5zTu1jCOci+Oci+KJiEkZ4JX4r57PJFKL+e+qZVHf3kUbFBUhYve1o7To7Lv4UHWXV7P3gcHyGUMrnvXMhxlHrQSN+ZkjtTjQ4SubCTfl8TOGvjOrsLZEETz6JT96RqKg2kUp4r/7Er0Uu+CQKdTxeXQuGxZOQG3zsB0lqBbpybsxhHQURwqdtGaO9a859bwkV/uZjQxEzhSEXTxw/dt5k8uaOETP9+FMXuP3Bj1srY2/LLMV/wez0nCsc1X7jOFEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPFqs9ghJLfYtv3VZz8oitIHvPeFFlIURQG+Axy0bfvLL+P8Tit7tqDRTBVRFBvV6UFRVUbiOd73/afnimYAoj4nN3/0PGoiXjbUh7n/0NhcAAnARKrAY50T80JIOraPoekql7xzKaZhU8wb7Lq7l9r2CA6XdkJz7BxPzwWQAOSKFt9+pJvNTVE8zhMbw5zM4WoO4WoOYUxkcVT4sDJFzGQBzf/aDSJ5JbKKJsbUTDEaQO5ADN+Wapy1AYoTWdytEdztETTPyYVwFAyTbz7cNRdAAjCZLvDwkQnaK4OMJ/N8+MfPMJrIcUFrGdUhN0PxHABOXWV1bQhFgbDHQUN/GvWJERKaMhM4kipgJAqUvH0Zha44aAq+c6txlB4r9LRtm55YhtFEjvKAC3+5j19958BcrZQn4KD1rAoqV0SY8kB/PM9dSpbL39CAZthMqhZ/+cAhvt8YpMTvegm/tDhTpCbzdOwYY+O1TZiGjaYrdOwYo7a9hFCZb0H/mrYI2VQBYjmMkZngJS3kxFkXRHUduwQXh9NMfHvv3HUgcWcPVqpI6KqmEy48tjIGuYOThK5qpNCfQnFrOMo8xHqmCbh1pjMziQBP90zRNZ5mSXngBUYEh6by/Sd65rU9cnSCT2xtA0BTFSqCbgzTQtfUE5qnODX6JjO893tPk8gVuWRpBR+/rJWh6RxF0+KCtjK+eOsBZmtqebhnist6dvC1N6+lbjxPY8SNXTDwrC3DzlkY41kCVzZgTuZxNc1cn9UTvC8Q4lk942kcfXHKLq2nOJ7Fzhk46wLkDk2S9FtYTae+Xl/zOxfcO7qq/JjZIvnuBPmDMQKX1qOHXVg5g+QD/ZjxPKXvW4m7NUIhazJ4eIrVF9diWzYdO8ZITeVZcV7NghASgNr2CH/8mY3EJ7K4fQ5Kqnw4PYv9OCVOh01NJXzjHRvYP5TAtm3qS7zs6J1keXUQuz1AmaseR8Em7VD4++1duJ0af31FO997vIeWMh+TR+JMGTbR82sxp3OYqSLOWj/5rjjO2gCx7+/Hu6ECLepGscFRG6CQLpKv9OJvCJ7uzRevMrpDY/kF1VQtCZGOFwiUuIlUeYkNpOjdG2O0K0HrxnKq6wM4tg2T1FVCVzVSHE5jTudRA07CNyxBc+nzQkacVX6cVYsTzFQwTL7+YCe/3DEw13Z5WxntlpvgZQ0UxzPYORNnnZ/sZAanfuw+dTSR5469w/icGl+5cS39Uxk8To1V1SFW1ISOtzqxGAzjhfsIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEGJRLHbV3K+A9c9+sG3bVhTlZ8CGF1juXOAdwF5FUXbNtn3Gtu3bX5ZZngZW0SR5fx/F/hTOxplCs0JPL/5zqxldG50XQAIQSxfom8xwTkuUTU0l/Hz7wIIxn79MtNbPwz89wpFto3NtK86vRnOceOF490R6QVvneIpMwTjhEBJFU0g9Moji0dHDLvKd09imjf/8mhOehzi9bMPEmMqT3R8ju3cCLXisADj9+BCKSyNweQP+s6te0noMy2ZgKrugfSQxEzQyOJXh7OYoZQEXO/um+dSVS4lni4Q8GhdEArjSBk+8YzOHU1m0W/uxAUybxD29qH4HkTe24lkWxbuydOE22jb3HBjl4z/fRaZgsqE2zJevWo7To5Offat2Nllk74MD1KyJsnlJlMFnBrj76Dh3Hx2fG0dVIF0wKXlJv4Q4U7h9OvGxLNtu6Z5rU1QFT+D3B+14/E4sh4qqK2BaKE4NR4lnXp/CaHougORZqSeG8J9bjR5xn9jkNAUzWSB+Zw96mRe7YJLZPorjsjoKxvyxx5P53zPIfDnDJPe8ecHM8dFa7udjl7Xy9QeOsqo2RFXIc5wRxMulL5Yhnp0Jlrlr3wibW6Ls6J3EtOGi9jKGZ8+TzzWdLVCugKUrFEcz5A5PYc2ezxSnSvnH1uOIyt+jODmpgkm9x0H8lm60sAvFpZHdH0Mv9xJ3KMc9l7xcNI8D7/IonvYIUzd3kN01hhE7dkxkdo2BruB36WQSBXbe3TdveW/g+KF4iqIQqfIROU5AiXh1S+SKfPHWgyRyRTwOjaNjM6GMa+sijCfzfOr+w/P6u3QVfTZEbDyZx9nkwrQgcW8PatCJ6tGxswbuZSXku+NgQ2b7KIpTRS/1oAadfCNo4+/P84ml0UXfXvHq53BolD8v4Mbl1XG4NXLpIgceHWbpu0IUBmb29cneBM46P2gqXr8D/SRDJk+VsWSe3+4cnNeWyBsobpX4HbPXIadGdv8E+vk1ZPLmvL6HhpP0TmY4MpqktcLPRKrAlpYoq2vDOHQJ1ls09rGAMsuUEBIhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ4kyxKCEkiqIsBVYAIUVRbnjOV0HgBaurbdt+FFBeqN8rmaIoqN6ZQp5CT+JYu0fH69JQFbDs5/aHoEenN5amO5bh/ec1gQL//UAHvZMzoQ3nL5kfrFDVEqKkysvkcAYAb9DJsi1VqOqJ/bSmZVN9nCLzrcsrKPEdv1jzuNvq0nDWBSj0JylmZwpNfOdUgfvEQkzE6WVMZknc14de4iFxTy8A3nXN5I5MgTm7k1o27lPw1navU+cd5zTw6V/vndd+cXsZk+k8KgrTmQI/f7ofgDv2jXBOcwn/s2UJqW/tIzM7nw1vaiXh1rALx4rPrFRxQejDs+KxLMP9SYqTWb569QqcKFh9GR741gHWXV7PU7/rmquXqt9SwTAG+aEEIY8DRZlXS8U1q6ooD7pe8m8hzgzhCi/n3NDCY7/omGvb/LomwuXe37tMcjSDsXOU9IOzYVEqlLx12bzwG/U4xY6qS5tJsTlBtgq+jZVM9ycxxmbO86pXx1/lnxdCoihQFvj9+2QyV+TQSJJYKk9txMP1a6q4efcwYa+D162tIeJ1sL4+TP9khn+5/SDvOqeJTN7Atm0U5VV9qT6jBD3HbuE2NpWQzhl8YmsbE8k82aLJ1mUV3H3gWOhYa7mfLT4foVQGLVmgOPS8UDH7VX6jJV52jVEvzpxN0e/AnD4WdOTbXIXVO4LzdBR1KwpW2pwXQAKAqjD1s8Ogq7zxgyv46Vd3w+y1e9VFtdjYFPIGTtdi5zWKM9VoPM+hkcS85zGAoeksS6sCC/pft7qKmrAHh6aQyBnUt4ZRkwbuFVFy+2M4yjxoYTeJe/vwbaycW84uWBSH0ii6Sp9V4PL68Mu8ZUIcYxk2m69v5rFfHOWy1zfhShUoKMycH20o9M0EkihnQEiH16lRW+KhZyIz11bqceBZESV/dGruOqS4NHxNIWIPF+Ytv7mlhHsPjWJYNgeHkwDs6p8mkSsQ9Z9gAKA4tYzi6Z6BEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFmLVZlXTtwLRAGrntOexL4wCLN4YxmW/ZcwQyzteKKcyasoybs5k8vauHrD3TO9X//eU1EfU7+896j/HQ2gEFTFf7fG1fxwyd6eO95zax7XtHaaHeCqiVhWjZUgG1jFC1GexJUNIVOaI6pXBG3Q+PPLlnCD57oJZU3uGplJVtaoi+q8NwYz+Ko8eNqi2AlC2hhF4X+JHaqCH+gMF6cfsXpHOmnRsjsGCNwSd1ce+qRAUJbGzCTBdSgE3drBGe1/yWty7RsuifStFf4+fRVS/n2I914nBp/dUU7tREPt+8Zxu/WuWv/6LzlNpUFyfyu61ggChC/rZvQ1U1M/+roXJsWduGomT/HfLZIbCTDfd89QDFnsP6KBnb+upcV51fz9P0zARIHHx9m47VN6B6dftvg152j3GiW8N6vP0Z91Munrmjnu4/1MJHKc/nyCj65tQ2XLgE7rxbJqTxdu8bZfH0zRtFCd6j07o3RvK6MSLlvXl/TtOh6ZhxtOofz2QASAAumfnUUZ40fPTJT5Oio9qNF3ZjPKZQPXtWEHjrxc2IuXSCzc5TQFY0YiQKKrqK6NMz+JFetrOTWvcOU+l2899xGIr7jv70+kS3w5XuO8r3HewDQVYWvvnkd9WEPYb+LbzzUyUSqQEPUy7u3NHL73hGCHp0/+eEO/u7a5VzYXn7C8xUvzZIyPx+8oIknOmJEfE6yxSx/8sMdcwXyX33zWgDuPjBKY9TL/165AqM/RfbBftRNlegVXozRY4W7/kvrUcNyDRYnL+p3kZgYx7e5CgwLK2+ih11knhnlY9c2c+/gNK3lAUr8Jx5c91IpqkLg3GpyB2JzaX6KQ8VR6iHz9Mz9g9Kb4Lw3LSGbMtB0hf6Dk8RvybLqwmqqWyM4PRJEIqBoWVzQVsaDh8fntVcE3aysDvHvf7Saf7njELF0gYvby1laGURRFP7jxrWMx9JM7JukvTWM6tQIXlaPGnLN3ZeqXh3Fo2PPhjOiwPiqErq2dbGk/KXdTwtxosb6EgwcmuLgY8Ocf2MrobEM+fEs3rXlZHaOzfVzLY2gl/3+8L3F4nPqfOKyNj55027M2fP7R89uJHlnH6HLGzGTBVAVVI9OcSLDO86u56btA6iKwp9dsoTasIeiOT9V6JKl5di2RLItquekd1qmhJAIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEGeKRamqs237d4qi3Ar8tW3b/7wY63ylsXMGqSeGCF3TjBkvoGgKikvDmMwyHNLZ1TfNX1zeRt6wcOkqu/qm2DeYmAsggZnQhn+94xBfeN0KdvXFOa+1FI9z5q/Ysmx69kzQszc2b71VS0KsurAWRX3hYhu3rlAwTEaTOd64vgaPQ8MwbYqm9aK2VfFopJ8cBk1BdetYmeJMIdMZ8EZlcXx20SKzf4J8V5z8kSkAFE0FFbDAjBeI39mDXuah7AOr0IIvrZA9XzT59TOD/MMt+8kbFssqA3zrXRuoL/FRNAz2DSX5x9sO8qELWxYsW+dzYiWf86ZrXcXdGkaLuil930ry3XG0kAtXcwhH1ENiIsP0aBYU2PfQIG6/g8R4lnWX1/P0rd34wi5iQ+m54eJjWbbd0k1pnZ/HGnQuWVXJ/z3WQ8G06BhL8T8PdXL96mquW1PNmroQbocUL7+aTI+kGT4aZ/ho/HntmQUhJFPDGe75vwNcfW0jKOBujaBF3RT6khQHU1gZAyIzffUSN6XvXkm+expzKo+rOYSzPvCi5ubUNfIhF/E7e1A8OhgWdtEi9PoWSvNOPnLxElI5g6Jpkc6bxx3j0EhyLoAEwLBsPnfzPv7lhlV85Mc7Kcye73tjGb79SDdvWFfDcDzHWzbX8xe/2M3NHz2P6rDnRc1bnByvS+cjF7Vy2bIK7j80xo+f7JsLIAH485/t4paPnsv7zm8inygw0ZWkdPZSb8RyBC6owYwXMKZyOJpCbN81jjOWY/XFdQRK3Kdno8Qrnup1MH1LN4pDRXGoWBkDd3sEW1P45zsOsrwmQIk/uqhzcjYEKfvQGvKdU9hFC73MS2bnsQCzYm+SzmSR4Y5j53VFVVh6TiWjvQnqlpYs6nzFmaloWlzUVsZ4Ms/+oQRuh8p7tjThcahYts2u/mmuWllFyOvgyc4YX7z9IDYQdOtcXl/KPXcdoK7GD70JMrvHCFxSPzOwAoW+JOHrWzDGMhi2TaLKy95khgvbyviPew7zD9etpD7qRdfkWUm8PNKJPImJLH37YkyPZhjcP0m5YVIcTqNXeAld34IxnkGPevCsjKL9njC7xXRwOMG/332Ej13aStG00DUVv67iiLiP3QubNnbBxPOmVh7vjPGecxsBuHRpOT2xNG/ZVMcvtg9gWDZbWqI0Rr0v+t82xKljGRJCIoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKcKRatOt62bVNRlK2AhJAch+pz4DurEnMyR+7IFIpDw7u6FEelj5FEnsc6YzzWOT9A5NJlFQvGmUgVKJo233yki83NJXN9jIJJqMILzwshidb4TyiABGA6Z/Kz7f24dY0tS2YKSJ/ommQ0meO6NTUnvK2KohC4pI7UI4NY6SJa2IV/SxWqUzvhMcTiKoykmfr5YVxNIfSoG3M6T/qZUYKXNZB8cAC7YKIGHERuaH3JASQTyTyHRxN8+jd759oOjiT52M928R9/vIZ7DowymsiTNyz2D8U5u7mEJ7sm5/oOF00c1T6KQ2n0Ug++zZWkt48y9aODeDdX4d9ciRZyMTWWIXNkiru+uY8V51Wz96FByusDZBIzASaqplLImRAv0LByYcF0SX2AC5f5GU3mear72PoTWYMfPdXHhsYIm5sXt9BavPw0XUXVFKznvDldURW044QopSaz2JZN0aERvbaZ7J4J8jvHcLWE8a4rQws55/V3lHlwlJ18gIfqVHE2hiiOZykOpEAB74ZyLLdGPFvE73HgdqjctH2AoNsB7QvHGEvmF7RNpApkC+ZcAMmzBqezlAWcqAo80RmjMuRmMl2QEJJFFPI6SGQNdE0lmTcWfD8Uz5LMFamydBQFsgqUXdNM7kCM6Vu6cDWHcLeXMJQqMjKQZnLbGLpTY/N1zadha8Qr3XSmgO7S8J1dRXrbCHbRQK/w4l5aQl++SN6wGEssPMe83BRVwVUfQNEhfk8vqceHcDWFCF7ZSOLuHpzLSoj9pmveMpFKL1PDGYJlbib6kwSiblze0190L06fyXSBf7ztIFeuqOSSpeUYps2te4dorfAzmshzcDjJjr4p7OeEQR0cjvPezQ0UEgaRKi+H98a48JI6Enf3oigKilMleHkjuYMxpn/bgbs1zMSaKNf+bAfF2fsMr1PjJ9t6CXudvPOcBoIe5++ZoRAnb3IoTXIiR7jCy+CRaUb6k1ibK3GHXWh+J6mH+lH9TvQKL4rr9AcsHhlN8lhnjL7JDF++58hc+7Xv2EiwKYQ5nac4nAZVwbe5ipxbpXM8TedDXTg1lXNbohwcSbKzb5oPXdSCqsCe/jhfu7+TdfURquRe9rSwioUX7iSEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiEWx2BUkjyuK8jXg50D62Ubbtp9Z5HmccRRNxTZtUo8NzbXFh1KUvn8lfpeGU1PnFYAHPTpVIQ+qAtZzit2WVgbQZjNFDg4njoWQFE3K6gOEyjzEx7MA+CMuatojJzzHgEtnS3Mp33yki+29U3Ptb1x/4gEkwExRfiyL/9xqUBWsdBHbtJlXtSfOCLZhURhKURxMgQ357jiha5op9CcxYzlSTw4TuKQOZ50fvdSLHnpxASSTqTyD01k0VaFvMoPHofGFWw9w1aqqBX17Yxme6p7kkY4JyvxuAO49OMYHL2imvTLIoeEEnzu7ieqUgevSUhJ39eBdV0H89m6Y3bVSD/SDbTNU4ubIo8PoTo1cqoiiKhSyBhODKdo2VTDanQBmAicKWQNVU2cKkkcyAHhDDkKrInzm1n0srw6yqamEbc8JIgFojPpe7M8tXgFUp8q6y+vZcUfvXNvay+rQHAtDSLyzx4Nl2yTu6cXOmQDk9sew0kW8GxYGSb0kRQtsG73Ug7u9ZKZpNI1i2jzcMcFE6lhh3+8LCinxORdcV5ZVBQi4F94u+F06y6qC/PWv9nL9miouaS9nz8A048k8q2pClAZeWiCReGEHhuIMTmcYT+apL/HSN5mZ+86hKZR5nSSyBkenM0Q6kqzfUkXyrh6s1Myb5nMHJjFiOVzn15CYvTc4+Ngwqy6qwSt/f+JFiqUKlFs2VrZI4KJaAMzpHLZpE9A0oj4nJb7TE6BgTGaZ+M5+rPTsvn9wEnM6T+iqJvQqH6vPq2b7PX0A6A6V5edVk5zMkUsXiY9nefCnh7nkHcsoqZJr+2uV16GxtDLAbXuH57WXBVx0jaU4uznKBW1ldI2n+e2uQc5viPDZFbWYHUlcJW7WX1DD47f3YOsq7pVR1BIXJW9dxtSvjmIlZ67P2X0xAiMZ3rOulm9u7wdgfX2EfUMJnuyaZG1dhPNaSxd928Wrl2XZDB2dJjaQxOHW8IVcBKJuzKKFszmIM16YeZYCzHiB+GAKPejCs/z0BS3migb/ftdhlteEqI14uHFjHUXTQlNV/ChgWjMBWMtm7oXNnIGmKtx+2XLCHgfTfp3BosWSMj+HRpIcGknOjX39mmoCZ0DIymvKc/4NyDKKp3EiQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYR4rsWusNgy+/8vPKfNBi5Z5HmccayMQeap4QXt+e44hWY/H7+sla890EGmYBJ063zysjZMy+Lvr13Bf9x3hOlMkbYKP395RTsdozMBCpUh99w4RsHC4dJo3VSBosyklKiagtOtnfAcx5I5zmoM0zFezv2HxtBUhRvW1dAQ9b7IjQUFBcWhYZsWWtBF7ugU3vXlL24c8bLLHZki9sMDBLc2zDTYkLy/D//5tSiqgl7pxVnjRw+7//BAx3FgKM6f/XQn57SU8kzvFB6nRtDtoHM8jUtfGOhQGXQzMJXl4HCSay6v4oHDYwB88+EuygMubr5xA+aPD5HOGqQ1Be/aMmyHOhdA8qz0UyOklkXxhV0Md8aBmWMBIDdbmF+/ooQj20bYcGUDT9/ew867e1l+QQ2rttYxmM6T82r0GQVqIx4ePjrO31y5lEzBYN9ggqBb5++vW8HyquCL/k3Emc8qWgTLPJzzhhYKOROnW8Pjd2AZ5oK+JVU+zrmhhYhHo5Cb/32hJ4E5lUfzOE7Z3FSXRr43gR6dCRhRFND8TtSiRXXYw0SqgMeh8aGLmnEfJzQFYDpd5C8vb+ebj3TNXVfetKGWomnxts31/PipmSJ9VYGPXtzCP9x8gKjPSdTv4jO/3Tc3zuvXVvP561cS8p667RPzTWcK/MVNu1lbF6bE5+A95zbywyd66ZpIU+p38ueXtOJ26YwlczwzOM1bV5bhNm3yqfkFnsZoBg8KRnEm6MwfcaE7TvzeQIhnuRwqdtFG0VUUVcG2bPQSD+kDExTq6/nyjasZS+ROy9yKE7m5AJK5tuE0KAqxb+9j2euX4H1LG5n4TBhE184xVl1Uy54H+ll2bg2Tg2l23NHDxe9ciq7L8fFaFPU5efeWRr7xUBed4ykCLp2/vKKdoekM5QE32aLJD57sYWNjCTduqOHjpRGyPzoEQAFwtkc469I6CskCeokHczSL6cjPBZA8y5rIcsnmMr5JP41RLxe0lfHPtx8EoGM8JSEk4pSaGkkzdHQKy7QJuHWMoknTmlKWt0Uwtw2Rs5UFy2R2jZ3WEJJYusiDR8bpnUzzV1e08xc37caYTc9bc80Klu6MzTwHGhaKpmIl8riHMwTvHcACQk6VyFvb2W0X+dCFzXz30R4KpsXGxghXrapEWbjJYpHYRuGFOwkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQYlEsagiJbdsXL+b6XklMw0T1OyE2vzhTden43Do/eKKXd29pRFUVCoaFU1fIFEye6Z/ii69biWHZWLbNr3b0s7ExwiVLy3HPK5JUyKcNnB4Nl8eBokA2WZh78Wx8PEM+Y+ALu/CFXMedY7Zg8pV7O3jb2fW8fl01qqJwaDjBUPzFFZQqDgUrWSCxe3z2s0roumak4ufMYqYKTN/SCTYUBlO4l5eQOzCJlTFIPthP6TuX424vOamxk7kin7t5P53jaa5fW8OB4QTvOLuB3+0eBOCBQ2O8e0sj33+iB9sGr1Pj01ct5bO/3Ydp2TxweJy/vLydm3cPgg0fu6wV71CaRNaYnbxNZscYwasWBuSofgeZVJGx3iQ1bWEOPTHCaE+CxtWl9OyZYM/9A9Qui7D59c3YAQeX/vlqMvE8g/kC+YBKV8Hi0YNjPN4Z44b1NVzUXkZ12M1/vnkdRcPC59KpK5lZby5dJBnLoTtUguUeNO34wQ/ilcPh1IgNpGb+7NJJx/MkYzki1ZUL+upOjVUX11I4NMmCkjpdxbasUzo32wJ3S5jp27qxZ48FR7UPd3OIf7ygFduhMmGb3HdojPHwsRnliyYdYylGEzm8Lo3vPNrNDetq8Lp0+mIZfvRkL/9x41qOjCb5i61t+N06TaU+fvREDz6Xzt9c1c5Hf7Jz3lx+u2uIt53dwMbGkztHiBd2cDjBwZEkb91ch41CMmfwkYuX4NAUusbTOHW4a/8IPbEMPpdOr9NiU9BB/vkDqaBpx8LJ1l1ej9O92Bl14lWhYGKOpjHjBTI7ZoLC0FVCf9zK05NpSvwu3A6N+ESWfLqIL+TCFz7+PeepprqOc/1VFazMTDBJ6u4emt+/ir6emSC/YFk1j/2qgxXn15BPFynmTbp3T3BOsog/IiEkr0Ulfhd7BuP8+SVLsLHRVZWvPdDBVSsrmUgVGUtk+filbXzl3iP87u1nYX3/0Lzli4enKFlXTtHvQI9lST46QPCiuoUrUqC+0s/X3rKWRzpifOmuY+PUhj0v92aK15hkLEcmUcQXcrLz7l6a15RTt7wEa/8EVtLAUbnwWUqLvPjwyVPBNCziY1nUgsFHtjQTjbr55sNdcwEkAF/Z2c//rqojcXfvXJv/igYy20fnPtsFC3vnOBONHoans3zlxrUULQvTtPnGA518/e3rF3W7Xuts4Nl/BbJM43RORQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQz7GoVaaKooSAvwcumG16CPiCbdvxxZzHmUixbDyrSyn0J2G2kEYNONDLvSwp8/OuLQ38vzsPA+DQFO74s3PZPZTE7dD4s5/txLahMujmIxe3sKwywMNHY+QNc258l0cD20LXNZKxHKqmEIi6cbhUjmwb4cGfHKaYMwlE3Vzx/hVUNIUWzLFgWly/tpq//e2+ufCSxqiXz16z7EVtqzGeRQ04CW5twDYtUBRSjw/hXhaF01PTJI7DLlqYiZmggtz+GO5lJQS3NqAGHLjqg+jlC4vSTtREqsD2nikADHMmiKFjLMWqmhCPdcR4pm+avGHx8UtbqSvx0jGW4rGOCVZUB3mqe5Jt3ZPsHYjzia2tvHF9LVG/i6k7uxeuyLJRo26sZ8N9FPBcUsfh7x7EKFq0rC+jui1M794YrRvLOf9tbcTTRaY0myczGey8wg+f6mVFdYibdw8RcOl8Ymsb+4ZmTlm37B7iH65bweXLK3E55hclT42kue8HBxntSqBqCmdd3ciqi2txex0n/buJ08/p0aloCpKazFPImTNF9BEXxZzB1EiaSKVvXn+HQ8PwOnC1hMl3Ts+1+8+uRHGc2lAa27BIPjqE/+wqUBUUBYx4nmxnnO2PDWMZFk1X1TM0lSXQPnP5LxoWP9/ez9/fvB/bhs9fv5w3bqjlu492Y1g2Ya+DP7+kFcO02NwcxbJtfrF9gA9d2IymaWxqiuDWNfLGwkCVVE4KCV8uHWNJYuk8m5sjTGUM+qcyxFIF7j80E/zQVuHnn1+/kq8/0MW/X7WCqcE0dsImE7FxL4+SOxCbG8u3oRJLnzlHhco9VDYFT9dmiVc4J5DbOY6zPjB3j6eoCsmJDPeOTvLhi5YQGC9y03eeppA18EdcXP7+FVS1hF/2uellXjxrysjOBuAB+DZVzh0LVt7E5dYpqw8wcGiKfMZg5QU12Bb0HZgEoLTOT3wii6qpeIPOl33O4swS9bs4pznKh3/8zFxbS5mPeLbI/qEE7z23kc/+dh83rK/FbYJZXHhdNDMG3RM5ViyPgGGT70ngWVlKdt/EXJ/ChnKeGk/ySHeMEq+Tojnz0HXVqkpW1y58PhPipXC6NY48NcLm1zfTtqkKBchMF/CmDYyxDL6NFeQOTmLP7s+KS8O7pnTR55lNFdh1bz877+7DtmxqW0OUXxxgaDo7r1/XZIbuOi9N717O8ECCytogRkcc4/lBr/E86byD7liGD//k2DH9z29YSW3k5J9xxUtjmwtiG4UQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIcZos9qvuvwvsA/549vM7gP8DbljkeZxxtKALy7BmijbzBmgq2DZa0InTqXNhaxlRn5OxZJ6mUh9hn5NUrsjhkSQfu7QV07KJZ4v87Ol+3n1OA36XTt1zCmhcXgfugJMj20YJl3sxixaj3XFCpR7u/b8Dc6EiyViOe/7vADf81Qa8gYUFlo8eHedTV7QTzxZxOzTSeZPCcYrs/hBHbYD4bd08t2QocFk9+nHWJ04fLejEt7GS9JPDAOQOTpI7PEn5n67F8byghRejfzLD0HSGpqiX7lgGl66hqQpPdMX422uW0TGWYjSRZ/9QgtYKP/Fske8+1gPA289u4KzGEsYSOc5uLuGcllKifhcAhbrAgnWldYWH14Q51+fBU7RIFG1275+kaW0ZR58eZccdvTSuKeXiP1nBSNFgj1Ek47FYH/ZTPV0glijw/rV1HEnPFK4l8wb/df9R3rKxnqqQm3NaorSU+dG1+WESpmHxzN29jHYlALBMm223dFPRFKR+efSkfztx+oXKvYz2JEhO5nB5dVJTOYyixY47esililz/8XWU1vjnLeMIu9HLPLiaQ1h5A9WtY+YM9NCpTV3SvA5868qI39Ezr928rIFcqgjAkd/18MH3tjM5G1LVOZHi87ccuwZMZ4o8cGiMj1y8BNO2yRct/vehTr7+tvV87f4OVAXec24Tqgp1JR58Th3DslhZHWTfUGJunQGXTmOpFHG+XDrGUgxN53jX2Y0cGEnyi+0DXNhWxscva8W2Z8JlErkif3veEvb+4Mjc33/Z5fU0BRwEtzbM7YvFRJ54xqRqSYiatgiafmrDccRrRyjqJb6hjOz9A2T3HgtV0N7azptsBcd4nvt/eBhrNmwvNZXn7m/v501/fRa+sOtlnZvmdRC6thnv2jKMiSx20aLQk6A4kgFmAkm0kJPSEjeBqJup4QwTA0n23D/A1EgGp0enZX05v/vyTlo3VXD+jW0SKvYadOnScr5y41oOjSTwODSSeYPvPtrNO89pZGf/NLqqsqEmxNhAmvI6P2Z/am5ZxamiRt2U+hwcPDBJ7ZIQ+Y5pPKtL587JqTI33+scQ48Z3L53hC+9aTUNUS+KotBW4cfrXOxHd/FqlprOc+ipEZaeU8WTv+mkbVMlnoCDQNSNf3Mlk0emSD7QT+DiOuyihRZw4moOvaTnwJM12p3gmTt75z4PH43jKvXwtk31fO3BTt6/oZ6NIR8RXScxnmNHUKNDKZLvHeMdVeEF4yXbwvzHfQd50/paLl1WQTpvsK4uzAVtZYu4VQJg7iEEsAwJMBRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghzhSLXcnUYtv2G5/z+fOKouxa5DmckRRVwbemnMwzo2T2TqCFXYQubcBR5aMnluat33mK6Uxxrv+v//RsQh4Hw/EsX7n36Fz7312zjKjfyfWrq6ktOVYAnprK8/ivOmhZXw6A5lBJDeaJj2efW/cBQHwsSyaeXxBCUjAsrllVxd/9bj+qolC0LC5uL+f81hf3NmRXrZ/o25cxfXs3VrqI75wqfGdVvKgxxMtP0VQCF9SgKJDeMTqzT17TjKPa/8IL/x49E2ne/X/bGEnk+NQVS/m3uw9z0/Z+/uaqpfxsWx9fu7+Dz1y9lBKfi+6JFKW6hgeFAw0Rnuyd4kdP9uLSVT533TLeuKEOgGS2SNdEiqfH42y6up7S3THUvIVjYwXDRZtgFqZqXTx8UwfTIzPRN7VLI2y6rglvxEWHVeCJsQST2QIBj4NLa0t44Jv7KeZnQhrcPgfXvrONX+wYIJU3mMoU+e5j3fzk/WezJOpnejSDWbRw+xxkkwUcHh2nS6Nnd2zB9k8OpSWE5BVOVRVq2iJ4g04mh9JoukrnM+NMDc8UsvfsmVgQQqJH3fg2VpJ4oI/iUBrP8hL8Z1ejuk/9JdizthyrYJF6bHBm/LMq2LZ9dO5727LRMiahipkAlIlkHtM6dhEYnM7hcqj8533HriufuKwVj0Plo5csQVcVHKpCz0SGm57uJ5Ez+PIfreHLN67ly/cc4YFDY6ysDvHZa5bRVHry5wrxh3kcGvsGEzhmA5DKAy4eOjLOQ0fGURT4/964mqJp4x7NzwWQAGy7v5+a9yzHOjJJsT+FsyWEsrKUkN9JpMqHqiqna5PEq4BL1yi0R3CbNvknR1C9OtrFdWRRKP3/2bvreDvuOv/jr5Hjet39Jjfu2qRJkzR1L6WF0uK2yBos7P6WZVlYlmUXL06xttAW6t5U4+7JTXLd7bjLyO+Pk94k3MIibVrg+3w88sg9c0a+M9/vmTNzHo/Pe5xWson8ZADJKxLhLMlo9nUPIQFQPVbUmYXv4GxfjGxPFKXIhnNJBa5FFUinP082h4XKZh/+CidFFU6MRB7ThC2P9WCacGrXGI1zSymrc+OvOP/F+MIbx2ZRmFHp4WubTtIfLtxDlXlsVPvtxJJ5Pr+6lVpN4djuEWqubMQeSJPaMoRSbMd5cT29Yym8xXb2bxrAtrGeMo+NXE+UfFpDXV7JN44NMqvOz/88cxKAgwMRnj02xkQiC8APbl/MJbMq38hDIPwFGe+N0b51hIpGL4subcDjs+HzWvCVO7G7VIpvaSP24gCpwxN419djbytCtr0xQTjjvbEp0wInwmxcOp2265ykt47jbrLhr3fh0QwkSSHutTMSSdNllai/tAFl+zAYkFlSTqjcgWnC3bv6kSRYO62U965qwvUG7Z9QYOr5/3smQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRDOi/NdZZGWJGm1aZpbASRJWgWkz3Mb3rSMZI78SAJbkw/ZqSLZFCRJonMsfk4ACUAkpWGYJjctqcMwTNJ5nXKPjUROQ5Ulfrytl4+ub6Xa7wAgn9WIjKfZ99SZJwj7yhxY7MqUdjg8Fmyv8nR3h1WmczzOxzZMA9NEliUm4hmKXX/Yk+Ali4JjTinWJh+mVniqsiQKn9+U1GIHvqtacF9Uh2yRkV9lXPw+Aokse3tD9IdS9AYLYQ3ffrGT96xuwmVV8NpV/unSNmqKHKRyOv2hFIsUC5W7JzAn0ny51c+hSyv5+LPt+J0WvPZCO/K6wX17BwincthUmZueO8XbF9RwQ1Uxux7oJpsqPE25zGohHsxOtmfwRJjBE2Eu/dAc3CfihA8FuOzSBnqPBujuz00GkCiqTP2cEmwpnTveMo+jE3G8divz6/xM8zvZ/Xg3B58fZOX1zRx5cYhYII2sSmx45yzKGz30Hwudcxy8pY4/6vgJbx65jMbOR7pIx/LkMhpjPecWRUbHU6+6nJnXkWwKtlYfhmaAbrwu7VN9NqRF5SQtMqpDZcuj3STOGvsApUV2QpZCsX2134HDopDOF8b8/XsH+MCaJt62tJ6eYJK2Cg8uq0IsUyjClyWJ50+Ms7atnFhGY3a1F5ddwe+w8PWb5xNM5vHaVRIZjcFwiiqfA0Wc319zM6u8DIY7WD+jnK89d4rbVzZOXgtU+ez4HRbKPFYGJibOWU5RZUzNAEXG1upD1wziw0ncTaoIIBFeE7/uGOOiWaXkap1IikSZofLS3SdIRrIsv7Z5yvw2p4r9D7yOfC3YGryU3DYLM6+juKyvOo8FcI+lSL40AMCGpeUcGUhy6lCA0HCS3Y/1cM3fLsBTbD+PLRfeaDOqvHzpxnns7QtjmCaprM43n+/gnmsWsO1Hx5nzgTmsrnWRuf8kit+O88pmTvZGKc3o7H2sh4omH+WNXrY+1Yu3zEF9s4/gRJq6gSQbDQc7h+OkcoXv5BmVHnZ2BSdDSH62vZcNMyrE96rwJ9PyOrFA4WeYwGCCxcsrsB+dQO+Pk2jwoF7ZjHNBOba2IpAklNchOO8PUVQ5NfCppMZNzzMDtC6rQFlVQzyQ5oWft6PnDTzFdhbf0oq9TOFEJMn2bB5ppptLZlWwazzGDx45wocvakGSwCLLrJ1eRoVPnMvfEGcl4hqa9gY2RBAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCEs8nneXsfBr4tSVKvJEl9wB3AB89zG96UtEiWyLN9WGo8yA4V2aqQPhpAj2axKFODQrJ5neFIhkxOZyyWwe+wEEhkmVvt4/1372drZ4BIKnfWEib1s4vPWUd5owfFIjP7wurJabIqsfTKJiy2qUOjwuugwuvApsjs7Q2RzGosri+mqcz9R+2z4rIUCuZFId2bmqRIqD7bOQEkmbxG13iC/mAS86zCoVcMhdMcGogwEkkzEc/w/Ze7+KdfH2Y4kpmcJ5jMcccLnfx8Rx9um8p/P32Sl04G+PgvD3KB20nl0wOYoynQTeSTYZaejPOTty/ixkW1jEYzdIzFefnkBAvL3Fxsd3CTYeW+9TPYUFfM9ns78RTbmXtRDSuua8buVJm+tOKcNjbNLyU0mODES0PMXFnFjoe60HMGyUih0NNiV1h5QwvjvTF2PdBF0bjG0pTKjJBJpVZ4Ivf+Z/ppmF1Mx56xyUI+QzPZ80QPCy+tx+ZSz9leeaP3Ne0b4fyLjKUIj6SonVVEy6KyKe83L5g6LT+RInlgAkuxA9mqoPpsxHcMo6dfn6d9d+4bZ9O9HYwMJJi/rg5ZOXOOnXtRDTaLjCJLbO2YwG5R+O47FlJ0+vNd5rYxu8pHqcfKiuZiZAkePTzMaDTLd17qZDyWYe30MkqcFu54+0LWTCslmMihyBJ2i4rbpvDg/kEu/fpmNnzlZf732ROMRUXW2Gut3Gvnm29biMsq8+9Xz+bunX38eFsPsXSeljI3+/pC9HRFaZ5bes5yS9bUYBuKYy0pjEVbqRN/KE1y7NXDcwThD3VqLMGH7zlITAGvamHrfacmv1d7Dk0wb13t5LyyIrH+tplvWECXbFUw0jrJ/WPEtw+R7YsVQnpOy3ZGiD/Zg5HSMFIa2tZhZjV7sblUnB4rkbEUgYH4G9J24Y21oM7P/Fo/p0bjhFM5fnzbEnq2jFDV7EM9FkA7FgTNRA+kST7YwYzZJbx890mKyxzMme5n7fwSrru+hRlzSziycxSLUyU0lKTj5WGWWhx4HSqXz6nk4ECES+dUTm7Xa7MQjGd/R8sE4fcTHU+jawayLDF/VSW23SPovTEwTHI9MSJP9ZDpCJPaP07mRAgt+MZey1W2+Kibdea3DIfHQu2MIoZORrBaFPS8wZ4netHzhXN4PJThyCM9HOoKMaPKR7nXzvf39fP2X+7Hoij8/cXTODoUJZLMs3paKbNrfG/Urgln01+feyNBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEP5w5/WRtqZpHgTmS5LkPf06dj63/2amRTPY6zzEnu6dnKZWOLHNKKKu2EFbpZuTo4kzC0gwu9rLeCzLndt6eGD/ECtbSrh4ZgW6buKwKBSf9WR3XYdZq6tRVJm+o0G8JQ7mXFTDRH+CsgYv6xo9ZBIa7mIbY70xkpEc9t94MvxoNEsgmcMiy7RVefHYVY6PxFjZUoLL+sY+HVk4f/qCSf7nmZM8cWSEYqeVL1w3h6ZSF1V+Bz6Hhc2nJvi7+w6yZlopG2ZWkMho/HhbL7phUuG1TVnftfOr+eKT7SxrKsGa1rh7Tj2uwQTxvHHOfFpvDM+SUppKXZS4rPztvQe5fn4VLSEdy+5xdKAUqJlTQvkt0+k5HuLU7jF85Q6mL6vEU2Jn2pIKJgZizNtQTy6V5/jWYaDwAOZ8VmesN8aCi+sZ6Yoy84Iq9j3VSzqeZ+X1Lex8pAstZzBrdTW9R0Pks4Wn1BdXu+k5FAAKwSVLr2gkMJhg/1P9XPiONmQkXG4rxdUu7C4Lwp830zSpnVHEjge6aF1Szrz1tZzYMYqiSiy7upnqaf4py+ixHNp4itSukclp7lXV6NEciuO1HRO6btB9YAIAxSKTSebZ8K6ZJEJZHB4ruqaTjufZO5bkv546QZXPzo/ftZTHP3YhwWQWTTfoDab40lMn6Q0mWdVSyrtXNXJiJMbtKxvZ3ROkfaRQdG9VZP7lihlUeuyUuAuf7b29YT776PHJ9nz3pW4qvQ7eeUHja7qfAtQWOcnkdR4/PMy3blmAZsCPtnTz9/cd5I51Mzl2XxfqzCIWXdrA8W3DGLpJ0zQ/+v4xYrvHJtfjXFKByybCwITXxtrpZTzfPk46b2DRDaITZwrXA/0JZqyo4uJ3zSQZzeErd1Ba/8cF2b0W8oE0gR8dRo+cDu2ToORds3G0FQrdk/vHpixjGYqz9m1tDJ0MUzuzCF0zpswj/OVzWFXWTC9jzfRC8Fg0nqUvrbFyYz3ZB06dO7NhIk+kWXVZPT7DRH+uj1din2pr3NR8ZB7xjMbmXxaWS7RH+I8rZ3Hv/kGeOhriKzfNA0CRJZY0FXHbj3fxg9uX0FDiOl+7K/wF0nUD1aaw+uZW6rw2EocmJt+T7Ar2Vj+BO49OTlPLHJS+Zw5qkf2NaC7uIhvLr26iaUEpWtbAW2KnfXvhPs7htRAanRqmFhxKctm1DXzhqRNMJLJ88tIZtI/E2NMb4oaF1Xzn1kVY1alhr8J5dlagqaFrb2BDBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEE423lNjpAkqQT4LLAaMCVJ2gr8h2mawfPZjjcjSZJInC6keYU2lsKI5BjMwtrp5WyYUcFoLENjiQurKjMay/L5x4+TyhXCEHZ0BYkkc7z3wiYqvXby+pnCSFWVeOnuk3hL7cxfX0cykiUZyeKvcPD8T9pJx888dXbW6iosjqlDo3Mizg82d+N1qNT6nfQFkyRzOpfMrqDc+/sVJCXiWSIjKTLxHP5yJ8XVLmRF/mMOmfAGGA6nuHtnH48fHqHMY+NDa5v5zyfbCSVz/MPG6UyrcPPRew5Q7rFxxdwqPnzPfj6yrgXdKBQXPXlklE9e2sbdO/uIpPJcPb+atioPd+3Mc+W0MuaN5chtGUTa2DBl25JNAYtMf0+E5voiVtT4uLzCD8+2nzNf9mgQZ4OPzn3jAIz3xgkNJVl5QwuVzV6a5pfy4j0nqGj04i6yEwtkUNRCAb6WMwiPJpm/oQ6rQyUdz+PyW4lOpNFyBpUtPrKpPO3bhll6ZRMA8WCaoion4ZEUCzbUse/pPrKpQgHVQHuImZfUserqJmwWEdTzl0BWZBRNZ/2NLWRSGsGJNDNWVtK2vJLyBu+rLmOkNXI90XOmJXaN4FhQ9lu3o+V1QsNJ4sEMTp+Vkmo31tPnZfP050mSzwRHZPM6VlVGUWRq24oY64lhc6mMdETZ91Tf5HyeEjsb3jWTeLjQnpFohh9s7uLLN86npsjBvr4Qn37gCLnT3x9bOwNkNZ23LKpBkpgMIAHI6QYP7B/k7y+eRiydx+uw8OKJ8Sn7cv/eAW5aUotThFW9ptI5nS8/fZJUTqPa5+Cn23s5PhLn/Usb6H5qAMMw6T8WIjCQYNqScvwz/EiSSaY9dM56UvvGKD49Fg3d+KO/k7OpPMGhBOl4Hm+pneJqN4oqvt//2lhVhX+9YiYvtI+xaEkTLr+V5OmQj9lrqjn43ACxwJlgkmlLy1l320ws1temEDybzhMcTJCK5/CWOij5HeMw1x87E0ACYELs6V7UKhdkdZxzS8kPxDFSZ4qi1WIHex/vITSaYsV1zdjd1ldZs/DXxDBMMlmN6Ssq0QGLy4KRKNxX2Vr9WOs9qEU26kyIPNR5zrLaUAJjOMFLT/ax8JJ6dj/Wg7XUzne39HBiLM6sKi8+h5V/vXIGmgE/2dbLYDjNrp4QtX4HiriHEv5ILp+N0FASj11BNgEJOJ0F4V5dQ3LHyDnzaxNp8kOJ3zuERMvrBIeTxINpnF4bpTVnrmP/GBP9cR78yn4M7UxgxbKrmhjuiIIJDtfUdXtL7ThdFg4PFa55v/hkOw0lTiyKTHcwxbx0Hp8DEUTyJmJquf97JkEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQzovzXRF8L7AZuPH061uB+4CLz3M73hCZnM6evhDHh2N4bAoL6ouYVe0rvGmYGJmpT341NINgMs8PNnfjsCgUu6w8fGCI/75xHqZpTgaQvKJ9NM6tK+r50ZYevn7zgsnp6USezOl/472FIvLpy8pJRfPnBJAAnNgxytyLaqH43CKjVwrIY2mN4+kYAEVOC+pZhfC/TcdojJ6ROByM0LGr8GR5SZa49H2zaVlU/n8uL7wxklmNgwMROsbiKLLE8eEYmzsCALxtWR1ffvokWc3g05fP4NsvdvKOFQ28b00zyYzGpuNj6IbJgf4IF04rZUtHgCNDUfpCSW5aXMeaaaUcG46Sy+rce/EsaseypE8Hh+THktha/GS7IpNtsV/RiCWt87aOFJYJg79r9cNYmpgxtd25xLkFTOVNXhSLzEt3n2TplY3kMzpDJ8Osess0xnpiDHdEmLakgo69Y3Ttn8Dlt7HuthnIsoRqUdBOf85q24rY+1RvoVA5mKFuZjFd+ydYeUMLux7tBkmaDCB5RefLwzQuc1FTWYJNtb1mfSOcP1ktS2+sF1VTKBl0MrPcSX4oQbY7SnWVk0y5k1T8txfNmdqrDFLNfPXpFEJGTu0e48W7TkxOW3JlI/PX1zLRn+DIi4MYpsm8dbWYpVYePjzCc+3jLGko4sZFtbQtr6T74AR6zqTncOCcdceDGVKRHPazCi739IZJZDWKVCsDoRSSBH9zUQtWVcY0obHUSS5voBvwyUvb2NIxwc7uQpBFfyiNy2YhmdPwOiw0lbmm7M/0CjdWUSj9musPJdnUPkaR08I7L2jk+EicWr+da2ZVsnnTmTCYVCzHkZeGaHDL1NX6pq7IBDNvcHhbH507A/jKndQuLmNY1ilyWvHYLdQWO7D9jiLdXFpjzxO9HHp+oDBBgkveM5tpSyte690W3uQ8dpV0NsclC2Wy6Rwrr2vlpXtOoOUN7G7rOQEkAB17xll0WQOlNZ4/edu5tMbex3s5eNY4vOjWNloXl2NzWKbM/2rX3XoiT/rgBJIJ8W1DuNfWkto3jjaeQrIrWGvcWE5EALA5LWy57xTX/t0CnF7x/f7XaF9fiLt29nFbcyUTHREylU7qV1bDpj68lzSQ7YqSaQ8hO1RMA8xXGXOSCfmsjmmYWB0q7tl+up4Y4MJppaxuLeXwUIQyl43vvHSKcKpwz9Y9keCxw8O4rApNZR5ay93ne9eFP3PxUIbwSJKWRWWkjwRwLa0kuXsU9wXVWKpd6Kn8lGWM3/jd4bfJJHJ07Btn872nJoNNFl3awOIrGrDa/rifnwZOhs8JIAHo2DdGw5wSQMLptTFrdRXHtxbCUyw2hQtubOVILE1TqYueQBKvQ2XjXBtOZ5zW6jQ7OgP8cGsvy5qKuGVpPdMq/vTvIeEPZ5pn+lXPixASQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQXizON9VwcWmaX7eNM2e0/++APjPcxveMFs7xjk8GCWcyjEczbL5VID2kcKTebHJOOaUnruAKqH6bRQ7rcgSpPM6Q5E0hgl+p4VkdmohkN0io8gSb11SR+6sAndZlSipmVocbrxKEbwkS8jK1GARp0Xm3asaJ19bFZmPrW/Fay8UduqGyYG+EI8fGubpoyOcOL1vo5E0d+/qp85UJwNIoFBo//IvTxIPZX77QRPeUJuOj3Hrj3aRyOp85pFj9ASTNJY6ATBMyGoGLWUu2kdiLGsqYUlDEU8dGeapYyPopwuKtnQEmFnl5R3L65lW7uai6WWUeWx84K59PHZohPV+F0VP9WGEc8jOwlhKHw6g+Kx4L2nAc1UTw1fVEy+yUfJUP0Y4i63VR+yZPvKjSSyVznPaLLstRLOFce3wWFh1UytN80rZ+0QvAJJUGNumCfuf6WPJFY1UtfhoWlLGstvbWHJzKy3XNnAkk2b+VY1EJ9KU1p4p7nzlk3FixwiqVWbxZQ3YXSpr39aGwz21yFmS4OneJ/jm/juIZCKvVdcI50kwHeTr+7/OTY/dRLR7DK0zQuZ4kPSRAEYyT74zimXzAEWuqX3/CsVnQ/qNp79bG7yT4/03RSdSbLn31DnT9j7Ry3hvnEe/cZCewwH6jgR57JuHGOmKkdUM1raV4XVY+K+nj9OXz3HVx+ZT3uDh1SKiTODwYGTy9bq2MmyWwpw+h4Wv3byA+/cO8PXnOhiOphkIpRmNZYhmCgWpl8+ppMbvAODKuZWEU1mKT+/L2ull1PjPBFi5bSrvXtWEKkJIXhPDkTQvnxxnT0+Ind1BGoud+BxW7BYZj03lv26Yx0A6S3mz99wFJWio8uAosiN7zh13aoUTxWNly11djHREObFthJe/f4wSTWK0PcyxR3vY/kQP/b2R39qu4HDiTAAJgAkv/eLklMAJ4S+fJGdJOTbzL7vfRTAd5OiWQTa8ayZr396Gv8LxKguA9CpnqkA8Szj1hxUjB4eTZwJIAEzY/kAXo92xV53fWuPhNzftmFtKctsQiV0jOGYWE3u6F++Gejwb6vGsriE3lCCf05FliXQ8R2g4SToxtVhf+Mu3pzfEbXfu5rFDI8hpna79E6hWhc3bR/C/eza5wTjZrgjOBWVEn+ghczyIfWbJuStRZZKnr5dtXivz3jWdkBX+cWMbmm7y9NERVreWUuaxccvSev71yplsnFWBz2GhayLJUCTDPTt7ODwQYTCcegOOgvDnKjqRprLFh6fYTn4ogRbJ4ru6Gcmtkh9J4pxXdu4CioSlYupvCa9mpDvKjge7JgNIoHDPFRpOTr7WDZPxWIZEdur5M5PX2dIxwdefO8U9u/o4NhRBM80p88myRNuKCgzTYOfDXRRXudj4nllc9I42VlzfTDKtISNx+8oGyj02/vFqlWcin+InPZ/gn3a8i5hlN4lslju39vK+n+9lICQ+Q28E0zjzm1baFNeNgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgvBm8cc9ivaP96IkSbcA959+/RbgifPchjfEaDRNKK3xjec6yOmFgASfw0JruYuZVT7MpIbis+FcXEHmZAi1yI5jQRlaPMtILsunLpvBT7b1MhrLsKq1BLsqU+G1ceXcSp44Mjq5nX/c2MaTh0eIpDXWTDsTamJ3WJi1uprjW0cIDiVweCyoVgVfmROX30Yykp2cd966WopepcjI77LSUOLkHy+ZTiZv4LYplHttVPgKBed7eoJs7gjw8IEhFEXipsV15HXQdYPGUhf5+NQio3Q8TyKawVNsn/Ke8MaaiGf44pPtTCt301Di5IaFNeiGwVXzq1nRVMTC+mJa3jofE5Nyj52tHRNE03kW1RcRTGS5dFYl6ayOz2khldPJ5HW+cN0chscSNKsq1799CfTFsQ4lyZqQaQ/i3dhA9KkeMCG1fxzZZyV9QwtdkRR1aQ09q2OpcZPrLRQUpw9N4L2kEaUkTq43hlLtIttWTP70eJ63ro5dD3czZ20N2VTh6fPpRB5/hZPIWIpULMeuR7tZfF0zd/eNU1PkoLbUycunRqgoTlFR7GbBu6djUVQ2vHcWw+0hWpdU0LGnEKbTcyhAaCRJ/axijrw0xOLLG3B4LKTPGusNG1x8eeQ+xlJjLCpfwobGtee5J4X/y1BiiIHYAB6rhxnFM1BkZfK9I4Ej3N1+NxXOCopG7FgqXcSPBc9Z3khpKInfXixvpDW8G+rJHA+SH0tha/ZhqXDCq4RAAaQTGlp+6nvhsanFkYFDQfaQYE9vGFWW+ODaFgKJLB5FITaSYNqySk7tOvMd4a9w4iuz89yDhTE8v9bHwvoi9vZGmF/no30kzmA4TeD0/qxqKeVnO3o50B+ZXMffbZjGOy+oxzAl6vwOfE4r3RNJZlb7aC338Mv3r+D4SIy8bjKj0iOeLP8niGVjdEY6AfCr9bzzR4cZCKf52PpWXjwxzt+sa+Wrz56iezzBLz+wjM7xJP/80BG+uH4GpbpJoC+O3WVhwcV1BI+GaF4g41lTS+ZUmPxwEmuDF2u9Bz2SZdbSCuqqC9/9A8NJ9PEsXQ/1Tralb+cYN/zjQiRZIjCQQMsbFFe5KKlxk3mVEIZcWiOb1s7LcRLePGTrKN/Y+b8AbMs+T1lyCc/88BgAsy+sprjKSWjkzLmsbnEZw1qeV6IZAvEsjx4a5okjQ1wyX6W5QmJ2eSM2VSWUCVFiL6HYUfyq205Fs1Om5dIawaEElU1ebL8R/GStcVPyrtnEnu5FT+Rxzi3FyOno0cL5T3ZbwYT8WJL4CwNIFhnfNS24BxK0rajk6MtDlNS4SMVylFT/qUdO+HOS1w0O9odJ5XT+dc00pFzhO3voVISaGcXoQKY9hOy2oAULYYu5vhietbXIi8rJnAojl9jJzypl++M92JwqqSKNJ/v6eWRfnP++cR6fvXoG47Esn3vsGJfMquSitjLSOZ0FtX5imRyBRJ6nj41y8YxyTo7FyQ0ZVPntrGsrnwzcE4Tfxu5UyaTyWGXQZ5WQOR5EtqsoRVZS+8ZxzivDtayS9PEgis+GZ20tlqr/O4QkFc8SHEySf5Ww1FfO0X3hMAeHe+kZz3JyqBBWt7SxGN3U6Y/10xUIMBJy0FpWTJHLSiSdp6bZi2qV0XJnro/nra8jPJYiHsgyc1U1W3/VOfmezaniu6qWE6E0dlXhX6+p5mvtHyGSjQCQ0TP87/7/4L0rvslXHs/QF0xxeDBCXfG54ZbC68/Uz4yVoEUEwQiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjCm8X5DiH5IPAPwF2nXytAUpKkfwBM0zS957k950Ve04mn82w6NjoZQAIQTec5MhTj4lmVSIpMctcIiteGY1YJeixH9Iluim+ZgQR8+8VOrppXTbHbyv6+MD3BFOOxLK0Vbr7cNpdgIke514bTotJS7sFhUcjpZ54YrOV1DNOkoslL47xSdN1AUiTGe6Osv30GA+0hImMpGuaUUlTlQJKnFq8VOaxYFQnDMJEk0E0oc9twWlVSWY3Dg1G+81LX5Pxf3XSKSp+dGZVu7tszwNp1M5EkOPtBxmX1HvJyhv5YP/Xe+tfl+At/nLxucsPCGsbjWb626STvWd3ML3b18dLJCWwWmU89eJgPr23lof2DVPsdNJS4+K8nT/DhdS08e2yUX+zuY3lzCXfv6OMTF0+n2ZQpj+Rp6U+jj6VQy5ykD03g2NgAgJk3SO4dw3tpI0Yqj1FkZ9itMGHoEMmiVtjQASOjYSk/XSBmQuyZXiy1btwX17P3RJj2u09S1eJjxbXNyBYZLW/QeyTItKUVHN86zNGXB1mwsR5JksjndYqavaQ8MpVjESpLxqly+qhv3MUPj32fVaUXMrPqoziVIrb9qpPFlzWSTebxltoZ743hK3dSUu2ia/84AIeeG2DxFY0kQhlS8SzFc1WCRQPcmLuRPWN76Ix0swERQvJmkcqnOBY4xt6xveimjkW2cDJ0ko0NG/HYCsEZJ0MnAYjn4uT8BhgmKKdPgGdT5d+6HdkiE3qiG1uLH8fsEnIDcTKnwjh+8ynzpykWCXeRjUT4TEG9apGx2JSpM0uQOl3oqRkm332pkx+9cwmbOsZZKdtomltCeb2bgfYwZXUeamf4CY2n+NbbFnJqLEHneIJ//NUh3rqkjmKXytPHRil2Ws/agHlOAAnAD7d0c8/7lvPJXx9mOJLm+oU1vHVJ3eT79SUu6kv+70JV4XfrDHfyYv+L5MwcEhJWeT8fv2wBn/xlGhOwKDLbOgP891vm0j2RpD+Y5oUTE6TzBp98rp2n372CYHuEXFrjyMuDbNxQh6zIhJ/owdrkK4zF4QSxZ4IUv3sObekcxsshANpq3eC3ntOeZDjLSHeMfU/1ER5JAoVxee3fL8Rb6kBWJIyzPhdFlU7cRbbzdryEN4dgdmTy75/0/JBPXFLGktASxrrieErsNM5rZbwvzkR/jNoZxWyKxWg/PkZVsYNSj53nTozxwIEerlg5xA9PfINsR5ZKVyWfWf4ZHu58mO5IN59c+klsFhs5LUeFq4JGbyOKrGB1qlPGob/CSTaZf9VQBkmVURt9+G6cRmrXCOljgckAEoBXFlFLHRTd0oYeyaAFUtRM97H/mT5sDgszL6jmuR8f5y2fXiIC9f6KdAVHKC0b5NPXq0wPypzcNcriyxvpPxZAkl0odgWlyI6RyCOd9d0df3kQtcyBa1U16Wo3x14eonVxObWz/Rx17KJbvo+/v+Z2fryth09d1sZQNMOnLpvBru4g7/zxHi6cVsp1C2vANDFNk5uX1DIay5LI5RmLZXjhxDhVPjszq3xv4NER3uwMw2SsL0bb3FL0SBbJKuPd2IBSbEMLZ5FdFhLbh1GK7YXfJRI5TM045/cBXTfIpvJYHSqqemaMG5pJNl24V4oFMpPTFVVGsUucDJ7ijoN3sHnoZdbWrOWqFVfTEVTIqR0cCx3i+4e/T97IU+up47PLvsT24zaGIhnaKtys//BcBg4HyCXy1Mwu4cTWYeZeVMt4T5yyejer3zqNgeMhfOUOnC1e3vnoIfxOC/9yxQzcnlEC6cA5x0E3dSQ1wvvXleLxhJAdPYQzDorsRa9/JwiTzg4hWeoafANbIgiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjC2X571fTrwDRNj2masmmaltP/5NPTPH+pASQAE/EMqioxFp/6hPaxWKE4R5cMPBfVoQXSJHePkjkRwlLjQSm101Tmoq7YyS9293PHC53s7A7SVuFhW2cAVZbpD6XRDJOH9g9hmCaPHx4mlslhmCbbOgN0jsdJxfPseawX1VLoclM3SUfzxIJZ+o4EyaTyVDR5MQyD1FkFmGc7PBThK892EE7lkYD9fWH++cEjhJM5knmN506MT1nm5ZMTuKwKHeMJFFVm5fUtODyFJ9GX1rlZdFkDPaluOiOdU5YV3lilp588/eCBIa5bWMt/PHacQCKHTZX58dZerp1fw5efPsHxkThNZS7ueLGTDbMq+P7L3WzvCrKsqYQvP32StW1lLEgalD7UjTucI3NgAvv0YtJHCoVgeiyLpcYNgDaeIvZML1qtm01Kni/t6qEoqbN2VwBz2wiuZZXowQyWSheS9czpSxtLkbEotO8aA2CkK8rOR7vR84XQn8hYCkmCuRfV4C6yM9wdIVliIdTq4B1PHGIoHWWeF7SXFI7cGWFN5grumf0It+Y+zv67xxg6ESE2kSEezLD9wS4OvzDItKWV9B8P8fK9p6ibXUJFkxctb3BwUz9qQ4aGG1Q+P/pPfHrPJ/nOoe9QbCtmun/aee5F4bcxTIMdwzvoi/dxZOQo9ak2GsbmURVpZSAwPDlfvacQjpTSUhz39JDpi+JaXnXOupQmHwnLb4SSnE2ScC4sJ9sZIbl7lPxIEu/GBgzDeNXZtZzOgo31+CsKYTsuv43l1zXj8lk5u45ekqBlYTmZs542b5gQT2sMRdL4a91MDCTo2DtO9XQf3go7m+/rwDShQrXw3LExTo7FedcFjWi6QTpvcHI0zsJ6/+T6MvmpbUzmdPK6Scd4gmRO5+5d/fxiTz95/dX3R/jj9MR6eHHwRb536Ht899B3eWnwJYr8Eco9Nl48Mc60CjcL6/1s7wpyaizBsZEoqiLxxevn8Kv3LycTybH3iV4OvzCIt9SJ2h1By+Sxzy4h1xMtjMXBBK4VVWimjjGYmNy2MZjAGkhjd1vOaVM2lZ8MIAHQ8gZ7nujBXWLn0vfPwektBJcUV7tY87bp2J3nLi/8ZRtPjSPJZ5+PDL7c8QWs09LMuKCS0Z4oOx/uwtANSms9WKwKa+ZV0FDiZCicJpXT+Pn2Pq5frvC9Y18mqxeum0eTo3x1/1d5y/S3cOusW+mJ9fDhTR/mg899kJseu4mne59GMzQyyTzr3jHjnHE4Z00NxdWuKXc8pmky3Bnhie8c5uUnetB185wAEse8UjJdUWwtPrKnwkSf6AZZRvXbcblszFlTS810Pzse7iIVy5FN5V//Ayy8KQzEBviP3Z/k33Z/lBfG7yQbyBAYTGBzqpTUuClyqsQf7sK9sgpTM5BtCrLrzLlQC6ZRy530HA8yc1U1PYcCRANpvrzny1zbei0P932HtbNNOseTOK0q9+4ZYFaND1mCZ4+P8ZNtPSRzOsFkjr5gmmKnhXROp2ciQaXPTsdYgl/s6iOcfPX7OUHQ8wbd+wP4VQk9nEEbS5Hri5FpDyHbFVxLKkACPZQpXCsMJbDWnfmpJjSSZPMvT3HfF/bw/E/aCQzEAcim84x2R/CWOpi3vo6iqleuY62se8cMToZP8p+7vsC0olY+ueSTRHIRskacLeHv0x4+wh0H7yBvFM6lg/EBvn34K3QGgtQU2bGpMi9NRGCeD/eaCjAMRjqjpOM5yhs8HHlxkO4DEzTOKwEJNvUFSGQ1/v2qmdQWOdA1Fz7bueE8EhLzayp4IfYZftjxz/zT9g/xyc2fZDgxjHD+mLo2+XezmsQ09N8xtyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAI54t6PjcmSdJ7TdO886zXCvCvpml+7ny243wzgVROZ+PMCg4PRs95b0lD4Um7UsZAi6Qpeut0tEAaxWPFzOuYaZ2MpvFvV82ieyJJIqfRVOIkms5x2ZxKvv1SJ7G0ht9p4SPrWnFaFD64phm7ReFXewdY2VLCSDTN4iI3kgSHXzzzdNmyBg82h8qJnSPUthUz2hUjMBTn0vfPfdX9CMRzBJM5frq9d3Ka3SKTzGl47SpNpU6GI2kGw+nJ96v9dgwTVrUUk89oSLLEyuta0DQDq00hHc8SUUPs7NvGmto1qPJ5HZLC7zCRyPLQgSEANMMgpxtc0FLKpvZC0IciS8QyGhe0lLCtMwhAldeGwyLzT5fOwGVTuOPtC2lSVWz3doJdRY8XCiJNTUeyKZhpjeSuUVzLK3HMKsG0K3TKBu2JJP/22HHefUEjJSfDGCmNXCpeeFL3JQ1gV/C9bSa50SS5jEZclkjHfqPY0gRvhQObSyWb1Di2ZRiX38bKm6dxx9EBkt0jTC9z87k105mrOHn556Ok43lWXNfCnof7mbOmhoPPDOCvcJKMFAqhR7qiNMwpoe9okG2/7mDRZQ1Ex1OMDoRputRFmZLnVPwk96f3w1HoT/RPNueZvmfY2HDZ691twu9pID5ASkuxqXcTbzHfz6m7YkAaSDPtgjJabspjc1jw2rxcWHMhW4a28B8d/8Vdq39GZdyJ98ZpaKEMaVliIJzlVPB5VpYuocXfMmVbRlbDSObxXtKAmTeQLDKpw+NYat2v2jZFVeg9HKC80UvLonIyiRwnto9w4c3TueCGVsb742BCeYOH/U/1cOuyav7j5Q4AZAn8Tgu1RU46oimsWQ1fmYPRrhh9x4L4y514fTZe/tYh/m55BfnpHv7pqeNUeG1cMquSKp8dm0Xmfaub+MXufrwOC06rQip3piBwzbRSrKrE6tZStnYWwoQe2DfIR9e1UlvkfM376q/VkYkjHAkcmXx9aOIQx4LH+JuLrubfH2tnWrmHhhIXBweiLG7w01zmonsixa7uILMkK+HeOE6vlVQsRz6rYZbZMKN5LJUu7NOL0CNZlGI72ngSKTA1JM3oi+EvdzCaKBQEW+0KVvvp72gJvCV2simN8GgKI2cQGIrTvLAMm9NCLJDmiW8f5vpPLKa83jO5zsh4isBAHF0zKalxUVrrmbJd4c/XgfEDSBLcOO1GHuh4AACPxUPcFibWoWIaUFLroX3bCHaXhWXXNuP3OOiYSDKRyOKPWagtdmC1909Zd1eki5SWwqW6+OWJX04WqueNPJ/Z9hnmlMxBz1s5vn2YC2+ZRi6tk4nnUCwSumbSfzRI47xSVIsCQHAoyaPfODgZVmZdUs6ca1qw5XXUIjtgok1kyI+nSB2cAArhERjQOZKi92hwsm2ldW5ScRH48Jfu5GiMcCpHX24nR4KFc/O7yj9EkeJm1mqVrgMThIYTzLy4Fn0kSWL7MN4N9Zimif+GVvRwFj2WxVrjQc9qIEnsebyncI2pydy5+G7euecWPrnsk2TScQZHUtQXu2gpcxNN5VnUUMSWjgB7esNcPqeKYpeV3kAKr13FaVVwWFUOD0SYU+1DAja1j/HWJXVv7EET3pQUVWLW6iokwyS5ewTXsiqs9R7C958i0xHBs6GeopvbCr9JeK3Ymv1YSh0ApBM5nvvpcSb6CsEjnfvGGemOcsMnFjHWE6Nr/wQVzV5kWWLG8kocXisWm8rhFwcIru7gwMQBVlav5K72u1hVtYp4Ps624W3MLp19ThvLneUMJvp573QbD+4O8OnLZvDr/YM0l7polC2MRpNYbAqpWI6TO0cpaygEW+18pJvm9TVU+hzc94EVjETTBIbjeJwKH5r3Yb6x/+tk9AyyJPOu2e8ilY8wnjoTpLprZBd7Rvdwbeu1569D/soZ2rnfn/lEGKu39A1qjSAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIrzjfiQ8bJEm6EXgvUAr8GHj5PLfhvJNM0HWTar+dD69t4ZGDQ9gsCjcuqqXa7yAyNoLVsJDaPU5q9ziSVcHM62CC/7aZ5GWTsVAKSTIp91iJZ3VsisQPNndz85J6bBaZTF7nBy938bWbF5DVDMo9NiLpPH933yEAvn/VXJZe1cSRlwaJjqfxVzix2hR85Q4WXlLPSGeUokonsy+sJpd+9ae5F7usyBIY5plpl8+pwqpIxEdT3GD3cEmdDcdqN1/Z38upQJILp5UxHs3wz1fMxBjMgARDp8IYuomvzIHdbUFWZQ5NHCKZT055QrHwxslqOmUeG4PhNIosAxBJ5Shz22kudTO9wsPH17fisCpU+uy8dUkdoWSGd69q4vNPHOcj61r5+nMdfO2i6fhyOkigeKwApA4FcK+oIv7iAADJXaNIpQ6OXlDOBx85wkfXt2KYkEzmsIwbvPJ85GxXlGxXFPuSCp462MP05VUceKYPXTOoaPKy/Jpmjm8dJpfRqF9ZQcypUXtDA9axHKlwBku9myFF55Z5pSgTWUqLfWx/qAvtykpq25xkswomGi2LPLj9IRZdaiUWsFBU5aZr/wRDJ8PM31CHt9TBeG+MeDpO3dIiYvko2ZhOrFOhtmgmM5pm8pnjn5pyTEdT4snabxZpLY1maKxwraH73sQ573Vsn2DuqjqqWvzsGd2DJEn8zfy/wTANnkltYqmyhoHHdZLhLE6/heorJL7b/U2yzve8agiJmdPJnAyTORk+d3reYPuDnVjtKrUzilAsMqoqIytQM91PIpKj70iAkho305dVIElZ7K4J6mfpJKNudjzUhWGYlJ3+fNpUmc9cNRNFkrhnVz8fmlvL4mYvyUiOse4YCy+up7zRy0h3DEM3ObV9lHrDpMprp2MiwYfv2cd/3ziPQwMRDg1EeMeKBsrcVj512QwePTRMTyDJhdNKWdpYjM0iY7fI3LSkll/tHaTMbcOuKq9fh/2VyeY1jgaOTpl+LHiMj864HVkCqyrhsiik8zpfefYUn7tmNp999BiPfWAle390gkw8x/Jrmzn68hCBgQT62hrMVJ74CwMggWRRME+Hy/jfOm3KttRWDwt8Yaz2evpO5amoLSYZyVLTVkTdzCJCw0kcHisVTV4M0+DgswPks+c+vT40lJgMIQmNJHnkGwdIRQrFpqpF5uqPLyAVyzF0MkRpvYfatiJ8ZSLI5s9VX6yPnJ6jM9LJRxZ8BM3QyBt5/nnHp7lnwSNER1IMd0RpW1FJzXQn4dEeMFSm213csX2YS+ZU884VjSSU5JR117pqqcjXkeqV+EDs3yhvcRN3TfCDgW9zOHSI4eQwVZ5p1M8qoedQkEwiR1WLD9WqsOOhLmZeUI2/wjkZfBMYjE8GkAAc3zvOiQMT3PyvS/FWuQn+op304cA5bdBGkngubWBWq8b4QJxUNEd5g4dpSyp4/qfHecunl+Ipsr++B1k47zTdYMepAJZwnlRvnHrvfP5n9re4a+xHGMd8ROQUrYvKOb5lGF+5AzmawwD0cJbYpj4ALFUulGI7eiyLmdGx1HmQJIlsSuOCG1o5tnWYWVTxjQXfZdDspsJVwvcODfOJjW1k8gY2VSGULJw7XVYFmyqT1w3sFhmHVWFzxwRtlV4aSlz8aGs3f7thOr/eN8CK5mLqi11v4NET3owi42lMw8TUDBzzykjuHEF2Fn4WMtMasce7AZAsMmUfWTAZQAIQnUhPBpC8IhnOEhxKEBpOMnQygqfYjqzIqFaZjr3jOL1WZl5Syq2HvgXAwYmDlNhLmFc2j8FEISDVptiodFbygcaPMMMyj4mOJHkpT5FUwbTyABlNx2dXaVWtjHdE6T40zlUfbSEVCeK5woKi2hnu0phzfRPF9V4smRyHBsLUFjvZ3hVizZwMPznyY26ffTsAqqSyqW8TLsvUz8fJ8MnX/qALv5Whaee8zsdDIoREEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEN4EzmsIiWmab5ck6WbgCJAC3maa5rbz2YY3gmkayBJ47Cp+h4VL51SiSBI1fjv12hjpmIrVUYbssWLEc5NFwZJVIe+z8MMnOlg/s5yGEheyBIcHoyxtLGJ+nZ8fbume3M5tK+rxOVTmVnsZiGR45tjY5Hu5RJ6xoTQrrmsmm9IY6YgiyRKbf3mK0jo3jfNKCY8keer7R7nyI3On7EMymyOT1/n05TP5xa4+xmJZLp1dyexqD1owx4ktw1S3+nG5rRh5ky+ubOWwmSeT1wnEsxS5LADk0xrOIhtun41MIo/NbWEkO8zyquW4Le7XuSeEP4QJvHNlA1986gThZI6r5lbSXO5mcUMxP9rSzb8/eoyPrW/lrh19vH1FPf/15Ak+sKaZbzx/HLdNZTyWJZjMkXepSGV2zIkM+bEUjnllpA9PkOuL4b2yCS2RZ0w2sdZ7GB+Msrq5mGqfnSqfncNjccxZdTCWOqdtermTeCiLqRsgFaaN9cQIj6ZoWVjGtNUV9OS6GAsOUVNaxXjVID7VgzdpxW3YCfWG0XMDJAwrzbMnePGn32PZNTcSGh4GvYq+Ay+w9+FuPCWlLL7yBhy+LJd/uI5cKovFlkexSBRVpnEX2Qh1HUdWJUr9pcjeOBIOHMEKfrroXmL6GEY0jWSR2ZzeTZOv8bz341+rVDyHBDhOB9/8pipXFc/0PEOLOpu+swrRX5FNFQriKlwV3Hn0TjYPbgbAqTpZNXsDjXP9KKqCpmnk5CAAnZHOV92WWuwARQL9TIKTWuYgZYKvzAESdB2cwO5U2f1YDxfc2Ii/Io6pDzDrAhfuEj96PgB4sDlMdE2nosHk8g9WsPXXUdpaivhGxQK8dhWPTeVdP91DIqtTUeRg8y9OUVrvoX5WCYHBOPuf7WfplY2T7Rg9FOTStaV0TCQwTBiLZWgudVHksvKtFzqJpHKsaytnQZ2PhXV+7BaF+mIHmm7yXPs4H1vfik2V+fTlMyj12P6InhJeTSSVZ0XVCvaM7Tln+rLKZdgUmWf+bg2d4zGOj8Z5+ugo18yvpi+UQjdMJM0kEcpgGiY7HuyiZXE5LYvKyfqtFFW5kLYOYeaMyWsN2WuFEgtqiwetq1BYbGny0DWyl+H9Hcxesx63M0g6NoCvtJR5F3l47md95DOF5V1+K5d/cC42p3pOCImsSrj8NnRNR1EV+o+HJgNIALS8wcHn+knFcoz1xAAob/Ry5Yfn4vSJsfTnqNJZyXBymEMThzg0cWhyeqmjlGxa4/BLg8xfV0dRZYzIWA82hxW7qwyl5ySfXVyD5MwSjcdw2cr48NwP8d0j3wPAoTr47PzPc/ieEJGRNABd20LMvaiGv/V8Ds8yhWK7C2e1C1M3GekKIysyqXgePa+z+i2t5DI6qWiOcT1GKpbDOOt87PRZaV5QhmKRyaUL5377zJIpIST2WSVIqoxpmDTNL8PushAaSbD9oS5MwyQdy4kQkr9AR4ciFMV0tJSOu9aDrIDWXc2/zfsihw+PMW1DMeHxFIoq07rIia3KhXbkN8ZOWxGSXcVI2pBtKqm8gZbTKW/wsOvRwr1cJqHhGi9l3kwvh0fghgVeNMNA0w1UGY4NF86T77ygEZdVpn00QWOJE49DZU1bGaORDM8eGyWcypPOa1T57PQGkiKERJgim9LY/XgvtTe1YGv0YilzovhtIANnXRKrZU5U77nfx7IMCzfWMzEQZ/BkuHDDCORSGrFAGpffSi6jY+h5hjsilNZ7KGqwMSh38fPZD2LmJdRSnf859QUS+QSqrKLKKrlcnq9U3IkZk9j6QCfG6dRT+aUo7/3YPPojad4zt4LYyBDF1RIr6hwcfPpeYhPjNMxfiK+8irZlVWi5MIneDoqQWFtWwwtjKSq8NgaCKWLZGD84/INz9ucd9ndMOT6t/tbX9HgLv5tpnBtCoiXCv2VOQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRDOp/MaQiJJ0jTgb4EHgJnAbZIkHTBNM/W7l/zzVlPiYbg7gNduoabYTpXfjlWVmFckYcTh6MvPER0a5bIbP0bi6X60QBrFZ8N/fSsTDpW3L6/nH+4/hHa6GGdDWzkXTivh1hXVfGBNPYcGEmiGSWOJC6uqMK3CzZNHxyh1W7lqXjV+h4UF9UUc6U2y9f5Oaqb7aZxXiqTAgo317H2yl9HuQmHb7AursdiUKfuQTOdxWBW2HR5mQV0RpR4r+/pCrJleSiaWxdBMXvj5CQBUi8z6d86k2mUhnNcp9djYdHSMqyuKKap2c+CZPoJDSepmFVPZ4icQG+eyxstQ5KnbFd44xU4r5T4b33n7IsZiGcrcVh45OMxwJMOWjgAfW9/Kl585SUuZm+1dQTTDRJYlMnmDljI7igx2i8xILk/ZRTWUbR8lfXgC+5wSit45i2hWI2SVyXktGHGNnT85SVGlk08sqGX0SJQfb5hNYDCBXOtBml2CdjwIioRlSSWBnI5pmHTsHWPh6TEMkEtrWBwKWlLH1qNi0WWcFp1abTpun4WJ/h5ynjGOPv9LMok4My9cx77HH2LhZVez78lHMHSd5kXLmOjrxl1cwoJLrmTrvT+jbeVqtFyOgfajLL78GrbedxdLr76Bnb/eQioaYfXbbufJb30XLZdl2vJV1M+ej5Go5PB9dzHW3YGsKCy49AZmNLW9sZ36VyARzdB9YILDLwyiqDILL6mnfnYxzt8IyPDZfFzccDFaCqLVMSLD6cn3LDYFR0nhfFTtqqbJ20RPrAeAdzW+j/ZfJEjFQpPz21wqt9/wXppr6161TZZaN8U3txF5rAsjnketdOK9qpkHftZObCIDQHmDh0WXNeD0Wcmne3nkB18Fs3DOL6lr4OL3/g3HXn6OI889g2ka1M6aw6q3voPlN3l46FgfTo8Huyrz8KFhEqeDILKazoKN9ex+vIfhUxEA2pZXMnTyTHGfq8jKiWR28rVuwn880c7H1rXykXUtPH54hEqPndWtpWQ1A90wsSgyp8YKYRWj0Qw/uH0xbeUiROq1kshq5A2D+eXzWVC2gIMTBwFYVL6IeaXzyJOizOGjpdzDs8cLxevJnIbbVrikyykmzQtK6do/ga4ZnNo1ChJc/pF5bE6nufDmNmKPdqNHs6ilDpyXVvPMPd9kzVvfiW15MYahs/vFB1FMCy1LlvPY176EoRcKRD2lZcy5aCNLr5jO9gdjODwWXH4bY70xFl/eyMu/OAlAZbOXlkXl7HmiF1mBhZc0oOW0KfsaC6TxljomX4/3xgiOJEUIyZ+pYnsxKS3Fsspl7B7dDYCExCeX/BOybsVf4cTtn+DhL3+JXLpw6V8/dz7Lr78Z0zB47rv/S2RkGNViZdWt76L1gi9zMtGJS3Vhi3qJjAyes73j20aYv76ORI+JGcoTd8RIhLIsWF+PapfZ82gvrUsr2PSTdszT19ALL6nHX+FEscj4K5wUVTrxVzhp3z6CJIHLZ8Vf4cLe6se9tpbE1iEwTZyLKlArnFjKnXhlmfatxyaL5AGqp/kZ74tTVu9BkqTzdMSF11teM7DqYLMqvPhkx2RAWW2bn7qZxUxbVoGhGfQenmDje6p5+a6vEKlvY/Gqy8nuCoJuYptbjKXRQ34ggWRVMWwQNWCkM0oymkPLGyy5opGu/eM0zi3FHypifCTAmlllyJrBLKudbFbjnlsXEzJ0ilxWNN3gorYyKrx2/u7egxweik62+e83Tier6Uyr8EzeOwrC2RRVwu5SOdkVY1qJHatVxsjpFL1lOtEnezASeSy1bopumIbsPPNz0XhfjG2/7mSsJ05ls5dVN7ay4+EuaqcXYXNZ6D0aZOnljfQeC5CJ55m1uprKVg95M8/AY35ePlW4ZrG5VD77/v9mi/EM3aFuHljxBBbdzuHdA5TWeVh+TTP5nI7do+IrddKxY4QZK1zs+OWd9BzYA5LE7LUbKK1vKuyPopKOR1EtFnY9fD/jPV0AuPxFbPjYp7mnG06Mytw646Pc2f6/k/tzY+uNuC3uyWstWZK5uvlqqpxV57E3BFPLn/M6Hw/9ljkFQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQTifzmsICfAY8BHTNJ+XChV6/wDsAWaf53acd4FkFquqktMM/A4LubyJQ5UYGhnm8KanAHha/gYb3/oRLLINS5ETS6mDeqAnkORrNy+gYzyO3aKwcWYRGXmEzsxhNp3axIziGayvvQQz42cinsVjc7K8uZgKn42f7+jj21fN4YWftpMIFwrNT+0eIzic5MK3TgNg+TXNaHkdRZWRpEKICEA6Gaf/8CFObH8Zp8/PrBUXsrDOjyTL6IbBmmllTCt3oo3kObV7bHJftbzBrke72fCBWUhShGwmxux51WihPM/94Bj50wXyvYcDpOM55l69gF+0/4LFFYtR5fM9JIXfpshlI5Mz+NsHDvHJS9v4n2dPccOiGrZ3Fp7ubgKpnI7bphLPFIoyVVni71Y2McfuoNSq8O63LUWO53FYLUzMLqV4TRUpDR79STuLL2tg16PdLL60gZ2PdmNzqNRML2Lb/R3M31DHnke6cfmsRIYS5NMaF7y1jfGBOEf2TTBjZRU2l0oskKH3cICV17Vgc1uQ5RyyHCWXDLHnoa+hWCwsu/YtjHV3UdbYxOa7f8wFN72d0NAAM1atpWvPTgDsbjexiXHq58xnoq9QIDdrzXp2PHgvpmngKSlj54P3suiKa9nz6ANIkoykKMQmxpi1Zj0Hn3kCLZdlxgVrSCcTHH3pWTylZYx1dwBg6Dr7n/wV1dNnUF5Vcf4786/EkfEjSKeK2HJvx+S053/azqXvn03r4qnHXULiU7v/gS+85X/pfVpl7FQcX4WdxTfVMKr2MzjaTSwX48rmKymyF5HX88zKL2Z7bPic9WSTGsvsy5lWUUNwsJ/xvkJgSXljM56SMgaOH2Hg2GEW33IVqmlF9lrY9nT/ZAAJwHhfnHgoQ/NCDwee+sZkAAlAdHyUyNjI5HcFwODxo5zatZ056zYyK3GI/z1ZTW8oxddvXsBgOMWe3hDzq31s29LB8qsL53hfmYPhjihDpwNJJFmi/MIqHn/+OACt5W4iqRwA33qxk09cMp33XNDEvv4QFV4bdSVODMOgN5hkZ3ehQHBOjRenKlHpd/4pXSecFkhkeOxwH29ZWMf3jj1GmbOMD83/EAAd4Q6e7HmS9bXr2XRqP3PL5rJ+gRWbpYJD/SlcVpX3XdgE2SxFlS4a55n0HQng9FmZt66OdDKHL5DhmJxk9q2tkNJI5qL84o5PkYyEcRcXUzt7PslwkFP7t7Pu3R/k8KanJgNIAOKBCXQtT8+Bp1n3jhsY6z5IcPAU+dRSKlqmsf4dKplkAm+pnyObRwmPZMmmNYZOHuLyD81l5Q0t6DkDLW9w6PkB6meXcHzbuZ8nQzPO6zEXXkMSyJLM9a3Xc2XTlaS1NJWuSmrdtRztjbNkYxVb7/nKZAAJQP+RQyy49Cr2PPJrIiOFsaDlc7z80x9w1b98Bs3XQi6dwmLRsLlUsskz49HQDCRZIhnOEhpJkormqGr28cR3j3DR29tYdk0zj33r0GQACcCBZ/u56qPzOL59mLkX1QCw5b4z3xnbH+jCW+KgZVE5vksacS2uwMhooErILiuyXaW4xs3622ey67Fu4sEMtTOLqG0rYt/TvTTOLcFdZH+9j7RwniRzGmUOKzse6ZsMIAEYPBlh2rJKXH4r0fE0lc1+xrv3ER4eIjw8xNzLLyeYjyIB3adeZlblGkrK6rH7XATiAyRHh1HkvTTNbaSmbRn7nw3SvLCcoZNBbC6FC30uEok8g88PMdYeAcBVZGPpbW18e2cvn7lqNlX+QoDTV2+ez57eMEPhNCVuK+UeO8FklueOj/G1m+e/AUdNeLMLjyaZu66WyGiSqMdCsd8GdhXK7JR/bCFGWkP12s4JIIkF0zx+xyHS8UJgxNCpCIlwlg23z2T4VITAQJxL3j0LTdNZfGkD+ayOrEiYukRiTJu89oTCdfPhTUNceuPVLM9sRM4oxGNpZl7gR5JVXrqnB0+xnRkrKolNBKlqnmDgyKFCAAmAaXLspee4+H1/Qzw4gaFrtCxZwXhfz2QACUAyEqbr5WfxNW8kbrdSaqzkiyu+T8YYp8Tpx2Vx0BnpZEXVClZUrcCiWCixl+BUxTXt+WTkc+e8jiVGKH2D2iIIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIwhnnO/FhmWmaMQDTNE3gK5IkPXqe2/CGWFZuIRUcQ1c1TEcpPzkR4YLaaka7CoWP89ZextJpVxH/eTfoJtYWH/6rW7BWuphX6+fQQITGYicOm4psifB4x2MMxAdYXLGYRD7Bf+7+HP++4kssq5xBfzCB06rwzec7ASg3FI4k8iy5shF/hZNEMMOhFwdIJ3LYXCpazgAkJFnC4baQzxt0DbaT6RnB7S+macESTMPAzOe4oU5lb8pFTjNoLnNR4bEz2pGcsr/R8TSKplH+wCWQDDDxrg5i0dxkAMkrxnpiVEQNQpkQ8UCGQH8SLadTVGbBE+ohPzyEUlaGtbUVW2Xl695PwhkD4RRf3XSKOVVOVpSk+cBSPw0lEte3lPPIqRTXtKjU2+ooddtpK7bhNFowgxmsNQ7yg0nknI5xIkxy7ygZE4pn+hgMdNF96hAXXHc9R7aMkc/oGIaJLEss2FhFKhrDU2LDX1Eo/vKVO9HzBsMdEQ7uHaOkxo27SCYdC3HxuxvBjGGxWEgnxjC0PMc2P4/L56fvyCGyqSSrb3knW3/5c2ZeuI4DTz2KarPhKSmlatoM6mfPp3XpCoZOnaCiZRpLr72RdDRG/dz5yIqCv6qGhrkLMEwTxWqltL6RsoZGWpaswFtahrukhPXv/jCuIj/e8gpsDifu4hJGuzpQFIUjLzw75ZiGhgeA5ee3I/9KdAVPsrl/MxXbllM7s4gZyysxDDj80mAh8Kg6QGNJPZ6JbhjYAR3Pol/2OUZTo/xq6Bdcu+4mZlzcSFiaoNt5FGfGyR377+Ajiz6CRbbw02M/JZqNcsf8HyHLEsZZRe2SBA0ldeRGJnj8m19m+vJVAMQmxiltaEZVLSyZfSWxe7oxUhpKsZ3Flzeh2jT8pUm0vE486CQymsJbKpOMnPsE8LLG5slAm7P1HNjLnPWX0PX0g/zyS99C1XOAhlJs4+YyB4lYHws3etnx0BDxYBYkWPu2Nmra/GQ1A0uRlQnF4H0XNtNc6qLIaeEjv9g/uf7hSJqr5/upKXawvy/MoaEox4ai3Li4jqeOjLJ2eikzqrzMqfa9Pp36Fy6jZRhODhNIBdg7tpcyRxnDyRG2BDYzcmgRi8oX8fldn2dT36bJZdbXrSecC/PjY3fyvrnv494T9/KxtrfzeXeQtHcddUUzCGsd9O2QqGzxcvG769ByBodeGGHZ1RX4m7L49RKiPz+Fkcij+Gzc9ndf5VTvDnTDoLiqCn9ZORe//yM43B68ZWWMdXeeE0SSS6fJpeLYnUlKa0uwWJp5+e7v03bBGloWLwPTIDo+xKJLGhjrGUXP2fCU1ZAMd4Bh4CsvQrEYbLjdDlKYxZeVcWJ7jNBIEqffSlGVC0PXiYyNYmg6vsoKLFbbG9FFwh+oVC2mkSLy8Qzu4mKO0ceSRDnSeIyK4RPk5i0h0N8LgGqz07p0OclwGIvVxkjHySnry4wHmWmrY+tdP2Jn+hkWXXYN7pI2Nv14EIDmhWUMnghRP6uYwFACm11FkiX0vEHvkQDT7BXk0tqU9aaiOaYvqcTmUlEtCld+dC7BoQTZhEb3wQAnd43SOK+UVCKHFEwTf6gTPZZD9ljwXtwAskS1W+GSa5oI5wxO7h1n58Pd1Ez3M94XJ53IU1bneV2PtXB+mCaYeYPAUGLKe/FAhnAgxYKL61AtMlvvPjOGU/EYaomd2ro5tE5fTvSJbtLhAbIuCyXXNZIrytK8aCn+iiry2Rirbiph39NRWhZXcGLHKPWziij3quw7HUACkAxnGdo5yscvLILoIKg+UKy0WuO0LqogEouRTUbYF8iyoizHTdeV4iAAYRPsPkgEwMiCpxKcxTB8CA7eA8EOWHArNK8DV8nvd2C0LCTGweKG8SOw76eFaW1XgKsUymeBJINqBVcZ6HlIjIFqBy0HigXcZX9i7wh/DC2YpsKmoDpV8FmJPdNHKp5D9lrxXt/KiCSRTWrYIll8ZXZMoxAwGh1PTwaQ2JwqpXUeFFXCXWxn+vIKZEWiffsI05dVko5nScXyODxWTANigcw5bbDaFcKjSfQcOD1WMAKkwkcZbB+mqKKaDe+cSTYRJRU9jNXhpKxpGgeeenjKvox2drLk6rdwavc2kCVi46NT5+k6xfuu2EDGVoSsg6TUk477yYVTqH43PZJCWkvjtDiRkFAlFYfF8boce+HVpXMJzr6TCMWHaX7DWiMIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIwiukQhbI67wRSfon0zS/fPrvm0zT/NVZ733RNM1/ea23uWTJEnPv3r2v9Wr/KCNdpxhqP0Y+k8YwDFSrjdqZc3AVF3Nq+xZ2PHAv7/vEdwj/4twCTOfSSnILZXQ9D4ZBLp3G6nCQ8slsntjGlqEt7Bvbh8/m4/ZZt9Psa2Zd3QYGgjEeOjjKN57vpK3CzffWz0KSJY5vHWa4I0JpnZsLb5lOPJDh4HP91LYVo+UNVIvMxECMpVc1IckxUqEgT3zzf0hGwgBYbHau+cT/I+BpwMAgltZYVukjEc4QGU1y4Nl+ImOFIqPKFh+XXW/FlTwGWo5A5XUEB5M895P2c/ZRtcjY3jbExurL2HxXJ/WzinH5bHTsGWPF1Q24j7yAZLNhqiqeDetRXa7z02m/P+m1XuGbZeyeGo3R3XWKWW6ZWCSO2+8jHgxgtTtwOD3ooQzFtXVkxqK4i4qJPtSNrcWHHs6SG0ngWVtH/Pn+c9aprPQxKHVgdThxFpWi53XymRROn59gfxcOrxdXcSmJYABvWSWZRAxD1/GUlBEeGcJfUUl0bAS728t4XxdaXqO4ppaRk+1MX7madCKBzeFAkgBJJjo2gquoGLvbg6Hr6LkskbEx/JVV9BzYCyZUz5hJeGSY0roGNC1HLpWmuLoWLZdFUS1Y7A5y6RQuv590LIqkqFgsFlLxGE6vj2QkhGEY2N0eoqMj2FxuPKVlZJNJxvu6CQ70kY5F6T20n8v+5pPMXL36jenQqV7zsQtv0Pgd3MtLRoIZntlEO3Si42mObx1GUWXmb6jF5lSxNWn0ZDpZ17ENtvwP+Otpf8d9HB86BdsqGT2cAsBiU1j9/nq6Hcf4wZEf8NkVn+VI4AiDiUEssoWb/LczfCjBoecHJjc/Z20NMy+oYujkFooqqzENA1lRMU0Dh8eHx/ARurMdzgouUYrtZNbIvHT/j6hpm0VFUwv+ylb2PB3B7dnDsZeempy3qLKGeRdfxst333nObs9cfRFtF16Er7SCfDZDPpVCsahYnS4yySSPffWL5DMZ1rzjgyBPR8uZjHRHmb++jnGXyWOHRqnw2tjbF+YDFzbx9/cd5m3L6qgvcWJXFfK6gSRJeGwKXocVRZbwOSyMxzOoskxzuQu/w/o6d+5v9Wd97u2OdvNo56PUeGr4/qHvM71oOnkjz86RnZPzVLuqWVe/jnva70GVVOaXzucD8z/Itw58k6PBo8wrnYfL4iKUDvHN5g/jivQTLZlPX28nleUNDJ84zsGnH0NSFK74+CfxukpwKV4iPz+FmTcmtyO7LbhvbWRo6AQ15TMgrIEqoftMejoPUNHcimKxICGTCAawuVyk4zEwwTB1Rrs6CoFNus5Td3yF+jnzmbH6IgxNQ7XZyKVTjPd0Yeg6jfMXYbEXztG5TIbNd92JxW5n9dvfjaI6sDsd5DJxkpEo2+77GdOXX4jFbqO4poHq6W3YXS7y2QzBoUGyyQS+ikr85X92AWV/1mP3t4mHQ3Ts2MLQqROU1NRhc7momjYDi2oh8q73oI2NUXzHDzhwai+LrriWZCSCy1eEiU5wcABFUfFX1ZBJxslnM2QTCVxFxTg8XmRFJRWL0HdoP57SMqqmzaZjTx7ThNBwgqYFZZQ3uNj/3ADltV52P9ZDTVsRcy+qYduvO4kHzxTAy7LE5R+ey4t3nyAVzdG6tJzqVj8nd41S1eKjdmYREhID7SGmzyomd/8pzLOCTCSbgntlFXoyT64vjn1WMUqtG6wKumGSVSTSWR2bXaWk1o3VbplcNp/TMfTT3wNSYSDIsoRqVc5XN70W/iLHr5bX0fIGkmlimIX7E4utkNUZGkuw74k+RjqjtC4tI5fOI1sk6meWMtwRpGmuQmRsGFlWsHs8aNks7qJivGop+kiK2PMDGLHcmY2pEt53t/LQ979IdHSYKz7+CUzDRFIs9B2V8ZUXkwincVVY2f2rvnPa6Smxc9O1Qzh2fglWfBj2/hiycbjon2HHt6FqDlTOL4R+2Fxw9EFYeBvICpx8Ck4+Cb46uPJ/4b7bIBs7s/JLPg8XfPz/PljjJ2H8GMjWQqjJA+8tpLVMrucLoDpg9Ah0boINn4XIQOHay1sNS98HJ56A5R+CaZeAxf6ndN0f4i9y7P5ftGweLZTBTOqg6YQf6MCzuhYjlSexcwTzrIBQyaGSWltLIm9iscmM9sQYOhHGW2Zn4cYGnvj2YWatrsbhthANppm+rJL9T/ehZXVWXNvMeF+M8YE4VS1+dj/Wg6GbtC4po3FeKdHRNBaHgtNrJTyapKzOi9WeRZbjTPT3YnW4sNjtuIuKiE5M0L75BUY6TrD4qutpXriE8NgIRi5PSV0j+VwaTJN8Jotqs2LqBlo+TyYRwzQMkCUG248z1H6U9e/8AIrVSi6bwdAK5/IDTz1K76H9eMvKWffeD3FfZhOqoqJICksqlzCrZBblzvI3qst+m9fjvu31/yHw99C97ymaH7tl8vVTjbdy+bu+8wa2SHiNvS6/OQiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAivP/U8becW4Mun//5n4FdnvXcZ8H+GkEiSdBnwDUABfmSa5pde60a+HpLRKIlwkNDQIEdf3IRpGhTX1OErr0C12yita+D2/7kD7UByyrKZ9iDeldMZG+nkwFOPMnD8CKV1DSy77iZUh8S+sX0ARLNRvnXgW3xj3TdQZIkiBT6ysISPLK0gbCgQM3jxrhMEhwrbGDoZYWIghq/UReuico5vGyE0nKSmzU/bikpMI4SqWOk/dngygAQgn81w+Lmn2XDF9Uh5DWuZndGJMLlUCoslx6obHSjWUrb9Ksyyq5twjf4COp7GuOU+tDENh9dKw9wS+o4EJ9d5wVuaaV2wlHw6xcZ3VaJpbl66u4uWRWWkkgbGwSOkHnmQottvx1pfh7pgwevbYcKkWp8Fh8eJx/Tg1YqxWtx4w06sVS6S28ZRXC7ygyHM8TTZOgkjmUctc5I+HEAtd5IfnTqm5X6dlhtXko5FMDGxWG1Ex8ewOqzUzp6LlsvhcHtQFBWLzYbFXoqey2F12HCXFCOrCp7ScrKpBI3zFhEY6MPt89MwfyHhkWF85RWEBvtxl5RhGDqZZLIQYDI8VCh8x8Q0DXKZDN7SMpAkTMMgHgxQXFXNcPsx8rksEtC+9SVmrV1Pz/49WGx2nF4/R198lpVvvZWjLz1HWX0j2VSS/qOHWH3zbTzx9f/GXVLKgkuuZPM9PyEeGKftgjXUzJzNkeeeYeMHPkZRVeN578e/BkYuyxz7IuyojMWD7Hm8Z/K9Lfd1sPG9s5DyCgPxAcKL3klR72bwN5HRsyyWV3HUGGbJFeWYpwuP04NQU9nKO1reiYnJotKFVLur0QwN4ipjPVGWX9OMxQZ2l0Tv0RhaPo7d6WKivxdT1znw1GNkU0lmrF7LBYtuOieABEAPZSj3z+TCW99dKEJXrSg2nTW3VJJLX4GJzGD7AW7+zBfIpdO4Skrp2L2N4VMnAFh0xbWUN7dgs9pJhAIkggGGO04gSRJV02bgr65m/bs/xJPf+h9e/MkdrL71X+k9IjFrdTV2t0pRfIKP2YbJJnSuXjaNa+85wLQyN+9vkDBCQ0g5kIqL+c/DaVa2ljK3xoEnEUaSdBqbSs5r//6lSefTfGXPV5hXNo9ELkEil+CymoupjMDnve8ARcERTmGpqkIPhXhf9XLC7iaO7w4T7Zf57NLPc5/nLjJyjmuKLyd3sJsH/vf7XHDzrWz+7udQrTYWXHolex97AABJlyn31JN8uB8udp8TQAJgJPIoKZmaojZiP+2cHKtKmYPGaxYyPtHNaFcnNreLxrkLSUbC9B7aD0DVtDZqps/gia//Nzf8y+e55T/+l3wmRTIS4sBzT+Erq2Du+o14ikuJBcZRLBZ2PXQ//UcOUFJbz4obbsbh95NLJZFI8sx3fkx4ZIiKplaWXXsTT337q4WCYmDexitou/xiOl/YzIHHHwbA7vZw/af+jerpM/+wTjD0Qumt8mcV/vCmNt7dQSwwQToWZceOLQA0L17Gyre8Ddfffhz9xRdRzRyzL7qYrn27cXh9yLLEY1/7EorFwvLr3srOB+8lNDxIy5LlLL36RhSbHYfHy+CxQ5zcsRVfeTmVrW1Y7VZmr3UQDSjUz0pyYtsD9OyLM235KvwVVi5+bxNOVw7TmODyDzcgGSaSKpPLKKhWJ5gG626fQTahoSbz+DWDsul+0m4rHTtGaV5cTt2MYiyaQfasABKgULAvS6T2j+O5sIb4y4N4N9STPh7EWudFSebwzSjm6L4JknNL8JbZ0XMmkiKRS+bp3D9ORZMPq13FMAwMHZweK1XTfFht5+u2TAAwTRMtozHaFycVzWF1KMgSSKqMapGRkLC7TfLZLLNWKdTN0HB4kkz09RAZGyURmEZFvY+Hv/wNUtEIAEVVNVzxnn/AMe7EdOXRo7lzA0gANBM5YHDtZf+ApdFNypIkPDyEw+WktGaEktpG8rVOAonQlDY3zfZhq3bCVV+DdAhWfgSKmyAZKISSKJZCsEiwsxAAUjodcnHo2QLtjxZWEuyAvu3nBpAAbPkqzL4RfDW//aDFx2DPD2BoHzReCNGhcwNIALpfArsP5t8K+38KD30ALv4caBkIdcOz/wrr/xXuvw3e/TQ0rPyD+k34/ZimSaonSv5kGMVrJbFzGFuDDyQJLZhGdlvOCSABMNMaZcV2yOoUFdupdVlYWGpHLrZj2GQu//Aceg8F2Pd0H1d+ZB5b7j/F2lva0DSDE7tG6dw7zvJrmtnxUBd1M4qYvaYGm9OCaZgE8wmaF5URC8ZoXeIFM4+WzZJLa9TOnEs2lcRit2FzuLC7fXhKSrDYHGi5DLlsFm9xGZIs071vJ3sefQAkiWXXvoXi2jqGT7bTuWcnoaFCQKDD6+OSD34ch9tNcHiAYy8/T9vKC7E6nUTHRmlYsAhFtdK1byfPfvvrvO1f/oVedRyn6gSTN2MAyV+05G+ci8LhwBvUEkEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQzna+qt2k3/L3q72eurAkKcC3gY3AILBHkqRHTdM8/to18fVhmgbpaIwjLzwzOS00NMDxzS9w0e3vx1NVjaKoyF7blGUtFU4kBbbddxcTfYWi+vHebp79/je59lP/xv/+xvzVUhnJri7UcITwiy+S3r+PojvuJBTLTAaQvMJf7gLdxFfuZMbKSkzdxOZW8ZTacbisAETHxqa0KTo+Svr4ceTmBuIZJ9lEjM33/ITQ0CCe0jIuuu29XPKBmeRTMqnmG6D8EuRIHEOzkU1pzFtXS8vCMrJpjdo2G5KcJ9jXw9GXn2fo+FGq22ay6i2X8MQdvcy5qIaZb7+N1CMPEv75z3GuWP4n9obwh8hGovgyPmIPduNZX0fk1x3IHitmziA/kMCxsYHYpj5srX60QBoA6fSnWY/nsLcVTV1ppYWRjhPs+PUvWHzltex66H5sThdz1m3kwNOPM33lalLRCAPHDrPs2ps4+OwT1MyYBaZJ94F9LLvmRvY+/hBNCxeTikYZOnGMZdfdxI5f/5JZa9ZzYuuLhIaHWHbtW9jyi5/RdsGFDJ88zsCxI6y6+TY23/Nj5l98OYefe5rY+BgXvPVWnrrjKyy87Gq2/PJnJEJBVr31HTz3o28zd8Ol7Hn418QC46y44Wa2/+oXzFqznsObniI8Msz0ZRew/Vf3MPuiiznwzOPomsbcdZew+Z6fTBbNH9/8AoamY3O5ePb73+SaT3weqD1PPfjXIZ3IEdJmM34sQvOCEk7tGZ0yT/+xEMsaarhkcwLd+QIRz3uwTJ9N864ewrUV5DIaB57tZ9WNLex4uJv86aLMGUuaaZyeIfHjOyi99nKsi5eSdaoYugkE6T+ymfHu4zQvXkU8MI2t993Fqptu5alvf3Vy20eef4bVq942pU2SQ0WxWQid6EOSZUY6TzHaeYq6OfOon7uAC972NiL9K3j+J98jNDRI69IVrLntvQwcOYTT50eSJbKJBHpxKZl4jE0/vANDL7T76IubuPLj/4RitTDv4ss59OwTONxZZq1qwl/hRMJE+vTfE+jsBEAtL+epr30Tr2KSfOYZIvfdD4D/lpv5f6tWEbelkR7+NYP3349ks1H8nnfjWrMG1e1+rbvzr8J4epx4PMhGpQrL/hNc3foprAMuwnfdTeToURwLF+J+z3uIP7uJ8E9/Cp/7Ic8/0D25fM+hIO+65a1YS4sY6d3P9kd/TcuS5YXzja5T1TqdgWNHJudvW3oh6edG0UNZZLtauOo6q2ZcssjINguxJwbOCcvRJ9LIQZ1nvvdNVr/tNly+Irb+8mfo+TzNi5bStW8Xx17axOUf/QSrbr6Nif4etGyG3Y/8mnw2y8YPfBSAeDDI8z/+Li2LlhEY7GO08xQAE309PPO9b3Llxz+Jruu8cOd3Jgv5q9tm8OLPfjh5LgU4vOlJGufMnwwgAcgk4jx353d562e+iP33GY+pEJx8Cg7cDZ6KQsF99ULwi/PynyoZiWAYBgPHDk9O6963m4a5Cyif3YZ9/jzyksTRpx5GVi34ysrZ+8QjZFNJVr7l7Wz55c/QclkAOnZtJx2Lsvpt7yYbj+GrqGLR5dcy0tHOnkd+xdJrb0LP5XF7nYRH0hx76TkAuvbu4rpPfZaJzj3se+JhdE1j+srVzFi1lsH2Y3hKSnC4PXjLK7FY7UiaDcvWQdKpQtCIRYKZlzYRz+hsvb+Tq2+fAYoE+lkfGJnCxY4kFT5HJpiGSX44iWN2KcldI3hKnUwvs/PcIz0sv7qJA8/3Uz+zBNM0aF1cwUB7CKfXiqGZIEE6nkNLJCkJHgETZLsd2W4j9uwm8oMDeK+8CktdLRgGpm4gmQbYbKAoyF4vztbW89XNb1r5ZJJ8by9mPo+ZyyGpKtroKNFHH0PxePBecTmGrmNmMliqqzGiUQzVQtg3ndBIGrtbRVEtZFJ54oNJxnonqG1Loao6do8XLZth4NgRdC1Px65tLL7yemwOJye2vzx53gKQJAl33oeR1zAzOhgmklXGzJ0V/iSBkdBIbBoARcLxlmqsDgeP/M/nWX3L7SiWHG45R8KWZsaack5sGQcTyhrdzKk6ijwUBX8DxIYhOgADu6B8FkQGoH4ZhHugZjHsvRPWfhqS43DyiXMPmPQqt9+SDLnU7z7Qo0dgz4/gwk8U1lk5/1XWI0E+A+ZZ+xwdAEcRpMOF6fnCfQPD+0UIyZ8odfQoZiZzOgvGBKeXiYhCcXER0miS/GCcvAmK24oWzJwJjZEkkKVzQ/IUidGhJKbXCscCJLaPTL5lafEhzSvnxM4xJAlSsRzrbp3Btl93Mv/iOrr2jQOg5XUcbguN88sK5zMlg26EqJ0pkY72YrUpBHoDSJLMSz//ERVNLTQtXEJRbR3ZYILdm39F/dyF+CsrSAQDKKqV6NgIJXX1hIYH2fLLn022afM9P2H1LbcDTAaQAKRjUTp2bSc8MoTT50OxWHj5rjtZceMtjPd0kUnEmX/JlSQjIUa7TqEF4yT8CWRJZlE6B4d/BVYnWBxQ3ApF9a9b/wkQT8fPeZ3MRt+glgiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCcDb5PG3H/C1/v9rrV7MM6DRNs9s0zRxwL3Dta9W410siGkHX8qRiUwsphk+2o+VzOFxuLLIVpcyBtcE7+b7kUHGvriGdT0wGkLwil04THx8/Z5pNsdGgliJnMoz/1xcJ3XknclkZRs7AYlOQ5HOLzUzDJBrMoNpkyhs8VE33U1LlIp/RMCWTXDZN3Zx5U9o9fcVqEo88glLXSD6T5rkffYfQ0CAA8cAET3/n66RCIyCBLMvog4NkM6CoMvmszkR/DF0rtMnikNEyGV74yfc59uImImMjHN/8As9+9xtc83cNHNsyjG45U1SsB4J/eCcIfzSr7CCxqdC3SBJGSsNa4ybXW3hasakXigtzQwmsTYWxmxtJYm3yYaY1JFlCLXdOrk/2WpBabTz9na9S0dzKkReeJRkJM2vNejb/4qdYbDYyiTjtW15k3oZLefnuO0nHY1jtDo688Cyz165n6313kU0lsDlddO7Zwaw169n14H3oWh6by8XwqRPMWLWWvY8/hGkaFNfU0XNgL9OWX8Dh557ENAycPh+hoQEa5y/ixLaXAbC73cQmxqmaNoPewwcAcPmLiIyN4K+sYqK/DwBPaRmh4UGsDgfpeOFz7S4uITpWCL7QcrlziuYBTu7cwqw16zENg/BQ1+vRVX/VgiNxJgZibH+gE0mWcfmmBjq5/DaUdJzUM5vIneog9MDD5Do6CP76YY7vGGOkM0rLojKOvDw0GUACcGJvmHDAwNbYRPSf/x3HRBhFlVlyZRkHnvwup3Y8T2RshEwiRGR0GIvVxljP1D6W3RZcKyrPmgDejQ1IVpnI+ChHXniGjl3biAcnOP7y8+x++H7iI4M8+tX/onvfbiKjw+x97EEOPv04M9asx19ZzckdW8kk4jhcbjr37pwMIAEwdJ2O3dvx+EtonL8QJAlfeRmlDR5kGbJpndzpABIAbXwcee8uMu3tBO74NtrEBNrEBIFv3UHmxAlcWpqx//xPsh0dZI4eZfgf/pH0nj2vUQ/+9XGqTj6tbyT74U+R+OFPsNgcjH3hP0nt2YOZTpPavp3Rz30OtbgY27LlHO/4je9vE/r7NEITOTr3bAPA4fGSjIQBiIeCeMvKJ+evqW8j318o8DQNA/eFZwVuSOC9uB7JoWDG81PaqsezlNTUYXO4eOIbX6b/6CGGTh5nyy9/RuvSlZimSTwYoPfwAWRF4cWf/oBkOMTsNesY7+kil0rRc2AP6ViU8uaWyQCSV+SzGaITo0imeU4hv9XpIv0q106pSGjKtInebjKJ+JTpr+rIr+CRv4H+7XDsIXjo/dDzIuSS//eywu/kK69kvLtzyvT+o4cAiUw2QzISori6jkB/L9lUikB/4frWNIzJAJJXDLYfY7y3E13XyKWTWJ12ImMjGJpGLp0im0qQTSVx+YqonTkHAFlWSUXC7Hr4frR8DtM0OLl9M8On2nF6vex55NdkkgmGTh4jmwjj0w2M0wEkhYaAcmwCm1XGW+agbyCO69LGc+ISPRfWkj4SwLWkgvSxIJJdmQwpeeW6KD+aRBqIM212MeGxFM3zytj7ZC8Wq8podxTFIpNNaSBDLqMTC2ToPBpF9vnJHD2CqeUZ+tu/JXr//aS272D0//0/MoePoEdjGMkkWiSKEYthTExghiPo8d9z/P8F0wYHMbO5QrhIKkW+v5/hf/hHki+9ROyxxxj82McxkylktxsjHGHk3z+H3DSN4EiafFZDtSqER5Ikgln2PtlLaU2IbCLAsc0vYJ4OwrO73bRveYn6uQvo2L0dwzDOCT4AWLD6CvRgBjOtYegGhmbgWVt37hhaW0e6/fQ9jW6SPxRj8NhRLvngx3nhJ9/HyIexJILUJQ3KLjJZ/fFqZrzPjv+GOMmZM+Hg3RDqhJHTgT/++kIwSCYEgQ7IRCE2CO4KSAUKd9ruinMPmGmC3X/utMXvBlfJ7z7QydP3n6YBoR5oWFUILzlb01qomAV65sw0uw/yZwWcKJbC/66y37094XdKdXRgRGOY2Sx6YALJYmF41OTgzijGeAozbyC7rOixHPnhJLYGD0Y8j1piJ310As+FNWfGpgTqhbX09MZwSSaZnSPnbCvfFcV1On/J6bPiLbMTGklid1nIJPLISmEcqBaFaUsrOLipD9Umk0+PoWUzGPkcSBKD7UexOV089Z2vFq4NmlrwlFeQT6V47Gtfwl1cgs3hIBWNEZsYJzDQi8XhIDwyROfuHVOOwdCJ47xapu14bxf1c+bRtW83dTPnAnDg6ccoqqphtKuDZ777daavWIVisWDkNeqslTRN9OAd2AW7vw+92yDcVwj5SYxPWb/w2glMnHttpxMjGc3+lrkFQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQThfzlcIyXxJkmKSJMWBeaf/fuX13N9j+Rrg7CqvwdPT3twMk2wyjb+icspbVdPa0PN59GwOPa9hZHXc6+vwX9eC78omit86HVQJLZ9HtVinLG/3eFFldfL1nRd9D8VqJT84SObYcQC8b78d/XQgwrx1tecsHx1PY3Uo9B8N0XM4SO/hAEOdURRFIT0wiJHXKa2rZ9XNt+Hw+rDYHSy64lrq2mbhfcc7UBSZVDRKInRuMEg+myEWCCDLMrIkEX92E9mBQWQV3H4bnmIHiixTNc2DrKhEJ8anhKxExkZIhkZQrQrayeOT0y21b/4u/0uiZzKYmUJx7isPTNcm0liqXIUXJkg2pVBomdZwLiwn0x7CWuPCfXEdudEEttWleN7aRG61wkjrMMcOvwRAUXXNZL9ruRyYJnWz59K9bzcAhm6g5/P/n73/jpPrvO+77891yvSyHbvonQBBsFeRKqSobklWc5GbJMu2bMd3nDv3E8e54/hJcZzkSeK4xbbiJskqtlWsZkuieiFFEuwECaJ3YPvu9JlTruePARdcLkiC5C5my/f9ei25c82ZOb/ZueaUeeH3PazZvpNj50JBjOMQNOr0rl3PyLmgBy+ZpFGtkCl0UR4bBSCZzVKbnsI4zkxTc7arm9K5++NznwnH8wiD4Nzra7/AMGjhJ5Pnxtqbx9rUFPnevplak5kszXqNTLELgOFDB843QHvnP5NPy3X3YM9dDd71594vL49rHJ78QbtRMgpjdr1yNa53fteWzHis29mNa2Jyr3kN05//PEP/+XeY+sQn8W5+NUcfbwc35HvTTJ6tzXn+etPB62s35baOHAUDQX2M0ujZmWWiICAOQ6wxZLt75jyHjWKSO3vpfs92Cm/aSM+P78DtS2KjiGxXN5NnTs9afuLkCSZOnaRVn13PU3d/j+r4KFEYnPtcGJq1OmFzbqNe0GrSatQ4/vijvO6Dv8bUaJKDe4aZHq2TuMA0TAz0U/n6N+aMV77+DVJbt84d/9a35z6JXJTupkfiz/525nZcqRI+K1gsOHECt6sIUYTjzn0OJ+FTnqhTXDUEwLHHHmbrDTcDMHHqBKs2bSGZbW+rx4ZP4PanAJj63CGSW4p0/9i5ufgTl+GuylCfLmO2p+asx3a7OK7L8ccemXPf0UceZGjrZSRSKZLZLJPPaMZPZLI4bjsAbezk8fZrajbwknNDglLZPBiD455/oVNnz9C3fuOs5YxxyOcKPNvqy3aSLswdn6MyAt//vdljQR0mj7ab6eVlyRSLrNqybc746ssup2vVEKXRYc4e3I+XTBJHEVMjZ9l6wy3AhfediXSaOIywcYwxDmGzyYbd1zKwaSvGQhyFREGIcRyufN2bARjafhmn9j8557kOP3AfXiLJul1XcvLJvRx/9GEcz8XW5wbv0IggtkwN12jUIkYTDu47tlF41zYK79lOFESkr+4nrgbY2FK4cwPV+8+CAeOfa8LvSxNVWsRxO5wkaMUz/z/04CjprE+xPw0WHMfguAbfN5g4xkkmCYeHiauzt/+TH/kIxnWxzSZEEeHwCCaVIpqeIhhRg7xtNLHNBnGjiXE9Jj/5qdkLhCHNp/bhpjM09u1j8Hd+h3qYwHEM1oLnOTSqIa1GxNDWHAfvuwton6/5qSSHH7yPOIpwPJeoFeAnk0RhwNqdu2avJmhhfIeo3CI4WcHvy0DCoevtWyi+dTO9799F8/AUwcnKzGNMA84c2IeXSBBHIZWJMYhCfBzKUYnp3Ai/+dSvc6R+kAkHmDoOrRqs2gmJDJROt8NGCqvh7KPtUJJHPgWv/H/ge78H/dvhpg+dP5gHOHYPvPUP4Jqfga13wu3/bzs8JDP3GGaWns3guLDvi3DNT8Pdvw8/9jHY/WOw823wlv8BfhbW3QT3/O/2Y7o2nPvjnDtWGdzd3h73bIW117/Yt1qeqVIhrlWJSmVMIkmcyPDgD6ZYs70bawzGdWgemSJ1WTe2GRGVW2SuW0X5ntNkrxskdgzFd2yl+M6t5H72cr5z/zB+yiNqxhDPXV2z3OKymwZpVENSaY8otEwO12jWQ3be2j4eOfLIKKs2FohCSyIV0qhUiMKIoNkkaDYZOXKI8vgYYbPJqi3bKI2PYMOQ6ZFhgkYdP5mkXi4RNOqk8wVGjx4m193D8ccfpXiB71Ryvb1kisU541uuv4njex/DdV3iuB3U53o+cXQ+eOr0gX3c+cFf5d5/+DvWT5bY8LXfBteDk/dDKt+epzaCcYVILqSJyXboXKvV3ocaZ4LjexU+KyIiIiIiIiIiIiIiIiIiIiIiIiIiIiLSaZckhMRa61prC9bavLXWO/f707f9i3iKuZe3bV/XefZCxvyiMWaPMWbP6Ojoyy/8ZQoaDeKwRffqdex85e0z49mubm5593ux1hJHIaEJcFIuwdkqNozBdwjG6hjPxfd8bn73T8563u23vJLChjX8we1/wG/d9Fv8t1f9NzbnN2KDAJzzb2n1G3dhY4PrOazb1c3rfv5ybnzbJu583056hjJkcgl612bpXpWhOJCmZ3WW8FN/wuQvfgjrgJtIsfXGW3jrv/hN3vEbv81Vd7yB6Xe8G2MMUatJOp+/YEBKtquLdP7pt8xS+t3fImxYUjmfdD5BMu/humCjCC9x4bff9RNc/+aNVP/wv2IyGQb+1f+H5M6dL/9NWaQW29wFaJoG7u4cAHErwi0maJ0ok9xSxMknqN57hsLrN+APZak9MEzkRRTft40j9cf51oMf5VBhLx//03/FYwe+xec/8V/53uc/OhPwMXX2DL3r2k2JTzeeV6emyPf1zxorT45THGhfvd11XTCG6ZFheta0Q3Wic6Eglclxus4140+cOsnApi3YOMZPtpvqjz/+CFuuuwloN7L7yRRHH3mQ7TffCkBpdJT+DZsYPnSAdbuuwhiHY489xM7bXkOjWiGRSlMcWMW+H3yH69/6DgBGjhzm8lfdwdHHHmbztTewass2KhPjDGzacv6PaAw3vePHefAfv0C2u4euwdlhQMtFJ+evccxM6Eh5vE664PPmX97NK961hdt+bBtv/KXd+AmXU//2tzCOA2HY3qEYQ3zqKMVz4QxjJ8oMbpnbxJix5Vnr8j2D683ebh24726616zlqjveQCqbm5nHAK7nEScsnGv+dBIuwXgdExsmK8M4zoV3w25i7rbVSyQwrouXSOAlkowdP8qj3/zKrP3L0y6/7XYc18PGET1rr8VPpuhbl6fQl8bb+4M5y1vj4K9ZPWfcX7OaYGJizrjb1XXBupeaTsxdG4bElfMN6E4mPXchx8GGEY3772PX1tmdwI5rWDsEhx+ZZtNVN5LOFyiPjWJjy5V3vpFEOsMT3/8Wr/+lf87t7/8lujasJv36QUzSJa4GTHz8SeIwxutK0Tg4TW3PMOPjxznWfJLkDX3gObjFBOaOArVkBazFS154PkZhSHFgkEalTDKXf0b57XndqtXYuPsaAB6965+4+Z0/Mes5Nl1zPel8gamRs1z/1nfOjB+49we84t3vpX/jZgDS+QJv+tkP4nz+S9z69vfM7CPyff3c/r5fIpnJvvAf3jjgXCCBxzizm/OXiMV23OD6CS57xSvp37BpZmxo+w6Gtu0gCkO8RIIHvvQ5+tZuYMPuq8n39LH95tvYfM0NjJ84xvrdV816vlve9ZOceOJRbHx+/gdBnZ41awlaTeIoBgOWmHqp3cA8efY0PYNzA+t61q6jWa1Qm54i19NLHMdEQQtvfXbOGYZ/7QBjIw2CRkgi7RLFcNfnD/PUSJ3P/N0BvrVvmkdPVTFX9pG5ZYjS149hUh7FN22i9sAI/uosOAYu7ye/Ns++e87inQsncT1D39octXITL+liY3A9h2wxwbZdeeJyiXZBF5iPxmBtjHHMubvtcy+7BMz3/LWGc38S8/QKLrBSh/Yf3cFJ+ATNiO5VadL5BK7v4iVdXM/QasYkMzkc16VVqxEFId1Dazhw7w+48rVv4NhjD7P9pls5+eTjFPpXse3mWzHGwXE96lEFsi42sPirMjQOT2FrISblEfTGBEGD1vHyrLKCjZazh/eDAdf3yXV1Y6MIi6UclMn7eQYzg1gsBRzY9npId8PxeyFsQa4fXB9aVSiuhbABuQHovwze8t8hjGD1dfCej8Ab/yu8+y/hHX8Ka66FXT8KN3ywHUSy4ZYX/kOvvgbe+odQm4DqKFz9Xjj0rXaYySt/A/oua4ehJPJw7c/Ae/4afuZzcNlb4G1/DO/8c7jj38GmV8HPfLYdarLELKZtrzXnz7fb/zVE7bwNWlmfYKxGckOBuBWRvWGQ+qNjBJN1cm/ezHTKo7G5wAMHp/n7Tx9kOobp8QajJ8qUWhH+htnhXk7OZ3S6RRjEXPuG9YyerJDvSbaPwc99K3PjWzdR7M8A0L8+f27bZ+HctssYg+O4pM4dL9Smp3C9RDvEyX36eNjgeB5eMkEcx2S6umlUq6SyObbdeAuJdGampvbYKxg5fJAr73wTrueBMWy7+VbW7riCM/ufZNdr7uTQuZDLK1/7Rvbfe/fM43M9fRzf+yg2DMi0hmnvVJ7xFZON2z/nQkxkYUxX29vEI08UmLJZvte8g3/xtSf4+b++n4/98BiNQH9/EREREREREREREREREREREREREREREZFOMNbOyfJYdIwxtwD/X2vtG87d/k0Aa+3vPtdjrr/+ertnz55LVOGFRVHI2InjhK0mrufRrFYJGg1yPb34mT6C+gReKkHUDMj4ObyWj21E7QbDtEsjE9CqljAYSuOjlEdHyBS76V69hpFMlVKrRNpLU0gU2NK9hfqJE9hymZH/9DvUH3wQgIFv3kvQiKlXAhIpD5x2A7PjGGyuQS5OUKsZ3KjF1K99gNah9pV+je+z9ht3YXwf4ziYsXEm//KvmP7MZzD5PANf/AfiKOLIA/fz3Y//5cxrvv5H3sHOV7+Ngh+AheDJfZz8tV8j8yu/jv+Gd2Fcg+d7xHFEItUgaNS593N/x/57vjfzHJuuvYHbfuIXSAYB5tBTOIU8ycsvx7/AVY4XgXnv/lwMcxeg2Whw5qG9JM66mEMhuVcMEZabhJN1vMtyxK2Icnmc6dooA+s3cc+XPtW++nsYcuzRh3jVT72fu//u43QNrWZo62U89s2vcuPb382+u79HdXKcV/3UB/jhZz/F1htvYfToEUaOHuZVP/1+vvs3f8WW626k1Wxw7JEHec3PfpC7//7jdA+tYWjbDh7+6pe46R0/xoH77qFeLnHDW9/J3Z/+BFuuuwnX89j3g+/yyvf+HE/+4Duksln61m7g4a/9Ize87Z1MnD7FyScf5xXv+SkOPfBD0oUuBjdvZd8PvsvO215DGLSoTIyxducVnHrqSbqH1pBMZ5gaOcvg5m3UylO4nkcqm2d65CyFgSGwMWGr2W6SK5VIZLOMHjlEo1qle2g1jusycuQw6UKRNVt3M7h90wv/8S+NBelcvtTz9/SxcSojIXf9xV4cx/DOf3UNrXqI47kQQxRHpPMJJt5+B6krduGkMwSlEj0//mOc/Xe/jfcf/5S7vlbHRpZXvHMrT9x9mskzNTzf4cZbs/Q+8Fm8hMv0Zz7Dhk9+gpE/+EPSv/27/OBTf8aRh+6bqeM1730fXWvWEoQh2UKR6dFhwlaLnqG1ZLq78UqGpJOGCPAd4kTM8MRhDj1wP7XJCQ7u+eHMc2298RXc+Pb38JX//T+YOHVyZvyWd7+Xa37kHYwdOcipp57gB5/6GNe88UfYcNW1hM0mD/7TFwDDtW96Gz1r1vHx3/znvOvX/zWFgU00Ih+nNE7Vg3yjQvMzf8/0F78IUUTuzjvJ/eKHSFRLnPzVf0ZcrQLgZLOs+eM/wgYBJz/0yzzd2epkM6z78IfJXHfdJXmPn8OS3vaOffjDjP7P3wOg+30/RzQ1RekfPj9zf/fP/gzZW2/l7G/9O+jpw/ngb3BsNIMbNVhXLJF86Bs03/CzPPVUmc1XRkwPn8BxXdZu3UIUGcqlGq7rkcwkGTl6kK7B1VAKcWowuO0y4qkWjacm8NfliVcZDhy4l73f+TqDW3aw85rbaLUaPLHn24wfP8Zrf/5DtOp1vvwH/504CoF2mNOdv/CrpLI5/GSK6dGzZLt6+P6nPsLEqZP0rdvA9ltuI5Vrh5WdeuoJnvjuN1m/+2p23/56KpPjJNMZHN8n191DvTSNm0gQtlrUzwVFpHJ5Dtx3Dxt3XUnBuEz9v79FetcuYsch2LaZ0HXpvvkm+q+46oJ/4wt66G/g8796/naqCG/8b7Dr7eBfIAxmYSzpuftc4ihi+MghjONSm54Ea8l09RA0GzR7PNJTEXu++DlS+Ty773g90yPDRGFI37oNNKtVHNehNjVFrTRNptg1ExiSSGew1pLKZjl9YB8Dm7ZQm5rC9Xwy3V0QW/Z+5+tMj46w/cZX0D20hrs+/IdMnG5vO1PZHG/4lV9nemSYez79SV7xnveSzhfaTe7+JgaSSWrfOYGtR6RuHGQq5XF43yR963J0DWQ4fWiK3tU5pkdr3PeFI1gLxVVprrhtDQfuH2bb7h7W7+jBb4aYRkTcCImSHkExybc/c4B8TxobW4JmRKE/xdZrB2jVI3DARhYLpJ0WhfpJ7NQU9UcfIXPV1Zz5rd+aFVY08K9/A29gAJNIEjcbOH4CG8c4Pd2kd+zAKxQu/MbMv0U5f+v79hHX6sTlMjZoYWs1Tv+r35i53/g+g7/zn3ALBbCW4f/+P+j5g/9DuZXARmBtDBamR+sce3ScdTvrTA/vZfjIQTZdcz396zfxpd/7L6zffRWrNm+j1agxsHELcRiSORca4qfThK0m2VQXmTCHrUQ4GR8n5zEydRy8mMrYBEPpTbTunYQIop0uex76Il3r17B622U0KhU2r92ALZUp9aa4LzrEqswqjpSOsLmwmZvv/zhsuKm97Ro/BCfvh+wAdG+AJ7/QDhS5/8/htb8Nm1/9ct+aCwuaMLIXauOQG4RMHzz2d3Dvn0BhLbz238LGV4LjLsz6X7pFOXdfjvr+/YRjY5jYElUreKtXc+S4x5FDTbyUw823rcGdbuK4BjIeUdKjaS31IKYy0cRPueS6kxhjqJaaNEoB3//7g1z3pg0M9adJnigR7p/CDGbwrlvF2ekW933hMNbCNa9fz9CWIlMjdUaPlcl2J8h1pUgXfJw938TfupOTlSx9g9NEQevcNjdJbXoKC5x68nEe+soXedVPvZ/e9Rshjvn6n/8xm6+9gdXbL6c0MUr3qiFajQYTJ4+zbteVnNy3l7U7djF24hh+MkmjWiWdL9C9eg3WxjgYojgmmcny6De+wprt7QDTE088xvorrmLfD77DsUcfAsDzE7zuF/8Z3/yrP+NHf/btrE2Mw/f/R/sztO9LsPm10Lu5Haiz8RXtgJ/OWojztkXxReB/+g8f4N/Gn+Eze2/iP215P5PkGLCGXH+Ow2NV1nSl+R8/dhU3b+7tdKny0izNtDQRERERERERERERERERERERERERERERWTIhJB6wH3gtcAq4H3ivtXbvcz2m001BT6tNT9FqNmlVK1hr8ZJpHD+PxRC3Ygyl9tW6bbsLJJ3I4CQ86vUKFoONQhzHAWIcz8daw4g/RRiHpJwUMTHberYBUD91ut14NzFB7b77aTzyCJnbbsW5821Ya4hacfsq344l5U9x1gYY49A7DdRqtA4cYPJvPo4NArp/8idwb70FL4zb9RmDqdVoHT5CePYM7itvpZVK4cSW8uQ4pdFhcj19FIY2UHVSbHDHqJcNxDHR0aPtZnfHoevn3kdyy2b8XI5GvU51epJGaZqx40cZPXqEvvUbWbV5K13ZPPbsWUwuR3rLFnCc5/szd9Kya2h7ppGjRxg9cYTmeIneTZs4+dTjuL5Pvq+fQ/ffw+Zrb6RemqY0Psb6y6/gxJN7Gdi4maDZYPzkcTZedS3DRw6R6+7BT6YYPnyQ9VdcRb1cIoojelevpTIxTq6nl/L4GFEYkO/uZWrkLPnePoJGk2a1StfgIKXREVK5PK7vUx4fo2fNWmpTkzieTzqfY3pkhK7BIcJmkzAIyPX0Up2aJJ0r0GrUiFot0sVuGuVpEukMrUYdLLi+R71UJpXLUy9PE4URmUKB8vg4ma4izUqV8tgYfRs2cObAU+R6ekikM4weO8raHZczcfokXiJJpljk7MEDrNmx89xnNcLxPIJ6TNgM6RlYxeCORRNAAsskhCS2MeOnypTHWhx5dIxcT5KdNw3SbEQYBxJuSOOznyRzzTVMf+5zJLdsIa5Vydx+B9Hp01R+cA/21T9CLdWP2yzTtaZIfbKOGT9DujqMm89R+fZ3SL/hdTg7L8M/M0b1/vvhdW9g5PQxxo4dYaCvj8G+fopreqi5OeqNJn4q1b6Auo0ZPXYEz/fJd/eTzXcTnjxJMDKGd90VBLUKlakJKhPjTJ09Q+/a9aRTGYp9/TSCJmcOPMXU8FlWb99B77oNeH4CjMGGAVOHDjI6NszAth30rV1H0GxircX1PI4/+jD9g0N0Gw+TzeE4Bn9oCCeZBKB88DDxmdNgHMK16+jduI76+Djx0WM09z8FQPKyy3DWbyCZz1Hbs4faffdhUimyN91E5tprL9l7/ByW9LY3GB2l/JWvMvmJT+CtWkXP+99HPD1NODGBv2YNbm8v4eQkXqGADQLcgQEiYsInnyIcHsFGEYmdOwiLXVTLIZk1q8n7kyQnHoOuDZyubebkwTIDG/NgodWIcL06zcopmtUyq7duI5FIMzV2llazjpdM4bgOcRiSyucImy0cxyGKIqIgoDiwitLYKMcfexhrLZuuvo5MsYsoCEgWCkydOkkURSTSKaZHhonDkL61G4hqDWLPwfE8HGPAgJtIUpuaJApaZApFMjj4nk+cSmAxNJp1mtUKp/c+xuot2+lJpmnedx92aIDEug3UvvMdbBSRv/128ldfi3FfRKN7owSHvw2PfxZy/bD1Tlh1JRSHFuy9voAlPXefT6NSoVYuETQaYGMcz4OuFM2wQj9pRoenGDt+FOO52Cgi19tH7+p1tJpN4ijERiFh0CJsBfjJJG4iQRxGuJ7X3u7FEWGzBcbgp1JgDM0M5Mng+0mCZoOgWceGERNnThGHIcWBVYStJuXJSbKFIq6XwE0l8BNpbNzD1EiNQneKQm+SVmSxIURBjJdyCBoRbsLBcWByuIprXFzfIZn1CVsR1akm6XyCKIgJWhG5riT1SkA671MrBXi+wcYQtCK8hEsq62EjS6MZ4hqD6zmkw2lS5bMARFNT2CjGJJM4qSTV732f4NQp8m94Pe7gICaOsbGFOMJJpcBxMfkcme3bL+XbvCjnb6tUIjhxAlotCEKsY4gnJih9+R9xCnnyd7yWOGgRhxHewABUKpjuLqq5tbRCQ9hqv9eucYiCiFYzxMQjRFGFOI7wk0n8RJLpkWH8dJq+9RtplMsk0mnAYIlxXZ84DAiDFp6fJLYxQb2BMRbXT9CsVYmCgEQqTSqVY/L0KY49+TCD2y6je2gNBigkUriNOt+uPcpD4WFev/H1hFFIPpFnZ+jgxnViDLZehjjAGINjI2hVId0F9SnoWg+rr770ISCVUfBTkMxf2vVevEU5d1+u2qOPETcb7fN1IMp0ceYs1AKPRjWkezCDcQ2FnhQYaFRDPN8BA44xuL6h1YxIZ3xarYg4tNRKLYr9aWwck/Jdmq0IN+UShxaMwfMdHM8QhRbHg1Y1xPVciCNSpTMEj+zBKRTwNm2hlc1Ta5XB0p6vvk+zVsXzfRqVCo1qma5VQxjjEgZNTj/1BLmeXgp9q4jCFn4mQ9wKiKOYRDpFq9nA9xMErRbJTJZWo046X6A2NYWfSuEmfFzjEmNp1qo0qxW8RJKg0cDGUTvAJJFk9WWXQWOavJ2g22/ByBPQsxHGDsLg7nYwmZ+FwhoYvLyj7/E5yzKEpF5u8Tv/42e5IzrDh5r/gg3OWd7c/ZeMjX+QD33g3ZzwYv7N5x7j2ESNX371Fn79zu0kvEX7vZBcmEJIRERERERERERERERERERERERERERERJaoJRFCAmCMeTPwvwAX+Etr7e883/KLoSnoOTXrEIc0bALXc/AT/pxFgloN4/t4vo+1FmMu/t/ut8plHN/HS6Wee6GwCda2m8WgHdzQbMLkJHgeYzlLMVkkn2o3kgX1OgQBWItfLEJtGlwP6ybBdZ+zviiKiOt1TCqF53kX9wLCAKIAXB+8uX+bRWZZNrQ9W6taA87NQwt+NkOrUsEag3EtBBY3nSaqt4M9SHjYVoDxPaJmiLGQKOZoTZdwEj5xbCGypLryNMsVcB3AYGxMIpulWalhXINxHGwYk8imadXrGMfBdRyiIMRLJQmbDbDgpdOEtSpuIkkQBZg4JpHJ0ayWcRJJDGCDED+ToVUq4XgesYGo1WwHk1RKOI6D4/qErTrJbIFmrYIxhkQyTdhs4CWStFpNiGMSuTytWgXX84ltjI1jkk+vz/PAGuI4JJnJdfaNe37LIoTkaVO1KTJeFhtbfNu+4jp+ijAMicMQ30aQSGGDACeVIqrV2v8Pw3MNlT42jjHPFXjUakJQAz9DEFuM6+L5z799stYS1+sQxzjpNFG5QlyvY1tNnHweN5PBtlq4hQI2jgmDoN1cn0gswF/o4rWmpwFIFIsdreMFLIttb1QqYRIJ4mYTJ53GOffe2zAkqJwLL3PALXZDZYyKSdCaHieVzJHp7YdWHYwDfrK934yCduO5myC2EDQC/JSPEzWIoxj8NDa2OJ5zfr8d1MFN0u4QdgiCiCiMcQ04vkMctAOV4mYdaxycxLljkyjCRhGxaW+/MZYojEim2kE3cauFjWPiKCKOwE8n283yxmlvf6MY4hg3mTwXtvYsQRNiC8SQzFyS9+MSWRZz9yVpNSGO2s3yrRp4afBnH+s9fV4y3ZomZVJENqIZN4ltTNIkiZ24PZeBhOsSxAGtOCKKIyyWvJchn8iBe+FjzpntfBhCFGAdh7jRIJwuYzIZrONg4nYAnzm3X6DVwvo+xlpMItHethsXzzVElQpYg1/InRv38BMuURhjrcVxDLVSk0TKw3UharUAMH4SL+FCFM06jrZhSNxqtceA2FoMYHy/HcRy7nZs7cUfV8+vRT9/w1Y7qMYE7ffXOA42CHGTCYznQa2EjZqQLGCMQ9BqEUQu1sYYx+AYME77mNSGMSR8XM/B4uAY2mELUdiey35y9sqDJuBA1Ghvb50LnyPVghoJJ0EUR5SbZUJCrLX0pHpoxS1C2w4e8ZyOvMfL1aKfuy9H2GrhuC5Yi4X2+ZcBG0V46SQ2au/7W9VGO5/G9Qgjc+6YwOD7Lo7j0CpXoFnH5Ivtz5GNiEsVTMLDSSax57ZDNNvnRBYgjDCe296uGkPggPE8TBDgGLe9zU0msQkXrCGIW+RSeWqtGq5xiYIGGS9Jw1p8P0EYhTjWUglrWBNT9Iu4z9pPXNR3I80KON7M9x1zWHsuDPbc/6tjENP+3XEgVVxMQajLMoTk8MOj/NY//A73NW5nc/UUH0n/F+7tD/ls88f5+S0f4lU/eRnVZsh/+OIT/O2eE+wcKvBf37WbK9d2dbp0uXgKIRERERERERERERERERERERERERERERFZopZMCMmLtZiagmRZW9YNbbKsLasQEllxtO2VpUpzV5YyzV9ZqjR3ZSlbliEk//4P7+OvTw1zjTnI79z/YTbvHuGBriS/sm6ANx76BX70Z27m5h3XAXDXE8P85mcfZazS4pXb+njrVat5zWX9DOSfJ3hXFgOFkIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgsUbq8soiIiIiIiIiIiCyoUiPg//dP+/jYqVFWJQ/zMX6X2lt/hvjgR9g4Adk+ly9v/1O+fO+f8pM/+BWus68kDi2/s3kt99kW/3R8jH/16UcBWNud5tr13Vw2mGd9T4ZVhRSZhEvKd/Fdg2MMrtP+eebvnmNI+S6uo4wMEREREREREREREREREREREREREREREZELUQiJiIiIiIiIiIjIChXXAhpPTWIBrAUL527M/G65wDhAbGeGnnnfmXqL+8bKlIOYUhCxv1znB6NlmtZyVStm3do/JTvWpFTKYWOX/oLHW/cMcmp7gT0Dx/m0+XMOTuxnVWMNq57YQU8rw08bqGQyHHUijo8HfGfiDF8wp1/Sa04YQ8oxpF1nzk9q1u1zy3guaceQ9hxcx2AAg2FtNsFVXVnMTKaJAXP+15kbz848efawef7lLvg8M0Mv4bHPvH1u3S82lsXtSZHcUHiRjxIRERERERERERERERERERERERERERGRxc5Ya194qSXIGDMKHOt0Hef0AWOdLmIB6HXBmLX2jfO58pcwd5fT+6DXcunM+9yFjm97F9vffDHVs5hqgZdfz2LY9sLy+7suBNU022KZuwtpMb7nL5deU9tim7+L8X1RTRfnUte02ObuhSzG92mhrJTXOl+vcyHm71do17dYLOU5sZRrh4Wtf0G+cxARERERERERERERERERERERERERERGRhbdsQ0gWE2PMHmvt9Z2uY77pdS0OS63e56PXIi/HYvubL6Z6FlMtsPjqeakW2+tYbPWAalqJluPfV69pcVqMr0E1XZzFWFOnraS/yUp5rSvldc6Hpfy3Wsq1w9KvX0REREREREREREREREREREREREREREQWhtPpAkRERERERERERERERERERERERERERERERERERERERERERKSzFEIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKywi3rEBJjzF8aY0aMMY9fxLKvMsY8aIwJjTHvfsb41caYe4wxe40xjxpjfvwllPLhl/CYpUCva3FYavU+H70WeTkW2998MdWzmGqBxVfPS7XYXsdiqwdU00q0HP++ek2L02J8Darp4izGmjptJf1NVsprXSmvcz4s5b/VUq4dln79IiIiIiIiIiIiIiIiIiIiIiIiIiIiIrIAjLW20zUsGGPMq4AK8FFr7RUvsOxGoAD8P8AXrLWfPje+HbDW2gPGmNXAA8BOa+3UQtYuIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJyqXidLmAhWWu/ey5cZIYxZgvwx0A/UAN+wVq7z1p79Nz98bOeY/8zfj9tjBk599ipBS1eRERERERERERERERERERERERERERERERERERERERERETkElnWISTP4cPAh6y1B4wxNwH/G7jjYh5ojLkRSACHFrA+ERERERERERERERERERERERERERERERERERERERERERGRS2pFhZAYY3LAK4C/N8Y8PZy8yMcOAR8Dfs5aGy9MhSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIpfeigohARxgylp79Yt5kDGmAHwZ+LfW2h8uRGEiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKd4nS6gEvJWlsCjhhj3gNg2q56vscYYxLA54CPWmv//hKUKSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIickkZa22na1gwxphPAq8B+oBh4LeBbwJ/AgwBPvApa+1/MMbcQDtspBtoAGettbuMMT8N/BWw9xlP/T5r7cOX6nWIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgspGUdQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIvzOl0ASIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItJZCiERERERERERERERERERERERERERERERERERERERERERERFZ4ZZtCMkb3/hGC+hHPwv9M+80d/VziX4WhOavfi7Rz7zT3NXPJfqZd5q7+rmEP/NO81c/l+hn3mnu6ucS/sw7zV/9XKKfhdDp16SflfEjIiIiIiIiIiIiIiIiIiIiIiIiIiKy4i3bEJKxsbFOlyDykmjuylKm+StLleauLFWau7KUaf7KUqW5K0uZ5q+IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiDyfZRtCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhdHISQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIrnNfpAmSuqBIQ1wKcnI+b8Ttdjoi8SOFUExtEuMUkTsLtdDmySITlJrYe4eR93LS27dI5UaVFXAtxcz6OjjNkBbPWEk01sWGM153CeMpnlIVlY0s42QALXncS42rOyeIU10KiSguT9vDyiU6XI/KizGxrAa8rhXFNhysSEREREREREREREREREREREREREREREVlaFEKyyDQPTzPx2f1EYw28wQzd79pOcl2+02WJyEWwQUz9iTGmvnCYuBqQvKybrrdsxh/IdLo06SAbW5oHp5j8zAGi6Sb+uhzd79hGYnWu06XJCtQ8PMXEZw4QjTfwhrJ0v3ObjjNkRYrqAbUHRih97Rg2iMhct4rCHevxelKdLk2WqajSovLDM5S/fRKsJXfLanKvXINXTHa6NJFZWqfKTH7uIMHJCm53ku53bSO5pQtjFOQgi19UblG++zSV750EC7lXriH3itV4BW1rRUREREREREREREREREREREREREREREQuli69vIgE43XGPrKXaKx9xdbwbI3xjz1BON3scGUicjFaZypMfPIp4moAQPOpSaa/dpQ4iDpcmXRSOFpn7KN7ic5ty4MTFSb+dh9RtdXhymSlaR9nPEE0fu4440yVib95gqik4wxZeVrHy0x/6TC2FYGF2p5hqg8Md7osWcaah6cpf/04hDFElsr3T9F4crzTZYnMEpVbjH9iH8HJSvv2ZJPxjzxBOFrvcGUiF6dxYIrKt05AaNvb2m+fpLl/stNliYiIiIiIiIiIiIiIiIiIiIiIiIiIiIgsKQohWUSiiQa2OTusIC61iCbVHCyyFIRjc5vzGnvHictBB6qRxSIcr7eb4J45NlwnmlIIiVxa0fjc44xoukWo4wxZgVpHS3PGag+NENe0z5aFUd87N3Ck9uAINoo7UI3IhUXTzZmwsqfZICacUAiJLA21R0fnjj000oFKZKUY/eM/pvbQQ50uQ0RERERERERERERERERERERERERERGReeZ0uQM5zMj4Y4Jm96q7BSbudKklEnkc41SQ4U8G2YvxVGZysP2cZtzeFSegzvJI52bm7WpN0cVKz50U41SA4XcWG7fnkr8peqhJlGbJBROtMjXC8jptP4A9lcTIXOOxzDU5ah4OytNjIEpytEozWcNMe3uocXj7xop7D603NGfMHs5iEMhplYfiDWeqPzG6OT6zJEbcigtMlolILryfV3l7r2FE6xKRcjO9gg9nhOE5m7nnOc4nqAcGZKtF0C687hT+UwUnqWEMujcSaLM19E7PG/LX5DlUjK8HYH/4RY3/4R+zc92SnSxERERERERERERERERERERERERERERGZN+oEWUScgk/21tVUv396Zqxw5wa8vkwHqxKRCwkn6ox97EnCM1UAEluKFN+wkeSObpr7JtsLOYbut23FzV18054sP95AluwtQ1TvOTMz1vX2LXi96ZnbwVid8Y/sJRxtX2Xe+A59v7Cb5PrCJa9XlofaY2NM/t3+mduZawcovGkj+TvWUf7miZnx4o9sxutLX+gpRBatxtEpmvunILaUHh3DG0jT/Z7teIXkRT9HclMRbzBDeLYGtMOh8revxXgKf5CFkd7VS/XeM0RTTQCcrE/6ulU09k3SOlqi9ugoth7S9aNbyN44hHFMhyuWlcjrTVN82xamPnNgZiz3yjX4qy7uO4m4GVL+1kkq3z2J8R3Su/tIX9FHYnMRN6Wvn2ThpbZ1U71/mLjUAsDJ+aR39Xa4KhERERERERERERERERERERERERERERGRpUVdIItION7AX5Oj+JZNxPUQN5eAlEtUauJ1z71au4h0TvNYeSaApPC6DbSOlRj9k0fI3jRE9r07APD60/irsp0sUxYBN+2RuaoftyeFrYe4hSSJ1blZy7SOTM8EkADYIKb8zRP4P7UDx1dDvLw44USDqS8cmjVWe3CE7I2DpHb24GQ84lqIk0+QWF9Qo7ssKcFYjcZj41T3DGNcQ/bGQcKxOsHpyosKIfF60/S97wqCs1VsGOMPZPAHFPwnC8cfyND/i1e251xscbuTVO4+Tf3BEZxcgvyr1lJ7eITpLx8huaULv1/zUS49YwyZq/vxB7NEE3XcQhJvMIOTvLivjsLROpXvnsTJJ8i/ei3Ve05Te3CE5LYuim/ZTGJQ50aycGxsqT4wTOHO9RBa4lqAjSyNA5N4vWncrMJBZX5ZaztdgoiIiIiIiIiIiIiIiIiIiIiIiIiIiMiCUAjJImKDmOnPHcS24pkxbyBDYkiNOiKLTTTdvop9YlOR1rESjf2TAFR/eIbqvWcY+NWrSQzlnu8pZIUIRmqM/cXj2OD8tt1fnaX/g7txMu1GuHCiccHHEcSgEBJ5keJmhG1Ec8fDmImP7MPWw5kxtydJ/4euelHhDSKd1DwwRfWHZwCwIVS+d4rC6zYQ18IXeORcXlcSr0tzXy4dryeF15PChjGTnz9I/YERAOJyi9JXj1J4wwZKXz1GfIFtuMil4vguyXV5WJd/0Y+Nzx1j5G4aZPorRyBsN+g3D0wx+dkD9L1/F25aQRCyMGwUE47UcLMJyt8+Mes+fyBDZnd/hyqTZUshJCIiIiIiIiIiIiIiIiIiIiIiIiIiIrJMOZ0uQM6z9XBWAAlAOFKbMyYinZdY1w4YSW4s0DgwOftOC8FovQNVyWIUjtdnBZAABKerhJPNmduJTYU5j8tct2ompETkxXC7EvjrnhWC5BpsM5oVQAIQTTSJnjEXRRYzG8XUHhyZM946XcFXaJ8sIVGlRe2h0TnjNojx+lJ43QrHkaXJ7U5iki42tjMBJE8LjpeJpnTMIQvH8V2yt62heWR6zn31R8c6UJEsewohERERERERERERERERERERERERERERkWVKISSLiFtIzBkzvqMmdJFFKLE2T9e7thG3Itz83M+uk/E6UJUsRk567lwwCQeTcmduJ9cX6PrRLe0xx5C9eYjstQOXskxZRty0T8+7tpPcUmzf7knS975duIULNLW7BucZc1FkUXMM3gXCRvzBDH5/pgMFibw0TsLF65m7TTYpj5737sTNzT22FFkK/L4Mfe/bhUld4Pg36eIkdcwhCyu5sYjXl5oz7q3ScYIsgFih0SIiIiIiIiIiIiIiIiIiIiIiIiIiIrI8qUt+EfEGs2RvHKR639mZseKbN+H1zm2gWC5sbImqLZyEp4YkWVKcpEfuhkGC8RrJDQUmPrkPzl0EN7mtC/8CTdKyMnmrMmSuX0Vtz/DMWPEtm/F6zm/bnZRH7ubVpHb0YCOLV0xivJefExbVAwjtBYNyZHnzB7P0/szlRJUAJ+Xi5hLEzZDcK9dQ+d6pmeWKb9iI15fuYKWy3NjYElcCTMrFSczvsZ0xhtxNg9QfHcU2IgCcfILMlf3zss0UeTmiSoBxzEUF0TkZn64f2cLYX++FuH0A6a/Pk768B79PjfKytCU3FXG6EgSnytQfGZsZ73rbFryezh9zxM2wHSSZS2CM6XQ5Ms+8fILcK9ZQf2ICWw8BcHI+mSv6OlzZ/LORJa4uzDGXXCRrO12BiIiIiIiIiIiIiIiIiIiIiIiIiIiIyIJQCMki4qY8Cm/YSHp3H1G5hdeTwl+dxTjLszEmGK/TPDxNXG5ikh6J9XmS6wqdLkvkotgwpnFoitJXj0DKo+dnLycutXCyPol1ebzC3Kvby8rkpn2Kb9pE5sp+okoLrzeNP5S9YNOj1/XyQ6fiWkgwXiNuRNQeOEvzcIncK1aTuXZA83KFcVIeTur8oZ6T9Mjfvo7Ujh6i6SZe97njDFfhDTI/grE6zWPTxFNNTPrcsd3a+T22S6zJM/ArVxOcrYIx+ENZfAXpSAdFlRaNw9NEEw1wwB/KkdxYwPGfvyE8ubWLgV+9imCkjpNy8Vfn8IraT8vy4Hen6XrrFrLXrSKqBO3j39WdDWm0saV5okRwooxtRbhdKZKbinjdyzf0daVKrMkx8MtXLetjhWCsTuUHp4gqLZKbivgDGRLrCgr3vcQUQSIiIiIiIiIiIiIiIiIiIiIiIiIiIiLLVUdDSIwx64CPAoNADHzYWvv7z1rmNcDngSPnhj5rrf0Pl7DMS8rN+rjbujtdxoKzYUTrRJnKt08QjjfANeRuGcL4DonBXKfLE3lBrZMVxv96Lxgovnkz01883G4+dQ3FN20ie8MqnKRynqTNzfq42xd+2x6M1Jj49H6C42UwkLlmgOSGAqWvHMUYyL963YLXIIubm/Fxt3R1ugxZhuJWROt4ifJXjxGVWhjfIfeqtTi+h78qM6/r8gcy+APz+5wiL1XrRJnafWdpHpwCILWjB+M5pDYVn/dxxjEk1uRJrMlfgipFLj03l8Dd3tPpMmYEZ6rUHx6leu9ZiC1efxrjObiFJMZdnsGvK9lyPlaIWxHTXzqM252EZsz0Fw4DkNrVS9ePbFawzqUUx52uQERERERERERERERERERERERERERERGRBOB1efwj8S2vtTuBm4FeNMZdfYLnvWWuvPvezbANIVpJwqkn1njPtABKAyFL5/mnC0XpnCxO5SK0TJbCQ2tFL7eGRdgAJQGSZ/tJhgjPVzhYoK46NLZUfnm4HkABYqD04gj+YBQOVu08TVYLOFikiy1Y40aD8zRNEpRYANogpf+M44YSO7WT5ipsRzSOlmQASgMa+CVpHpztXlIhcUDBWo3rPGYgtAOFonep9ZwmnGx2uTOTFiSYbNI+VML5DY//kzHhj7zj1Jyc6WNkKZG2nKxARERERERERERERERERERERERERERFZEB0NIbHWnrHWPnju9zLwJLCmkzV1mo1igpEarTMVokbY6XIWjA0treOlOeOxGuRlEYhbEa2zFVrDVWwYXXAZJ+0B4A9mCE5W5twfTqqZTWYLJxq0TpUJy80Fef64HtC4QNNZNNXEyfo4uQR4usr7Svf0PIzKrU6XIsuMbUWEY3MDR+LqpTm2C8fr7bld0dyWS8jhguc0zWNzxy6GtZZgvE7rVEXBYbIkRKVW+/h2CZz7xOW5n6nm0WkIFSKwnITlc3NyGYegGc8hMZQlODU3+LTxxHgHKlrBFEIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiy5TX6QKeZozZCFwD3HuBu28xxjwCnAb+H2vt3ktZ26USVQMqd5+m/K0TEFuS27roevsW/L5Mp0ubd24xgT+UJTg9u2nC7U51qCKRtnCywfRXj1J/eBQMZG8cIn/HOrxictZyiQ0FnGKCcKKBN5AhHKnNut8tzF5eVi4bWepPjDH52YPYeojbk6TnJ3aQXF+Y1/U4SY/khgK1ydHZ44UEcT2k58cuw00tmt2+XGI2tjSeGGfiMwfa87D73DzcML/zUFYutyuJW0gQlWaHgCz0sZ0NY2qPjzH1uYPYZoTXl6LnJ3aQWJtf0PWKADi+S3JTkdbR2aEjyY3FF/1cNoioPTrG1OcPYVsR3kCanh/fQWJNbr7KFZlXzWMlJj65rx14l/Hofvd2Ujt6MM7iDL1zu+eenyXW5nELiQ5UIwuheaLcnpMTDUzKpfud20jv6sW4Hc1fnnduT4r0Vf0EwzWah6Zm3Zfc0tWRmlYshZCIiIiIiIiIiIiIiIiIiIiIiIiIiIjIMrUo/iW+MSYHfAb4dWvtsy8b/SCwwVp7FfCHwD88z/P8ojFmjzFmz+jo6HMttmi1jpeo/vAMuZvboQfGc6j+8Cw2Xn7/qN1N+3S9bQsmfb4hPnPTIIl1K7NhdKnP3eWkvne8HUACYKF675k5jT1xPYTY0vf+XaR29lB4/QZM0p25P3fbavyh7CWsurM0f59fMFJj8rMHyFzZR/72dSTW5pn4u/2E5dYLP/hFMJ5D7tVrcYvnGymT27vxB7P0/9KVJLe8+Ibo5W4lzd3gdIXWmcpMM3s02WTiE/vmBEbI0rAY565XSNL97m0Y//zpRf6OdSTWLlyAQtQIaZ0s0zpexrjtpvdwrMHEp/cTVYMFW6+8PItx/r4c6WsG8AbPh0b6a3Okr+i76Mfb2BKM12mdrlD5wSlsKwIgHKkz+bkDRHXN5cViuc3dlyOabjLx8SeJppoAxLWQ8Y8/STBaI6oHBMNVolKzw1XOltxQIHPdqpnbTsaj+OZNOCskpG+5z9+o0mL6G0fJXNlP/jVrcYtJJj65j2C49sIPXmKMMaSv7Cd9RS/e0Pn9jzeUJb374vc/8vJZhZCIiIiIiIiIiIiIiIiIiIiIiIiIiIjIMmU6/Q+mjTE+8CXgq9ba/3kRyx8FrrfWjj3fctdff73ds2fP/BR5iZS+dxJCS+X7p4irAYn1edJX95O5qh83uzyvzhuM14nGG5i0i9+fWYoNQPN+meelOHeXCxtZRv/sEVrHy7PG07v76P2pnQC0TleY/OwBgpMV3EKCrndtI7Wtm3CiTjjRxE17eANpnOSin8sLcolyzd+56k+NE47UqXzvFFGphT+UJXPdKpKbijOBEPMpnGoSjtUwnoM3kMHN+PO+jkVA296LFLdCao+MMf3lw9hGRHJrF8mNBUpfPw7AwD+7msTalRkA1iHLeu5aawnHzu0Psx7eQAYn4b7wA1+C1nCVqX84ROvINE7WJ3fbGmoPDhOO1gEY+PVrSQyunECwS2RZz9+XIyw3CUfqGGPwBtK4uYs7d4sqLSp3n6b8nZNgIXN1PziG2p7hmWVW/d/X4Q9knudZ5CJo7s6z5okyo3/88OxB19D3/l1Mf/UowYkKTiFB9zu3ktreg3EW5NTjRYsaIeFIDduI8PpSeD3pTpd0MTR/L0LzZJnqPaepnQsUzVw7gA1iMrv7SO9avsEcYblFNFrDWvAGMnj5RfXd4UJ88BdV6kdULrP/hhsB2LnvyQ5XI/Nocey0REREREREREREREREREREREREREREOsh54UUWjjHGAH8BPPlcASTGmMFzy2GMuZF2zeOXrspLx+tNU/raUeJzV25vHS9Tf2wMzPL9t89eVwpvKIO/KrsUA0hkmTGuIbG5OGc8sb7doB9VW0x8ah/ByUr7dqnF+EefIByt4fdlSG/vJrEuvxQCSOQSchIe0185SlRqARCcqVK99wwmsTC7YK8riT+YxVu1bANI5EVonaoy9ZkD2EYEQPPgFMHZKv6aHCbl4mS0vZL5Y4zB60vjr87g9aUXLIAkboRMf6EdQAIQVwNKXztK5poBAJy8j6u5LZeQm03gDaTx1+YuOoAEoHFwivI3T0BkIbbUHhzBzfqYVPuz4xaTmPTCfI5EXg4342GSs+dmencfU186THCifa4Ul1qMf+xJwpFaJ0q8IDfltY+ThzK4XalOlyPzKDhVpvbASHt7Gllq9w/j9aRwFlcox7yIGiFRuX1u6eUTJDd3kdrStdgCSFaGDgd7i4iIiIiIiIiIiIiIiIiIiIiIiIiIiCyUTnfn3Qr8DPCYMebhc2P/BlgPYK39U+DdwC8bY0KgDvyEtcvzX3nbejDnmp6tIyXiWrgsG8lbZypU7zlD/YlxvMEMxTs3kNw4NwBC5FLKXjNA/dFRookmAIlNBdK7+wGIpluEI/XZD4gs4Xgdf1X2UpcqS0RUDdrNcM8QjtaxrXje1xVON6k9NEL1njOYlEvhzvWkd/Ri/I5mjkkHhaNzG38b+yfJ3jhI/jVr8XrSHahKlqvWSI3a/WepPTyC15Om8Lr1JLd0YeY5UC8qt2gemp49aMFGMbiG7ndtxy0k53WdIs8lGKtT+f4p6o+P4a3KULh9HcnNXRjnhed9Y+/cbM3m0RKJNTmax0p0v2sbXl5zWRYfrzdN97u2MfGpfRCDk/PJXNnH+MOjsxd8+lxpcHGcKzUOTVH65nHC4RrpK/rI3boavz/T6bLkZbLWUn9kbM5461SF/GvWdqCil85aS3CqQut4CVyH5PoC/lD782MjS/PwFNNfbQdc5m4eInPdKryi9hMdE8//Ob2IiIiIiIiIiIiIiIiIiIiIiIiIiIjIYtDREBJr7feB5+3Ostb+EfBHl6aizrpQs6RbTGCSy695PCy3mP7yEZoHpwBoHZxm7MRe+n/1KhIDi6NBSVaWuBURjtQIxuoU37CRcLKJ150kOFtj4m+fIrWjm+TWLkzSxTajWY81vq5QL8/NvcAVqU3SxUnPnjdRPaR5eIrqvWdxu5Jkrx8kuT7/otZVf2SU0leOtm9Mw8TH99H3gStIbe9+qeXLEufm5oaYef0Z0lcNENcDWmcq+P0ZjLf8jjXk0oqDiMp3TlJ7YBiAVjlg7K/20v+hK0muK8zrupykh1tIEJVas8a93jT9v7CbuBURDFfx+jMXFQQh8lLFrYjpLx+i8eQkAK3KNOMnK/S+73JSm7pe8PH+mhz1x2Y3zvsDaVJX9JFPuwpHkEUpqrQIhmuYjEf/h67CtiJax8sE4w1M2sPWw1nLO9nFEajaPFFi/KNPzJzLVX94hmiqSc97d+AkdD63lBlj8NflaB6eHVCWWJvDSXQ6e/nFaR0vM/rhR2dCLE3Spf+XriSxOkfrdJmxv3oczuVelL52DBtbCq9dP++Bb3JxlmlGtoiIiIiIiIiIiIiIiIiIiIiIiIiIiAjqOF1ETNIhddkzGsUdKLxx47K8+nU4WpsJIHmabUYEZ6qdKUhWtDiIqNx9ipE/epjJTz3FxN/uxy0mqNx9hvK3TtA6Mk3pn45SuusYhTvXz4pOSl/Vj1W/jzwPrydJ5sZV5wcMFN+8Ca8nPWu5xpPjTHzsSZr7J6ndd5bRDz9K61TlotcTllpU7z0zZ7xxeOqlli7LgDeYJbHxGQEQnkP2hlXUHhpm/C/3MvIHD1F7ZBQbq4FOXp5wtE7toZHZg5FdkGM7t5Cg6x1bZ53JpC7rpvHEBJXvn6L8jRMM/8FDNPZPzvu6RZ4pmmzMBJA8zTYjWicqxEH0HI86L315L273+XM9J+fj9qYZ/6u9BMfKlO46TlQL5r1ukZcqnGww8cl9jP2fxxj/88ep3n+W6buOUfraMSrfOUn+lWtmnStlbx7CW7U4wnSCM7U5YZKNfROEY/UOVSTzxQYxyS3dOPnzgTduMYFbSBI1wud55OJiI0v5e6dmAkigvU+pPzkBQHC6OhNA8rTq3aeJy7ND2UREREREREREREREREREREREREREREREXq6ldUnQZa51vILxHQqv24CNYozrULn7DMmt3Xj5RKfLm1/GYHwHG8zuoDCecnHk0guHa5S+cuz8QGwJJxq0jpVmLdfcN0lyW3f7MxrGGM+hebSkqw7L8wonm0STTQqvPz9vqg+cJbW9G687BUBcCyh/88SzHhjTOjZNYk3uotZjjMXJ+DDemDXuJHVl95XMtmK8nhSpbd3Y2GIMlL5+nNzNQ+cWgMnPHSSxPo/fvziahGWJMuCkPeLq7MAE4y7MsV3qsh5637+L1pESxnVona5Qf3wMDORvX0frWInJz+wn8WvX4BaWX6CfLBKec8FzGqyFiwh38gcy9P/ildSfGMfWQ2wQU/r6cQCaR0pEkw2Cs1XczV0LULzIi9c8WqJ5aHrmtltIUrt/GIC4ElB7aITC6za0AyC60/iDGdy0/1xPd0mZC+yOjO+Aq3O5pS4YrRGcLJG5ZhVOov1Gx82IqS8cIrE2h7sm3+EKL5KNiUvNOcPRubELndc5OV/fo3VSHL/wMiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJLkEJIFhEbxtQfH6f++PjMmNudXJb/qN3tSZG9eYjK907NjCU25PEHsx2sSlaq6AJXDjbP0TfqZnwmv3h45nZyWxf+Irm6tyxOcaVF88AUzQNTs8drIXQ/fcvMumr8eRffFOnmk+RuW8PEp/bBufnr5HySG4ovoWpZTmoPjczMiQsK43ZwRP8lK0mWIa83Tf7Va5n+xyPnx/rT+EMLs480jsFg5gY4AZwLB4vLAVE1VAiJLBivJ0X+tespfeXozJi/Noc3kMZJXtypttedonWiQv3hkdl3nDsEiEtzj1NFOiUcrj7//aN1Sl87xsCvXEVifeESVXVxvIEMiXV5WifKM2O516zFH9C53FIXVwKIofLdkxe4d+mEzBjPJXfLaiaOPzVrPHNFH9Dev7jdSaLJc0ElBopv3NQOopTOsC8cOCYiIiIiIiIiIiIiIiIiIiIiIiIiIiKyFCmEZBHxh7KYpIttRjNjuVesxiumOljVwvCLSdK7+/D60oQTDdxCAn9dHr833enSZAXyulPtK2BH5xtIwqkGiS1FWs+40ndyc5HkpgJ9H7iC1ukKXm+KxLoCbi7RibJliXC7U+AAz8iTcnuSOIXz88bJeBReu56JT51vODO+Q2Lji2ve9Nfn6fnJy2idqmJ8h+TmIv663Mt9CbKEeb0pMtevonb/8PmxvjRRJZi57WQ8hTTIy+YkXBLbuuh69zbC0Tpu1sdflyexOr9g6/QGMnj9acLR+sxY+oo+mgcmz92fxi2oMVcWjjGGzLUD7SCR0xXcrI/blcRf/eL2vdkbV1F/5BmBUQaSGwtM7x3H7dH5kSweifWzt+nhaI3EhgKtY6WZseS2btz+xTdvE6tz5F+3nvBsjaga4A+k8dcXMGbphFTIhbldScJHm/irswSnzwflpC7vxetd/N+ntU6UqT44TDTdJHfLarresZXyt0/gr82Ru3GIxIb2587vy9D3gd20jpeIawGJtXkS6xbuOEsugkJIREREREREREREREREREREREREREREZJlSCMkiklido+udW2nunySuBCS3d5Pa1tXpshZEVGrRPDRF3AgJzlSxUQYn4RAPZnESbqfLkxXGZDyKb9xI6ZsnsPUQt5ggtaMXtytJ88AkzSPTJNcXSO7swetK4XWlSG3v7nTZskT4Axl6fmIHk589gG1EuF1Jen5iB15+dnhNakcPvT93ObUHhnGLSTJXD5B4kU3MwYkyU184TO5VazCuofyNE7g9yXbzmhrUViTHdyncuYHE+gL1vWMkNxZJrMsz8cl97fvzfns+9iz+Bk1Z3OIgonloCpoR4dkqcT4BnoPXk8IrLkzIjVdM0vuzl1PfO07z6DSprd0EY3Vaj43h9qTofs9luFkFhcnCiqaahJMNwrEa0ZRDwjU4Yw38FxEkmVxfoO8XdlPdMwytiMSGAtUHh+l61zb8ocwCVi/y4iQ2FMi9Zi2V756EGOJaSPFtm2kdLdE4MElqWzfpnb246cUXABWO1wlH67TOVLCNCCflwtkqiX59xpY6ry9N+ope3GKC5KYiwUiN1PZu0lf04aQW99eerVMVRj/8KDZoJ1Y2npig5+d20vWOrdTuO0v13jPtYKoNRYzv4Pen8RdhyM9KZRVCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIsvU4v7X+CuMm0+Q2dVHYk3u3O0kTnJ5BnKEE3UaT00SjjdIrM7SfHKC1uFp/LU5EkNqlJdLKxyuUf7OSbLXr8L4DnE1pL53jGiqRXC2itebonLPaYJSC+/1SdycGprl4hnXIXNlP4m1OeJaiFtM4ubnziEn5ZHe2Ut6Z+9LWk/UCCl98zjGMTgpj9qeYVonynAI6g+P0v/LV73oUBNZHrxiktwNg+RuGJwZG/hnVxNXApx8YsECImRlCaYaxJNNqg+OkFiXJzhVob53HH8ws6BzzO/P4L8mA6wD2k3muetXPee2VmS+BacqlL99gsT6AtFEnfrj43S9fQt2YxHjmot6DuM5pDZ3kdrcRVQLiCYapHb24vWmMObinkPkUnCzCYp3biB77QA2tLg9KdyUR3JNnvytazpd3vMKJxqUvnIUry+Nm09Q/u4pcreuJtrY0v5iiTOOIb2jF39Vlrge4hYSS+acvXVseiaABAAD0VSL6c8fAsAfzFD57im43SG1qdihKuU5KYRERERERERERERERERERERERERERERElimFkCwyxnPw+5b/lXjjRkRiXR5/KEvrWInEhgJeX5qoFMBQp6uTlcSGMXEYE1cCKt87NTOef+166g+NtpdpRuRfs47GU5OMf+JJ8reuIbm1CyepTahcPK8nDT0Lu47Eujz+qgy1PcM4KY/iWzZT+cEposkmrZNlhZDIDK8rBV2pTpchy0kQE7cicjcO0jg0hdebJnPtANF085KW4fWmL+n6ZGWLmxHhVIPcTUM0Dk3h9qTa836y8ZKf0834uBl/HqsUmV/Gc/AHsp0u40ULJxoUXr+B1rEy4WSD7A2DxPWQOIhYntGvK4/XnYLuTldxcWwYE043sfHsEAt/MEvz4BQm6VK4cz2t42XCiQatQ1N4PSmFBy42CiERERERERERERERERERERERERERERGRZUod9ItQONnAtiLcYhIntTzfIifjEZyp0jw4BUBwuorbnaRny47OFiYrSjjZoPztk2DA60sTjtVn7nML56+cnL1+kOr9w6Qv7wWH9mOA9K6+S16zLF3RdJOoHuIVEjgL0Fzspjz81bmZq2YDNA5OUXz9Bqa/chRjzLyvU5aOuBYSlpo4aU/Ni7IgTNrDGEP5O+19ZHCyQuPAJD3vXdhju7DcJK6GuDkfN5d44QeIzCOLxQYxlbvPAO153zwwRfe7t2HcF7ffDaca2GaEU0ziLtNzQFnebBgTTjbAGLzu1Iv+DCw0tzvF5N89hW1EAASnKmRvHMQkFUGyXES1gKjUws14uIXFe7wbTjQoffsEtT1nKbxhI3gOhHH7TmsxniF32xpK3zg+a77GQUTxDZswzuL6bK1oCiERERERERERERERERERERERERERERGRZUrdTYtIHETUHx1j6ouHsI2I5NYuut62BX8g0+nS5p0N45kAkqdFk03iWtCZgmRFqj4wTPX+syTW5ci+YohwpE44WiO9u4/k1i6S27sJx+pYLMlNBcrfOwmxJb27j9ZIndRlMcZzOv0yZJGzsaWxf5LJz+wnLgd4Q1l63r2NxJr8vK4nqgdU7zkzezC2RKUWTjGBv25+1ydLR+t0hcnPHCA4VcHJ+XS/cxupy3oWXXOwLG0mtFQfHJk1ZpsRUbm1YOtsHJxi8u/3E0038frTdL9nO8n1hQVbn8izxeUWtfuHZ43ZVkTcjC76OWwYU987xtTnDxHXQvwNebrfsY3EYHa+yxVZMOF0k8p3TlL54RkwkLt1NflXrsXNL55wKFsPZgIdnlZ9cJjcbashu3jqlJemdbLMxKf3E56t4RQS9Lx7O8mtXYsusMNaS/X+s9TuOwtA5bunKL5+A8FonajSInfDICbh0DxSmjNfK98/Te7G1Xi9qU6ULhcSx52uQERERERERERERERERERERERERERERGRBqHt+EQlOVpj8+/0zjQbNg1NM33WUOLj4JralwgYxLK5eEFlhompAMFKj8Lr1GN+ldaSEP5Ahfd0qcjevxu9N0/2jW8m/dh3Gc6jeexYiCxbqj45hm6HmsFyUcKTG+MeeIC63Q5bCM1UmPvUUUWV+Q5dsEGO8uZPSpD36fvZyNTOvUHEtYOLvnyI4VWnfrgSM/82TBCPVmWWCsTqlb51g5M8eofy9k4Tj9U6VK0uYDeMLBtss1K4yGKsz/tEniKabAISjdcb/5knC6SZhqUl1z1lGP/woU/90hNaZ6gs8m8hLY4MYLhBIZ17ExA/OHRfEtbB9+1iZqX84yPRdR2nsn1yW54Ky9LTOVJn6pyOMfvhRqnvOEpaas+5v7JugcvdpiC1Elsp3T9E4MNWZYp/TBfZRrkPcUojAUhdONxj/xD7CszUA4lKLsY/uJRytdbiyueJqQO3B4Vm3p//xCIm1Wfp/bhfpy3tJbiri9abnPNa4jr7FXWSs7XQFIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgtD/3x9EQnOzm2QbOwdJ57nRvXFwEl7pHf3zRrzV2cv2GghshBMwiG5Lk/pq8doHpyi/tgYU18+jJvyZpbxelLkrhukda55/5maB6awsTpO5IUFo7V2gM0zhKN1wqnGvK7HSXtkrh+cNWYSLon1eRJr8vO6Llk6wukm4ZlnNWDGlnC8Pf+iSouJv91H6atHaR0pMf3lI0x+/hBRffkde8jCcjI+2ZuHZo8VErg9qQVZXzTRwLZmhzPEpRbRZIPKD04x+ekDNA9PU/nOScb+4jEChevIAnAyPrmbZu97nbyP23Px5zTheB2edUjZOloCC2N/+TjNw9PzUarISxaM1xn7i8eofOckzcPTTH76AJW7T8+cC9nYUnt4ZM7j6o+PXupSn5fXm8ItJmaNZW8axMn6HapI5ktwpko08axzq9ASPntsETAJF68/M3fcP/89hHEdkhvzuF3JWcsU7lw/Z0w6Td8JiYiIiIiIiIiIiIiIiIiIiIiIiIiIyPLkvfAi0kleXwaTWH5ZMV5fGn99Hn9jAcc1GM/Fyfv4A3ObMUQWgg1jqnuGZw9GlmCkRvry3lnDidU56g/ObqxLrM21r0R8AVElwAYRbj6B8Zbf51denAvNAZNy5309ju+SWJej651bZ8Ys4PUr3Gklc1IeTsYjroWzxt1cu+E2GK0TnJgdtNTcP0k03sBdq6ZcuXheMUlyUxG3O4kxBuMacMBftTDHdk7WB8Ps3k/PgOtQ+f7pWcvGlYDwbA1fYXcyz7xiksTGIoXXuwRnqrj5BN5QFn9VhrgVEVcDnKSLk3nu7emFAhDcYpK43A6DqnzvFKmtXc953Cmy0IIz1TnBqJXvnSJ7wyB+bxrjGBLrC7SOlGYtk1i7eELwwukmTj5B4Uc2ExwtEZVa+ENZvIEMnkIdljQbW8KJJibhYFvxrPuc3OI7lnUSLoU71zN2rIQNYrz+NJnrBkisyc5azutJ0/eBK2jsm2h/R7Gzh8SmIsaYDlUuFxTHL7yMiIiIiIiIiIiIiIiIiIiIiIiIiIiIyBKkEJJFxALJLUWah85d7doz5F+15nmb1pYqN+OT3FSk+sMz1B4cwc35FN+yGRvEGF8NdrKwwok6wWidC/XvGHfuYOqybqr3pglH60C7WTR70xDGmb2sjSyNAxNMfeEw0VSTzDX95G9fj9+npueVzKRcMtcOUHs6yMZA/lVrMcn5DyJxcwnqI6NUfngWJ+VSeMNGXF3dfUXzulN0vWMrE5/YNxPWkL15CO9cMIT6GGU+eX0pGk9NUr3/DE7ao/iGTRh//rd1AN5AmvydGyjfdWxmrOutWzBZrx1O8mya67JA/LU5MO3gEBIO/qoscaXF9FeO0tg3gbcqQ/fbtpDc3HXhx6/Okr66n/rDo+0Bx5C7bTWlrx8/d/vSvA6R53LB0INnDWWuGaD20AhxqQWA250kfUXfJaju+UW1gNrDI5TuOg6xJfeqtSR2dGNLAU7Ww1+bV6jDMtA6WSb/qrWUvnH8/PHuLUOLKuQ2OFuleXQaG8QkNnXR/ytXE5WaNA9MUvr6CSrfP03xzZtI7+rFSba/qvUHMovqNcgFWPvCy4iIiIiIiIiIiIiIiIiIiIiIiIiIiIgsQQohWUQSQ1laR0oUXrcBG8UYx+D0JJdlU0ww1aR6zxlqe4YBiKZbTHxiH/0fupLkxmKHq5PlLJxqMPaxJ4nG6uTvWEdw9nzzMp5DcvPc+ef3Z+j7+d0EZ6sQW7xVGfzeucEiwZkK4x95YqbxqfZAO3Si+x3bMJ46SFcqryeNhfPbds8hDmL87vm/6nr1gWEq3z8NQFyJmfrMAdy8T3pH77yvS5aO9OW9DPzaNYTjddysjzeUxU23w2m8/jSJjQVaR0szy6d29OAqPEleguqeYar3nNsGlQMmP70ft+CT2t4z7+tyfJf8batJbS0SlVp4XSm8wQzGdci/ai3lb544v2wxgT+oJl6Zfzay1O490w44OCd/+zoah6cIjpUBCM/WGPurvQz82tX4A9k5z+FmE3S9dQvZGwaJpppEU00qd5/GNqP28922FuPqOFI6xx/M4BQSMwEjAPlXr8PrTs3cTgxmGfjQlQRna+AY/FWZWfd3SvPwNNNfODxzu3zXMfJ3rKPy/VPYVkzxLZvI3bZmWX7nslIYx5C9ZoCpLx1un2+F7fOt5NbiTJhHp7XOVBj98GPYetgecKDvl64kOFOdde42+Xf7cT+4m9TWrs4VKy+OQkhERERERERERERERERERERERERERERkmVoc/yJfAEisK5C7BaoPDkNkyVy3iuT6QqfLWhDRRJ363vE5463TVYWQyIIKzlQJz1QBaOybpPDGjbSOlXC7kmSvHSCxJn/Bx3ldSbyu5w+NCEZqMwEkT6s9NELhtRvwejrfhCed4RWTFG5fR/3xcVonqqR39ZLZ3o3x3HldT1hqUntwZM5488i0QkhWOOM6JFbnSKzOzbnPzSboec926k9O0Dg4ReqybtKX9eCmdIgoL04wXqf+0Oic8eax8oKEkAA4SY/khrnHjblbVuP1Zag/Noq/Jkdmdx9ej4J1ZP6FE3VKzwi8AcAwE0DyNBvEhGONC4aQALhZH3dLFza2tI6XCKeb2CAme90AyQ3L83xQlg6vN03/z19B7bExglMV0rv7SW3rwjizgzu8nvSi29bWHxubM9Y8NE1iXZ7moWlKdx0jfXkv3gUCJmXpSG4s0vXWLVQfGMZ4huy1q0isWTzbzsb+yfMBJAAxNA9OU39weM6yzcNTCiFZQqxCSERERERERERERERERERERERERERERGSZUofpImJcQ3JTkeSmFRDCEVncQoLwmY0YgJPWlJSFZYN45vfWsRKt4yX81VmKb92C/zKDQi40f918EuPr6vUrnd+fwb89s6DrMI7BLSaJJhqzxp1MYkHXK0uf15smf9sa8ret6XQpspQZcIsJounmrGEne+mP7dx8guy1A2SvHbjk65aVxYYxRPZZYxaTcLGtaNa4Sb1w+JhxDMmNRYUyyqLjr8pSXHXhEJ3FzOufGy7iFpMEZ9uhlDaIsZFCBJY64zuktnYt2vCOuBrOGYsmG7i9acLx2edu7gsEn8oioxASERERERERERERERERERERERERERERWabUGS8d4RYSZG8YnDUDvVUZkj0wO1cAAQAASURBVOvznStKVgR/VXZ2KIgFfzCHV3j5QQ3+6hyJjc+44rKBrrdvwc0rBEIWnptLkLt1NXjnr0rvdie1XRWRS8LrSpF7xWpwn7EN6k2RWKttkCxfXneK5LauWWON/RMU37Rx1lj6yj78JRjgILLUpXf14mT9mdsm6ZJYmyMcqQGQuqIPt1uhD7KwUpd1zxlLbixSuGPd3HO3lRBKvJwohERERERERERERERERERERERERERERESWqUt/afJnMMasAz4KDAIx8GFr7e8/axkD/D7wZqAGvM9a++ClrlXml78qS9yK6HrbVqJqCzfrk9hQxOude6Vikfnkr8rQ98HdVO4+hW1FeKtz5K5bhfFefiaTV0zS85M7CE5ViOshXn+GxGo1nMqlk9iYb8/BszWMZ0iszZNYpwAAEVl4xjH4G/P0/PhlBKN1HN8hsV7bIFnenJRH19u3UL3nDPXHxvDX5im8dh1efxp/KEs4XsfJJUiszuE+IwhBRC6NxFCO/g9dSXC6irUWrytJ5e7TuMUE6asGyN40iOO7nS5TlrnE+jy9P3c5pbuOETcj8q9aS2pnD07GY+BXriY4W8X4LonVWX0nttQohERERERERERERERERERERERERERERESWqY6GkAAh8C+ttQ8aY/LAA8aYu6y1TzxjmTcB28793AT8ybn/Lzs2iGkeL1HdM4xxIHP9IMn1eYz78sMRFqPkugL+qiy2FeNkPdp5MyILz0m6eL1pWqcq+L1pzDw2nnnFJF5RV9OW2YKRGvXHx2geLZG+opfUZT0LMk+8XBJvV5JoY4DxDE6y07t5WWzCqSaNpyao7x0nualI+ope/P5Mp8uSZcLvSuN3pYkqLUzCxUl0rrE7DiJax0pU7z+L8V2y168isb6AcXS8KfPL78tQfMtm8revwyTdmUCD5MYiyY3Fi36ecKpB46nJ9vZ5c5H0Lm2fZXGIGyHNw9NUHxzGyyfIXDtAYl2h02VdNL8/M+uz5K/JYZsRTsbXPmGZiIOI1tFz+/ykS/a6xbXPd3yX9M5eEpuKmNjiZM6HUiVW50isznWwOnk5bBx3ugQRERERERERERERERERERERERERERGRBdHR7mRr7RngzLnfy8aYJ4E1wDNDSN4OfNRaa4EfGmO6jDFD5x67rDSPlRj788dmbtceHKH/F68kuenim9eWGifhQgcbVGXlCcfrjP7l48SlFgDNpybJv3Y9hdeuXzRNSrK8hNNNxj/2BOFoHYDm/kkyNw7S/bbNGG9htn9u1n/hhWTFiYOI0jeOUbt/GGjPxdpDI/T//BW4Ck+SeeTmEp0ugeaRacb/cu/M7dqDw/T/0pUkNyzf42rpHOOYlzXv41ZE6WvHqD04ApzbPj88Qv8HduMWOv95kpWt8dQEE598auZ2dc8w/b981ZINTnB8F+YxhFI6r3l4mvG/esY+/4GRc/v8xRWW46YUELns2E4XICIiIiIiIiIiIiIiIiIiIiIiIiIiIrIwnE4X8DRjzEbgGuDeZ921BjjxjNsnz40tK9ZaKj88/axBqD400pmCRJap4GxtJoDkaeXvnCSabHSoIlnuwpHaTADJ02r3nyUY15yTSyscb1DbMzx7bKRGMFLrUEUiC8NGMZXvn5o9GEP9sbHOFCTyAsLxOrVnnfeFZ7V9ls6L6yGlb56YNWaDmNbR6Q5VJDKbjWIq33v2Pt9Sf2K8MwXJCqMUEhEREREREREREREREREREREREREREVmeFkUIiTEmB3wG+HVrbenZd1/gIRf8V97GmF80xuwxxuwZHR2d7zIXXnyBMat/0L4SLPm5u6Rc6DNl1TryMmj+vgBNrkVLc1eWKs3di3Dh3b0sApq/z0Hzc9FbsXM3njs59TXF0rOc56+90ITUJJVLIb7QF7kiIiIiIiIiIiIiIiIiIiIiIiIiIiIiS1/HQ0iMMT7tAJKPW2s/e4FFTgLrnnF7LXD6Qs9lrf2wtfZ6a+31/f3981/sAjLGkHvF0LMGIXPNqs4UJJfUUp67S403mMXJ+bPGcq9ci9ed6lBFS5/m7/PzVmVw+2bPr8x1q/B6NOc6baXNXa83RebagVljbl8abyDToYrkpVppc/fFMq5D7rY1zxqE9O6+zhQks2j+zuX1pklfPftv4fWn8QfSHapILmQlzl0n7ZG/Y/3sQc8hubHQmYLkJVuu89e4DvlXrp096EB6V29nCpKVRWE3IiIiIiIiIiIiIiIiIiIiIiIiIiIiskx5nVy5McYAfwE8aa39n8+x2BeAf2aM+RRwEzBtrT1zqWq8lJIbivR98AqqPzwLLmRvHCK5Pt/pskSWFb83Td/P76b28AjBqTKZqwdIXtaDcUynS5Nlyism6fvZXdQeHaV1eJr0lX2kdvTg+G6nS5MVxvFdCnduILE2T/2xMRKbi2Su7McrJjtdmsi8S24u0veBK6jcdwbjuWRvGiSxTk3zsjg5CZfiGzaS3FBob5+3FMns7sctaPssnZfa0UPPz+ykev9Z3EKS7PWrSKzR9xSyeCS3FOl7/y4q957FJF1yN2qfL5eGVQiJiIiIiIiIiIiIiIiIiIiIiIiIiIiILFMdDSEBbgV+BnjMGPPwubF/A6wHsNb+KfCPwJuBg0ANeP+lL/PSML5Dams3qa3dz7nMqck6rShmdVeKpKcGdpGXIjGUJTG0aV6eS59JuRj+QIbinRs6Xca8qLcizk7XSSVchorpTpcjz6EZRpyeapBwHdZ0n3+fvO4UuVtWk7tldQerk+VsqtZiotqiK+PTk+1cgILju6S2d5Pa/tzH1SKdVm4EjJab5FM+/do+yyLlpj0yu/rI7OrrdCkvqNIIGCk3yaU8BvKpTpcjl4jju6Qu6yF1Wc9LevzZ6Tr1VsRgMU06oXN6eRGUQSIiIiIiIiIiIiIiIiIiIiIiIiIiIiLLVEdDSKy13wfMCyxjgV+9NBUtXpVmyBcfPs3v/tOTVJoh77x2Lf/XHdtY35vpdGkvm40txnneaSAyb+Zrvl3wM/nabazvWfqfSVnaFnKbemSswn/9p6f46hNn6c4k+O23Xs4bdw2S9NWst5gcH6/yv75+gH94+BS5lMe/edNO3nrVarLJTmfPyXL3yIkp/v0X95JwHRKew6/dsY0bNr20hmCRpebF7n+fPFPit/7hcfYcm2Rtd5rffcdubtvWhzE6LxJ5KZ46W+LffX4v9x6ZYKiY4nffuZtXbevHucDnUt9BCEAziLjryWF++/N7Ga+2eN3OVfzmm3ewuT/3sp9bc2yFsHGnKxARERERERERERERERERERERERERERFZEM58PIkx5heMMdvO/W6MMX9ljCkZYx41xlw7H+tY6R45McVvfu4xSo2Q2MKnHzjJJ+87Thwv3ctutk5VmPz8QUb//DGqDw4TlVudLkmWsajaovrQCGN//hiT/3CA1snyy3q+C30mP37vsSX9mZSlLZyoU/7uSUY//CjT3zhOMFqb1+dvhRF//K1DfGXvWayFiWqLf/6ph3n89PS8rkdenji2/M29x/nsQ6eILZTqIf/6s4/xyMmpTpcmy9zZUp2v7znBf9w6xO+T4T/ki1ROljgxXu10aSILKhirU/rWcUb/7FFK3zlBOF5/wcdM1Vr8y797hD3HJgE4OVnn5z+yh4MjlYUuV2RZmq4H/OvPPMa9RyYAODPd4IMf2cP+4dnnfMFojelvHGf0w49S/u5JwokX/rzK8vXEmRL/7BMPMV5tfxd115PD/N5d+2mG0Ut+zmC4ytRXjjD64Uep/PA04VRzvsqVxcjq+x8RERERERERERERERERERERERERERFZnrx5ep5/Dvz1ud9/ErgS2ARcA/w+8Mp5Ws+K9ejJuU3en33oJD9/2yb68skOVPTyBMNVRv/Po9hGu7mjdXiawhs3UnjNug5XJstBVA9pnSgTnCzj9qRIbixQf3SM6X88AkDz8DS1h0YZ+JWr8FdlX9I6LvSZ/NyDp/iF2zYvyc+kLG1RI2Ty84doPtVuZm4dLdHYO0r3e3aQGHppc/zZRistvvjI6Tnjh0aqXLehZ17WIS/feLXJ5x48NWd876lpXrGlrwMVyUoxOt3gx900fPMUAeAdgcsyHnZNEXrnZzsksthE1RYTf7uP4EQ7PKR1rETjqQmyNw1CMyaxsYg/kJnzuDNTDZ44U5o11opijoxV2bYqf0lqF1lOhksNHjoxNWssjC1HxqvsGCoQTjcJRmqU/vEwwZl2UF/raInGwSl63rsDNzVfX43JUnJ4dG5Q2pcfO8NvvGkHa7vnbrsvJCw1aR0rE4zUSG7MM/l3+4mm26EmraMlsjdV6XrrFow3LxnQstgohERERERERERERERERERERERERERERESWqfnqtAittcG5338E+Ki1dhz4ujHmv83TOla01V0pfvTqNWzqzxJGMa5jeOpsiUzS7XRpL0nrZHkmgORp5W+dIHPNAF5RAQ7y0llrqT0wzPSXDs+MFd6wgfJ3Zzfl22ZE63T1JYeQDBVTs37/iRvXk3Qd9p0tscsp0J3VPJZLJxqrzwSQPC04XaPx5DjG4SXP82fKJVw29GbYtbrIxr4sURxjMAwUNNcXg5FSg31ny9RaIb96+xb+5137KTXCmfsHi+kOVicrwRrXo3bf8KyxuBaSn2p1qCKRhReO1mcCSJ7WOlwiuamL8jeO4xR8+j945ZwgklzK4y27B7lssEArikm4Dn//wAm6s4lLWb7IspFNeKwupnjHtWtxHXCNYaTcpDuTIKoGTH7uAMm1+ZkAkqc1908SjtVx1yr8ZyXqziZ4xzVrZp3bfPfACJnExX3PFtUDpr90mPqjY+2B166fCSB5WvW+s+RuXXPBQCpZ+mwcd7oEERERERERERERERERERERERERERERkQUxXyEksTFmCJgEXgv8zjPuU9frPNg5WOAT9x7jHx5uBymkfIc/++nryCSW5hV7bXCBq4UaINI/4JeXJ5poUPra0dljpRbGMTx71hnz0tdz3YZudq8pcmi0wgdfuYn/8k/7CKL2Gt521Wp++62X05tTOINcIs81mWNL88j0vISQFDMJ/tOPXsF/+8pTfO6h9r4o7bv85ftueNnPLS/Pickav/aJh3j4xBQA+aTH//367fz7Lz4BwJVri1yzvqtzBcqKkLFQN8zZ13q6QLwsZ8+x/316OC4FtE6U5zSf51MeCc/hf961HwDXMfynH72C7asUhCDyUqzpTvO779zNr33qIUr1dgjbzsE873vFRsKRGs19kySfI2jEvJyTQlnS1vekOTVVn3Vu879/6lp6LjJQNBypnw8g4Tl2Cca0v+uS5UnHuSIiIiIiIiIiIiIiIiIiIiIiIiIiIrJMOfP0PP8O2AMcBb5grd0LYIx5NXB4ntaxoh2bqHHvkcmZ240g5ve+vp9yPehgVS+dk/NxMrMDVLI3DWHSSzNURRYPG1lsMDvMpv74GLlXrZk1ZtIe/urcBZ/j7HSd7x8Y5e5DY4yWGxdcZl1Phv/zs9fxV++/gY/cfXQmgATgC4+c5onTpZf5SkQuntebInVF76wxf02OcLxBXAvnbT1TtYA9x87vi+pBxH//6lOUG0tzX7Rc7DkyMRNAAlBuhnz/wBh/9tPX8ic/dS1/9tPXsbZbV1+XhVWLY7I3DM4ac3I+5P0OVSSy8Pz+NMnNxVljyS1FWqcrM7fj5tz98JNnynzuodMzt6PY8nt37Wei2ly4YkWWsUYQ8rf3n5gJIAF48myZh09MEZ87NwzHG/hrZ5//pXb14vWmLmmt0jlTtRb3H53g20+NcGy8yuHRKvcdmZi5vx5E/NE3D1K5yHMbG0Szbsf1ELdn9nzKvWI1Xrfm2PKlFBIRERERERERERERERERERERERERERFZnuYl8cFa+yVjzAYgb62dfMZde4Afn491rHSnp+pzxvYPVyg3Q/Lppdfc6eZ9cq9cQzTZJJpukliXxx1I4y7B1yKLi9uVJHVFH43Hzl+R2DZjUpf14PWmqT08it+fJn1l35yr0gMcHCnzix99gMNjVQB2rynyhz95DRv7snOWHSymaQYxxyfmfj7H1EQql5CT8uh6y2Zq6/I0D03jD6TBMVS+d4r+X7xy3tZzZnpuKM9Tw2UqjZB8StvvTjk6Xpsztu9sma89McwXHznDn/z0tQx1pTtQmawkw3FMf9KhcOd6WifKuMUkbk+KSd8wdw8qsjw4GZ/ud22jvm+C5sEpEuvzxOWAyt3nAkYMJNbm5zxu/ALHiSPlJt/aN4p3ucO6HgVHibwYDx+fYu+ZuSGQh0cr+Fv6cXI+tYdGyL1qDcmNRYLRGukdPaR29OCkFIS6EoyWG/zHLz3JFx5pb597Mwl+9Y6tc5Z7arhMuRmSu4hzG68/g9uVIJpqAVC55zTFN2/CtmJaJ8ukd/WR3NaF8eYr/1kWHasQEhEREREREREREREREREREREREREREVme5q3bwlobApMAxphfBr5grT1ljPl14H/N13pWqvwFGmNed/kq+nKJDlTz8iXW5IkbEc1jJXAMJuOR2tTV6bJkGXASLsU3bsTrSVF/eAR/KEf+jnUkBrMkBrNkruh73sd/7qHTMwEkAI+dmuab+0b4wG2bAKg0Ah49Oc2h0QqDxTS7Vhd49fZ+vrN/dNbzbOpVy7VcWl53iux1q/C6UpS+cwLjQO/7dpFYN7f5+aUaLCTnjN2xY4BsUs2bnXTN+q45Y6+7fBWru1Js6c/xv+7az86hAqsVRCILyEt5fNuNuHaqRd5a6o2A44kkvrGs7XRxIgvI602Tv3UN+VvXEJaa1B8dw+1O4uQTFF+3gcSaufvhjb1ZHAPxM3qXr15X5N4jE6R9l5+8af0lfAUiS9upyTr//ktPcPtlA/z13Udn3XfDxh687hR9H7iC0rdOUH94lPRV/XS/fSteT6ozBUtHPHZymi88cppswuWDr9yMxdKdnRs08pbdg/Rl557zXIhXTNL7c7sof+ckrcPTJHd0k9rWjb9K3wWsGHHc6QpEREREREREREREREREREREREREREREFsRCdQ3ngI8aY5qAi0JIXpZWGGGAX7tjK3/1g6NUmiE3b+7hndeuIeG5nS7vJTGeQ/qyHpIbC9jQ4l6g+UPkpfJ70xTfsJH8K9dgki6Of3GfkyiKuefQ2JzxB45N8oHbNhHHlr+9/wT/8ctPztz3+stX8RtvvIxqK2TP0UmKaZ9//7Zd7BwqzNvrEblYbi5B5qp+klu7MJ7BmedwkGzS4/967Vb+8vvtfdEtW3p521VDVJshhbS2451yzfou/s2bd/I/73qKZhhz+/YBUr7Df/7HfbiO4d+8eQdTtZZCSGRBZXwHtzfFbx0+yc7uNONBwK2JPP2NsNOliVwyXiFJ/rY1ZK7px3jOc+6Ht6/K81/fdSX/+R+fZLIWsGt1gR+7fh37zpZ55OSUQkhEXoTJWgvXMewcynPnzgG+sW+EhOvw/ls3cvnq9jlZYnWO3h/fTtyIcNI+xjUdrloutdPTDQB+5fatfPi7h5muB1y3oZtfvX0LH7n7GJVmyO2X9fNLr96C7zkX/byJoRw9795O3Ahx0h7GvfjHytJnrX3hhURERERERERERET+/+zdd5hdV3nv8e9up7fpval3Wbbce+/YxmB6xxACgZsEkgspJLkkQBJISAghEIpNsanGNrZx792W1ftoNNL0enrd5f4x45FGI8kqRzozo/fzPHo8Z51d1vasvc/e55n3t4QQQgghhBBCCCGEEEIIIWagolQnK4pyE/Cq4zg9403/CswBrgD+pRj7OJW5dI2u0Qy/fr2L95zVjMdQ2dAV44fP7ubcORUzNogEGCvMO7JJZoU4KoqqoAVch3zfcRxM28HYr0ioP5HlisU1rNkTnbTsJYuqAOgcSfHPD2+b9N4jm/v56AVt/OjDZ9Iby+J36TSUSaG/KK0TFezUG8vy69f2fRat74rx1Ye28YtPnn1C9ieOTNjr4vYL27h6aQ07+pP84LkOvvv0LgAs2+G+tT1cv6yuxL0Us10sa/Llezdx7fI63AE3RiLH39y7if9+/+ml7poQJ53mP/Q9KEA0nef7z+7iptMaCHh02geS/M29m/jsZfOYU+U/Sb0UYnaoCbm5fnkdX/ztBla3lvOZS+dhWg4PbezlmqW11IbHns0UXUMLzNzvTsSxKVg28UyBmpCHqqCb3miGWKYAjIWN9kYz/NV1izijtZyGMi9+19F/Taro6mG/exCzmGSQCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghZqmihJAA/w84DUBRFAO4C+gGlgNPAd8r0n5OWfFsgd5Yloc39VEVdLOpJ0ZTmY+cac/oEBIhSmFzT4y7XtnDxu44t57RyJWLq1nfHeMLv17P21c1cPHCKp7eNoiiwG1nNHLhvLEQkmzeJmfaU7aXypkEPQZBz4kJfhBiusiZFj2xLI9v6Sfic7GxO4bHpWJaUn1VaoqiEPYajKTyvLp7ZNJ7g4ncUc3oLsSxKFgO8azJL17dS8irs7AmSMirY9lyfRDiQDnTpn0wxfb+5KR2r0tjaX24RL0SYmaq8LuZXxPEduCVjhFe6dh3H5TKWyXsmSil/niWJ7b2oyoK33+mg9qwhw+f28rGnhh+l8biuhC9sSzd0Qy/WtPFrasbcWny3Zo4Ss7U74eEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBiNihWCIkBoChKGPgt8ITjOP843uYt0j5OGWYih6IqEzNoO47D4voQ//eaRewYSNATy/KJi+Ywp9I/o0MP7IKFOZQBy0Gr8KB5Z+6xiOnhzTHlWA76QcaUYzsMD6T45fO7+eW6HnKmzRt7owQ9p/H5X62jYDn88PndXDi/kr+4eiHnz6tgYW0QjzF2qWws83Jma/mkAv+AW6etct+M9Zt6Yvx2TRfb+pLcenoDFy2ooiLgPjn/A8SMZ2UKULBRAy4UVTmmbdhpk8JIBkVXMCq9KEUMqmoq8/GlaxexqTfOUDLHpy+bh9+lEZHrd0k5jsPavVF+8mInI6k8f3blAp7dMcSLu4YBeOfqpoNeh+yciZ210AIGiiYhJeL41IY9nNEc4U/ObKFKUenK5lmXzBBwF+tx4/iYiTyKoqAF5HolTjw7b2EOZ8B20Mqn3pPWhjzcenoDv3ytCxi7n3zn0lrOqgrSPpikLuThtT2j/OzlPbg0hfee3cIZLWUYcq0WJeCYFlbKRPXqqK7ShzRE03me3znEL1/bS3O5n+uW15HImMyt9NM+lJpYrirgprVi7DnNiucxo1lUr45e4T3m+2wx/Ti2g53Mg6FOXGst2+GOF3bTMZRiz0ianYNJdg4mGUhk+fzVC2mr9PPGnigXzK+kscxLY5n3iANI7Nz491iAXulFdZf+nBAl5EjYnhBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQYnYqVlXgL4CtQBAYAF5QFEUBPjD+WhwBK5EnvW6QxNN7UQyN0FUteBeX05vOk8tbfPeZdqLpAgAvtg/zmUvn4TgOY/+rZxYzniP+2B7Sr/aBA+45YSJvn49RKZk14thYiTzxx/eQerkXHHC1huDaVpI+jdYKP3YsR/KlXvKv9nF70OCWa5byuRfa6Y1n6Y1lKVj7ikee3THEszuGuG/++RMBJABBr8FX376c7zy1k0c29bOkLsQXr1vEnKoAAO0DSd77/ZeJZcbO0+d2DvGX1yzkjy6eOyPPU3HyOLZDriNG7MEOzJEs/jNqCJxXh15+dNfEwmCakV9tp7AnAQoEzq8neHETWtBVlH4WLJtvPb5jYlb553cO84WrF9IXy9FaNT2CBk5FG7tjvPt7L5Ezx2bifmr7IH9x9UJ2D6e4fHEN1aGpv//c7hjRhzow+9N4V1QRvKgBo9J3srsuZpG8afGdixZg3dOOnSoQcWk0XVrPVx/fTmvlKmpCnpL0y0oVxu6vn9yLoiuErmzBu6QC1SPXLHFimPEc8Uc6Sb/WD4B7XoTIzfMmPecYusrbT2+kYDmMRDP89ZIGgi/1Y2/fRc1ZNex2xfjAD16ZWP7363u5+xPncFZbxUk/HnFqy/enSDy+h+y2UVwtQcJXteJqDJa0Tw9u6OVL92wcfzXEb9Z08YWrF3LbmU280D7Ma7tHWNYQ5gPnttBQ5iXflWD4Z1uwRnOgK0Sun4PvjJppEagijo85miX5Yg+pV/vRy1yEr52De26EnmiGHzzXwR9fMpeHNvZNLG9oKo9t6ueXr48FQL24a5iakJuffvzsI95f9IEOshuHAPAsqyRyfRt6WWnuccQ0ICEkQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIWaooUyk7jvN3wGXAKuAS4E+AGHA78Mli7ONUkN06Quz3u7ATBayRLKN3byPXmcCtqyTz1kQAyZt++HwHPbFsiXp7fPK746RfGQsgAcjtipF+vR9H/oBfHKPc7jipl3onxlR+d5zkK7388U9f55FNfSSe7Sb5dBd22sTpz1DxYCffuHg+n750Hl5j6qUw6NaJ+KYW7s+rDvC1ty/n0T+7iB99+ExWNZdNvLelLz4RQPKmbz+xk94Zep6Kk6fQm2LoBxspdCdxMibJ57qJP7kXZzxU4kg4tkPyxZ6xABIAB5LP9ZDfEy9aP7ujmYkAkjfd8cJuHOTaXSq27fD09sGJAJI33fNGN5+6ZC5PbRsAZ3IIUr5/fLx1JnCyFulX+og90IF9wO9WiKNRZitYvxsLIAFw8hahR7t4R1sVOwaSJetXdtsIsfvasRN5rNEco7/cTm538a6LQhwo1x6bCCAByO2Mkl47NZcz4NbZ2B3jb89oxftAJ+ZwFjtZwHqiC++uOGU+Y2JZ24F71/aclP4L8SYrlWfk7q1k1g/h5Cxy26MM/WgT5kjpnm2GEjn+4/Gdk9rSeYucafP1P2wlU7C47cwmDE0lkSlgp01Gf7tzLIAEwHSI3ttOoS9Vgt6LYnIsh+Rz3SSf6cbJmBR60gz9aCOFniRDyRw3rqynqdxHfXhfQMglC6v57Rvdk7bTH8/ROZw+on1mtoxMBJAAZDcOkd06UpwDEjOSY8tzsBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQYnYqSggJgOM4XY7j9DmOM+o4ztsdxwk5jnOh4zi7irWP2cwuWCRf6p3Snt08jK4qhA4yW7sy/m8mynXEprRlNg1j56QAWhybgwUt+DqTrKgOsndPjPSrfZPftKE27/DNR7dzzxs9fPDclom3NFXhq7cup7ncN2mVkVSOwUQWl65RF/biP+C8PNj5qCjKjD1PxclTGEiPVRjvJ/16P1Y8f8TbsDMFslumFsHlu4tX/K+rU28bFAW8MpN8yXQMJ6eElMHY7yWaLhDxGjQecC0zBzM4hcmhJdktI1jR3Antq5jdjIyFnTxgLNoO5Vbp7lcdyyb18tT768z6wRL0RpwqcruiU9qym4anBD3Nq/bzyQvn4OmZGobg2TDMlXMrJ7WpckMpTjJzJIvZOzmcwU4VMIczJerR2P2NcpBzwa2rVAfdOI6D7UBTmZfz5lVipfIUeqbeC1slDFIRxWElciRfnvqMn+1N8cPndlHmM/jbezfx0QvacOsqhqbQEPEcdPwc6eU1u3loSltm8/DRd17MHhKkLIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEmKWmJlscA0VRGoFWx3GeG3/9Z0Bg/O2fO46z85ArCwAUVUErc1M4oFhci7jpT4wVoZf7XYyk9hWk337RHOr2m9V1JnE1BMlGRvCuqETRVfJ7E+gVXlQpZBfHyKjzT2nL1vnozya4tSaIGojvmwF7XMwaK8JfuzeKAvzZlQuoCLg4o7mMudWBieWSOZNHN/fxjUe2kzdt/viSudx4Wj0Vfvek7S2uC1HmMxjdLxDgs5fPpy7iLeKRitnoYNc+1W+gGEeeFaa6ddwtIayyPK7WEFgO6Y1DGDW+t175CNWGPQTdOomcOdH20fPbyBYkQKpUukezlPtduHWVnLkvWOSm0xp4dscgH79wDt99up3VLWX43WO3fQcbb4pbQ9GLlk0nTkGWW0Xx6XjmlaFXerCzFpl1Azg+g+byEn0OKgp6uZd8Z2JSs1Y2M++fxfTl2A7maBYsB8/CMtKv9k963z03MuUzvT+eY0NvjBWGlymf1CEX/el94Q+qMnZdF+JkUgxtLLbWPqDdXbpn9oqAm89dPp//+9sNE20Bt046b/K3Nyzhnje6+e3rXZzWHGE4maexIoBW6cEaGgsd0Su9eJdWoIZc2AUL1ZDvH2YqRVfRgsaUZ3zLUKiL+PjJi51kChbff3YXf37lAhrKvfxhYx83r2rgV691TSzfGPGwqDZ4RPt0zy0jt3M8UFdX8C2vwrO4HCueQwu5D7+ymKUkhEQIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCDE7FSWEBPgX4Gf7vf4k8D3AB/w98L4i7WfWUjSV4IWNZLeOgDn2R+yqT8ezqBy3Aabl8LEL2tg7kqY3luX05gjzqwMoB5vGdQZwtQbxra4h+Uw3Tt7CPTeC/4waFJniWxwj95ww7gVl5LaPAqBEXAzMD/OlUQ/BR7oInFdP7OHdEzUieo2Pzfa+IIU39kZ5Y2+Uv71hCYvqQgB0j2Z4Zscglu3w17/bOLHs392/mYBH5x1nNE3qw5yqAD+//Rx+v76HbX0Jbj6tgfPmVZzYAxezgtEQQG/wY3anJtoiN8xBC7qOeBuKruI/v57E43tJPLl34rXeeGRFdUfCcRw+c9k8dg4kGUzmWN1SjktT8EiAVMlUBd386PkOPn/1Qnb0JxhJFVjdUsbjW/t5dfco6/bG+NsbFqPu9/mq1/pwzQmR3xWfaAtf14ZeLsEM4tilvSqBdy4g+0gnmfWDqAGD4DVtVFQa1IVLE0KiqAr+c+vIbBzCKYxV0SteHe8y+WwWxWNlCqRf7Sf+aCeOaeNZWkn4hjZiv+8AQKv04D+zZtJzW860+Oaj27l3bQ+nXbGQswMGdnI8xE5TyJ1ZzRWZLGVBNx5D49YzGjmtKVKCoxOnMqPSS/DSZhKP75lo866qRq8qbcDitctrKfO7+M3rXUR8BpcsrGZDV4x/eXgbHcNj4T3P7hhie3+Ce//4fMpvmc/QHZtxt4XQK70knu8h8UwX3hVVhK9uQS9VUJY4LlrARfj6OYz8dMtEm17rQ6314+ofxdAUMgWwbIemCh+fu2stecvmllUNfOayeazvinJGcxnXLa+joezIQhu9yypIrx/EGsoQuqaV1Eu9pN8YQA25KH/HAtzzIzP2OzpxjBwJIRFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQsxOxQohWeg4zu/3e512HOcbAIqiPFukfcx6ruYg1Z86jXx3EkVXcDUGMKr9NANrHtvBb97opj7soTLo5jtPtbO0PsQVS2twaTOv+NuK5Uk8tq+YKdceJflKL2X1fhRNPcyaQhycHvFQ/u6FmANpookc93QOsyJVwP9kNzaQemOA0FWtYDvolV5cTQGCvbFJ2/C5NM6ZUw5APFPg7+7fSPtAiiX1oSn7+9nLe3jbynpc+uTzb3FdiMV1U5cX4nD0iJvK9y0h353AThYw6vwYDYGj2objOGQ2DY+FWQFOwSb5VBfutjBUFKe4sjua5asPbaWp3EvE6+LfH9uO361zVlsFtSEp4CyFuVV+Pnv5fL583yb+6vrF/HZNN//8cD/2eD1cpmChaypeY9+1Sg+5Kb9tIfnuJHYsPxZKcpTjTYgDZRMmuT904PRnALCTBWL37CD0wcXoJby3czeHqPrUSgrdSVDH769r/CXrj5h98nsSxB7smHid3TiEXuGm6o9X4uQtjCofWtg9aZ2eaJb71/UA8H+f2sE/XDSPpYpGpdsgU+7mc09s469uXMIHzm09mYcixCSKrhI4vx53SwhzKINW5sbVGETzGiXtV9jr4uqltVy9tJbXdo/wxNYBFtUF+e+n2yct1x/PsWs4xXlzK6n+7CryXQlG79428X5m3SB6pZfwlS0n+xBEkXgXllP1Ryso9KZQfQaupiB6uYfzU5WYlsN3n9nFxy5oY2tvnLw1FkZ2zxvdeA2NhbUBbj29kcbyIwsgATCqfFR+dBnWSIaRu7dhjeYAsON5hu7cTM1nV2FUH/n2xMzn2Pa+nx1HQmiEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCzRrFCSDwHvL58v59lqvEjpCgKrobAQQuBI34XAD2xLD2xLADVITe6MjMDO8z+9JS2zIYhQle0oB9QpCfEkdJ8BlprmGrH4apaH6GtUbLj75n9aeIP70bx6tR87nT0iJuLAgY//dhZ/H59L5UBN1cvrWFJfRiAjqEUj24eoDLgosznmrKvhogXTZ2Z55+YnvRyD3r5gR+nR85OF8isG5zSnu+M411YfjxdmxDxjRWd7h3JsJexoIHKgIveWIalDeGi7EMcHZeucdvqJk5vKSObt/i3R3dMBJC8qWDaU9bTIx70yLGPNyEO5M6a5MYDSCbYYCQKpenQflz1AVz1ErQjToz8nviUtsy6IYIXNqIFpt5DAnh0lZDXIJoukDNt/vKJ7SgKfOGqBZwd8POt955OW6WE5YjS03wG2oIyWFBW6q4c1BktZVQF3byxZxRFAeeAe6A3Q9iMSi+pV/umrJ9eO0Dw/HpUX2mDVcSxUQwVd2sYd+vk55Cz2yoo97tZ3hBmXXcMv2tycGimYLFnJIOuHX1ghB50YUVzEwEkE0wbcyQjISSnmv0vOrYNMzAkWgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIQ6mWBX0CUVRFrz5wnGcEQBFURYBycOtqCjKDxVFGVAUZeMh3r9EUZSYoihrx//9bZH6PKMsqg0S3m/GYbeucuXiWlR1Zs6yqYWmFuTpNT5Uj/zBvjh+iqIwpyqAv8IDB5wiepUXZXyceV06F8yv4mu3ruDzVy9keWNkYjl7vJhkKJmnJuSZcv599Pw2tBl6/onZSXVrGHVTi+z1Sm/R9rGkLkTzfrOFKwq868xm+uK5w6wlTjS3obG0PswZreV84qK2Se+d3hwhd5AQEiGKTffqqL6p+YaqXwq7xeymV0wtODfq/CjuQz/X1EW8/NmVC1CUsftKGPuM7Y3lqI14JYBEiCOkKAotFX6GknnecXrjpPduXFk36VwyqqbeExv1ARSXfAcx26iqwsLaINevrMfn0hhI5FhSF5q0zF9dt5ja8LE9J6leDcU19evUQwVPiVls/+AjW565hBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQs8fUSsFj82Xg94qi/COwZrztDOBLwOfeYt0fA98G7jzMMs86jnPD8XZyunMsm3x3kkJ3EsWj42oKYFSOFbU1Rnx85PxWLNvBdsZm9G0s85S4x8fOaAzinhchtzMKjM1gG7m2DdVdrCEpTlUF08bOmzi9aczhLOHr2jCHMqRe7psYZ5rnrcdZW6Wf1S1lvNY5yn8/tZOPXzgHXVOo8LtY2RSZUsQkxOGYsRyFrgRmNIdR7cNoDKJ5i3u9U3SN4GVN5HZFcbIWMH6tPWBm8OMxvybI3924hPXdMXIFm6BH5541XfzLO1cUbR/i2GUKFme1lfP5qxaSyBbwuXX6YhkW1QUnlrESefJ7E5ijWfQqL67GIJpPQiLE8QtU+8ld04p9z86Jgkzl9Cqcyul1v5rvS1HYmwDAaAjgqp8a3iTE0XC3hTCaghPjSnFrBC9rRjUOH2xwTUs5l918GkqiQKHMzbPJNKOmRU3QTbZg4XmL9YU4kRzHodCTJN+VRNFVXE1BjOqpgTul5jgOmYLFaU0RXt41zOevWkg8WyDo0VnVFCHi2xcK4Z4TxmjwU+hOAaB4dUIXN6LoxcrmFSeTGc2R70pgxXIYtT5cDUFUj06mYOHS1InA0IsWVPHO777I+89p4eqlNaTzFquaI1yysOqY961XeIncNI/RX2+fuOcJXtqEfpCgGzHLOfuCRxzHOTADVwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIWasolRAO47zB0VR3g78BfDZ8eaNwNsdx9n4Fus+oyhKazH6MdNld0YZ/vGmiSIGrcxN5ceWYVT6qI+4aanwc8+aLlRF4X3nNLOyqay0HT4OesRN5Lb5mONBEapHQw3KrLHi2FjpAmY8T2++wPdf28PtoRD6Y3sn3ne1hqj4+DL0oAuj5shmlo/4XPzrO1dy/7oeHt86gKLA9cvrmFN1ZMXKju2Q705i9qdQXBquxgB6uRQlnYqsVIHRe3aS2zoy0Ra+tpXAhY0oanHLlFz1ASo+tIRCbxrVUDEaA+jlxQ0AKA+4CHp0Xt7VT0XAzd+9bSlL64sXdCKO3u6hFPe80c1jW/p528o6FtQE+OVre3Ec+PD5rZzePHa/YGVNYg91kF4zMLFu4JJGwpe3oBhSgCuOj6GpaAsj+D+8FGcoA4ZKqtJNzLGpKXXnxuW7Egx+bwNOfiyoSTFUqj6xAldT8C3WFOLQ9DIPFR9YjNmXxi5YGDU+tKCL7I5RzKEMasiFqymIHnJPrGMl8igPdcKOKA5jD+VXX93CyOJK/vp3G9nQHePtpzdy7bJa6iNy/yhOvvyeBIPfWw/W2JcTqk+n8hPLcdVOn+Cm9oEkv3p9L89sH+KWVfW8/5wWfv7KHnIFi/ef08qq8fufwkCK/N4kju0QuWkeTs7CzlsYNX6MSjm/ZiIrkWf019snQm0BAte38bDb4s6X9rC8IcwHzm1haX2YlY0R7rr9bH78/G529Cf48PmtnDenAo9x+K9DzWgOJ2+hBl2YwxnM3hSKe99zvW9FFUaND3M0ixYa+55BQnVPQY6z72fbPvRyQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIMcMU7S/kx8NGPlis7R3gXEVR1gE9wOcdx9l0gvZTMlamQOwPHRMBJADWaI78ngS5gMGX79vMxp4Y7zi9kVimwD89sJm7PnEuteGZWTRjJfPkd8UZ/eV2sMcOWitzU/nR5Rgye6w4Crm9CUZ/sx2zL41R5uYzN7Rh/3LH/qcS+d1xFBuMGj+pnIlLUzGOYMbr1ko/f3L5fD55yRxc2tHNRp9rjzL0o40wXoeiVXqo/MgyjAoZ36eaQn9qUgAJQOzRTjxLKjCqijujfK49ytCPN00UjGphFxUfWVq0gtHt/XH+9eFtvN45ymnNZXQMpdjQHePsORVF2b44erFMgS/+dj0v7hphZWOYNXuiPL5lgO+873TK/O6JWeABzIH0pAASgOTTXfhOq5pWRcVi5gr0Zhi+c/PEvZ1a5mbPBdUE3Ma0CFJIrx2cCCABcAo2qdf6JIREHDc95J4IGXEch+Rz3cQe6Jh437O4nLJ3LEDzG8D4vcGO6KRtFJ7Yy3bF5O5Xx4L0NvVspnM4xd9cv+SI7luFKBbHskk80zVxPwlgp02yW0enzf3CUDLHZ+5aw5beBAA7BhK876xmvnrLMkJeF7o2ds7ku5MM/WADdtoEQHGpVHxoKb6llSXruzh+hb7UpAASgNSjnWTPqWRTTxzHge8/u4s/v3IhTeU+zmqrYHVLObbjTIyNQ3EKFulNw8Tua8fOWURumkv0dzv3e673UvWRpegVXlyNQVyNcg9xKnP2DyHZ/2chhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSY4U5YNZOiKN8r0qbWAC2O46wE/hP43WH2+QlFUV5TFOW1wcHBIu3+5HBMGztRmNJuZy06h9NUhzzcvKqBJ7cN0hfP8f5zW+kcTpWgp8WR70mSer5nokgVxkNXOmMl7FXpzOSxW0pWPMfIT7dg9qUBcEZz+HrTODlryrL5TIE7X9jNrf/9Ap+56w3WdI4e8X6ONoBkLFRo90ShEoA1lCW/N3FU25kpZPwe3sHGI6aDYxZ3pmQrbxJ/Yu+kglErlie38/ivqyOpHL96bS9/+ot1WA58/uqF7B1Js6knznefbmcgkT3ufZTCbBi7ncMpNvXE+evrF1MVdNMxlGJZQ5hdQ6lJASTApPCFfY3g5GXW7plmOo5dK5kn8fieSfd29miO0xydnQPT4/PPHJl6rTJHZ+b1ayabjuO3mAo9SeKPdE5qy24ZodC17zxwclOvu07BpiHg5pMXzZlo+/nLe+iOZk5cZ8VRme1j902O7WBFc1Pa7djUtlLZNZicCCBZUhfi81ctZENPnI/e8RqPbu4nmR0LHUmvH5wIIIGxe57UK31YOfOg253NZtP4tQ9yT+vkbSIujS9eu4iWCh+buuP84tU97B0Z+65AVZW3DCAByPekGL17G3baxLu4nNSLvQc812dm7XO9OAb73fdiyzOVEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBi9jiRUyqvLsZGHMeJO46THP/5QcBQFOWg09Y6jvM9x3FWO46zuqqqqhi7P2m0gAv/OXWTGxVwNQRw6SqKAv/7bAfb+hM8uW2AbzyyfdLfus80hf40ViI/pd2KT207FczksVtKZjSHdUAxXH53HNfCskltiqGyV7H52/s2sbUvwcOb+njP919iS2/8xHSs4EzpF4Cdnho0NBvI+D08vcqH4pkcZONqC6NF3EXdj5MqHHTcHaztaP1hYx9f+PV6NvXEebF9mH96cCvvP7sFALeuoSnKW2xhepoNY1dTFT58fivfemwHj20ZYHt/kh8+v5v71vVQsCYXwukVXtSAMbmt1ode7jmZXRZFMB3Hrp0sHPQ+Tk+buPSjC/M6UXxnVE9p859ZW4KenNqm4/gtJjtt4hSmFiJb+wUh6NVeFNfk88KZE+J/N3SzsSfGJQvG/r/omoI6Mz9iZ6XZPnbfpBoagXPrprR7llSUoDcHp6v7vsq66bR6vvrQVl7vHGXt3hif+tkaXtg1BIB1kPApK5rDTs3O57LDmU3j16jyobgO+DpzbhgzYPC9Z3bx0MY+dgwk+faT7fzzH7aSOVgQ3yGYQ/uCn1S/cdB7m/2DbcQpznH2+3EGf0krhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQBziRISQDxdiIoii1ijJW3awoylmM9Xm4GNueThRFwX9mLaErm1EDBnqtj4oPL50IIfndG92Tlk/nLXpjM3fmdkVXD1rE5GoNlaA3YqZSvTroky9juZ1RMqurMVdWovp0XG0hwh9ewh89tHnycqZ9wkJI1KCB/6ypRc2u+sAJ2Z+Y3oxKL5UfW457XgTFq+NbXUPZLfPQvMZbr3wU1IAL7/KpGV2upuBxbTeazvM/z+ya1GbZDoPJHAG3zuevWkBFoLiBKuLItVX6CXsNErnJxZB3v7KXnmhmUpte5qHyI8vwLCpH8ep4T6ui/D2L0AKuk9llMUs5Xg3P0vKp7bU+FtRMj88/95wwZe+Yj1bmRou4ibx9Hu65kVJ3S8wyasCY8tmruFT0in2BT0aVj8qPL0NvC6H6dMwVFbw8x89D2wZ5fucwK5rCAHzm0nk0lvlOav+FAPAsKid84xzUkAu9wkP5exbhaj6+e8pimlvt59KFVcytCrC+Kzbl/Tue341p2XiWTP1ccs+PoOon8qswcaIZ1T4qP7YcV1sY1afjO6uW6Lk1dCayDKcmh4b8fkMve0fTR7zt/QP7sjuieJdO/d7KqPcfe+fFLLNf8Ig9NYBMCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghZir9BG77OkVRQo7jHLbKX1GUu4BLgEpFUbqALwMGgOM43wXeAXxKURQTyADvdmbp9JJ62E3wsmb8Z9ahGOpYwALg0lU8hkbOnPwH7V7X9JhV/lh45kZIjWTxn11Leu0gqk8ndFXrcRfLi1OLXuElcuMcovfsnGjTzq/j8ZEEq86vZeE1rRheg6G8SdacWhDi0k5M8dmboUKOZZN6qQ81YBC5rg1Xg4zvU5W7KUjFB5fgZExUv4FyAgofVUPDu7QcJ2eRXjuA6tEJnFeP0XB8RXKaquA/yOdNxGfw3+9fxarmqcWd4uTxuXTqw94p7W5dRVOVKe2uhgDl7110QseiODXpQTfWnDB+0yG9buzezndBA+vMAhf5p0dQkeY18K+uxbNorKBYCxQ3DEoIAL3SS+DiRtKv95PdEcWo8RG8tGnKfaC7OcToNc08/EYPj+8ZZsP6scdmTVVoLvPxPx84gzNby1APci0X4kTTAi6C5zfgW1EFqoLmn17Xy7DXxVduXsYbe6O8sHNqRm3Ya6AqCq62MKGrW0i+0AM2+E6vxtUSQgtNj88lcezcLSEqP7wUJ2uOBYdkC8wzzSnL6aqCfhTXUaM+gHtxObktI1gjWdAVAhfUk3q1X57rxVSOhJAIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFmp6KGkCiK8nPgjwALeB0IK4ryTcdx/uVQ6ziO857DbdNxnG8D3y5mP6czRVHQQq5JbXVhL39x9UL+6ncbJ9oaIx6W1IVOdveKxqj2ETivnsJQGu/KKrSAC6NaZvkWR0dRFXynV2PUB7BGs2ghF0atnw95Jl/aqj06f3n1Qj7/6/UTbTUhN0sbTtw5pJd5CF/dRuDcBhRDQfO73nolMaupLg1OcHiUuzmM4jPwLqsETcEo96CFj6/IMugx+NMrF3L7na9NtIW8OlctqWFh7cz9HJpNljeGaav00zGUmmj7sysX0Fh28M/VkzEWxalHVRW6ghrxWjfz5s4l7Th8b1s/N53XUuquTSHhI+JEUg0Nz7wIWsRF4Px6FK+OUeVD0aYWwVeV+3iuN8qGnn25nR89v5W3nVaPS5frtCg9LTh9n2Eaynw0lPloLPPxmzVdE6GtmqrwofNaUVUFNeJBPasO15wITsFC88v3DrOJ6tbAPXatLPe7OWdOBcsaQmzs3ndN/aOL59JcfuS/cz3oovzW+RR6U9hZE73Si17lI3BBozzXiymc/YJHZmlethBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ4hRV1BASYInjOHFFUd4HPAj8JWNhJIcMIRFH5m0r62ko8/LM9iFaKrxcMK+Klgp/qbt1XPQyD3qZp9TdEDOcami4m4LQdPjZiK9ZXkd1yM3T24doLPNy4fxK2ioDJ7RviqqgR2SWbXFyuSp9UFnc4soL51dy1+1n89S2Qcr9Li5aUCUBJNNIY5mPH3xoNc+3D9ExmOL8eZWsbikvdbfEKWhZY4R1KNy1bQBDU7nl/BaWN4RL3S0hTjrVo+NufOvPycqgm39712m82D7Cpt4YZ7dVcFZruQSQCHEUVjaG+dUfnctT2wYpWDaXLKxmZeO+zx7Nb6D5JXzqVFAd8vBf7z2dF9uH2dqX4Nw55axuK0fX1KPajhZwoc2fHDaiynO9OJj9c0ckhEQIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCDGLFDuExFAUxQBuBr7tOE5BUabO+CyOXtBrcMnCai5ZWF3qrggxIwXcOhctqOaiBXIOCXG0PIbGuXMrOXduZam7Ig5hTlWAOVUnNlhJiLeiaypntJRxRktZqbsixIzRVO6nqdwPNJW6K0LMSIqisKIxworGSKm7IqaBlgr/jA/sFTPI/sEjtl26fgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUSRHd10oG/tf4DdgB94RlGUFiBW5H2csnKmRfdommg6X+quFIVjORSGM5jDGRxbZgwVR8exx8ZP4QjGz3AyR/domoIlRSFi9rOSeQoDaex04YTtQ86p6ak3lqE3lil1N8Qpzs5ZxLsTxAZTpe6KECVlxXMUBtPYOeuIlh9J5ekeTZMzj2x5IcQ+8UyBrtE0qbx50PetxNj9sZU5cffHYnpwHIfeWIa+WPao1jMTORkj4ug5NsNB+MWFKpZ18OuPEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCzER6kbd3v+M4//HmC0VR9gAfLfI+Tkntg0n+/dHtPLixj7ZKH3934zLOm1uBqiql7toxMeM5ki/0kHy2GxQIXtxE4Jw6tKCr1F0TM4CVyJN8sYfEM13gQODCBgLn1aOH3JOWyxUsnto2yD/8fjODiRzvOrORT1w0l6ZyX4l6LsSJleuIMfqb7ZhDWfRaP2W3zsfdFCze9gsWT24b4B/u38xQMi/n1DQxksrxm9e7+c8ndoACn71sPm8/vYFyv/utVxaiiFI9CeIPdGC1x1ADBpmrW/AsLicSkLEoTh2OaZPZOkL0dzuxkwXc8yNEbpyLUX3wz0rLdnh+5xB/f/8mOofT3Liijs9eMZ+2ysBJ7rkQM9OazlH+/veb2NAV44L5lXzx2sUsrgsBY4EUuZ1RRn+7A2s0h9EUoOzm+bga5PyajYaSOX752l7++8l2NE3hT69YwM2nNRD2GYdcx7HHx8hvdmDFchjNQcpumYerTsaIOAKOw3evU1k3R+Wm6HZW1dSWukdCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghRFGqRt/eb/V84juMAdxd5H7OaYzuY0RxWIj/Rls6bfOX3m7l/fS+W7bBzIMVHfvwK2/oTJezp8cltHyX5VBdYDpgOicf3kG2PlrpbYobI7oiSeGIvmA5YDsmnushtH52y3MaeOH/2y7VcM7eCz5/XymsdI/zo+Q4s2ylBr8WpzsoUMEez2AXrhGzfHM4wdMcmzKGxWb/NvhTDP9mMGcsVbR8be2L80U/X0BPLkrdsfvLSHn78gpxTpfb8zmH+8cEtxLMm8YzJVx7YwvM7hw+5vJU1x8Zi/sSMRXFqsvMWiYd2Y7XHxl4nC5i/3Ulq7/S5X5WxL06GQl+KkZ9twU4WAMjtiBJ7sOOQ465vMEl7+whtES+W43DP2h6+/tA2MnnzZHZbiEOy4jmsePHuJ4upczjFh3/8Cuv2xrAdeGb7EJ/5+RqGk2P9NQczDN2xGWt07HVhb5KRu7dijZ+fYmaycwf/PH9q2wD//IdtJHIm0XSBL9+3iZc6Dn1PDGAOphm6YxPW+DNTYU+CkV9ux07LGBFvzXEcYr6xcOiCnX+LpYUQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEGLm0IuxEUVRFgFLgbCiKG/f760Q4CnGPk4FhaE0qVf7SL3Uh+rRCF3Vgnd5JX2xLE9uG5y8rOXQPpicmOF3pkmvGZjSllk3iP+06hL0Rsw06fWDU9oyW4fRq32k1wxgpwv4z6ihYBX4xeWLKX+5HyeR5MrlNTytKQwmctSG5dIkTp7szlFiD3VQ6EvjXlBG6PJm3I3Bou7DHMniZCcX4tnxPNZoDj3sLso+tvZNDRP41Wtd3H7hXDmnSsRxHH7zeteU9t+u6ebGlfVT2nMdUWJ/6CTflcA9J0zoyhbczTPzXkJML1Y8j7kjOrnRgcxAChZXlqRP+8vtiRP9/S4K3Uk88yP4VteQWjOAf2U17nlhNL+r1F0Us0RhMAMHZHNlt45gxfOolV4AzFiO3HgAo/FyL1d2JbmyJcj7rlvKJ/+whYc399EfX0RrZVEe14U4Ko5pk+uMk+tKQM4i9VIvKAqhK5vxrqhC8xml7uKEzuE08czkwJ72wRRdoxkqAm7MkSyY9qT3zcEMVjSLFpg+xyGOjJXMk+9Oknh8D/nuJO45YcLXtUGVFxz4xat7p6zz4IZerl5ae8htmkOZsXDc/dt6U5ixHK5pNNbFNOWANn6JyRTSpe2LEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCFFGxqpoWAjcAEeDG/doTwO1F2sesl35jkOTT3QBYOYvRX+1A9Rt4a31EfAbRA2ZiDXtmbkGE0Rggtys2ua0hUKLeiJnG1eAnt3VkctuSSgb/Z/1EAVFm/RDLPrCY6B/2YI/XFBlrBrlsdTU+Qz3ZXRansHx3kuE7t+CMz9Sd2zJCNJGn8iNL0PzFCQcBUH0GKEwufNYUVK9WtH2UeacW6TdEvHjlnCqJnQMJHt3cT01o6jiaXzP1M7XQn2L4J1uw02PFurkdUUZHc1R+bBl6mYTIiOOjujW0sAsrNnkWeMVb+vvVwnCGoR9twhkvVM9uHcUcyeFqDDBy11ZC17QSvLgRRVFK3FMxG2j+qY/YWtiN4h77rHRMm8RTe1E0lfTaAezk+DPerjgLozk+fkYTv9zcy2g6T5PtQ1NlXIqTK9cZZ/iOTQQubCTxxL5Qh+jv2lEDLnzLSh8sBTCQyJLMFaa0G5qC3z12Hqreqeej4lJRPBLwM9M4jkOuI0b0d+3YqbHfe25HlOGfbuGRVRF2pbK0Vfp5dffopPXmVPoPu13VP/U+RfFoqG4ZI+IIOA76eA5oxsyWti9CCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghRREWpGnYc517g48A3HMf5yH7/Pus4zgvF2MdsZ45kSK8dmNKe64jjc2l86uK5k9rPbiunudx3srpXdL5VNaj7zTyshlz4lk+PYiYx/fmWV6GG9oUh6FVecgPpyTMYK2D1pqbMRK9tHMZXOKBRiBOoMJCeCCCZaOtKUujPFHU/erWX4GXNk9rC17ahVxbvs6KhzMv86n3hFpqq8KlL5hL2TQ0nESfWQCLLH/9sDV//wzbaKgOE9wt6CHsNbjqtfso6hYHMRADJm8yhDIXB4o5FcWrSgi6818+Z9HRhzY/Q51HoHi3tGDOHMxMBJBNtA2m0yFiAT+KJPVixXCm6JmYhoy6Ad1nFvgYVIrfMRQ+OjTdzJEvq5V4Ul7ovgGScPZJjZcDDR85v4/Y7XmNDd/Qk9lyIMamXejEaglNCQwEy6wZL0KOpHMfh7lf28vOX93L98rpJ7/35VQtprRi7/zVqfPjPm3xPFH7bXPQKCV+baaxojkJfaiKAZKJ9OEuNrXDHi3uYXx0kuF94SLnfxdXLag+7Xb3Gh/+syctEbpqHXi5jRBwBx8YY/x4qa8kzlRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQYvYo2tSejuNYiqJcCfxTsbZ5StEUtJALa3jyzJlawGDnUIp71/bwhasXkilYuDSVPSNpBhI5Wt5iVtfpylXnp+pTKzH70gAYtT70Cm+JeyVmCqPWT/UnVxDfm2DXUJJdis3piQKTyoQcUPSpOUta2I1qaCetr0KorqnjUDFUFKMoOWD79mNoBC9swDMvghXPoUU8GHU+FE0p2j629Sc4b14lN6ysx7Jt3LrG/z67i3PmVFAdkkK9k2nXYIrt/UkA/vOJHXzswjZURaEh4uX05jLm7hcW86aDjUVUij4WxalrtNZD7rb5+NImlktlJzaf+/U6vvO+02koK919nuo+yCPP/tdGhymhZUIcKy3oInLzPPxn12FnTPQKL0btAc9sDijqQT6fFWiuDfJvT2xjKJXnpV0jnNZUdnI6LsSbHAc7mceon3ovoVdOj2f2vliW/3m6nVTe4uqlNXz+qoVkTYsldSEuml+Fro3d26gendAVzXiXVmAn82hlHow6P4pSvPtjcfIo2sHvZTPjv87/fGIH37htJdmCjarAkvoQc6qmjuP9aV6D0DWteFdWYSfzY9fsmpn5PZsoAcdBH88bzZjZwy8rhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQM0jRQkjGvaAoyreBXwCpNxsdx1lT5P3MOnrYQ+C8ekb2JGB8Fk0t7MbVGkZTLDb3xtncG5+0znvPbi5FV4tGURXQFBRNkQJocUTsvIXZn8KK59EibqK1bt5x96sA3HHNEuZqysT5A2A0BNBrfRNhNygQvq4N1VfsS58Qh6bX+PEsLCO7bXSiLXhxI3q1r+j7Uj06asiFnTVxChZOzgZX8bavqwp3vLB7UltDxIt2sEJqcULt//88lbf4j8d3AvDAZy84aAAJgF7tw7uyisy6wYm2wHn16DXFH4vi1OQoChsHEzQ6KjlV4dG+KOm8ha6W9j5Pr/HhW11N+rWBibbAOXVkNg4DELy0ES3iLlX3xCykunVUn46Tt8B2cGwbRRsLwdPLPfjOqiW3Kzblmpw7q4b/+9R2tvYlAHAfJFBPiBPNf04dQ/+7Ef/qWrJbR3ByYxX2ilfHu6KyxL0bo6oKLl0llbd4eFM/D2/qB+Db711FwHPAs5499nyoGBqqV5dAyhlKC7tR/QbeFVVk1u+7bhoXNPCDzV0AxLMmA/Ec7z+35S23ZxcszP40ViyHFnbjbg6iyNgQR8lxHNTxr6AKdr60nRFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQogiKnYl/nnj//2H/doc4LIi72dWcs0JU/GBxRT60iguFVdjEHdTkLZsgWuX1fLQxr6JZS9bWM3ct5jRdTrL9yYZ+tEm7PjYH+nrdX4q3rcYY5rMrCymH6dgk3q5l9gDHWMNClS9fT5/dFEb332mgy++uItvXjufpj0pXAWLwNl1uJuDVH5oKfnuJE7OQq/24TrIjN5CnEhGpZfQNa14llViJ8Zn1270o7mLH4aT70oy9MMN2GkTAFdriPJ3LUQv8xRl+ysaw5T5DEbThYm2P79qARUBKd4/2eZVBzh/XgXP7xyeaLtlVQOtlYeeuV0v8xC8tAnPgghWLI9W5sHVGED3GSejy+IUUJ80cT87gJO3Afjj+WHmXjyXBTWl/ezVPDrha9rwLq/CjudRAwbmcAYtZBC8ZAGeBWUoioQpieJwbIfU2gGiv90x9iQMhG+YQ+DsOhRDRdFVQpc1k906gjmSJfKO+WDapPw6//jKbtb1jAVP+l0aZ7dVlPBIxKnK3RKm8uPLSL8xQPiGOeCA6lIx6gMYJyBE71jUhDx8/uqF/NU9GyfaqgJultaFJi1nxnKM3rOD3NaxMEDFrVH5sWW4mycvJ6Y/RVXwLqlACxq4WoJg2jgVHv761d1s7B0Lbgq6dVa3lr3lthzTJvVaP7F72yfaIjfPxX9mHYom9wPiKDgOypshJFbh8MsKIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEDFLUCmjHcS4t5vZONbrfhb6oAu+iycVmQY/B39ywhMsWVfNKxwirW8u5YF4FYe/MLBp2bIfUy30TASQAZm+K3I5RCSERh1QYTBN7sGNfgwOxe9v59KdWsLA2xAvtw+wyHFqvb6EqvK84TnXrRQtgEOJYueoCuOpObBG+Y9rEn94zEUACkN8dJ7c7XrRzYF51kLtuP4fHtw7QPZrhqqU1rG4pL8q2xdEp87n42q0reG7HEGs6Rzl3bgXnzq3A7zr8rZ2r1o+r9tBBJUIcKytTIPZAx0QACYC+I8a7z15MOFT6z2Et4MK7cPL1KnhBY4l6I2YzcyhD9N6dEwEkALEHduGeG564F9DDbgJn101aTy+YvN+rUlnhpSLg5orFNSypl6AEcfIphopnXhmeeW8d5lBKN66ooybk4bHN/bRW+rlsYTVtBwS1FroSEwEkAE7OIv7wbio+uBTVrZ3sLovjpAVdeJdUTrzO5E1uc0O4zENlwM0VS2pYVPfW183CUIbY/bsmtUXv34WrLYyrRu6TxVFw2BdCYksIiRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQYvYoagiJoihh4MvAReNNTwP/4DhOrJj7ORXVR7y8c3UT71zdVOquHDfHssnviU9pz3cnS9AbMVPYqcKkYk4ATBtXweGW0xu55XQpJBanNjtnUuiaeh01B9NF3c+iutARFfeJE6+pzMd7zmrmPWc1l7orQuBkLQq9qSntesY8yNJCzF52ugDmATetDtjJwxcnewyd8+dVcv68ysMuJ4QYE/K6uGJxDVcsrjnkMmYsN6Ut35PCyZkgISQzntelc8H8Ki6YX3VU69mpAtgHXKctBzsl9yziKDn7xpGEkAghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIWYTtcjb+yGQAG4b/xcHflTkfZyycgWLrb1xXmwfomu0uEXlJ5NqaHhXTC0S8SyY3jMtixPLjGYpDKSxc9ZB39cibhRj8iVLDRhoEdfJ6N4x6Y9nebljmA1dMVJ5KWgSJ5ZjO3iWVkxpdzUHi7qfdN5kY3eMlzuG6YtnirptcexSWZN1XVFe6RhmMJEtdXfEKUgNGHgWT70GqRWeEvRGiNLRwm5UvzGpTXFpqH5j7F43e2T3hCOpPK/vHuGNPaPEMvkT0VUhZqy+eGbsOas7Rjp38HPKTpvoZVM/g7xLK1B9xkHWELOdFc9R6E+jBgyUA0JoFK+OHnGxZzjFi+1DbO2LUzDtEvVUzBiOjaOM/Wja8p2PEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBi9tCLvL25juPcut/rv1cUZW2R93FKGk7m+NnLe/j3x7ZjOxDxGfzvB1ezurW81F07Ju55ETxLKshuGQZFwX9OHa6WUKm7JUrAKVikNw4Tu78dO23inh8hcuNcjGrfpOX0Si8VH1jCyK+2YScKaBE3+k1zeK4/zjzHprHMd4g9lMaW3ji33/kaXaNjIQ3vO7uZP71iAZVBd4l7JmYbx3LIbhth9Lc78K+uxT0vQm5nFDSF0GVNuJqLd20dSeX4z8d38qMXdgPQEPbwtXesIOJzsbAmgEuXGeVLoT+e5WsPbeGeN3oAmFPp47sfOIMFNfK5Kk4e1dAIXtqIHc+R35NAMVT8lzTSni+wwLTk+iBOGXqZh4r3L2bk7m1YsRxq0EXkbXMYuXsb5kAa15wwZTfNxajxT1l3KJFja18cUPjXR7aydm8MgIsXVPGVW5bRNM3ud4Uohc09MW6/8zW6o2Oha+85q4n3ntXCgpoAbmPssybfnWT0NztQvRqBixpJvdiDU7Bxzw0TvKgRRS92Hq84mRzHYXt/kl1DSUIeg0W1QSoCh37O3v95yU4WcC2IUP6ehUR/uxMrnkcLuSh710LWxjJ89M5XiWdMNFXhr65bzHvPbsJjFPurUzFbOI6DPX45KUgIiRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQYhYp9l/SZxRFucBxnOcAFEU5H8gUeR+nnGzB4rmdQ3zz0e0TbdF0gb/8zXp++clzD1tsMR3ZeYv4o51QsAle1gyOQ3bLCNaqavTwzDoWcfzyPSlGf7Ft4nVuR5TYo52U37YA1dhXsKwoCp4FZVR/ehXxkTS/2trPt+56jXTeoi7s4ccfOZOFtdOj4D5XsPiPx3dMBJAA/OzlPVy+uJrLFtWUsGdiNir0pxj+yWZwIPHUXtwLygjfOAf33AhGtQ9FVYq2r/VdsYkAEoDuWJbvPNWOAty2uombVzUUbV/iyL28a3gigARg11Ca7z69i6/fugJDkyJbcfJk2qOoQRfBy5rAcki/1o9JFZt9Oqc1l5W6e0KcNO62MNWfPg0rmQfLYfCHG3AyFgD5XTGiD+yi4v1LUF377nX7Ylm+8Ot1bO9PcOWS2okAEoCntw/y9LZB3n9Oy0k/FiGmk0ze4puPbp8IIAG465W9NER87BhIcMuqBux0gZFfbMUcGHsWs6I5AufX415YjlHnR/NIoMRM99KuYT78o1fJmTYAly+q5p/evpyakOegyxf6Uwz/dDOMLU5+e5SEZVP5yRU4OQstYDCCw2f/63nimbEgCct2+Iffb+aMljJWNkVOxmGJmchxsCZCSAql7YsQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIUUbErUz8F/JeiKLsVRekEvg18ssj7OOXsHEiyYyA5pb19MMVIKl+CHh0fK54nt22U/N4E+b0Jcu0xCn0pzKF0qbsmSuBgv/fsxiHs5OQCDse0cEwbPeLmzl2DfPWZdtL5sWLO3liW+9b1TNlOqUQzBV7cNTylfddgqgS9EbOdOZQBZ9/rfEeM7LZRFF0pagAJwN6Rqefr+r1RFtYG+ZvfbWTPsFzHS2FjT3xK20vtw4zOwHsEMXPZBYvcuiFyO6MUupPkdsWwRnOU9aYZSspYFKceLeRCr/JiJnITASRvym2PYsUnnxfru6I8u2OIedUBNnXHONCL7VPvLYU41UQzeV7aNTKlPZEt8Pf3bWLPSBormp8IIAEwR7LkOhPgOBJAMgtE03n+/v7NEwEkAI9vHWBTz9Tr5pvM4QzYYNT68CwqR/Ub5NvjYNq46gNoITdDqTw9seyUdXuikqssDsMBa/yRW0JIhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQs0lRKzAcx1kLrFQUJTT+empVrDhqo+k8bn1qXszcKj8VAVcJenR8VI+G76xajFofmTcGUbw6kVvno4Vm3rGI46f6jYmfFa9O4Nw6FEPFHMygGCpoKrmdoySf60b16AQubGTgIMVBb+yJnsReH17EZ3De3Aoe3NA3qX1Olb9EPRKz2f7nkGdJBe65YTJrB4n+voPgBfW450SKFkbSXO6b0rayKcLWvgSJnEk8K4VXpXCw38uq5ghasaPmhDgMRVPxnFGNatqk1w2hhV2U3baAnqEUhlbcQCQhpjtzNEt6/SCZdYMYDQHK378YcyCNnSqQfLEXLehCcU++SA8kcgDs6E9y1dJa3tgbnfT+eXMrTlb3hZi2ynwuzptTwSNb+ie1Bz0GsaxJLFOgwedGcWtoETf+s2tRvTrp1weIP7wb+4IGPPPLUCWMZMZK5kx2HiSk93CBZ2rYTfn7F5PZOIQ5kMa3qgpQUNz7xkFFwEVDxEv3AaEjDRFv0fouZiHHwRq/z83mJJBTCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggxexS1PFVRlApFUf4DeAp4UlGUbymKcthqKUVRfqgoyoCiKBsP8b6iKMp/KIqyU1GU9YqinF7MPs8EjWVeXmof5mMXtKGNF5KX+1185eZllPvdJe7d0dMCLlwNAWL37SK/N0Fu+yjR3+4ApED1VGTUB3AvKgcFQlc0k3y+h/jDnQz9cCPRx/eQ3TbCyM+2ku9MkN02ytAPNvDHC2qnbOdtK+tL0PuDc+san718Pi0V+4IBPnRuCysbI6XrlJi1jDo/vjOqUdwa7rlhYvePX1u3jjD0w43kdh96VvCjtaIxwscuaEUZv1w3lnm5bFE1L7YPM7fKT33YU7R9iSM3t8rPNctqJ72+bkX9jLxHEDOXoiooeZvYg7spdCfJbh5h9Lc7qGmNUO6XoDlx6rALFvHHOok/tJtCT4r0q/1Ef7cTO2uR2TxM6PJmIm+fhx6cfI2eVz0WVjeQyBHy6KxsDE+8d+nCai5eUHVSj0OI6chjaPzpVQtoKhsLhlAUuGVVAxu6o7RU+KgPe9HLvUTeMR/vskowHUZ/uZ3czij5zgQjP9tKdke0tAchjktV0M3VS6d+HzCn8uCBn+ZIhnxnnOhvd5BZO0ihJ0XyuR7snIkW2BfmWB308I3bVhL2jrXpqsLfv20J82sDJ+ZAxOzg2Fjj36wP93eXti9CCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghRRMWe/vVu4Bng1vHX7wN+AVxxmHV+DHwbuPMQ718LzB//dzbw3+P/nXWcgk2+N4k5mEH16RgNAfSQm4aIjw+c28JPX+rkM5fOw6UrrG4t5+y2mTkbtpnIkXyhZ3KjDbn2KJ65kZL0SZx8dt4i35Wk0JskcG4dwYsaiD/aiZOzUAyV0BUt2KZF8rkDCjkcoD3Gxy9o444Xd3NJazl/eXoz1ZZCdvsoRkMAzW8cdJ8n06LaEL/65Ll0jqTxGRptVX58Lplx+1RlJfPku5PYiTxahRdXvR/VXZzxoPkMwtfPIXBuPSO/2Db5TRtyu2J45kSKsi9FgaZyH3959UJqQl629Mb4l4e3sbguyD/fupLygIRelEJzmY+L5lewpC6EadsE3DrNZV4UZWq4l5UqUOhJYsVyaOUejLoAmleuTeL4mdHs1M9s08EZyNB8+vQMTzBHs+S7kzh5C6PGj1HnR1ElFE8cH2s0R3rNwKQ2O1lAr/biP6sORVdwCjZmPIce2ve5ubwhwldvWc4/PbiF7zzVzucun8eHz2ulcyRNddCN3yPXanFyOJZNvieFOZBG9Whj30tEplHQnAPvOrORhjI/fbEMj24eIJ0z+c/3rKJyPNxHL3MTf7gTz9zw2PPjfpLPdeFZVIZqaCXovDgaju1Q6ElSGEijusfG4qtDSVY0hhlK5ni5Y4SQR+dvbljCsobQQbeR3RlF0VXstDmpPf16P8GLmzAqvRNt58yp4Pd/cgHd0QzlfhdtlX4MrajZzWK2cRzs8SGSLWRL2xchhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQoomJXMpU7jvP/9nv9FUVRbj7cCo7jPKMoSuthFrkJuNNxHAd4SVGUiKIodY7j9B5/d6eXzNZhRn6+daJIxj03TNm7FtKezPGFX63j+hX1WI5Dx1CGu17Zy68+eS51Ee/hNzoNOaaN4ppayKHoUtxxKslsGGL0V9snXgevbKbQlwYgcF49iWe6xoqBDzIuMjhs7Inx5esWc11OI/2rduLj7/nPriV8bRvqNCjUrA55qA5No4I9URJWukD097vIrB2caAvfNJfAOXUHDYk4FprPOPS1VSteQf36rhh/d99mAEIenbed1sDf3LCEq5fWUBWUsV4qm3rjfPfpDq5bXouhqTy+ZYDdQ2kW14XQ9yuctHMW8cf2kHpxXxBY6OoWghc2ymewOG6O7aAYU8eRqinsGExyekt5CXp1aOZwhqE7NmMOjN17oClUfnSZBOKJ46cAqgLWvuQD1W+A5RB/ePdEm3temLJ3LUIPugDwu3Xec3Yz586t4MX2IR7a2Me3Ht85sXxjmZeLFlSfrKMQp7Ds9lGG79w88b2EqzlI+XsXTYsgkkze4p8f3sqT2wYJunVuOq2e8+ZVcOnCKlY0RoCxgKnczhh6uRsOcn+juDQJnJohcu1Rhn60Eeyx13qdn/YFfr72zE4uXlDFn1w2j2zBYlVzBI8x9fnfLlg4BRtMZ8p7qCoHexRrKvfRVO4r8pGI2cpxHKzxcWS9OVCFEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBiFih2lf6TiqK8G/jl+Ot3AA8c5zYbgL37ve4ab5tVISRWPEf03vZJs/Tm2mMUelP02AUSOYu7X907aZ2hZG5GhpBQcPCtrCa2NznRpLhU9Gop9Jit7LxFoSeFOZJBK/OA7RB7YNekZbKbhvEsLiezZgB0FTtVILcrSvjaNvKd8YlzQzFU9kYMXnpphHe0VpJ+unvSdlIv9+E7owZ388FnQhbiZDP705MCSADiD3bgmR/BqCzidU8B36oaYt37zi3FpaGXF69gdM9IeuLneNbkpy914ndpXL5IiqJLaVNPjD0jab779L7f/d6RNPHsAsr97ok2cyA9KYAEIP7oHjxLKnDV+E9af8XspBga/rPriP9h90Sb6tNRAwaj6ULpOnYIub2JfQEkAJZD7OHdGB9dhjYNgszEzKWXewmcW0/yuX33qP5z6og/0jlpudzOGGZvEj04OaDHY6h89aGtxLPmpPZdQ2kuWnDi+i0EgJUsEL1/16TvJfJ7EhR6UtMihGQkneeF9mEAEjmTn768B4DGiJczxsOu8l0J7Hged2t4LIxPV8EcDwdQwH92HYom4WvTnZ0xiT64i/1zHczeFCuXluE48NS2QZ7aNvaMdd7cSuZVByeWK0SzWANp7IKN6jModCXQyj1YI9mJZYIXNYx9NyHE8XDA0sZ+NB3z8MsKIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEDFLsCrtPAn8G/GT8tQakFEX5M8BxHOdYUgEONkXtQaYxBUVRPgF8AqC5ufkYdlU6dt7GTk0t0LQzJjVVHhQFnP2OusxnTCosnkkcxyG7K0r4ujYKPUkUt45e5saahgWqJ8tMHrtvxXEc0m8MEL1nJ4Hz60k+241nUTl2ZnKBRqEnRfiaNux4ft+MxDakXusnfG0bhb4UBZ/OlqDGF57ZAUBIVSfNMv8mO2ud6MMS+5nN47cY7OzUYiSnYOPkiztTsuJAbk987HzpTaJ4dLSIGzOeK9o+6g8SfHVGaxlhn1G0fZxMs2XsLqident1ztwKgu7Jv5eDjUVsB0eumTPOdBy7iq6Q70sRuqaVQm8K1W+gBQys0Sw1c4JvvYGTzE5Ove+0RrI4BQskhOSEmo7jt5gUTcE1J0zIq1PoS6FF3BjV3oM+61npqdflsNfF6pYyntg2OcCsuXwGhk/OMrN97AI4BQsrNvXe8cBnt1KJeAxWNUV4qWNkUntdeF+YhJ0qkHq9n7LbFhC9r53wlc2Yw1kc08ao9aN4tJPd7Wlhpo1fO29hjU4diz7LQVMVLHvsewBVgerQfqF7o1ly20eJ3tdO8JImtJCL5Is9BC9pwinYWNEcRq0f97wIinqwrxuFOAqOgzWeaVSQEBIhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcQsUtTpXx3HCTqOozqOY4z/U8fbgscYQALQBTTt97oR6DnE/r/nOM5qx3FWV1VVHePuSkMLu/Asq5zcqCoYVT7mVwf4uxuXoo0XSHgNjW/edhoNZTO0EM2x8cyNEHuwg9yeBJnNQ8Sf7sKomKHHUwQzeey+FXM4S+z3u0BVUDw6hd4UuZ1RvEsrJi+oK6hBg4oPLMazpBy91je2fn+a2IMd6HV+Ns8N8PGHNzOcyuPWVRpbI+hVk8eN4tUxKmRG45NpNo/fYtArvSiuyR+3RlOg6LPJO4aKd0EZsYfGr60bh0g8uRej2l+0faxsDPOes/Z9JFcF3fzF1YvwuWZmwf5sGburmyPcsKJu4nVjmZdPXDQHQ5887vQKD6pv8u9Kq/Sglcs1c6aZjmNX8xp45kWIP7ybQleCzLpBEs92Y1R6aa30lbp7UxgNgSlt/jNr0QKuEvTm1DIdx2+xGZVeki/2UOhNkXqlj9QrfbgXlU1eSFXQQlPHm9el8edXL6Q6uK+o/l2rm1jRGDnBvRZv5VQYu1rQhe/06smNCujV0+M67vfofOn6xVTud63+4LktLG8MA1AYzaBoKk7BJv7IbnynVRN7aDeZLSPkOmKkXu3DmIafSSfDTBu/WtCFf3XtlPZQUwhDG/tuTFMV/t9Ny5hXte8zvTCcJflcN1gOVixHriOGZ2kFiSf2knq1b+z7iM7YlO8RhDgm+4WQmI4EOwohhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIWaPolYNK4ryMcdxfrDfaw34a8dx/v44Nnsf8BlFUe4GzgZijuP0HmdXpx3V0Ahf3YKqK6TXDaFXeIjcOBejzo+iKrznrCbObitnMJmjIeKlrXJqUblp2Yyk8nSNZgh6dFor/RhaUXNmisJOjM0OGnn7fPK7Y6h+A1drCCszdXZwMfM5OQunYKN4dez02O84vzdBoK0R3xnVZLeOolV6iFzTNjYztaLgqg9S8f4l5HaOUuhL454Xwd0W4gKPzgOfvYCBRI66sIc5lQHM97rHQhd2RDEaA0RunIt+EgNtTMvGsh3i2cK+c6/Cjz4Nzz1RGkaVj8qPLCN6fzuF3hSeReWErmmdEgZx3LIWZixH2W0LyLVHUTw6nrYwhYEU3oXlR7QJ07IPOna7R9P0x3NEfAZfunYx7z6rmWTWpK3ST31ECvhKrTrk4Ss3L+P2C+eQzpu0VfmpDU3+vTiOQ49jE3jvIgqPdFLYm8A9N0L4+jb0oIQuiOKwsyZl79x3DXK3hMjuihGaV/bWK59krsYA5e9dROyBXVipAoFz6vCfVYuiKKXumpghBuJZ9o5miHgNWiv9aKpC3rRw6RpGtY+qjy8ns20EO57H1RICFRRFIbttBL3cS+CCejAOPt6W1oe599Pns2sohd+tM78qgN8zMwO/xMyi6CrBS8YC59JrBtDCbiI3zsFVX7xQu+O1ojHCvZ++gN3DKYIenTlVAQLusfPDKTikNwwSvq6N9Jp+rFiO8NvmUuhJ4moIYMyNoEfGAn7641m6RtNEvK6Jc1hMH4qqEDivDseySb3ShxZ0EblhDu75ZTz42QvpjmaoCrqZUxnAtX/wnmVjDmeAsTEcvroFxwGjxo85nMHdEsKYH0HzGYfdf8602D2UIp23aKnwU+6X+2VxEI6NPX7pkBASIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEbFLsSqbLFUW5FfgYUAn8EHj6cCsoinIXcAlQqShKF/BlwABwHOe7wIPAdcBOIA18pMh9njaMSh9lty4gdFUriktF26/IwaVrLKoLsegg62XyJi/tGsG0bb764FZ2DaXQVYU/uWweHz2/jaD38MUVJ5tW4SF6XzuoCt4lFZjDGUbu3krVJ1aUumviBFBDBnqdH7M3NWm29+QzXehVXsLXt+FZUo7mmTxOjUovRuXkInoNWFgbYuF+EyK76gJUvH8JdqqA4tHQijTek9kCiqLgdx/8MmnbNs+3D/OLV/dw8YJq/uPxHewdzWBoCn95zSLec1bzIdcVpx53W5iqj6/AzpmofgPVpRV9H1rEjZMxib28G/+qaqy0ycgvtlH2zgVvue72/gS/XdPFKx0jXL+ijmuW1tJQ5qM3mmFdV5S/uXcTg4kcHkPlKzcv520r63DpxT8GcfQ298S465U9bOiOc9vqRi5bVD0lgMS0bB7e1Mdf/Ho9OdPm3Svr+chVS/jV9gHM9V3cojUwrzpYoiMQs0Umb+LoKrHf78J3Rg2OaTN6z06ClzSi6NMvmEs1NHwrqnC1hcG00UJuFE0K0MWReb1zlM/e9Qbd0QwuTeULVy+gNuThjhd3c2ZbOW9f1cj82iBG7VhwQ2bbCNH72vGvrsGzqJx8Z5x8dxLXgkMH9NRFvNRJ0JcoAaPCS9lN8whd1oxiaGiB6fV9AkBDmZeGsqnnhxY0cNUHiD3UgWdROapbI/lCN6Fr5/CToVEe++1uLppXxVltZfzpL9fRG8vi1lW+eO0i3nVWE15Dnt+mE73cS+SGOQQvakQxVLTA2PcJc6oCzKkKHHQd1a/jWVBOdusI2A6xh3bjag4SvnEuiaDOLzb08uAv3uCstgpuPb2BeTVT74G7RlPc8UInP3iuA9uBxbVBvnHbSpbUh0/o8YqZx3EcrPHbXFOxS9sZIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEKKKiVlg4jvNeRVHeBWxgLDDkPY7jPP8W67znLd53gE8Xr5fTm6Kr6GWeo1pnTeco2YLJT17aw66hFACm7fBvj+1gRWOESxdVn4iuHjOj3EvklnnEfr+LxJN70cJuyt+1CFejFEDPJnbBIrczSuLpvQTOqSO9fpDcjijBy5pIPt+Dk7dwNQRwt4SmBJAcLdWlFS3UIZEp8NT2Qb7z1E4MTeUzl87jwvmVeF2TL5cvd4zw8Tte4+IFVfzkpU72jo7NtlywHL7ywBZOa4qwurW8KH0Ss4Pq01F9J66wUVEUvKuqMeN5Es90oRgawUubcLUc/traE83w0R+/Stf4GF6zJ8r6rhj/76alPLF1gP9+up3BRA6AbMHmC79eR9CtkzUtVjWV0VzhO2HHJA6vYyjFe//3ZUzL4fYL2+gazfCbNV1cOL+K5Q1hFGUsUGHXYIr/84u1FCwHgJ++0c1D2wd522n1/OiZ3Ty4oZe7bz+XmvDR3X8Isb+1e6OMmnnOPaOG5Is94IB/dS1mS5CNPTFWNEZK3cWD0oOut15IiP2MpvL8ze820B0d+9zMWzb/+OBWvveBM3jXmc3sHk7z1LYBQh6dmvBYSIK7NUTgvHrij3bi5Cw8i8oJXNCAEXKX8lCEOKRj+V5iOtC8Bp7FFZjxPJm1gyiaQuCiRtZaOdJ5i9Ut5Zw/v4Iv/mYDvbEsADnT5u/u38yiuhDnzKko8RGIAynakY9FczRLfm8S/1k1OAWbXHsU1afjP7sOs8zNY5v7GE7lOXtOBV6Xxp//ah3f+8DqiXvg/niG36/vI+zV+f6zHRPb3dKX4DtPtfPN21ZKEKOYzAFrfEhYEkIihBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSYRYpaDa0oynzgc8BvgMXABxRFecNxnHQx9yP2cRyH7liWurCHlztGpry/ezhVgl4dnpXIE/19O95llQQvbsRKFkg814W7NTQxs62Y+fKdCYbv2Dzxs2d+GYGLGjDqA/jPrMUxbfQyN8o0KuLpj2V5qWOYz929dqLtEz95nTs/ehYXLaiatOyaPVFyps2C2iCPbO6fsq2OoZSEkIiTrtCdRFGh/N0LcSyH5PPduOdG0A8TLrGjPzERQPKm+9b18MFzWyhY9pT3HAf6Yhm+fP9m5lUH+NFHzqSpTIJISmFbX5xousAXrl7I/zzTTjxjAvCtx3bys4+fxZltY4W0PbHMRADJm4ZTeXzj4UodQ2k29cQkhEQcl91DaVosKHTFKbt1PoqikFo7gNKbpj+slrp7QhRN12iazb2Jg7Rn2N6f4JWOEYZSORbXhSZCSFS3jhZyEbqqBc1vkB9IM/SjjVR9bDnutvDJPgQhZi1zOMPwnZvxLKug/F0LsfMm2e4kD2wZ5edvdPPh81rpj2fZPpCcsm7HUEpCSGYwx7SIP7aH9Ov9KF6d4CVN+M+phXIPjw/FcXeO8Hf3byJbGAuK8Ls0/uTy+ewcTFIT9tAbzXD3q3u465W9vOvMpinbf37nEP3xLE3l/pN9aGI6cxys8dtcS3GwzAKafnwhu0IIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCDEdFDWEBLgf+LTjOI8riqIAfwa8Ciwt8n5OOclsge39SYZTOZrL/cyrDqCpCgDr9kapD9expC7Ehu7YpPXK/dMv1MMczmD2pEn07JncPpSREJJZwjEtMhsG92uA7PZR7IJF1ceXo/iOriijazTNjv4khqayoDZAdfDwhfLpvMnGrhjtQ0kqA26WN0aoDe1bJ5rOs6Erxkg6z6KaIAGPzqaeOL9+fS/xrDlle/e80T0lhMSljZ1/e4bTzK8OsOOAQraAp9iXVyEOz86YJJ/txhzMkFk7NNGe2xXF3RI65Hrq+GfJ/hRAVRQqg26qAm4Gk7lJ75cH3ADsHEjyRueohJCUiKooVAXc9MWyEwEkAHnL5ofP7WZVcxm6plId9KAqYO+XQxJ06xQse9I6QhyP6pCbmp4Y+Y4E+Y59AQ36aA7n4toS9kyI4vJ7dForfOwenpyzGfEZ/G5tNx88t5XvPbOLl3aNUBFw0TOaxacqzHmii0Lv5IDI7I7RQ4aQ9MeybB9IYNkOC2qC1Ee8J+yYhJhJ8qbFzoEk3dEMNUEP82sCeMeD1QqDaexUgfTLfaRf7ptY58zL6/g5EPYaZAs2jWXeKUF7lfJdxLRlWjY7B5LsHU1TGXAzvyZIwD35edsczZFeMxYO6mRM4g91AFB4WxvfeqmdlY3hiQASgIYyL/XhsXvkX7++lx39SV7vHOXstvJJ3x28aXlDGL9bnvHFARwHa/xx2lYdCtkcWkBCSIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEDNfsaclP8txnMcBnDHfAG4u8j5OOfFMgW89voO3//cL3H7n69zwn8/y5LYBAPpiWUZSeSzL4lOXzCGyX7jDjSvqaCmffsVqinbwYafoxR6OohQKfSlG79+Fs3+1+zjVZ4AyNfDgcLb0xrnlOy/wkR+/yvt/8DK33/Eaew4o+jzQ79f18K0ndtAdzfLI5n7++rcbGEyMhSgMJXN8+b5NfOCHr/DyrhF+8Pxufr2miz+56w16YtlJxUxntpbx+asWsqQuxCsdIySzBXYPpfi3R7ejayp+l8bDm/p479nNhPYLHbn19AbcMp7FyaaCcpDwG8WlHXKVgmUT8RosqAlMan/f2c0srAlS5jX4y2sX4jHGxrOiwJ9cNg+vsW98j6YLRToAcbQW14WYV+Ujms5Pea83niVnWgDUhd385TWLeDNvxqWpfPrSedzzRjcAS+qCuDSVWGbqdoQ4UtVBFz7/1KJLxaszki3QGz38Z7cQM0VbhZ8vXL1w0j3ju89sIujRCboNGsu8LGsI0VrpY+9Imu5omu0DSSzX1HvDQ31Gdwyl+NCPXuYDP3iFD//oVW777gts708cdFkhTiW27XD/ul5u+M/nuP3O13nbfz3PT1/aQ64wds+jqAd5BtMUVE3h9gvbWFof4t43uvnzqxbg3+/8e/85zSysDZ6swxBH6fEt/RO/81u+8wLffaqdZG5fAJ+VKmAOZg76ndJItsBHL2hjIL4vVLE+7OEj57eyczDJ+q4YX75vE2e1ldNY5mV7f5KGiJfLFlVPLF8VdHP7RXMo97tP7IGKmcexsceHnaVCIZctbX+EEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCiSIoyjaeiKH/hOM4/O44TVxTlnY7j/Gq/tz8CfKkY+zlV2HkLRVdRxquFt/Un+P6zHRPvFyyHL/5mA0s/E2IomeXKJTUEvQY7BpJ887aVmJaDS1MYzRSAowt8OFLpvInjcEwzwSo+Hc+icrJbRybaPEvKUYMyW+hMZ8XzDN25GWskS/iaVhRDxXlztmEVAuc3TIzrI9qe7XDni50TASIA67pivNA+RHNFM445tu03i412DiR4bfcoYa+Brip8+4mdNJf7+NB5LewaTFAVdLN2T5R71/ZQE3Lj0lV+/sJuPnPZPHKmzcbuODef1sBT2waZVx1gWUOYf31k28S+/+XW5Ty2ZYCHN/cT8up89rL5KDiAwzfeuZJdQykKtkOhYFET9GBaNvohQnfEzHfgtbrUVLdO4Lw6Rn+ZgPEMIMWrY9T6D7nOE1sG+OOfv84nL5rLhfOr2DuS5solNVy0oAqfWyfic/G7tT186brFlPkM8qbNfW/0cP68CgB0VaG53EcyWyDgkWv4yZYzLS5dXEPYa3D/+t5J7121pAZ7PAyqO5phIJ7h67euIOTVaSv3kslY7B6uJuDR0VWFj935Gv/+rtO46bSGUhyKmAXyBRt/S5iEuxcnN1YMjgL+s+soNyyGUnnqIr7SdvIwHMfBKdgohopylIFp4tSRzVtYjk21383Xbl1OfzxL2Osib9oMJ3N86bpFtFX6Wbsnyv/9zQYuXlDJ+89pxa8r+GsjJH+6BSc/fv/q1tDK3Di2M+Ve4vEt/WztS0687opm+c2aLr547eKTerzi1GUXLBRVOWSAaKnsHk7xV7/bwP55l199aAvnzatgaX0Yxa1h1Psp9KQm3vefV09Nc4iHXtrNNUtr+fD5rbzQPszX37GCWLpAQ5kHVYGqgKcERyQOx7EdhmNZvvaHrZj7/dK//eROLltUzektZQBkt40Qf2wP/rNrST7XM7GcWuNDqfHxzXvW88FzW3l25xAAt180h1ja5D8e38lnLpvHV25exhd+vZ6qgJsL5lfy0MZe3n9OE9csrSVbsFhUF2RZQ/jkHryYERzHwZoIIXEo5HKHX0EIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFmiKKEkADvBv55/OcvAvuHkFyDhJAcEXM0S2bDEOm1AxgNAQLn1uOqDzCUmPpH7IPJHPGsiaoq7B5KURv2sGckw/aBFA9t6KU84OI9ZzXjNw4+u/axyhRMXtg5zB0v7MZ2HN59VjMXz68i6D3y4nNrNIvqNwhd0Yw5kkUv92CO5rDiefSwFP7MZOZIBmtkbObXxLPdBC9vxk7kUdwankXluBqPbnbpbMHk9c6RKe2JRI7M1hESz3aBA8ELGxksN3jX917itOYI0XSB1ztHAdgzkuZrD23lfz+4GoD++Fj/zplTwRNbBwBw7Vdc98PnOvj8VQupDrn5wq/XAxBw63z8wjbchsbDm/sBCHsNDF3lD5v6GE3luWVVA6ZlUxv28P3nOvj+cx3cenoDt184h+aKQ4dAiJnHHMmQXjdIZv0QrtYQ/rPqcNWV/ndspwuk3xggfE0r5nAWxaWh+nQye2J45kamLN8fz/LXv9uIZcN3nmon4jOoC3toiHipCY1dixM5k5YKP09vH+T5nUPMqw5w6+mNDCfyLG8Ic8uqBnKmRSJrSghJCTy2ZYCvPbSVSxZW8cVrF3H3q3vJmzZvP72BlgofmYJN0At9sQynt5Tz3M5BPrywDs/z/YS7U/yf+WE2RnQ+ft9GAL73zC6uWFxzTAFjQgyn89T35ghe0oidKuDkbfQKD+nX+/CfXY1pWqXu4iEVBtOkXu8nt20U98Iy/GfUYFRN38AUcfLlTZuXO4a584XdxLMmFy+o4o09o3RFMwwmcgwl89yyqp4rF9cyks5x77oevnDVQjoGEkSGstRsi5HLWoSvbcMpWFjxPKrfIPZgB+7WMHrYPWl/a/dEp/ThlV0jEnAnTjgrlSe7ZZTkSz1oYTfBixpxNQenTTjTaLpA9s2Qy3G2A8PJPADZnVHcbWE8i8qxYjn0ci9mLMeDr3cznMyTNS26oxm+/2wHHzinGa+hc9+6biI+F7qmcd7cylIcljiIfHeC5Iu9WD1JvruoiQfzWb71SufE+0PJse/JHNMi9WIv1kgWczBD6KoWzOEsWqWHjQGNn6/tYiiZ5/XOUT57+TzuWdNNU5mXhzb2AeDWVUzL4Z1nNNIby3LXK3uoC3uZXxPkgnkVLKqT8BFxGI6DNf61q61CIZctbX+EEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCiSIpVwaQc4ueDvRYH4ZgW8Sf2EHuwg0JPivSr/Qz9YCOF4QzN5T4OrPlZVBukOuTGpams3TuKR1MxbZtfvraXRM6kczjN1/+wlSkrHqf1e6Js60swtzpAU7mP7tHMRNjDkVIMjfTr/cSf3EtuZ5T4k3tJv9GPUuTAFHHy7f87tFMF4n/YTWrNAN5llbibQ1NmeX8rfrfBDSvqJ+9DgZuqwgz/eBP59hj5XTGG79iE3p1iNJ1nSV1oypgsWA4jqbHCtMYyL4oyVrRUOx5688z2Qd51ZhMAPbEsX394K6qiYI3PtvypS+byw+c72D6QRB8/hved1cI/PriFVzpGqA65cYDBZJ7dw2muWlJL3rT5yUt7+M5T7RTMyYVyYuay8xbRBzuIP9xJoTdF6sVehn+8EXN0GhQb6Sp23ib20G4ym4dJvdZH/JFOMorCM9sH+KcHN3Pni7tpH0wCkMqZDCb3hVxF0wW29CYYHj9XAAIujSe29vP4lgGyBZuN3XH+5eFthH0Gc6v9fPWhLQwkclQEXCf9cAVs7okB8NS2Qf7j8R1cMK+Sj57fwtVLavC5VDb1xPnHBzbTOZyhfTDFLfOr8T+4G/W1AQq9Kcxneli2Pso7l9UBY4FL2lFep4V4U8Cto7g04g93knqtn8zmYWIP7cbK20TzJlXB6Rk0ZyXzjNy9jeRTXRR6UySf6mLk7m1YyfxbryxOGZt6YmzqjlMzXpiuKgpntpWzdyTD0PhYCXkMHBxyBYfrl9dx37oebqyJUPVgJ/aOKPm9CaL3tuOYDukNQ8Qf7kTRVRR96nX3ssXVU9puWFkvASTihEuvHWT019spdCXJbhpm8HvrKXQnS92tCXVhD5UH3Hd6DJXGMi8Aml8n+XwPiWe6ye2IEn+sk3Rfip5klld3j/J/frGOxjIfC2oCRHwG5QEXLRUBGst8rOkcZXt/ohSHJQ5QGEwz+L8bSb/WT6Enhf+5Xm4paCyvDwGgqQpN5eNhYYoKobExkd02SvzRTnI7RsmZNkmvykgqPzE+dFXlC1cvJJYpTNyX+N0aqgJdoxnuW9dDOm/RPpjkqw9tJZopnPyDFzOK7Tgsy+X5+8FhUGwJIRFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQswaxapicg7x88Fei4MwR3KkX+uf1GanCpj9aebXBPmPd68i5NUBmFvl51/esZIynwsHhWuX16GpCn/YNHl9x4HO4fQR7T+bLpCK5nCcw/+6umNZ/uvJnfzo+d3c9cpevvaHrfTEMljWkYcs6NVevKdVgeVgxfNgOQQubsSo9B7xNsT049gOikfDf07tpHbvykr04/jd3nxaA9cuG9umrir87Q2LUTcOT1lOXT/E8oYwqZxJxGdMeb8mNFZktLg2yN9cv4StvXGuWVqLS1N5rXOUPcNpvnTdIv7hpqX813tXsbguyLvPbKKl3EdvLEs8Y/Lk1gFuOb0Bn0tjKJXDpal89MxmPrSqibte2cNPXurkO0+1c+/abj50XisAv369i/6EFKLMFuZwhuwB48+K5SkMHNm19kRSXRrqBfWggp0s4GQt1KDBaIWbD/7wVb73TAd/e+8mPvSDl9kznKIm5OGi+ZNne1cVaKv0T7xO5S3W7IlOWiadtxhK5ij3ufjMZfNpLPPi0iVEqhSuXDJ2bawKuPmTy+dj2ja7htJs6o3TMZThIz9+le8/28H/e2ALv3ptL9UFBYZzk7Zh7o7ztpYKFAU+felcPBIIJo6R29BIV3tRgi6crIWdLIxdj86rY26ZD7NglbqLB2UOZaYU2Be6k5hDmRL1SExH3dEM33h0Gz99qZOfvtTJNx7dht+lc97cCgDcukpzuY+Q1+CF9kFaKnz0xbPUjObhgMekzPpBXI1BACLXtaH5pwZ5nT+vkg+f24qmKigK3LKqnmuW1pzw4xSnNiuZJ/l01wGNDvmu6RNCUh/x8t/vO4P68TDJyoCL777/DFrLfSSjWZyGAGrAANMe+65BgcEV5TzZPnb/PpjIkcgWeNfqJvwug689tJVfvraXHzzXwQ+f301vTK7900GhP42TMSe16euG+cJZrVQGXHz7vauYW+lnMJEjlTfRz6yBNwOdHLALNtmmAJqqsKguxJ9esQDbcfjmo9v5ygObGU0XOL05Qm3IQyJjoqoKD2/qm7Q/y3bong5Bk2JasxyLLw2P8vZkinlmHjMrY0YIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCDE76EXazkpFUeKAAnjHf2b89fSc9ny6UZWxf9bkEBBFU3DpKjeurOe0pgjxTIHasIeKgBsYC2Uo97nIWzY1QTcjqcmztgc8h/8VW5bNno3DvHTfLtKxPEvOr2fZxQ0Eyw/+a2sfTPL+c1rwuTQsx8GlaTy7Y4BbVjXiPcK6Zc1r4D+nDqPejx0voJW5cbeFUXSZ2XumshJ5ki/3kny6C8/SCiLvmI+TNtGrfbgaAqiuYy9qb67w8Y3bVvK5K+ZjqCrNFT4S/e1TF3Rr5GM296/r5WMXtPGNR7ZPvHXtsloW1gXZPZSiL57l7LYyVjSeQX88yz/ctJSeWAaPrnJ6dYgKl07BdNi9fojLQkFuubaG53tHAdjUE6et0s9nLp1LmcfFf168iP7n+uh6KcqXVtfzWluevEulKujG59L4wtULeW7HIG4Z27OGoipj8V0HFBQr2vT4Haf8GuW3LsAZSIOuotb4yORtHn7nGRSATsfipa5RNvXEuXZ5HX9z4xL+8YEtPLVtkNqQh6/cvIxFtWOF0TnTQlMVvIZG5oDwAF1V+eHzu6kKuvm321aW4EgFwLlzKvjc5fPxuzW+8ch2cubYwHTrrazrGuUvrl5IpmChayqD8SzOIYZpedDN129dwdltFSex92K2KZg2XZbJkhvbMAYyULDRqn0MZiyevWMbdfPCuK5qpqIxgNs7NSysVA51/Z4u13UxPTyxdYDCfs9pBcvh5Y4RLl9UTXXQzXnzKugYStM+kOSShTV0DKVQAOdg94CGint+BPfcCGrYTXwojW1DsMKDNj7uakIevnj9It53bjOOA83lXjxGsR7dhTgEVUExpo5Z5c1wh2ni9KYIv/3gWWRMC2/EjStt88I97Wx7sY8zLmlg3vVzsIYyYNo4dX7u3NXHZy+fj22PncMhj0GZz8WX79s8absjqTy7h9JcvKAURyX2p6hTx5yiKdSkHH598yrUajdff3gbG/ZG+a/z52E+003o8hYc2ybv0ngxl+WRNZ3kCzbdsSx9sSxrOke5bXUTVUE35X4X//rwVv7PFfOxHYdXO0aoDLjpi08OkHgzCFiIQzFtk7ZCAYAG2yQvISRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQohZoih/Ue84zrEnDAgA9HIPwUuaSDy+Z19btRe9xj/xuqncN2W9TN5ke3+SlnIP7z+nhb+9bxPWeHFNS4WP5fWhw+63Z1uUh767AWe8pm7Nw53YtsN5t8w9aOHHvOoA33hkO12jYzMEG5rC379tKQ7OlGUPpTCQZvgHG3EKNoqh4hRsjPoAVR9fhuqbPkWp4shltw6TeGxs7GbWDpJZO0j5BxbjXVRelO37XDqLaveNZf/p1aRf64fxsY4Kg21B3lZn8KvXunhh5xD/9q6VDCXyzKsOsLwhTMdgiu8/u4tHN/fTXO7jS9ctZk5VgNHeNFWKRVPIx0s/2cGyixp449E9ZJNjhSS+MhdXfXAR/6m04zhgOw6mDcu8Xp65cyNvDv2OJ3u46JpmfjgyzE9f6gRAUeBrb1+Oc0BghZi59AovgXPrST7fM9FmNAbQa7wl7NU+4V0J4o/uGZsJ3HbABv2KZqKmw8v37mLReXWEIupEMM786iD/9b7TGYjn8Ls0qkNjAVQdQym++cg2fC6NT1zUxrce3zmxj/PmVhAaDxAYTOToHE5zwfyTf6wCKoNuPnv5fL764JaJABKAsNfg8kW1/PPD2ybaljeESa+oI9gcRN2TmGjXV1byg0093HBGA25DbufEsQu4dSoyNtmfbyOrMhauZzp4T6/BGzDo3DDMaG+KC9+1gGC5h4qGQKm7DIBW6cGztILspuGJNs+SCrRKyVEU+ySz5pS2bMFiJJUl6DX498d2sL0/CYwN/f949yo+fF4Lu1WVpS4VJ7/vGu1bVkXsod2Qs1CDBr2LK3jlsS6WXtzAqquaCZaNjT23rjG/OnhSjk8IAM1nELqqlZG7tk60KV4dV9P0GYeJkSxrHu5k07M9aLrClR9dyuYNQ2x5vhe3X6dGgegvto2FBo5/Dn3wHfO49v51JHJjoXoRn8F/vWcV6fzU89q05cFtOjDq/GgVHqzhfYEO2uoaXnmym1Q0x6UfXcyjm/r5yU3Lyd25FWyHeFcSNAV9TpjfkyZm2qiKwpmt5bzYPsT/uXIBd7ywm+5ohovnV/GVW5azrS9BRcDFY1sG+Oj5bXz94a0T342taAyztD5cov8DYqYoWHnSqoLfcqiwbLL5dKm7JIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEUh03pOE4qqEDivDqPOT3bbCEZdAM+CMvSIG4CdA0ke29LPjv4EVyyp4Zy2Csr8LlyaQjxbwMaLadn80y3L6I5mCLh0FtQGmV9z+ALPwT3xiSKLN215voeVlzURKHNPXjaRJZ4uTASQwNgs4Pes6ebmVQ1HfKzmcAanMFbc8+Z/Cz1JzGgOl4SQzDiOZZN6tX9Ke2bDEL6llce83V2DSZ7cNsjG7hiXLari3DmVVAbHxqSrOUTVH60gtXGIaLpAb7WbLz69naFUnisW13DF4hruW9fNlUtqWFgTYDCR4Y4XdvPwpn4ay7x8+pK5RPvS1Odgw4+3cdoVzTx57xb8ZW6Gu5MTASQA6dE8/VtG+dd3rOCBDb1EPAZbuqKcnlY5MHun8+V+2lYFeebN/zcO/PtjOzizpYxqpJh5NlB0leAlTbhaQmR3jOJqCOKZH0EPut965RPMShXIvj4w9sLcNzhdsTzxjMmqq5pZ93gX1310MTuy+8a436XTVrnvdiBnWnzzkW3cv74XRYGv3LSUf7plGb2xLGGvQX3Ew5be+MTyytS8KnESaapCpmBNarMch5++sHtS24buGBuHM8y7oJaqkXKM/jQ0BXk4meKCimrOai1OaJQ4dXldKq6hLBkAm4mgMHv7CHMWlfHGYIb4UJbYUIada/q58J0LcE+D+z7NaxC5cQ65pRXkOmK4W8O454TRvKXvm5g+blhRxyObJ9/vnj+vgsV1IXb0JycCSGBs6P/zw9v40yvnk3Qgf9t8wl0pnJSJq9JLeu0AjIch2IkCYbeObTtseLKLSLWXFZc2TWwrkzd5rXOUB9b3UhVwc/WyWpY1SFG8OHE8i8qo/NgyMpuG0cJuPIvKMPYLRi21ra/0sfHpboD/z959R8lx1fnff1dV5zShJ+cclLOsLFmWZVnOCQfAJJPTEtbsLjzsLuwuLD8WWNISTDBgY5yjLMuSlXPWKEzOOfR0jlX1/NFyy+MRBoNkyfZ9neNz1NW3bt3quV2hj7+fIhHT0TSd5v3J72ZhRRpy2zgqTDgPSW1e3A4z/mgyHGA8FGdnyyj3LirjWy+eC1wxyBJ1uZdP4Mq7mSHDQtYHphI6NUq4J0Ai28aJVi++keRvUd7eIF9dUQWj4XPBpACqTqJ5nDuuK2HQAP/27CkcFgN3LSzlG8+dSgX2bm0aRpbhh3fNIRRL8KO7Z/PH/d185Zo6dB0K0i3MLc2gIP3yCJoULl+xRJw4yRvibFUlHA5e4hEJgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiBcGCKE5DKi2E3YpmVhmzYxuKF7LMS9v9pH73jyKbCPH+7lK+vq+NjyCkaCceaVZdLjCfGN504zvTCNWcXpHO4c59sbz/DKl1ZRknn+P3MsmkAxKbiyrFTNzUFWJHwjYYa7AxiM8qT2cVVjwBeZtLzPGyEcU7GZ/rrpJFsnt5NMMpJZ+avWFy4fajBGfCiEIdtKrMs/4T1jju1v7rd/PMx9Dx6kdThZwPHkkV4+sbKCL6ypxajISLKEucSFscjJzhP9fPbhI6l10ywGvOEYmgb//GQDFVl2vnR1DUe7PQB8dkUlru4I4c4ALXhJxDR0HRJxDUeGJVXY9FqhoTCUWVg/PR/Jl0DrTmBIP/e+rEhUz8vFXewg2wKb20boGU/2M+SPYlAmf5+Ety/FacI2IxvbjOxLPZQJJJOMwW1BHZt4nDZkmMnNtxENqSy7sxoloeNLTH7y+6sGvVGeP9EPJIN0/uWpk0wtcPHxFRU8fbQPh8XAsD8KgM2kUJF1+RSmvlstrc7iof1dqVAxVYPRQHRSu35fmPufOE5hupVphS6WShZqKt0sqnS/xSMW3olGAnHSzhMqomSYcZe7mG0x0HRgAF3VSUQ1Ap7oZRFCAmBIt2CYY8E+J/dSD0W4TOU6zfzTujqeO548P66blsfTR/uwGBUSmj6p/aAvwul+Pz/f3sZ3rq5nXr4TuyThffj0xBA7CRKvCfM6s2eAKUsLMBiT90Xbm0b42O8Ppd5/YFc7j318EVMKRBCJcHHIZgOW6gws1RmXeiiTDI6FaH1d+KUaV7G6TOiaTkFdBnJ/EPomhgBEnUbGQrEJyzpHgxRn2vjkykpePj2I227m+pkFuB2mi74fwl9Hcpnx5zvYu3eQTFXHVehgTp6d5oODSLJEts2EXYXX/0Il240U5NiQVQ0NnT2tIyyrzkoFkLxqy5lhhvxRyrPsZDstTC1IIxBN4LabxP278FdLaHEsZ2/CXJpGOCpCSARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBeGcQ/2f928Cpfl8qgORV/7u5md7xMA6zgZ9vbSHTliyWOdHr5Xd7O9nZMkKey4rd9OeDPdS4hrvQTvX8HI5v6ebgCx2M9ARYcmsVZvvkoJBYQqM4c3KwxPoZ+bgd5r96f+Q0M5ZpEwueXWtKMWRa/uo+hEsr4YkQaffifb6dkZ+dwJhrR7Kcm2uy04h16t9e1N446E8FkLzqlzva6R4LpV6f7vfxyMFuYgmVn94zh89dVc13b5/BdTPzebFhkC2NQ6iaTvNQgM88fJT3LyoHoMxgouWlHuxp5lTgiGJIVn8Od/rIr0pPbcNsMzDjyiJq5+cyJ9PJrLw0IruGGDwzDoAjw4wkwaKbKxnq8rH7sRa6/9TG16YUM/fsU+pvmV1IXpqY28LFJxsVnMsKwXDu1K64LZBp4ZUHGzn4fAfRQBxXpuUN56TNpJDnmvj+yT4f4+E4iyvd3DSzEF84zrppefzj2lraR4OMBqJE4gn2t4/ywM52njnaO+H7Klxc4WiC/7hpGsuqs5hXmkFdroP10/MntDHIEoqcPNb1joc51OnhzIAfmwgAEy4Ql9UIWRYMrw0hUyTsy4vY+sdmGnb0MntNCWnZVjz9IUznCaUThMuV02rkp9tayXKYyXKY+cHmZg53jROKqWTYjMjSxPa3zClkWoGL/7x5GjPyXGiazomDg9iXFExoZ19cgC8YT73OKXWinC1+D0Ti/GBz04T2oZjKvvaxi7OTgnCZax8LYcm2TlimqjoLri+nfkkBB57vwDo7G+k1v4EoLhOOynR84YkBfEurs/jlznZ+v6+T8iwHAA/uacdmFuemy8VAp48tvz3FnKtL6G/1cfCFDk5s7WHKknwUo0S52YDU7MFcOTGUSV5Twj+8cBKjIvPNG6dx7+Iy0q2TQ8+yHWZsr5krdrOBXJdFBJAIb0pcTWA7G0Li0DQiUXEPLAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjCO4OosLjM6AkNNRhHthiQzxYGa+d5unZC1dE0nRF/lPUzC8lxmZlTks7hrnEAZAm+tLbmjcNBdJ2gN8ahDZ2pRWN9QY681ElWkR17+sQi9PFQnFyXmS+vreWhfV2Mh2JcP7OAZdVZb2ofE0MhJJLBI7qqIRlkwmfGsE7PwpAuwhoud7G+ACO/bsBxRQGhw0MA+DZ34VhaCBIY3FZMxQ6MWZMDa/6SaFzlWM84/eOvf54xqJqOpoMWUxkcCvDfL57hlZYRAKxGhX9YU8MXHz3O51ZXs6t1ZOKYVQ2rSUGRJdRAstCzv8VLyTQ3J7f30tPooXZhHo37Bgj7Y9Qtyqfr1Ciz15RweGMnx7f0YLEbWfGBOjw9yXCUIy91MeuqEtJzrZze3Y+nP1lskohptLzQzftvK6O2MI2PrajAZBBF/gJoURUtkkCxG5EMF764TYupBA4M4lpVjK7pSBJokQTqeATFKBMLJ9j7TDvrPzmddNvkQrxXZTnN/PuN0/jo7w7y6ulnTkkGx3u8PHqwhykFLm6cWcBzx/vZ0HCKr62vR9N1XmwY5POPHE31U5/n5Jf3zqMw480fC4Q3ZyQYo2nQT/zsse65E/2894pSdGDTqUGKMq3cs6CEX+3qQJJgVaWbj8wrQbYZ6RkNMbMo/VLvgvAOkGEzovSFUKrSsc3IQtdBkiWCe/uonunm9IEhdj7WwvpPzmDmVUU4L9PwOV3T0QIxMMoo5ylaFt6d6vNd3DW/hJ9ua00tsxhl8tPMvHBigG/cOI1f7GhjwBfhplmFTMl38sPNLVxflQVpdnY80sSshXkkRsO4ri5FTyTvgaJtXsqnZXEAMNsNTF1eiHQ20UTTIZrQJo0lElffqt0W3qVUXwwMEsobXC9eCmPhOHKtE3PTONFQMlREkiRi4QQHnmtnyoJcQjt7cS4vTJ6DJNBiGnFfjE+tquQP+7owyBLvmVdMUYYVXdfxhRNsPDkAwH/cNI2STPul3EXhNbxDIaYuyic0GMJ8NvA0HlXZ90w7d39lDvFXeog2erDNyj7321KujVfiUd53RTn//twpmocClLptFC63sqA8k/1nQ5wkCb5+/RRyXZfntYjw9pFQY1jOhpA4NY3RWPgSj0gQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBOHCECEkl5H4UAj/1i7CJ8cwFTlwXVOOudhJUYaNNKsRb/jcU7LvuaKEwgwbHaNBnj/eh6bnU5njYEVtDglVw2xQ+OP+LlZUZ5P5Z4JIjBYDkUCc0ulucstcqAkdxSBxenc/sajG68tvnGYDHaNBrAaF2+cVYTXKOC1GEurk4rg3ovnjhBtGCTeMTlweSkD6m+pKeAtpMZXEaBj/lm40fxz9NX93Pari39wFQM5nZr1hAImu63R7wsQSGulWIyOBKKqm0zjoJ6Hp/ONjx/nY8gqynWaG/dHUevcsLKEopjP6h9NoHT7+rdTJXeum8smNpwjHVRp6vVRm21F1HZfVMOlp14Fogv++bTrphuT3wT8WQTFIVM3LofXwMOYZBq78YD2haIKMfDt1y/J54YfHiYUTWJ1GZl1VQjwQx2hWiEdVNFXn8MZO5q4ro7/Fm9pO5Zxs3IUObBYTS+pzcGeKAAYBol0+vBs6iPcHsE5x41xZjDHnws4NNRIn3hsgcmJiCI99eSHz15eha9DT6CEciJOwvPHpf0VtNk99agln+n0M+KJ0e0I8erAHgFN9PtZOyeVEb3LeZ7vM6Dp88/lTE/o4PeDnZJ9PhJBcRN5wjPFgHAmJMwN+VtXmIElwoMND86CfdJuB/71zFtubR0izGKnPd/GDlTUUHB9De7IDtcxJ2rwcejwhisTfSfg7nerzMTeYwL+7b8JyyaJQvLKE0weGKKxOJxHTAInhLh+Z+Q4U44UPZfpbJUbDBHb3ETw8hCHTQtq15ZjL01KhEMK7V+94mGyniY8ur2B70zB5aRZW1eZgkBVyXRYe2NnG7JIMshxmSjKt1NvM/Lw4F9NRD3JHlDXrytCMEtF9/URPeyb0nT4zi2s+Ng13oYP011wbuKxGPrmyki8+ejy1zKhIuCxGDneOUZJpI8spCuiFCyfhixI6PERgZy+yRcG1thxLXQay8dIHKg4PBSmKSvTGdGa+r4aRoRBumwl7monBsWSAZWV9BolNnfjafRPWta4tRdPhxlmFlLttbG0cYk5pBl9dX89LpwbxRxKsmZKLySATTaiYRYDkJTfsj1CYaSHaMEJiIMjy6gxYX8bGR1vwDYcxRlWiZ//uoaPDqfUsSwsYdIHFqNA8FABg3bQ8/v25U6yfns/SqppkQKlRIdN+eYXsCG9PmhZK/duhafTHJgfqCoIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgvB2JEJLLhBqOM/ZYE/EuPwDRVi8jv2og51Mz6RgJ8MmVlTT0eun2hLmiIpNIXGUsGMWsyLxvcRm9nnCqQPxVGTYjcU3/s9s0GBXSc630Ncnsf7YdAFmWWHJ7FWbr5KmRZjfy+71dHO/xsqjSja7r7G0b45Y5hayqy/2r91V2GECRQD03NlO5C8l0+RShChOp4QSBHT2o41Fi3f7UcsmkoMfOPY3dkG9DybCQUDV8kQROi4GxYBRNg7w0C4FIgscO9/Ddlxr57OoanjnWy8JyN5tODTKrOJ2TfclQg9/s7uDzV1XTNBigbTjADbMKubUqm7EHGtD8yTAeuWmcqZ4IH5xdxC8OdRNLaHxoSTndnhCfvbKabz5/OjWuRRVush1mHt7XhV2Wef/qIs5s6eH4lh7yq9JY+fGpvNw1yrPNvXx1/RQKM2ycOj5E7GyQyaw1Jex/th2by8SMK4s5tKEj1bfFaSQjz4ZnIET1vFyioXjq+6QYZNZ8fBpKnoWSTDuyKGJ+V4qPhBl5oAE9mvyuhA4PkRiL4L53Cor1whW/xXWw1GcS3DUxAMCYbePYn5oJ++PULcrHlmHmpDf4hn0ZFZkZRelYjApXf2/7pPeznGY+tbISq0khkdAY8kcYD8UntQu95vggXBj942EkSWIsGOUrTxynfzzK166rZ0llVqrNlDwnbruJqpx8DnWO8Yd9XbgdJq4rdZO/qQc1cPY42jhO2ngMw921l2p3hHeQWELDkD45+M46xY03rpJflYYr28rGXzQk35Dgqg9OoXZB3ls80vPTExq+V7oJHRwEIN4bOHstPgtTgeMSj0641IZ8Uf7zhTM4LAYWlGUyEojy9WdO8s2bpjKtwEXXWJgnj/Ryx9wicorTKDzjQz08jE7yOprBDux31hIvchDvCZzr2CCDSSG3LA1H2uTvz/SidL64poZtTcO4rEaWVLn50Sst3LWgmNo8F3aTQn2BE5fFhFER91LC3yd8YgTfix0AaIE4Y384TdZ907FUpl+yMUXiKr29fg79sZnhjuR9aI/VQPUdFewJhlg9JpORlwzv0VQdS72b0KHBCX1IGWZmGox85YkTfGBxKe+ZX8yJXi//u7mZhRVu8tIs/Hx7G26HiSsq3BSLEMlLLjQYwvBIY+r+KXZiBMUTYcmN5Xi6Amh9QSy1mQSGeyespxc5SHh8KKZzv2cZFJlYQuPxwxPb/uiu2Rd/R4R3PE2LwNnTr0PTicRCb7yCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjC24QIIblMqGPRVADJq/RwgsRIhLFQnP/acIbqHAf5aRYe2tdFXNW5d1EZiiIz4otQm+fAbJCJJrTU+vcuLiPX9cZPx46GErS95smxmqZzeGMX5TOzJrVVVQ2rSSGmamxrOreO3fzmppEuSaStKye4p4/EWARzVTrWaVnIZvHE4cuRrmrEuv34t3RjyLFhKncRPjpMcG8/rrWlBPcPkBgMYa5JJ/3aClr9EX79YjuhqEp1roMHdrZTk+Pg7itKAfi3Z0+xsjabl04O0NDrY019Ll1jIZZWZeE7G/oRTWh8+8VGKrLs3L+ujrVT8wg1ewj4J4Yc6MMR5s3I5BfA9bMK+NKfjhGOq0wrdPHltbXYzQYCkTiqpvPc8X4OdiafPB+IJbjtphJm5bhwZVtoDUaYV5fNPbkVuJ3JAlBnmhnFIGNxGPEOhVHjGv7RCN2nR7nixgp0ILvEQXaJk+wiB8/96BgZ+bZUAAmAmtDY/WgzTVNtFOc5uXN+Ma4LGDohvD0khkOpArpXxTp8qOPRCxpCYrYa0LKt2ObmEDo6jGwx4FiUDyaJWCj53Tqzp5/SWVlI0l8XiFOcYeOaqbm8ePJcMWeWw0QoqvLr3R18alUVLUMBgrEE75lfzB/2daXamRSZmlxRuH+hjAaiPHaohx9taeHaGfkc6x7nzICfMreNDJuJQDTBIzu7UXWda6bmctWUXD798BH+9bopGGQJXYeMmIYWmHgc1QZDOCMiLEb4+9Xlu9CGw7jWlhHY04cWiGOd5sZY6CA0HKa4PnPCORIdtj3USH6FC1fWpS/4TnijhA5PLFxH1YkPhUQIiYDdrGBQJMZDcV46dW6eWI0KX3z0OF+/YQozilwc7fbiHQ6hvub+CgBNJz4cxrG0kNCBQaKt4xiyrdgX5qNFVXz9wfOGkKiazo9eaWFuaQYjgSjfeC4ZspfQdDY2DDCtyMWQP0okrjK3NIPaPNdF/RyEdy4tnCC4t3/S8mjr+CUJIdF1nWPd43jDcZyDsVQACUAsnMBzcIQTjjjXX+Gm/+QYU5cVYLYoGDLNWGdmEz4xgmw34licj+Y00X5mgF/fNpPnWkf4t2dP8aEl5Wg67GkdTfVbYFBwWMRPZJcDw3h00v2T2hNA80SpzLdBOIEWTWCbn0voyDCySca+rJBuh4Fnt/Zz7Yx8DLJEQtPZ1TzCNdPyeOHEQKqvNKuR1pEAGxr6WVOfi0GEOAl/q7MhJHEdnJpGNBa51CMSBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEG4IESFxWVCMsmgSKDqE5bLZoVshxmTItM8FKB5KPnk7DvnFyPL0DoUwGUz0j0a5H/umMkjB7oZ8ke5YWYBUwsnF6FFQ3F8oxHQdOzpZsKvC3UACI5HiYUnFyQ3DQW5dU4RO1tG0M8O02yQWV49ObDkjehRFS0Yx1Tmwjojm8RYBD2uob9u34VLLzEaxr+jF9mWPFQkhkLYZmShFjuJdfvxvthO+g1VmEpdGNLNjEUTfOqXe2kdCvAPa2r4fy81UZRhZX6Fm6893cBNswoBmFaYxo+2tACgnp1MrzQOsX5GPr/Z3ZHafudYiFxXsiAzzHnmhwSySeabN02lacBHOJ6ctw29Php6fSwoy+Cr66cw6I/w613tfGFJBfmyAc0g8XzHCEqmmTuKcihk8nelsNjFFXdU0fByN4nYue/DUIefoQ4/eZVpTFteiHcohCxL3PKluQy0eyf14xsKU3yFm/984TRT8l0sfZPfF+HtTY1rnDfewSAjGS9w8FJcQ4uqaHEN59JCtJiKGkygmhOYbIbU8T7gi5KZY/qruvSEYlTnOshyWtjbNkp1joNZxekkNI1QTOU7Gxv50tW1PH64jx/cOQuX1cCjB3soc9v58tpa6vNFMfSFsr1pmP/acAaAbKeZMwPJYtyKLDuNg/4JATAbGga5qj6X0UCM77zUxOevqmFb4yBr5pZN7lgG5U2GiQnC+cgSENPQIwmsUzKRLQZiI2FI6OSXuvCc55ozHlGJBBO4LuGpMRyMERiLYNRAthknBfWIkDwBoDrHycdXVPL9l5tTy0oyrXSPhUloybCESFxj06lB6pwWZLsRzR+b0IfJaUTXdBS3BWdJMYnxCPHhEEig/Zngg5IMG1dUuCcEQGbYjMQSGuPhOAZZJp7QON7j5dljffzgrtnkON84hFIQzssgIaeZYTg8YbFkvzQBimcG/GxpHGJGYRr+kclF/f7+EMWzHdicRlqPDONyW1Cq0pAsBtRwHOfy5LUwkoQcSnDjYS/6wXHunpdDqCQTl9WIy2LAF0mk+vzsldVk2P66a2Th4jndP066xTD5HsogkZlnR2scI+GLoWRaiLaN41xSgGRWUMtdaP4Yn51Zgm5X+MaN03hofxc9njD3Li6lPs/FM8f6KM+ys6A8k+9taiKS0HjuM0vFPYvwt9OT53qvruDQNKJxEUIiCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCMI7g6g6vUwY3FbSri7Fu6Ejtcw6zY3uNmMKh/nKujo2NPTT742wuj6X6YUucp0W3E4T7cNBnj3eT+dokA8tKcdqUnhwTyfzyzJZUZOT6i8wHqHntIeuk2OEgzGK6zJxF9pBgtfmO+RVpOHInPwkbrtR4Tu72vjWLdM51uPFYpCpyHZgepNPjZVkifDxYawzskECySATafJgW5D7Zj824SLSVQ3f9h5C+wZwXV2aWu7b3IV1ahZpM7IwV6RjzLUhGZJzoKPPS/NggKocB6f6fABcNyOfB3a0E1c10m3JIrZBX4SiDCs9njAGWcZskOn3RvBH4nxkWTkvnRwk12Xh81dVM70wPdm3rpI5NRPjybFzg1yYhyHLygMbzrCmfvL8WVqdzVefbqDMbeN7a6bS9kQ7Q2eLi2+dn01JuvXP7r8sS0xfXEBWsYOIN0bTgcEJ35MZq4rY9XgLZ3b3UzUvh8x8OxbH5CK93LoMnu5OjvnMgE+EkLwDeIfDtB8bpv34CKVT3VTOziYtxzapXSKucmpXPyMt49RVpKG1nQupSbu6FEPmhS0Sli1GEkNhJA0wyMgGmciZMSy5NiJn572sSFjdFrxof1Wf46E4P9zSSn6ahRlF6XSOhtjQMMCnr6xKtYkkVJZWuSl12/ny1XV8cEk5NpOCw3xpilbfiXRd508Hu1OvwzGVdJuR8VCchKrR0Ds5AMllTX7+XWMhfre3g5tmFeK3K+TPyiZ69Fwxu3VxAVGDjCi5Ff5evkgcV0wlcnoM6zQ3AIpRIdQ4yrGYTlZZGrJBQkucO5m6sq04MiZfc75Veps8nNzZR9gfo3SKm+JVxYSfbUu9byxyYMy3X7LxCZcPWZZ4/6JSKrMdvHJmiJJMGwZF4ntnQ0ky7WbMBpnyLDtjsQSO5YX4nm9PrW/ItYHTSKAvhDGcAKcJQ6aVyOlRtDwHxvNcQwLYLQb+9Yap/HpXOy+fGqQm18mymiy++1ITn7myivFQHLfdRI8nRHmWg7bhoAghEf4mslEh7coShtu9qWBU2WlkXJbwnRmjqC7zLR1P44AfsyLjjyQoKZh8nZ0zLYNFC/I49mIXdYvyOfBcO8M1GaQf6sc2IxtkCdmoEDw8iLUuEy2YvBY27+jjvauL+OBLTXzx6lr6x8N4w3HWTstjQflbu4/C+Y34Y2BTcNdlEDvjSS03Ly4kcXoUc4aF0MFBjHk2rNOyQJLAbqSnw0cipmPsDREcj1I/J5sbp+QRU+DGWYW0DAVo6PPRNx7mm8+fTvXbNRoSISTC306PAuBXFbJklVgsdIkHJAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCcGFc8hASSZKuAX4AKMAvdV3/1uveXwk8DbxaxfWEruv//laO8a0gyRK2BfkYCx0khsIo6SaMRU50mxGTLGMxyNTnuZhemEa61Uh+mgWjQcEkS5Rl2blnYQnHur38ZGsr0USyuPwLayaGHYz2BNjzVCvlM7JwZbsIeKIEfVFW3F3L3qdaiQYTZBc7uOKmCszWc4VwajyOjo7NbGDtlHy+8sQJCtOtJFSdJVVuphe+uYINPa5irkrHv60bNFDcFhxLCtCjGqIK+vKh+mKEDg4CEDkzhmNZIYHdfaDqxLp8OFYUYSp0TFjHbFAA8ARjZDuTRcVGRSYcTz7H2BdOMLXAxfPH+/ni1TV8e0Mjv9/byf3X1PHSyQH2tY/xwcVlPPihBeS4zNhM5w5Ruknht8Y4111dhDOi4bXIPDXgIatP456FxVRmO/nlzjY0HcwGmbtmFLLM7cJYptIXjjG6czAVxADQc2CY6Qvy3/AzUAwyReXpJOIq139mJkde6iIe05i5ugiDUebM7n7Ssq1Y7EaObemmep6bFXdVseepDmLhBNnlLrTpaex4KVnkVJTx50NPhLeHSDDOtofPYDAp5FemM9Tpo+PECOs+Ph2rY+IBzNMfZMcjTRgMMjnrS8iqzYBgAmeFC0tpGpIsXdCxadEExjwbocNDhBtGQALrjGw0VcNsNyLLEktur+K5rhFml2X8VX3mOM2Uum10jobQ9XFunl3I6voc6vKcqafHp1kMLChPBg7IsiSKny8CSZKoznWypy0ZaPT00V7uW1bB/3upkfFIgtX1OeSlWTAoMqFogscO9xCOq7x3YQm/39fFoC/Kz7a3kX/9FMqmuJFybVhUHc1houHMGEVdfpxZF+b49Oo1g8EoTujvNoVmE4mmfsyVafi3nr3Gy7TgWldG6IVOErEEK++uY+9TrYR8MTLybay+dwpWh5F4JILR8tYeO4a7/Tz3w2Mk4snr5p7THla8p5rCu+owhuLIdiOmYieG9InjSsRUgt4YRouCzSnm+btJpt3M9TMLqM938uHfHqRzNFloXJ/vZEa2k6HREJ9cVoHFHyN8ZAjX2lL0iIpklNHCCWIjEYIWA2ky+Dd3AaAUOjBMdZP+umvq1yrPsvPFq2tYVpXFs8f7eORAN59cWYUiQziucbDDQ1WOg77xCBbDmwuHFN7dIqE40WACq8OIyWrAVObC+b4pJAaCRKIqozGNXX9oAglu/uIccsveuqAGi1HGYTFyqNND0bQ8lr2/kpObBxjrDVI8003lFXmofRFaDg5RPiuLedeW0d7uZeHqUkIvdaLHVCSLguvacvwbOyf0ndXipSbXwdefOcnVU3L5xk3TyHWJ69dLSdc0ErEYRosFoyIzHkmQyLVTOMVNbCRC1CATSzehb+/BMCcH67Qswg0jxAdCGIudmK4uJd4WJeSPY083M9LjZ9fDTUxbV8pIkQlJkrCZDOxvH8UTik/YdpY4lwt/B+lsCEkgoYARiAcv7YAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAukEsaQiJJkgL8GFgD9AAHJEl6Rtf1U69rukPX9eve8gG+xRSrAaUqA6omFocPBqPIisTy2mw0TUfXdfrGw0QTKr5Igo7REJGERkW2nf+6eTr3P3GcG2cVsKjCPaEf32iEmVcWc/yVbkLeGMvvrGHXYy2YrApzrynFYFQYaB3HMxCisCaDeCxKd8NxDj73JJqmMuOaG2nsVfiHq2qIqRomRWZf+yjecOLN7aguEev241xVArqO5o8TOjaMfW7u3/sRCheQZFRQ0kyoY1FiXX60iIpzRRHGAgfmYieya3KxTnm2ndvnFvHooR6yHGayHCY6R0PU5jppHPTzm90d3Dm/mLVTcylMt/LHj17BSCBKmtXIDTPzkWWZTPvEfgORBDuah3nueB9XVLj50EuNhGMqaVYj37xxOkg6X370OBl2E19dX8/u1jHeV5tH9/Pd7N5yEqNZ4a5769i29fWHFYj4Yn/VZ2EwKpRMcVNYk874YIitDzeSXeQkPdfC3HXp6GoEo2GY1kOP4x8uZuVda/GOOIlqGs1nA1iWV2cxszj9zf8hhMuKdzhE6dQsGvcN0H50hKxiB3WL8vEOhVMhJMFxDwHPGLGIkfKZTjLzRjm248eYrHaq5q9DsWVit174069kkAk3ejDm2bFMcSNJEGkeR8u1sfDGckLeGCO9ATzEGA38+bk/5ItwotfLaCBGeZad790xk/984TQra3P5ydYWQjGVLIeJz19Vw9GucaYXpTG1QDw9/GK7fW4RTx3pxRdJMBKIsenkAL9431zaRoLYzEZO9g5xRWUWsgRfv24KvlCcpqEAX1iTPGebDTJqXKW300/IH6d5/wAAU5cV4h0O/8Xt+0aGCft9ODIysadPDrGJx6L0nGrg4LNPoGkq89bfTMm0mW95sIRw6aQ5zQz7Yqgj4XPXeIE4EW8UR7qZ5gNDxKP9VM/PxeY0UT0/h3h0mM2/+j19TaepXriE+qUrSc/Ne0vGO9juSwWQvOrI5m7iK4uYdWXxeYOiPANB9j7dRtvRYZxuCyvuqqW4PhP5AodKCZe3qhwn3751Ontax7CbFeZYbZx5roPwYJj02nQWXFdGYvsgvo2doEig6gDEryzBxyCjpk7cq/NxpGdizE4js8z9F7YIaVYTy2uyybSZGA3FiCY0/JE4r5wZYXpRGqWZNsbDcSqy/3yYiSC8Vn/rONsfbmKkJ0B+dTrL7qgmu9jJUCDGgW19eAaCoL+mfcv4WxpCMrUgjd0trdxTa2Lw4IucPrKfvJopXHHramLOHIozbOzZlbyWaT86Qs9pDwU16Zzq8qFcW0zQFwW7gbn+GFpwYuiEnmlhTXEa88szmVaQJgJILrHhznaObXqBvqYz1CxaRvHcJRwdUDn8dDsWh5H6RXk43VYyZBnJaiB0eAhzRRquq0rQdZBKnHT2BZEVmca9A8TCCarn5VJQnU7j1l6uv38OAIUZVr51yww++dBhVC05uT+6vILaXOel3H3hbU7Sk/fVwbgCVpBUEUIiCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCMI7wyUNIQEWAC26rrcBSJL0R+BGYHJawLvYFeWZPHW0j288ewpV1/nw0nI+sLiMpkE/f9jfzZ7W0VTbL66p4fvvmclvd3fS74uQn25NvWd1GNn+x6ZUMVHQFyWvMo2eMx52P96aald5Ngyk++QJnvz2v6WW954+yfs+eT93bmxOFW24rAZynOY3tT+mUifaFjX1BHBkiawPTUM2X+rpKLyW4jCSfl0lo787BTokhkJEGiWMc3LY1O3hoX2dZDvN3LOwlNkl6fSNR2ge8nPr3EKW12TTNhzge++ZhScQ4/a5Rfx0Wyu7W0c50DHGf9w8nSsq/nLBJcCWM4N89o9HAdjbNsZ75hUzrTCN/vEwBelm3v/rA/ijCfzRBN94/jRfWlFF/6beVFF9PKrSsrOfvKp0+ho9E/p2ZZ0rOBsfCtFxfIThLj/V83IJ+mNIikReqYvMfDsAsbDKiz9rYHw4TN3CTNRIG/se30t+VQ0ntrwEwFB7K+2H9zP/pn/k6Is+Kubl8MwnFlHotuN2vLnvinD50TQ4sqmL4Hjyiccj3QEOjXdwzcemA9DbeIrn//c7+EeGcWXnMu/6W9nyq5+k1u8+eZTbv/YtIO2Cj002KaStLmH4lydSBc9ymonRYILtz3cAsOC6cqaYbeT8mWLL0UCUf3riBJvPDKWW/fO6Oj68pILP/PEIibPH/pFAjJ9ua+UH75lJhtWIQZEv+P4IE00vSueJTy7mZJ8PWZKYWuBCQsdiVPj6Mw2srs/jOxsbU+3/+9bpHOgYY3/7WGrZN2+YiiTrnNzem1p2ZFMXy++sYfeTLRRUp5NfmY75NSE5uqbRduQgG3/6fcJ+H66cXNZ/+ksU1NZPGF9f42me+K+vp173nGrg5q/8KxWz512Mj0O4DPk1De+8bGxPtRHvP1uAmWFGr06n9chwql3Dtl7KZmRRVG/gmf/3dQJjyevY4c52hjvaWPPRz2B1XvyCYOm8uSESVoeRjoYRFEUmu8SJ1ZkMmIpHVXY91kJnQ3K8/pEIz//4OLf/0zyyi0UB87tNnsvKAzvb+dKiCg492pgKtOlvHGev3MncFYWwqTt1PjbVZxK2xdnzm+8S9vtS/dQtWcG8628lt7ziL27TbFSoynPQcKSX777URDShcffCEhZXZjIeinHttHz+7ZkGbphdxILyDKxGcW8lnJ93OMTzPz5ONJQME+1vHufFn51g9QemJH8r0PUJASTAeYOZLpbmQT+7Wob5QI3C4af+QOexw0DyPNFxeD83/Mt/0t8Wx55+7t4qHlXpPDHK1CuL6JQ0+nWV1cWZNPYEKLcZ0M7uq2RSaMq38PVnTiJJ8PgnFr9l+yVM5h0a5PH/+jpBT/J6dbiznZqONqbd/lH8RXZGe4Ic2dQNgC3NxI3XlRF6vp1om5domxdTVTqNAyGyq9PZ/nBTqt/GfQNMW1GII8OMw2JMLV9dn8Pzn1lK11gIt8NEba5zwvuC8GbJJENIwrHk/bCs/uVwR0EQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQhLeDS12ZVAh0v+Z1D7DwPO0WSZJ0DOgDvqTr+sm3YnBvNS2mEh8IonoiKC4zxnw7ssXAmX4/G473ce+SMiQdWgb9DHojdI2GJgSQAPx8exv/ccs09nd42NE0zJySjNR7IV9sQjGRrkmUTnXjH4vgHQojyRJTluQjK8kCo1PbNgMgSTJIyULk7j2buWv+Lfx+Xze1uU7uXFCMpr+uQukvMLqtZH9oGrHeAHpMw5hrw1ggntp9ObLUZJD9yZkkBkPIFgMRt5nnO0b58mPHU22eO97PHz6ykM/+8Qh94xEA6vMc/N/75lHqtqfazSnJYCQYxWkxkmk3/VXbD8cS/HF/N9++soYqSUGV4FA4Quuwn2um5tMxGmI8dO7J1maDTK3LRmNv74R+uk+Psfpj0wiMRfANJ+f6nLUlaJpOf+s4iZjKzsdaGOsNsuTWKl564CTxqJrs025g/WdnkV/qYnw4zPhQGKvTCNoAhzf8kQU33sahF56esL1YOATaKGCk/dAQS26qxCUCSN4RIoFYKoDkVWF/nLA/hn9shGe/961UEV1OWQXHX34BgNq5S6mfuhyjZsQcMqLHNSTjhQ/uMJW5yPnULOL9QVSgbyzCzqfaQIKquTl4BkNU1KbR44/y6MFu6vNdTMl3IZ8tLD3V55sQQALwo1da+I+bp6UCSF417I+yr91DXZ6T2vwLH6oiTFaV46QqJxl20D0W5FCnlyFfhLVT83lgZ/uEtj/f0c737pjFtzacYcgf4ar6XEozrEQ6PKy7uQKjqhMzyBw9NER/q5d4TOXIxi7WfHgqNfNzU/2M9fXw7Pf+CzWePNb6hgZ59vvf4u5vfhdbWjqKIXkpefK11wyArmsce+l5ymfNRTp/2oPwDtMxEuSTL5/mO9dWUyUpjMUT7A2GqYzFqF+cz5k9/eg6ZOTbyK9MY3ygJxVA8qrm/buZfc31FE+dftHHm1vuwmhWUud7gKnLChjpDmA0K6Q7DERCcaISGHJtJEyGVADJq3RNZ3wgJEJI3oXKsuz8+oPzGWsap+VsAAkkgxoKCx0YXWaUmyqxukwkVJ241UCo4zhhv2/CvVXjnh1klZTjyMjAnp7xBltMSrOauHdxOWum5BGKJXj6SC9fevQ446E4iizxj9fU8vHfHeJn75vL8prsi/kRCG9j3uFwKoDkVXmFDqz+GEowztU3V7LpyVbGBkIAGIwyBVXpb8nY+sbDfO6PR/jqynyGGk+kAkhe5R8dJjzSz2B7GgFPlMo52bQeTgZd5ZQ6kcvtnO714LIYCEVV7nvxFP++rIpaSSHDYqTHBJ/feJpcl5l/vWEq0wpcb8l+Cec32NaSuneSFQOamkDRDdiHQ1yzpgRPMM7e7X2MD4epXZDHsVYfs95bjz4WIRRViWVbafpdI7pJmdR3Z8MoS26rwvGasBqDIlOX76IuX/zdhQtDInmPFIkl56Cihi7lcARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEC6YSx1Ccr6q1NcnWhwGSnVdD0iSdC3wFFB93s4k6aPARwFKSkou4DAvPl3VCR4cxPtMa2qZ88piYvNzeGh/N6cGApwaCKTeW16Tg1GZ/PH5owkMZ4vJjcq5AvdIMIbyuoL3SCBGx/Fh5q4rRUJCMcp0NoxgsihoqorRauWKW+9Mjk/XMRhNeAb7Kcyw8alVVXSOBvn59lbWTs170/tryLRiyLS+6fXeqS7XuSsZZMzFLsiz8/yJfnZu7+F4rzf1viJL3Dq3iOeO9aUCSABODwR4+fQQH15anlpmtxiwW/78IScYSRCOq7gdJiRJonM0yJl+H1+eXUzOC13o4WShXIHbTOzGSm76+V7eM78El8WAL5JgRr6Lz04twjEcw55uTgVFFNZmUFidjkGHaz85g4g/xvhwmIZXerA6TOx9upXZV5cy1hsks8DOYIcPJKic7ULXYagrim8kjKfbj280wszVaWiJdoY7xwFQVRXFYEwW6EsSs9deh9XpRJIDLLgun/42EwbzhQ+buJxcrvP3YjCYFCQp+YD4FAmMZoXA6EiqiA5AjcdxZmVzxXW3kzmURXTHGKASY4CQ7MI+J3dS/38vSZIwFTgwFTjwjYYZbvWy6r11yAaZWCTBiVd6iJTb6I+p/L+XGjEqEr//8EIWVrgBGPJHJvXpiyRwWYyT9ttlNRCIJmgeCjBzPES208J4KIbTYsRinFwIeDl6O8/dztEQv9rZxlVT8lBkiWhCm/B+y1CAHJeZtVNzcViMHOv2UGAw4rQbiL2SzH8zA0uWFTJgUmg5kizgPfBcOyVTM7HYkk+F9w0PpQJIXhUYG6XlwB6a9u5izrobKJ05G5srjcW3342maujoKAYjvpEhEUBykVyOc1eWoNcb4cMvnuIr19Syu2uU6lwnqjdMuQxXfXAKmqojKRJHX+pk9prXzQ1JYvY11+MdHmT0pW6yS8rIqajEaLo4IV5ZRU6u+/RMGvf1E/LFKZvhxtMf4tjmbu763CwiTzUT9Cafbi/bjTjvrsPqNBL2T/w+mO2X+nbq7edynL9/i/llmTT6VVpes2zZulLcp0aIHxwgDkStBux31DA6EsZiM7PotrvQdf3svZWREy9vJBYKMD4yjNWVngoF+0sK0q3sahnhh6+cu3dMaDq/2d3BdTPy+dXOdhaUZWAxifl5Ib1T5q757Dn+VVXT3UyzKwQfaQQgLsG6m6o4eNqDrEhUz88lu+StCVvq7x/in6cD7cdxpGdQMWcBbYf3T2ijKAqubCuHXuxk2opCrv7wVDRNQzYrfH1vK7s7xrh9bhFmo0JhupUvbW5MrTuzKI0f3zWbokwrxZl23k0ux/mraRqV8xaSV1lNLBwmr7gKV7eL2CPtxACjBOturqI3qnJscw8BT5TSWdmcPDZCcX0m+nCEeEzFZJ5875GWbSW/Mv0t3yfh3UUh+TtVLHI2hESLvlFzQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCEt41LXZXUAxS/5nUR0PfaBrqu+17z7xckSfqJJElZuq6PvL4zXdd/DvwcYN68ea8PM7l0PN0QHoW0IrBnnbdJYjSM9/m2Ccv8r3TjqE6necg/ub2mUZBuw2yQJxQeX1GRicWgYDbIzCk99zTtWCSBy20lu8TJcJc/tWzhDRVsfvA00WDyf5yfurwAk92IrCjULFjMhh//D2F/8k+gGI1c/w//xP+3oZ3hQAy7SeGLV9ei6W/wUcfDIClgMP11n9W71GU7d4FAJM6xHi9f+NMx1k3LQzlbUO6yGviHq2poHQ5wrMc7ab2j3R6gfNJyAE3T0XSdmKox5IvQMx7h+5uacDtMrK7LIZJQeWhfFzZF4SduN4nwa57UPRrF2B1A0+GJwz18alUV33+5mY9OLaLxsXZMFoX515Wz96k2yma4kSSJ/c+1A2BzmVj9wXq2/v4MhdXptB8fIRHTUskK7kI7OaVG0rNDnNr+MCaLjSW33oMsj9DXEcGeZsfT8zLN+3dxxS3JgJ6mPTuZcdU1HHz2CWZdvZ7uk8cZ6e5MDfeaT34Zm/PiFFBfLi6b+asmIBEBs+PC9z3WDhEfRnMps9aUcOSlrtRbM1YWYbIqmC1OjGYL8WgyyKOr4TjrPv0F5DGIHhqb0N34062YS10Y3BcvjMlsM5Bb5uKV358hEdOQJJh/XTmmXDuP7+sAIK7q/GBzMz/OdRKOq2TYTJPOKwvKM3FaFO5bVsEvd7Sh6WA2yHxyZRW/2N7Gh5eWM+yP8qMtrWw6NcjM4jQ+t7qG6UVpF23fLpTLZu7+DVRd53ivjzK3jSvr8lha5eYD9VBkS/Bku4GHj3vxBKLML8vkS48d4xfXz0Bv9xPb0z+hn8SePkrfN4XTuweAZOjYa+PgrGnpk7ZttFjxj47Qc7qBntMNXPvZL1M6fRbP/eC/iYWTT/82mi3c+OWvXrT9f7e7HOdueZadq6fmkKlEuTbfxxXpEr8+4cFnMLCivpgtD54hHlWRJJhzTRlpOQayiktT58yZa9bRc6qBIxueSfV53efup3bxsos25vyqNIK+KEde6mLbQ03oms6sq4rROr1oZwNIALRgnPChQVbfW89zPz6e+o6UTnOTVXQRzjnvcJfj/P1bnQqFqZifQ9uBIUwWBTc6mudcAbIeTpA4Noyh1okimzny4nNEAsl7MYPRxNUf/xwHn3uC3rxZtJw5zZ0LiqnKSYY9RBMq3WNhZAmKM23nAibHuyHqp9RgpC7HxpmhUGp73WNhPrjYxZB/mK6xEDV5rrfuw3gXeKfM3Yw8GzOuLOL4lh4AqqvSUM8GlAGgQ2hjBwULCwjrOnnlb808ioZC9G95isbtm1LLZq1dT25FFYNtybifvMpqbPZ0uk4HmXFlEZFAnJceOAmA1WnkvlvKuGNBMSZZJRgI8P5FpfzPpiY8oTiSBLOK07GbZfyRxHnH8E52Oc7f9Nw8rA4nux75PQA3vv+fSJzwnWugQ+jFDmxXlWJLNzPzqmKi4ThVc3PZ8rvTZBc7mbq0AE3TScux4h0KA6AYZRZcV47NJX6HEi4umThxQI0kz9EmPYqu6yKIURAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCEt71LHUJyAKiWJKkc6AXuBO5+bQNJkvKAQV3XdUmSFgAyMPqWj/RvocahdTNsuB88HZBVA+u/B+VLJzXVwglQX1cHooMcTrCyJpvf7+ua8Na8kgy6PCH+985ZdHvCeMNx8tMsOC0GrCaZL6ypoX0kwBUV7rNrSJzY1svUZQUgQSKqklXsZMcjTakAEoCT2/sorsvElanQ13wmFUCS3J04DVs38c/r3kfraBhN0/nJ1haW1ZwnWCU8Dj0HYaQRDGbImwEFc0C51FNOeDNahvx89ckG5pVnArCtaZhPrqyi8aVG3ruwlO9takKRJW6ZU8SR7vEJ615Zm5P6d783zLbGYfa0jnD11Dw2NAywpCqLF473s6Aik//d3IzVqPDh6nL+8fET/MNV1ZwZCHDjlFwYDDHJWJgMu5HusTAP7GznM6ursA/HAYhFVI6+3MWctSU4Mi288rszqdVCvhiHN3ZRXJeJ1Wmkv81L9TwX9nQvaz/ipv3IbuLhLHY+/CvMdjszblvHK7/+MSaLlemr14Ik0bR3BwDB8TEKa6fQ23iK0e4ult71fkwWK0c3PjdhqDsefoCymTOwp2cgXESDJ6HvCARHILMiebxJL/r7+41HoOFx2PRVCI0RvqWZSCDOVR+cQsgfw+Yw0d86Tsgbx55uZumd72frg79E1zUKaus4/vIG5s1cD6gTutWjKlpEPf82LxDvcJidjzYng3ZIZu3sf66dVR+Zyram4VS73vEwv93TwU+3tvLDO2fx3dtn0OeN4I8kyE+zoEhgUGSicZXv3jGTUFQlP83CV59q4Mq6HKYXufjGc6c52OkB4OXTQxzq9PDT986lKMNKUYbtou7nu1H3WIhAJEG208z1MwuJRyP8bn4H0sZ/htAoXylZzBc++A1+1xXFE4zy7Run4fCqyHFtcmcJnXggjmcgCMDUpQVY7MbU21mFxSy98/3s/OODAEiSzIIbb+X4yy+m2pzZtY30vIJUAAlAPBqhae9OSqfPujgfgnDZGQvF+Oe5OqV7voH04F6yZ93Df02/Gp+xmid+2Uw8mjzm6Toc2tBBTuk0lr/3Q3SfPI53aIDs0nKOvfTChD63/OZnFNZPw5Fxcc6hkiQhIVExM5uSKW4MRhnvcAg1cJ6n2I+EySqyc9v98/AMBLE6jGQVO7G53tlBY8Ibc7nMuJbkYq9xYZcVjP0BXj971MEQ5mka7cePpQJIABLxGE37djL7mus4IZv55c42drSM8Iv3z8MoS/zvlhYeOdCFLEl8aGk5H19SQGbnRtjwjxD2UFSxkhdu+Dwv9Gbw6Q3Jc/CckgwybSaWVmfROBgQISTCeZksBuavLye/Kp3AWIQMl5Hg69pooQTuHCvNreMYzW/NPfxQd9eEABKAYy9t4NrPfonmfbvILq2geOoMgs3NHNsss+KuGrZtaUq1DfvjjO0cYt1tBsxD+5CHuuk2VeK4aj5tAQOKLHGse5wXTw5x46yCt2SfhDcWCQZo2LY59dqgGYDYhDZaKEF4LML0VUXEwgkMBhn/WAR0GO7yIysSZTOyWHB9BZqmARJZRQ6yCkVImHDxGSSVkCxDLBlCYpYSqPE4BpMIwBEEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAE4e3tkiZC6LqekCTp08BGQAF+pev6SUmSPn72/f8DbgM+IUlSAggDd+q6flk8tfUv6j0Cj98H0bNBHiNN8PiH4L4tkDaxON6QYUZxmVB95wouJLNCwmUi22VmSZWbXS2jmBSZO+YXoyhgNSo8dbSPp472pdb56vp6JF3nvzac4YtX16SWWx1G7Gkmtv6hMbXs6vumMtobJL8qjeL6TNSEjqxIxGMq0VAI3/DQpF3yjwxz6MwAT55IFrAvqnBjN51nGvUcgO3fge59ydd162HJ56F4wZv9FIVLJBJP8N2XmtjbPsbiqmTQTCimsrVxiPuvqcWoyPjOPkE6rmqsnZrLS6cGkSWJz6+uYkF5Jr2eEN1jIR473MNjh3q5/5pa/uGRY8wvz+DZY33sbh1ldmk6cVXnhpm5PHY4+TRu7ew3fHfXOL6ZpdheF0Sil7roPdwKwKxcFxW6gt2mpN4Pjsc4uKGTuetKJ+3XULuP6asKQQuy/PY0dj78SwxKHa2H9uHKymGstw2AaSvXsPfxPxIJ+Fl+zwd55Tc/54pb7kSSZXRN4/jLL1K3dCWLZ8wizZ1BfvgE/fG8SdsL+7x4h/3IBjtWhyhEuShGWpJBIXt+BIkouApgzTfAcQMY/s7PvGsvPPOpZNU8YJJCnN7Tz+nd/ROaVc3NofXAfo5teoFFt92FqibIq6xhx0O/Jqj5STfYIXHu1GXIs6GkX9z5EI+oGM0K01cUoao6ikGi6+QokWB8Qrs19bn8YW8X0YSGBjxzrJ+XTg2m3v/mTdOQgL1tYzx1tA9vOM7/3DGT98wvZnvzCJGYlgogeZUnFGd36wh/OtjNbz6wgLp8UQR9ITUO+un2hPm3G6ZwpHucL1QPIv3+46l5KnXtxrT1Gyxc+kNOd+jUKWZ84SABTcNtVtCj5wJwlDQTEV2noCaDotoMMl5XsGkwm5m5Zh22tHT8YyOk5eSy74k/4R8dSbWxOdPwDg1MGuf4QL94Evi7SSRA6d6vI3XvBXs2VK1B2vNDojVfIuRLBnXklrsoneZGTegoRoXuhjOYLDYKa6eixuOTugz7fajx2KTlF5KsSOx7pu3ca4PElJsq4MzE45pUnYFnKExGjp3cMnFME5I0XadhLMA3XjjFF6+uRc4wkf26NqbpGQyPtOAbGZ60ftAzxkh3F5QVM7c0g0OdHp473ofbbqKh14umJ7fx8+1t3Fc2Ak/cd27ltq3Iion1Cz/BY5VOBoKwbnoeDb1eGvq9fG519cXdeeFtzWI34nKb6T41SrrJiUGWzt2EAUq+nYaDQ5gv8vXqa6mxyKRluq6hJhIUTZlB84Hd5NfUYamswGjuJjA++fww1O5DbTyJsvdfoGgeJRUmsmWN9cfLaRsJce/iMp443Mv0wjRqRUjPJRcPh1LXrwBh/Dhly6S52N8foijfhsliIBqM43JbcWSYCXiiDLb7GGz3kZZr5bZ/nIvFLu65hbeOQoKQJCFFkvc7ZlkjHo2IEBJBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEN725Es9AF3XX9B1vUbX9Upd1//j7LL/OxtAgq7rP9J1faqu6zN1Xb9C1/Xdl3bEb4Kv+1wAyasCgzB4alJTxWUm831TMOTZkq/dFrLunYLHJLOxYQCDLPPZ1VXct7yCfW2jqBr4o+qEABKA777UhNVsxCBLVGWfKyQ2mBSyS5yYbecCQ8aHQkxZmk9Gnp39z7ZzaEMHhzZ0ICsSEb+P0hmzJ41zyvLV+OLJAJS1U3NZVOkmrmoTG0UC0LoFShfDivth5VfAXZ0MZRHeNkYCMTadDSI40evlmmnJgI2DnR5+8HIzFVn2VNsH93TiCcX53JXV/OmjVyBJEo0Dfr7w6DF2t43y+OFebCaFsWCcmKoxuySD3a2jAChni9MVWSahJouNYgmNDJuR4UCUjWoMbWYWyCAZZSJL8nnBE+DjKypZWuHmFqeL1kfakUgW072WPds6ab8qZ7txZfbTeewhTm57gcH2ZiwOB77hIWKRMGZ7cr8MZjORgJ+03DyGO9sBaD96kPqlK1N9ndm5lea9OykZf4WM/f+B2xxEVpQJ26teuIodf+pjy4OnCXgmF9YJF8BoM4w0w7IvwfIvwbwPw9GHYaztL6/7l4y1TiiMyz36FWatnhgiNW15IV1nxmjau4Oxvh52P/oH9j3xCK0H9jJl2ZXsevEhWOVASUsW4RuK7GTeUYtykQvk7Jlmpq0o5OALyWP7/mfbcRc5cefZKMqw4rIa+NSqSryROMOBKADBaGJCAAnA/3upkVhCp3HQjzccx203kWk38b2Xmzl0NnzEIE8OmVAkiQFvlD/s60TT3h7ZYW8bus4DO9uJJ5LnX2WsZcI8BZDat5Kryfie72W8O8Bgp4+BkQj6lSXIGcm5qOTYMK2vYP8rPUgSKAaJ3NLJBbnBcQ8v/fyH7Hn0IYIeD/7XFdJnFZeQV1kzab0py68UASTvIjnqAFLX2cv0eR+G9q3g68OebsGRYSa33EV2iTN1zbn78RYy8vLY+8TDvPLbnxMLh5CVicF2U1dehSPTfVHHnV3iJKvo3DWNpupE08yYlxSAQQZFwjQ3l1C6maf/5yjb/9Q0KcxJePdymA2MBWNoOuxqGSGabcG8vBDJKIMMlnk5jJtHScRilE6fNWn90hmzcReWMB6QWF6TjC+JJTTuf/wE66fnT2hr8LRMHkDLy0hRH9+/OoOaXCff3nAGRZGoy3VRLwLAhDcQ8ETY91w7mXl2IkYZ+63VyK7ktalS6CA0PYuOxjEqZuW8ZWPKzC/EnpE5YVlGfiE9p06w5Vc/JauwBN/wIP7xUZbeUY3BOPnnrMJqB+asnORvEGXLIDiKtfk5frPOwj0LS4jGVQZ8EYIxddK6wlssHiEtO5e0nNzUol0bH0a5On3SXAyG4gw2e3nx/07wyu/OsOnXJ5l9dUnq9y1JgoXXV4gAEuEtZ5AShGQJKWZABSySRjwavdTDEgRBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARB+LsZ/nIT4W9msCarIV5bGKyYwGA5b3NTgYPMu2rRAglkmwFjrh2bP8JdC0v412dOsa0pWfRb5rYhSdLk8A8gHFfxhuP8y/p63LZzgQzB8ShqQmP6yiIkOTmm9Gwbmbl2Xvx5Q6qdpurseKSZGz9Xwkh3J1d+6OMcfPYJErEYs9ZehzUtDYfXyN0LSzjYMYbNZCAv7dz+tA4FcCTGyM2sgG3fhuDZQmVnHqz+OgAhf4xETMXqNGE0TQxsEC4fDrOB6lwHp/v9bDo1yMrabL6wpobCdCvTi9LIcZq5YWYBzxxLBuHsbx/j3kWlHOkeZ9gf5WhinH1tYywsTxaSabrOqzkFo4Eo+WkW+r0RvOE45Vl2Np8e5D3zi/nJ1lZ+s7uD/7xhKkW6hC2hE8uyEpvh5nifl58c6aZrLMSKqiy+tqSKzT8+AcDhjZ3MXlOCpuooZoU+s87BcJCpq4s4taUHXYe0HCslU+DZ//kOdUtX0HvmJECqSH6ovZX6patoP3IISZKQFQNqPI7BlCzYH2xrIaOgiEW33cVoTxeFJbkUVU8lMdaKWrqSzPFD3PLR97Htha34hoepnLcCZ/ZCOrd4GekOUb/YjyPj/N9/4e+gJkCNwiv/kXwtScnjTTTw9/dtsk94aWjdwNxZlRR+7BN4PToWu5GO4yN0nBihsKqK7pPHUm0D42O4i0uZuvIq9u9/mqVr78ZpdZEwJDDm21+/pfPvmqqhKOcKLHVNxz+WDLNxZlqSx/M/w++LcWjDxACQk9t7KZ6WyYO3ziJilOjwhijJtLOtaZghXxRfJMHMojTcDjMHO8fwhROMh+LEVY2qHAdVOQ6uKM/EZEiOyahImAwyn1pVyQ82nyuOXlThpnHQDySDiyIJFZtJXHJcKLV5LgyyRM94GLNRAZNjcqPCubQ1BImGEvQ2ebA6TeiqzjgSRTdVYdB0VKuBznCUxe+pwWEzoGmgJiZfW0iyjCzLaKrKoeefYt2nv0DHsSPEoxFyyytp2LYZe3omy9/7IY5seAZN05i68iryq2vfgk9DuFwYTabk9Z5/AApmwtYNsPgz2Dd8kBV37GR0MMHep86FQ8WjKmN9vSx5z/sY6+shOD7Ouk9/gf1PPYpveIiapauYdvV1KIY3f+wIjEfQEhr2dAuK4Y0zF52ZFpbdWUNv4ziaqmMwyux+voNV760lc1Y2ajBByB+j4+QYAG2Hh5m5qpiC6vQ3PS7hnac610nveBi7WSEYTeD1xRlSZPJurUaSJRLWGE0vv0j53PkEPWMsec/7OL75RdR4nBlXXUNGXgGJmJmFLgevBAMsrnRzqs9HTa6DLKeJL19dQ4nbTiyhYbUcmzyAzAoY78KeO4Nnjp0mz2VmdX0OdXkuXFbj5PaCcNZIT4CuE2MMtPiYvqqQ/CluLOvLMZgNjIXijPaHWHJrNTmlzjfddyKuEhyPohhlHOkT731UTad50E/7aJAMq5G6fBfptmRwhCsrm5v/8evsePi39Defpqh+GlOWX0nT3l0svv0ehtpbyS6rIDQ+xnCvndJpbmZdVcyxLT3omo4ry8ziheOYnv9o8jcYRy4s/QKY7BSM7MLA1fzhQDcGWZoQqClcIpJMJBRgzX2f4dDzT9LbeIr0/AJ85nECNUMUVF7BiX0jDG7p5tp76hhv85FzQzlHDgwy1h/i0IudrLirlrH+ICab4W+aq4Lw9zKgEpBkTKqBoCxhUQzEoyJ8VhAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCEtz9REXwxWdJg/n2w/+fnli3+LNgzJzXVNZ3Q8WE8jzWBqoMikXlHDXnTsynJtPGFNTVE4ioGWaIg3YrVpJBuNWI1KoTj557iW53rwGqUcZit2Czn/rxqXKNhWy/1i/Lpb/UiKxKZBXZGeiYX6UcCceJxleySMjY/8FOmXbkWSYbTO17hqvs+TdOghy2nh1hVl8NV9TmYzhbIH+oY4+N/OMwXl2Vzp/8MFMyGwrmgqxCPoIYD9J4apXn/IONDIYrrMymflU12sSgWuRyl20z86/VTef+v9hNNaGxtHMZskLlrQTHZzmQx2deuq+em2YUM+iKUuW1YjQo/2dpKXNUocycLu3a3jLJ2ah4vNgzgtBixmxSeO9bPZ1ZX860Np/nN7g4+sqyCDJuRDJuJf7thKm19XuaNxpG29YIOkkkheHURcYeR/DQLayuymBNSGG/1oZ8NWEjENA4834FskFj0vkpWGA6h+PuJVRRTMX0BQ20hrM4o/tF2dF0jMDZKxax5HNv8Ir7hYbJLyxnubOfQ80+y5I57kAwKi2+/m51/fJCM/AIMRhOJeIwzO7diMJm5/f5/wTds5pnfjFI6fSaZeYvoaBgjO6RyzX1fpPmol46GEG0nvKnP1DcSfuv/kO8KGvgHYdU/gxoDSYbjj0HVVX9/15Z0qLwSWrckX8sGLLn5FJ++n4Pdn2ew3Z9q6syejjNrB/6RIQDGB/qYtnIN3SeOsWLe3YQfHSCmDyAZZczvtWKtnXwueJVvJEzrkWFaDw9RVJdB7cI8zDYjTfsHOL2rD+9IhNlXlzB9RSE2l/m8fURDcaKhxKTlIU+U7T9pIrvSRWxmOt/Y3sz33jOTf33mFLkuC1lOMwPeCPcsKKXfG6HHE0JRJFbVZmMyyNjNBsyKzMyiNG6cVUhc0/ngknLmlGRwuGscWZboGw/zyIFuAK6bUSACSC6w4kwbv/3QAg51jHGsZxzK85PzveXlZANZgUWfxr8vBkB/i5cpSwtIz7GSq6oEfnsKNB0MMlV31hK3GdjzZBsth4ewOowsu7OG8hlZGIzJoLC0nDzmXHsTB599nJB3nNZD+4kEA4z19nBm1zYA8iqr6Th2mNIZs5EkmbyycjLTRYHvu4mSWY6+4n6kjf+SLP5e8HEIjUD5CtSRLoyWkgnt/aMRiqbMYOtvf4bBZCYWCtLwykvcdP/XUYwmPvjcAMrjrfzsvRmU/pXF4iPdfk7t7meow0deRRoZeTaKp2TiclvfcL3c0jR0FU7v6UeSJFbeU4c7zYTnT43Eu5PXyjX5dtLXlbJzQyfx6ORjq/DulOUwU58WZ9f6MWzNDxLJ+C7+zR2Ew8k5YltaQOXMhex54mHmXnczoXEPJdNmYrE76G44zqkd26hb+nEcWSp1eU48wRhbzgzxnvnFfPmx46ksyzvnF5NWXMTqytVIrZuTCxUjzPsQGKwo4x38712zmV7oojzrPMFUgvA68UjyNwSjWaHUbsT/64bU7xD2NaVkZFvIq87A8CZDQ73DIbpPjTE+FGagzUtehYu6xflkFSbv+Xc0D/OR3x4kcfYe7ubZBXztuilk2pPXswPGTI7W3kjF3JsppZeXf/kTbGnptBzYQ0Z+IWabDVk24D0Spr/Fy2CHj3nrSrHkWKgwHMWx4QNQdz3kTk3+DmFxgWMeysEHuL9E4q6bp3NcquZAxxizitOR3yDQT7jIZAOSrqKqOmabnXnX3cJwVwe7HnmQBTfdQWtLkKwKF/Pn5RB66AzmuIZZhpVLC9l+cJiRviDhYAwdnZL6TNKybZd6j4R3IYOkEpIljKpCQJKxShKJaPRSD0sQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBOHvJqqCL6Z4EMqXQUYZhMfAnguZlSCbUk0CnlG8gwPYJBfBxzqThT8Aqo7nsWaMBQ6WVGaR47TQMRoky2GmNtdBNKGx8dQA37plOt9/uYn20RDzSjP49JVVJFQVoyITiWup7dgzzLiL7Ox6vAV3oR1dg5KpmVjsBiSJVIEbQHquFaPRhNnuYO0nPkdXwzEsDidXfujj9AST4RILy93saR3llTNDzCnJIC/NwuOHexj2R2n2yugON5K3B44/AvZs8PYwfN1mXv7FKcL+OAADbT78Y1GW3VGF6TVP6tY0XRQDXSYWVrh5/rNLaR0O4LIYqc1zkWk30Tjgo2kwgMUoM6XAxcqabE71e+kbj3Cyz4fVqHBVfS4vnRrkYKeHexaW8LHlFRzp9vCtW2dwpMtDvzfMj++ZQ48njMts4EDnGPV5Lp480su/zi9DerojNQ49ppKzox/PVAfra3OYoZvY9XIzxoV5ZObbGesPAmC2Gaie7yLH5eXIjnYaDx3BXdjJ4qvA5S5l8wP/zexrrueKW+4kHo0Q9vtY+4l/IB4NUzJjFrKiEA8FsSoRcsd2Yx45TulHbiVgMnLT579A75kTJDSF/OoZeH25bHmkkfRcG0aTwt6n2wEYaIWRUQV7poXxwYlPwM0sFAX5F4UkQ8kVcOQP4MqHkabkU88l419e96/pO296sn81Djn10LkHPRZiynzHhBCSwxv9XP/prxP29+EbHkRTExhMZlZe/0GCv+9MtdPjGp5HmzB+ejaG9MkBIrFIgh2PNNFxYhSAwXYf3Wc8LLklk/RsL1fcaELVHBx4foC0bCt1V+Sfd+hGpxGn24J/9Nw8lGUJiy35uQy3+ihwmajPdfKjLa387L1zueuXe1PnjpN9Pj68tIx5ZQVoGkwrSMNkkMlymImpKpXZdkYDEaYXJp9gv6I2h6kFLn6+vY3HDvUAsG5aHtfPLPg7/wjC+dTkOqnIsjOnJAOCB2HO+6FsGcQC4CoEs4uyGWk0HRgG4NTOPq66uYLoy13w6uVBIjkXE9eU0XIoGZ5TUGVmtPMoY90B8qtqQFIIB+zULV1HXkUVbYf343RnU79sFX1nTuHp7yWjoAiL00HxlOn4x0ZxF9UQGDXi7WrEWjoTk/WNAyCEdwijBbLq4I7fwob7k9d/wWFw5uOodzPWFUeWJbSzhefV892YzCZWvP/DxEJBdMBstRELR4hEEjQPhwDY2TLyV4WQ+McivPB/J1LHvMF2H+UzswCYuqzwDddVjDKFtRkU1maklvm296QCSADU/iA5pS7ScqykZVnE9aqAruvomkZh22Ok7/4PtNmfIvRSL3r4XEhNaGcf2fdUcOUHPkbP6QZyK6pJzyugr/E05bPnkV06hY2/Gmbl3fk0tHv4/b4uPrGikp9tb5twf/bHA90UpNdw5Yr7kepvAH8fGCwQC4MlHVmNcYM43wpvQkaeDdkgsWBFAdrW7gm/Q0Q3dVL62dmYMixvqk9N0+k4PkLXqTG6To4ByWNxx/FRbvrCHMIGnX9+4kQqgATgySN93DanmCXVZnyhOD/Y3MKWM0PML3KwZk0lK++9j97TDUxbeRXu4lJCPi9pOQXMWh1CkoJkl1o58tIQM++qxrr7FzDjPeDpPPc7ROBPsOpfkMqXY9vxbeq0BOHVD3P3yzrXzyygIF1co1wysowS8+ENaExZsRpNVcmvrkXXdYwWGx0nPNjkTEJ72tFf/W1LA3VHL7NXFHP4wCCls7JJS39z81QQLiSjpBGSZYy6gaAsY5E04pHIX15REARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEAThMidCSC4mgw2aXoTiK5IFwfEwDJ2CylUAJKJRRnt78I+OYLaZzxX+nKXHNVR/HEu2jfp8F/X5rtR7HcMBpuS7+N8tzcwuzWD9zALQdUKxBD/b2so10/NZVZd9bihGhVmrSzAYFdqODOPKsmC2GkgkNBbfVsWB5zqIhRO4sqwsv7MWXfcx0tVBPBImp7yKRDRM98kTWGrnsKGhJ9VvnsuCUZEZDUQ4M5Asxp9qHUfKqACTHazp4OuD2e9lbFhLBZC8qnHfANNXFpJTaqSv2cPpXf34xiLUL86nbFoWFscFCBEQ/i5VOU6qcpJPrvaH4+xtHeW+3x2kPt+FxSCzqNLNrOJ0/mdTE0uqksW+4bjKeCjOytpstjYO84d9XSypdHPt9Hx+uaONf762jpJMG5IkUea24zDJVOc68EUS/N/ds3B1hfC9bhyqN8bNZdk07BmgM5J8t/nAIAtvqmCwzYvFAS73AH1ndrPnqTAdRw8BEBz3YLanEfbtIuQdx+JwcOCZJ/AND7Lgpts5uXUTw53tLLrtLhpe2cRwVwc1C5dgveYDEAzgjPdgTy9GGjqGrW4WTe2ZbH1kgLrFQaatcJGeHSTkbWPetVl0ntAZH4rQ3+xl3cdLCAyHGWjzJQv8risnt9SFcBEYbckiw4oVMNYGcz8IY+1QnvjL6/4lJickImByJF+PdzFacR+9FjNGi4krbrJy/JUeFIPMwuvLySnL5syu02x98JcArLr3PhwO56RutUAcLRiH84SQjA8GyS6JkJEbRlYshPwuSqZa6W8+gXdoCIPRCDIsunEux7cO/tkQkowcOyvvqWXrHxrxj0Yw2wwsvrWKQxs7Um2GT3v43L3VHB8NIElMCK8CeORAD3cvLCbTbuZE7zgV2Q5+vauDQX+EO+YVU5/nwGJUUu2znBa+tLaW2+cXo2o6JZk2bCZxqXGx7GgZ4SO/PciZ+zIwdm+GgjkgKxALEgmpJGwm5t1eSePmXlRVIz3NzOv+xOhRlZgn+bTuzAIrsnQMSMNgNNNx/CBOdzboOvGwg8zCYsYG+mg9vJ/9Tz3KNZ/6PE53FgGPh4GWZpr37WbZXfcy1NHISE8n236/l7zKala890PkVdWgJjR8I2E0VceVZcVoVibvlPC2FldsmLr3wow7oHM3FM2H3KkoRLCnOVh6RxUh3xB2ZxBJGmF8SObQc09QOnMOueWVJGIx+hpPk11Vh9kgE01oNA36//KGgZEe/4TQJYD24yPkVqThGwsT9ETxj0VwZlpwFzqIBOL0t44THI+SVeQkvyoNo/nc8Sra5Jm0DXk4xFUfqKf50DDdpxrJKXdRvygfd6Hj7/vghLeVRFylv8XLia09oOtMn7oY/eYNaOZaEntOTWqv+1U6+46SU1pBLBqh4ZVNzFyzjsa9O+k4doj1n/gQRzcPccdVxcgWA1XZdkIxdVI/MVXjuW4T11sUpJz65MLxTmh8ARZ96mLvtvAOoxhlrv3EDMz+GInXXxyoOpo/DrnJl9FQnJGeAEFvjLRsC0FvjLG+IOk5VvKr0rGnmYlHE4z1h4hH1VQAyau8w2FGevzoeRb6vJOL80eCyeuQbk+IPa2jFKSZ+focA4PNZ9A0DWdmNkarlZGuDvKqaxlsOcPRjc+TV1VN8bSZLL+rij5dpbPuM8QCAazZVtxqN47WJ5OhJONdEBqFRHI7pYMvUZt7CwZFBEldUv5BdHs2Iyf3YpsxG8VoYHxwAACzzc7MqzIxBu0kDvRPXE8HiyKx4PZqEUAiXHJGSSUsGXDKJgKyjFVSiUVFCIkgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCG9/ojL4Yjr6B6i7Fnz9yaJgexbE7HhCUWRZIT46gBpPEPH7CFtCyEb53BNeAcmkoLhM5+06nFCJxFWWVGWRbjOiSBLtIyHmlGSwpDqb1qHAhKdhR0NxRnsChP0xahbkEQ3HMJgUdv6xmdJpmSy7o5pYJMFIbwCTzYCnv5fG3dupXrgE79AABqORwfYWqovKJozjM1dWkeU04w/HWVGTzeGucTRdB6MFdvwPRMaTDdu3I6/9IJkFdqrm5KCqOqBzem+yoKS/zcuzPzxGIpbc/76mcZbeXs3M1cUX7M8hvHneUJy2AQ8udQSz1cF4KEGuHOUn76knODZImtNOhtNEJNBHVbrMFLufX91ahGSyk6mEMDkK+drydBR0bGlZKJEx1lRUEQkH0L1jtIasmCKjDMs2tKjEnOwMQpt7Icc2aSxypoWuUx4G2nxUzMqm57QHTdPxDg2RltWHzWVk8wM/4Ypb7mTvk49MWDc9L5/WQ/sACPu8+IYHycgvIDjuofvkcRbefAe7HvkdsXCYijkLSM/L57kffhtJVpi19nYMQQdth6dRVJdJ04Fepq1wkFMaYvsfHmCsp5uSaTMpnT4Ld76HotocskrqiATj1C3KY8rKIrKLHGTm2ZFlUeh2Ueg6HPgF+JNFa3TshFn3gBp9832FPBD1J0NNTFbw9YKrGMxO0FVC2YuIBdzIsQCHt3dhMMmsuLuWzAI76dnJedt9qiHV3e5HH+KOz3wDJOA1OVOKy4TiPH/I0vhgIzsf+g90LXk8zKuqo2ruvWTkFRANBoiGQxRVTyUa8lK3qJyuNg+jaJjMBsqy7NjPFtEbEzp7n2ylbnEezgwLVpeJVx48Tch3LgwqLc/Gn471srFxiGhCY3l1FtubR3DbTVwzLQ+7SWF6YRpmRWJRhZtPPHSEWCI5ru9sbOSzq6uoz9OYXpSe6tNkUKjOmRy8IlxYY4Eo/9/TDayfno9vqAl3ySIIjYEaZ8S1ki2PjzDcfQpXloWZt5SzfzSAlm0GRZoQeiZZDTiLJNyFFkqmGuk63kJR/TUcfuFppq64im2/ewA1npwzGQWFLLzpDjb94kfousaxTRsonzUP0EjLyWXBjbey76k/kVVSSjwSIRGL0nO6gcf/6+ssv+eDdJ86RW7lAk7t1skqcnHFjRUEvTG8QyHsaWayS53Y0yYH8whvH15zHtmRcdj1g3MLLWmYblrJ3qfbWHGnm6i/k97Gbix2B1ani4W3vIfWA3vx2h043VkEvePEThxhasEcNB2WV2f/2e291qvXkK8lASazTO8ZD0c3dTPWHwRg/Sens/eZNkZ7g9QuzCMR14mGEhTUpGFPSxY0W+oyibaMT+jPWJnOib0DNGzvA6C/1Uvz/kFu+fIc0rInX7sI70wDbV6e+cFRABSDzJTl02g4NUZJdQRzkZ1ET3BCe4NkoG7KEnoHm3BkuimorWfzr3/G1OVX0rJ/D/1NR8kqrMHbF6TUbKShz0txhpVuTzjVh9kg894ZTnKII/lzIRaBvsNgdsCUm+DU01C6+C38FIS3s0gwzkC7l+YDgyy/spigWUGPvib4xiCnfoeIhuIceL6DjhMjzFhVRDQUJ+SNcWZPPwFPlIrZ2cy9ppSeMx4cGWYMZmXSdS+AltDIdVpYVJnJntZzISWSBGVue+rfn72ygluKNY5sehFHppvmvbuoX7aSwNgoueVVjPV0cWzTC8y65jpObnuZrpPHmX7l1WSXVzM8MEpw3IMaj5FbXkF59gKc276dDOkpWwHt22HoFJKu85Fl5XSPBvGG4lTniuvWSyGhG/EMDDLa203VgkUMd7ThGehHjcdIz83H5nKRlmnB4zCiBV4TZiuDo8SJzy2uGYVLzyRphGSJDMVESJJxESc4LkJIBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEF4+xMhJBfLWBeUXAHebmh4EnoPQP5s1GVf5n+3tCFLEvdNsxAJ+omGghhtFuxrMvC93I0eU5HMCq41pUhm+bzdq5pO00CA6jwnnaNBnBYjldkOZAkUWUJRZEaDMQozkgWRAW+EkD9GRr6d9qMjjA+GyC52MWtNCYc3dtJ+fBSA2WtKAB2T1c744AC7HvldapuK0cjcG27nY8uz8YTilGfZ2NkyzPoZ+aiazrzSDG6cVcAjzX5uzRxHejWABKBsGc4sC2XT3Rx4vh1dB6NZYfmdNWCLM3Q8NKl49MimTirnZOPIEE+3vVT8/U3UHfkR1p7dsOiTFO77GVLlKko1FblpIyz7Ivqu3yBl1zAjowB5x8Ow4n70g48hmR1oBfOQDz4AS/8BffcWpIgHfcpNSHt+DPM/TN7ASaRoiMi0b+M/GiCRoZE4Mkw4345zVTH+7T2g6sgOI6Zryzn28wYSMQ2r04Qry0LxFCfjvc8x2HqGsplzAEjEYxjNFtB1ssvKKZ85F4fbTdnMObiLSrA60wDIq6yh++RxACRJJhYOYzCayKusYvejD6U+g8bdL7Lo9g+gayqKUWbGSp3dj36bOetuYKynG0eGm7yqGnY8/FsAZq5Zh8cQRzFYkeQs7IZCzBaDCCC5mCKecwEkgJY9Fb99FpKei1PXkaTzf/aRRIRmTzOnRk+xtWcrtek1rDdmU/3cl6FiFaz5d3DmQaAfdn0fLa2aePXV6JubSY9prJ6ZTVtUZfOvT7H+0zNTIST5NdM4s2srANFQEM/IIHmrS/Bv7YGEhmwz4FxdgmSQ0XUdXdeQZQWAsN/Hjod+mQogARhoOUMiHqW/uRFZkTFZrIz2dOEuLiMtV+XMnmGwKQzlGhn0RVhQnonDYiTgiTLcHWC4OwBA/eJ80nPthHzjQPIYnL4oh5c2nkTT4SdbW/nKNXXIksSUAhePHuwBdGYVp9M+GqJ5KMD/d90UdrUMs6FhEICH9nXxrVtmXMi/pnBWXI2zb2Afz7Q+g4zM9ZXXsyBvAUYlGV4TjKlcWZtDQ58PU1YpDB2EAw8QzZ7P9s6pDHeHAPCNRNj5wGlWfXo6VqsBeU0p/s1d6HENyaLguqqE8cQIs1brKAYDaVlr2fjTH1A8ZRqNe3akAkgAPH29jA8OsOyue9n64C/x9PWSX13HoeeenDD2nLIradjyUup1JODHNzzE6R0vc2bnFlZ+4J9oOx6l+/QoRzZ1ExhLBgZVzMlm5d11WB3nD+gRLn+hkB8OPzhxYcSLS+tk2XsW4Bs5xLbf/yr1li0tnRXv+wgFtVOQFZloKIi7sBh3cQnXmE5zZOgAI8oqmsZmoeoqWdYssm3nDyUx2wxk5NnwDIRSy6rn55KeZ+PQC53MvqaE8YEwsgIhf4zRniBz1pbSdWqUxr3Jc0heRRpXfaCetBwbhiwr5so0oq3eZP+VaZhzbTQ+3T5xn30xxvqCIoTkXeT07v7UvxffVknQE6WgSmL3Ez/gipW3YvGbUL0xkCUci/IJHR9GT2hkrSolFPaw/6lHKZ46g+HOdhSDgVgkQnZpjN1PdrLonlpiVpl/v3Eq//HCaVqGguSnmdn13jSkXf+KVHpF8jvWfxSKF8H02+CZT8G674CnEzJKL90HI7xt+EZDSEhU12YQ2diBa3UJvs1d6FEVyaSQcVs1hiwrAP0tXjpOjFCzII9dj7WgqTqKUWb++nJObO2m7cgwdVfk4R0OYzDLZJc4mb2mhCMvdaW25y60Y3WZsFsMfPOmag72ttLribPtlM4X1hYRVs6wszfBvr59jChDDHStwmyzYTCaGOvr4fALzzBn/U0Exz1oqsqyez7Aiz/+HmoiTtnMuRTU1DM+0Me+J/9EPJIM75Ekmes/8zmcspL8zljdMO1W2DdMvO5G2gcCfPrhI2Q5THztuimsqcvBZhHXH28ln0fFnpZBx7HDzLvuZmKRCCarFYPLxVhfL1PqluN5pAnnyuLk/AwnkIwyjvXlNJwcZckt1Zd6FwQBo6QRkmSMBhMeScZKjP6hvyEMVRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAE4TIjQkgulkA/uGvgyY+Atye5rO8wynOf5bq1T7BzyILRakNLJDi2aQN1RYvw7+jFsSgfFAlUHf/WbkxFDgzOyU94DcZUqnId7GoZoXHQT47DzC1zigjGVH6xow2rUeG9C0sACPmjBMej5Ja5iMdUSqe5aTsyTDyq0nVqlIU3VJCIaxiMMu0nhqlfnE/CYmbF+z7Ett/9ilg4hMFsZvk9HyRhsvH7vc04LAb+dDBKUYYVfySOrsMDO9v4wJJyyi1BGGyZMN743PuIR1QObzxXjBSPqux+ooU1n65F0173qGRAlmVUdfJy4a0x6vPjPvJjrA0PwZVfhY3/jOTIBUlCPvRrWP4lePnrSAA1a5F3/D9Y8FHY+T9IwSFY+U/IW76ZLPY6/kekgRNw1b8ibfr/oHQJdO9HbnmZ6K27GH2oD/uCfMInhgGI9wfRYyrOFUWoGWZOo5EWV1NBNW1HBpi7ViEW7ubQ07swWa1YHA4AmvbuZNW99+EdHmSguZGAZwxdkph+5Vo2/vT7zFy7HovdgXdoAHdhCb7hIWQlGQCRV11LZ8MxAGRFYckd76W/pYnNv/w+82+8g7QcIxt/+nPQdaKhZJFzzaIlHNv0AgAzrlpHf0sTxzZtSH6IksSqD3wBTct8i/5q71L6ueNEYN6/MF71ITRNQvfqBKJeMvPtWOzG162is717O/sH9/NI4yMA7OzdyVMWN79Z8zXK9j0AZ54Dkx1e/jpoKrFZ38P/bEeqj8ShQcoXF3DGKBP2x1LLMwpqKZ+9iPYjewCwSw5Ch4dwLisECfSYivfFDtR0jW3PP0gsHGb2NddRPHU68UgE//DQhLGabXbi4Qgnt72Mf3QESIZCrf7QxzGYTGQXxtj5RAzn2gK29HrJsBkZDyeosJowmhXiZ59sf3p3P9Xzcllybx1xTeOUP8xXtjWivub4azLIrK7P4WtPnwTg3sVlbG0apijDRrbTzFgwxuziDFqGAjQPJQOw7Gbl7/0LCuexu283n9nyGXSSf58X2l/gx6t/zLKiZQDkuMzkpVn47Z5OHLILNn0dtAShus/Qvy00sTMdfMMRijQI7unDsbgAFAk9oeHf0o3hShfRkA/vcAuOjAw0NYE9M4vuk8cmjSvs9zLenwwf6W9OBkANtjbTc7oBgOKp08mvqePgs09MWE9WkqFquq7R37wbZ5qZ1v0e5l59NUe3gHcoStvhYWasLKKwJuNCf5zCWyCuaqCfPzyP4Ag2R5xtD04MrAl5x4n4vRjMFjb9/Iep5YX1U6m7eRU/7NvJzr6d3Fl7J7NzZvOZLZ/h83M+j0kxMTdnLm6bO7WOrunMXVeGJEPXyVGyi51klThQDAozVxcTCcYxmmX6mjxkryjC5bYQjyYYORvUBDDQ5qX9+AizriohsLsXyaTgXJ28no73BQjt7Sen3EXvGc+E/fhzYVfCO5MsJ+e50axgshgwmGWG2k7Q13SKZzpa+ci//B+JriBIEuGGERKDyWNyhlJEUBslLSeXudfeyFPf+Qazr7mOlgN7yCmfgmKQMegJ0swy0YTK1VPyuP+adOZla0gvfxHJXQlbvgGhseRAunaDrxeu+RbIRtj5fbj638HsvESfjPB2IUky0XCC0kwzgeEw/u092BfmIxmSv0NIaSYkWcI3GmG4y0/N/FwOvdCRumdX4xqHNnQwbUUhR17qQtN0iuoy8I1G8I+EKa7PpHJONid39JKRb8eZaUVTdTr6e/huw3+yo28HNek1/PPN9/No048pcZXwWNNjeKIePl75ISJeL1ani0jAz9z1N5FTVsHLv/wJ8WgESAZYLb79HiRZouvUCSRFxjs0mAoggeT1xpHNL1NesgxD/2EIj8KxPxK//UG+ts0Aig+AkUCMf3zsOL/90HyuqMh66/8Y72KSpGOy2Vn7qX+g++QJ9j35SOq92z79byQ6g2jeGP4tXdjn5SIZZdAgZjFiMP6Z6w1BeCvpOiY0QrKE0WwhioKVKP6xxKUemSAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIwt9N/J/7F0MsBIkwjLWcCyB5VXAYxtr53svN6JLEaHcnYZ8XxWxC88fxb+vBv6Ub/7YetHAC/kxRo9NsoGssRELVuX1uMVU5TjadGsRiTBaDf2JlVbIYFPANhYlHNQbbfQy2+xls81E+K4uAN0phTQb7nmnj0IYO9j3TRuXsHNJyrESDQXY98ntmrlnHwlvew5x1N3Dg6ccI+f0EYyqDvuSTPe+cX0yO00wwlmBKQTrv/9UBjOEhJGcu2M4FL+j9Jwn5YpP2I+yPE43GSc+1YrJOzMSZvqoQi0Pk5FwqcmAI6+lHky8SUdASULYUmjaebSFBLABF86Bje3KRJQ38/eCugv7jyWWZFTBwIvmery+5rGwJdO1BrbuDeMAJOqiBGEqGJbX9xGgE/5ZuogmNtue7OLOxm7nXFDNvnZMZK1U2/OgbhLzJIuBYOIzRYsGekYnZZqe/pZF9TzxC54mjHNv0At7+PvY99SciwQCHX3iGhTffgSPTTdX8K7A6XfQ1n6Fq/iKiwQA2ZxoAU1espmHry7Qc2MO0VWs48PSj9DWdJOQdJxYO40hPFskbTGbikeT3wZHpZqi99dyHqOscfOa3xMMTi5WFC8yeA65CsGYQnPJRAp44A61ehjr8jHQH8AwEJ63S4++h0dPIyZGTfHb2Z/nojI/yqVmfYmXxShoyC2H67bDl3yEwCJoKrgIiw/ZJ/UjNHkpqM7DYzoWc2FyZuHLXc+1n/51VH/gYksWAOhbB/0o3/i3dBHb2garReuwAzft20Xn8ME/997/TdeIo9oxMapesmrANs9OJp783FUACoMbjnNm9HTWeYNvvf0L1XDvGhM7v93UxFIiy5cwQn3jyOItuqZzQl2ySeHFwjFCWiadah/CE4hPe13SdzWfOhaBMyXcSjKr8z6YmfrSlhR9sbgYJvnnjNAA+sbKSYGRiH8KF8WL7i3x85sdT/31s5sd4sePF1Ptmg0KfJ1mMy2hL8hgNmMJdODImh5dlppuRDRKqN5a61ghs70WLJDBZLJzc9jIWhwPFYAIgp7SMqnlXTOrH5c6m+9QJZl19PVd/4mv0NjZisTtYdNtdLLrtbkxWG6e2bib//2fvvsPluAq7j3+nbC93797e+7266r1bsmRLcu/dFGOaTUtIcGgJeSEEAiShBAihBgzY2Lhb7pZt9d7blXR7L3u312nvHyuuEHICJNgGcj7Po0famTNnzsyZnT27j85vWtqmtmletIyhUyenXuvZDKOdHXTt38Xz3/k8My86F2SjZYz/3YkT3jKjsQynUm60i/7m/BWuQkiM4FASaJnMBdt5i4rZ9ov7z1s2eOIYxclzY4JHTj+C2+bmrxf8NTuGdpDQEjze+Tiv9b/GxFiEjl0jjHTHSIQzHHltgNoZRZTU+ek9EmLn412MdMVIRnLoOZPaGcVs/sUpZq6uYmLgdT4fzgaMKIVOMicmib/cR/zlPjInJlGCTprmlZxX3lfkJFh14eeD8OeraUkpkgQ2p0IqmsVmVxg6lQ9i0nNZLCMf8BR/uW8qgERyKGCAzemifu4COnZu5dL3fJDw8BCldY0YusrcdbXgMvH4IoSTGseGYrz3J/vwZ4aRTj0LqvNcAMmvRHohG4enPgwFVTDZ/WafDuFPUCqeBSxUe/7z10xoJDaf/R1iywCynP8NIjqWxDAsLIsLQkO1rIGsyLgL7NidKie3D+P228mmDQ5v6ufAC320LS1HliWObxnMj833anyg4ON8Y+m32FC/gZ54N8/3Po8qq4Sz+XvvyoJFJKNRug/uo7y5FdM06Ny/ZyqABM4GWCXj7N34OK2LlxPq68U0Lhw/ZFMpLNUNC+6C409AKoSSjfHRpT62nD43rs7qJj0TqQu2F95Y/mIX8dA4Xn+APU8+ct46KQHS2cAnM6WT2DKYvz63DmBg0Ti39K1osiA3F0JvAAEAAElEQVScT0sjS5CSZOwOBzlLwWVpxCfNt7plgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAI/2si4eGNIKngCsL4SZBksH7jP6A7/UAKI5MhFYsAkNESuOaUkD40PlXMu6ISyWfj9WR1g9ZSH5UBF0ORNHVFbubVBtAMk/dc1MjW02NcObMcyE8q7z0awuFSUVSFTEKj79gkLQtK2fdML0uuaUTXTFS7TP+JSVoXlaHnsqSikQsmgzgkk8tnljOZzLGyuZhF9YU4bCqZnMHpsTgfvbQFpyMF3Ydg3T+AZUEmApUr8WecIAG/Nn/JE3CwI7KZpcpaLr6zjYGTYdLxHNXthXgDDuwOcYm+VewuD6a/CjncDUp+UjrJ8XzYQ6QPlLPXZmIUStqB3SAr+eCcVAh8Zfn1po5Zv55cw3swlDqUG+/FiJnI17yL2GsjuEryk9wyJycpuLyB6EgSjPxFolZ4MFwqqixRPd2BTT3N8c3P4y/KTwAeOXOaullz6T1ykD1PPsr6938Yu9vNk//8j1PHUVhRRcO8hZzY9hoAWibNaz/9ISV1DTQvXsP8q+7D7U+gqG4MLYXLZ6P7wF48hUHCw4M4vT6yqSTx0DjpWAxfUTHx0ARn9u5k+c130nv0INNWrOL45k2Y+oVPvE2EJzFM8STcN1R8CFZ9HK10HqmIRs/RCYLlHiyg/2QIp9dGeaOF9GuhTgktgU2xsaZ2Dd848I2p5bOLZ7OmZg2YWj585FfXeSaG6nmdyUQBBxUthdjd54coNM6tIp2Ic+jF71LTPJt5c9eSPnju/u7fUMfzD//4vKr2Pv049XMWMP/y69HSOc7s2YKnsJBFV15PZHT4gl0nQiEiY8Mkw5M43QaJs01I5Qz8Lhsbmko4s3eUJdc2oudMFFVitDvGlfMqyDlU3rm8nv6Nx5lI5AOibllYzanROCW+cwEWqiyz8ci5fRumxfc2d/Pdd8znH6+bSedYnPbZlb+9j4Tf25KKJXx5z5eJa3EA/HY/9y2877wy7ZW+/D/Uc2ENnuM/4OL163jmlxLm2Xtp47wSrHAWucSJe14pqQPngma8yyqYiI6yeNUNBJQSLOCme/8f/cPHsCyL2ZdcxtFXX8LhdjN3w1V07t9N3ewFmDQwfCaGlh7nzN6dnNm7c6rO0oYmVtx6NxN9vXgCfroP7OLMnh1T66umTT/32rIY695Dw9xVDJyIUFDm/kOeRuFN5LIp2FWFWO0lFK37B+h+DYLN0LASRo6imiZz11/F9od/OrWNrKg4XG5SsegF9WWicT43+2/5yomvkzbSZPQMFhaFzkL+fvvfA3Bn7TuZ2Odm5Ex8arsl1zay4/FO5q+ro6jKS2Iyx/4XemmcW0JRlZfhrgjBCg8ldT7cAQfVbQF6j4YY683XUTezCABnYwGZIxOYqfxnuOxWsdcXEO4Is+LmZoZOR/AFnTTPL8Vf5HrDzqvwx0cpdrD8fTMYOTZJaYOfVCxH3ax59BzcDUBGSeOcVkjm5LkQOu/KKiTdwmcLEgtNkEslqZo2napp00nHY/iLfLzyswEqWgrwLrOhWxaNJR6aS73oioXiLTs3Jvl1spIPBTR18Jbmv3sKwm+hZQxcXjuyKuNeWEZq7+jUOs+ySpRCJ4ZuMNYXp/9kiAUb6pFVCVM/90W+sNJNaZ2XktpWTNPEG3SSS+vsfrKLacsrKKr2Eh5OcWzzELUzitj5RNfUtnULA8xYO4sD0X35uuyFfLTxHiq8VWj9E6g2GyW19RiaRnF1HcOnOi44htj4OM0LlnDwxWeonz2PqvYZHHrp2fzvD2ctWLcOW1EcRo7mw2HrVyID1VoPqd/IRw24X/93F+GNo7h96FoW03RhGue+L1/+rr+kqLSa1L4xXLNLSB8+9x3Ks7YWs8RFUZXvrWiyIJwvlw+zS0sSNrcLzVJwYpKOiVBFQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRD+9ImEhzeCzQ6KCwwDFrwL9v5gapW16D38+JQdSKHlstTOnMvxza/gDRZhRFP419dh6SaSKiO5VWSX8rq7MC2JF0+MsqK5mOpCN2Cx+fQ4yxqL2Hpqgo9fPo2qYH4ir5bWCZZ52PtsD9mUjt2lsvDyOpLRHAMdYQY6wufVnYrlUO0OvMEiEpOhqeWBsgq8hUFumO8EC3wuFaeab5+qyPidNr79aifvbKuHsplw8ik4/BAA6SseZGQywOrbWtn6yzMYmonTY+OiW1v44tiPmCEtJxXL4S10UFTlJTKaxDLzT13+tdwA4U3kKSwndckXcD9yJ0x2Qe1S6HoVNnwBhvbDwF6YdiWc3Ahz74QzL8KpF2D+O2Hff4KnFLylWMlJkg1fIPrsCL61TqKvjWKv8GDmkmj9SewVPmzVXrSBBIktA/jX1oJbwbQZhFJDDJ98nqpmC3QHmx98gMLKaiQlf911H9zL/CuuoXHBYnzFJWz9+U9oXrx06hhqZsympK6Bo6+9RNW06fQfOzy1bry3m3TMYN9zYRZcXs++Z3soa/DjLXSy7NZP4AvmZ6a5CwqIh/LvgxNbX2XlrW9n52O/YPh0B4lQiDXv+gDIHjyBClw+D5IsY5nnwiraL7qErPdcQIDwBvBXw+EH0MsWkYxkSUVzdO7LT1ZrnFuCltHRsgZ257mPvApPBR7Vw/ePfh9VVrmy8mranDMZ0QfJGbl88I4kQ6QfqhfCwF4ctuMogRkYkfy1Idlk7AvLSfbFUO2BqbozSY1X7j/J4mvqaV+5huaGhVjHs/jX1WEZ+ft7tivGvDVXsenn353aTlYVJEnC7goy94q7aFhwOcnJ4xx47mmW3XR7vpAkIUkSlmnSftEaJvv7CJRV4qot4TvPd2FXZMp8Tl45Oc5tTaUc2DjM0OnzJ/c3Ly5D91t8f0s371vVSDKrU1/kYWdXiF/sHeBLN87i+aMjxDI6sYw2tZ0k5ed1jieyaIZFwG1jdnUFs6oDCH94R0NHpwJIAGK5GCcmT3Ad1wEwEs1weCDKNXMqwe+BmiXQvwuyMWoP3sN1H36B/tNpJFliciiJP6WR2BbG0Rw4f6zhkPHY/JhPRcho+UnIqk2m8YqFPPC1jzP/8mu59L0fZKy7k4MvbMQTKKRmxhpe+/kZmuaXUDtjASe3Pn9e25sWLmXj1z+Pzeliwz1/TcP8JWi5LLKs0DBvAfufefK88qau07Y4x7z1bRSKEJI/WUVeB0gW/TkvQdNEysSgpBUevxeycZJL2iiqmcuK2yRObn0Nb2GQtuWryCQTNC1YTOfeXciKgmkYyIqCr7iYhXYvP5r9dZ5IbSKajVLsKub+E/dP7XOWtIieXwsgATi8qZ/mBaUkozlUuwvTNFl4eT17n+2htN6Px++gZJaPbb88w0R/AkmCtqUVeAIODMOipNZHeDSJ06HgXVEJ1tk57TLIqkRBqZu+YyFSsRwVTQXseLyTy++Zhctrf5PPuPBWkRWZCQeUTw8w0Z9g78Yell1fT/2c+fQc2s+xfa8wb8EG7DX+qc/93GgSM5bDXuVlxbrbGBg+wald2ymqrMLudJOMjtIw20nP0SgLZtRR4Zf5ybYe3jazit1DbpZc+R3sO74Ms2+Fw78415iFd+fH4pKcnwztLXvrTozwJ8PQLaqKHOQ6wiBL540LbNU+VK+d8f44fUdDtC4q58irA6y8qYUdj3WiZQ1K631MW1rBiz84jq6ZuAvsrL69jRPbh1l8TSOHX+mnpMZHLJyjcV4JB17sO2//vXsjVC6uxCbbuLbiCsqOZAgW1lMYqKY3vJ/w0CAl9Y0kwiHsThd1s+cy0nkKSZbP3pNNamfNZrS7i/DQAMVVtWTiCS65+15ObH0VLZNm/iVraRz8KShzYfgANF0C9SvgsXtwXP9d6oIOeidTALxrRb0IIXkr5NJ4CgJ4g0UU1dRhczionzaPuvLZZI5OgAyWZkxdn0qZm1zQKQJIhD8euQQAGSRsHieamQ8CM5IalnV+CKogCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCMKfGhFC8kbxBsEy8k+lXvt3oKfBVYRVUMP8QAUNFSXIikwuk+Lqj34SJnX0iTS2cs9UFel9Y9irvKg1F05qNAwDn9PGV54/91Tgq2dXsLCukCUNQdor/FPLJUVm98ZutEz+aZy5tM7up7u57H0zpyaV/4q7wI6sSJi6zuLrbubMnh2MnDlFRWs7s9duQJPt2BUZRZYIp3J0J3VmVhWgmxaXzSzHYVNwyAZo6akAEgA8JRgjFh2HR7j4jjZ0zSA+mUVRJTJ6BrtP5fSeUepmFpOO54iMpikscyPL4j/tv5WeTrZRvvpBZjtGyTZcj3NuimQ6jeO6n2NLDpO0F+Fqu5VMMobtmp+QnexH9VdBy4fQcjIFV96NFdOIPd0FqgQmmPEctkVlxDf1A5DcPYJncTmu9iJwyoxEOokkxtn/7JMYuRyzLtnAnicfYcn1t2AaBqH+XmZefClndu+gtKGRYFUNZ/bsoLSukcmhfroPqLSvvJhjr71M7cw5bPvF/SBJrH7b3STDk0wODSArKnMvuwnZVgb0odrOPTU+l9bZszHBRbc20LZ8Had2vsy0FRdzetc2tEya7Q//jBkXX0JpQwuJiJ94JMjOx7px+Wq46NZaLnn3fex96n5kRWbhVTeRSaaIHD2GVJ+jpq72LerJP3OZCBz8OVL72wmPqAyfORe60XVwnLJGP7puMnUnDfcRSIwxvWg6yVySL0z7V8LPOwkPpCktrKLi1kq0lnJs7mJ49r58yE7LZdicGu5rq8iMWqiqjOa20TeeZrQnTmVLkMKy/P3b6bUhyRKRkShGLorTcBM5MkTmSOi8Zje/bRGbOBdCMm/9VSg2G9lUir0b+5h7SQ0OV4bw6M/Qclmu/qtPERroxdB0iqqqMS2L07t3sPDt9/L+5zvRDIsv3TiL4WiahmIPVYVuTgXsJCPnHvUuSeDyqOwdiuO0yfx4ey/Xza2kyGtn2tnPja7xJO+9qBHdtKgKuLh1YQ1VhS6yuonbrnBmNI7PqeJQFWZWFbwhXSpAf7z/v12W1Q0e3jfAnOoCjGQYdfYt0HwJGBqR0qsYHcjRfXgC07AorfOg9MbI9cWRnQr2mvzkTdmtooczONI2Utq58CRLM7EPShRWVLL/2SeoaGmjfs58Vt35LnxFZZzZnwWgc/84lS0NrLz9gxx+6ZfouSwz16wj1N9LLp1mzrorePG7XyU+GWLtXe+ja/8eVLuD0OC545AVheKaOoY7T1KUSVLRWP5GnVLhTeCxKbR601A5B4wM7P0hZONn10V5+clJ5q8vYdE1N2LoGoMdxzmx5RVu/PTnaJi3iNjYCA63l9LGJrRMmsnhAewuN7eUXMEHjnycjy74KKZ17lqVNRtw/tPm0wkNh9uGZUFsIk1JrZ/oWAp/kRO7U8FV46Pv+CQT/fmJo5YFJ3cMs+bt0wB49jtHuPxd7ZjHJ1B9DqRfjUUNi/irA/iWVlBS60e1y5zaNcrEQILwSApXswgh+b+ixOdgbCyFPpJi//N9aFmDo68lqZk2i4qWaciKjOm0mPoaY4FiV0nuGcHnq8FT5CM8NISpaXTs2ErzomXkkknSsQ6a56+ic9cY6pIiPtNcw7Gn+jmsm8TnFLNi3Tfwpw9D2xUQH0XSktD1Cox3wEV/lQ8BbLscKHnjDt40YfQoJMYg2ACqHex+cInxwJ8KLWvQuW+U6mUVJDYP4llSDtavlzCJjqXoOjRB9bQgOx7rpHVRGbmMzsV3tmFzKOiayQs/ODa1XSqaY+vDp1l2fRM7H++kaX4psYk0iiphGmDq1gXtMLOwcWAjfx/8ADk1gqyohM/e83uPHKTn0H4qWqex9KbbcBUEuPIj9xEa7EeSZIqqasikEoQG+miYt4ijr75IzYzZuAsKmLnmUspK/JQ+eWM+IKD7Objh+7DvR/Dy5/LnQDf5u6vaGYpmCLhs6IaJS3398FfhjaMlJ1FsNlKRGNff8SlyhyZxLyhH60mQPhrCNacEW6kbSzeRHQp6gYNgjf+3VywIbxIzHUMGMigoLie6nr+P2Kw0iXAWX1CE0QqCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiC8KdLhJC8UUwTJrvByIKRA1mFoX1gwddechBJa1xqd+EtLOalH3ybd9zzL6SPTJA+MjFVheyzIyny61Y/EMngUGX+al0rE4ksxV4Hp0bjxDI6q1rPTTwzTQs9Z6BlDLyFDoqqvURGU8QnMmRTGguvqGf/830YuonDozJ/fR02p0wmmSCbTFI/dwFNC5agOpyMdJ+hpaQC8+Aptmg+fnwiyjfvmM+xoRjhVA7TMnn3XC/OTC9koue1V5O8JCYzVDQH2LOxB4dbpXVRGYlojvU1G5g8nqBlURmd+8YwTYumeaWk4hqZ3j4UtxtbSfEb00/Cf2kwkuIfn+skmtb50NolfPOJM2yYUcNAOM2xoRgfWruYb246g0O1c+/FS/ja/aepLKjmP9a2UzCmUVTmJvyTE/jW1GBpJpJDwdLyE4WNWA6l0IERzk9kT+4eAcC8xM1TP/kKy266g2R4kqaFS+g5tB8ASZZRVBVD1+k6sIcr/+I+4qEQ+55+HD2XxVNQCMB4bzcldQ0sufE2TPPsxGTLYsvPf0z7ytW0LV9FWeMcknEfHr+DhVfWMXg6zOy11Rx5ZYBlNzTTf2KSVMxCdS5l1Z2zUO0aS2+8g31PP4qey5JNm+haBaPdGbRsiLpZxfQcnmBiQOfQywbNC+6hojHDc9/6J6yzk6X9JeVcdt9nRBDJGyEdBkAaP8bQ6TkoqkxFSwGmbjF8JkJoMEEupeH22qFvB+z6Lpx6Dvf7XuK+WZ9k4lEHsbE0AIlwlq0/6MV/Xzu1894OgVro3wlOP/RuxSx08NgT9Rj6r02ClyWW39g89TqbMlh4RT3BKonjrySRnRd+1EpOBdXlYPqqtei5LBXNbWg5k7HeGLm0zvCZKP4iF4HSQta/7zO4fAYv/Mc3SEUj+X0qKlf/1Se4+H1/wdGkk09cXoqqSCSzOieHY/gVC1m3mLeujj3PdJNN6iiqzPwNtWhZnRK/k1sWVmNTZPrDKdKaycN7+/nZDU3UxMaYTOZ4IeHCqvTRH07xi73nQiM+e/V0MjmD2763i4fev4w5NYE3oFP/b8v19fFJ6zL6yxYxUmqjI9fPS70vsbZ2LWPJMXwOHxUFTq6aXcETB4ewkODgz6BlA7q3lmNHFYLVozjsLyOrNhpmX4rRAfRB5sQkmROTAHiXV5IbSyEpFwZ+WTGdBVfdwKs/+R5j3Z2UNTYTrKzmxe9+Dac3wMyLbufolhjDZ5J4Ak2se///Q5LSPPvNz5MM5+tX7Q7iofy4Jh2P03NoP7Wz5rL67e9m5HQHsqpSWtfA4Zefp2XJcg6/9Dxtyy9ClsVE4D9V86UO5F2PwNy3Qc0yjP5jaA13Y7n8eMwxFmyoJTI2Tiq8k6OvPI9is7HyjruYHBzk5R98e6qessZm5l9+Lb6iEpKRMHosyefm/i0JOUeNt4YqXxVtwTacSv4ebJrnJrjXzSzCW+hg//O9tK+oJJfWGe+L076igoISFwCDJ8PYHAoVTQVk0zqj3TEioykcLoVULIcqgeq2TwWm/YprTjGFBXYOvNjPSN/ZcJWAneEzEYqrvNhd4qvVnxo9FCLb2YmlGziaGrGVlf3WbeyqQomiMKBZaFkDX5ETT6ELu7uVgaMbySSjBANVeLfbMBPaedtKqoyVNfEGC4mHxpmz7gpy2QwFZWXseuIhLv/A5UyOpmh2OXn+2dNT23UfmsBZVEVHyWwuLYnQYo9DcgxqFkPtMjj2KDgKwBn8g5+jKVoWDtwPL3waVn40H3rS+RIUt8Kln4Xy2eApeuP2L/xB5NIay9bXIaU0UGWSu0bOrZSg+H2zObNvBD1rMjEQZ8FldXTuHyc0lKB9eQWpRA5ZksCCwgo3/iIX431x4qEMNqdCbCKDosrIqszAiUma5pcSrPQwOZSc2o3dpZL2RPj03M8w/uw2SurqyKaSWKbJweefZtlNtzM5NIDqdKKl0yiKyjPf+gq6lg/Wc3p9rHvvh5h3+XWkdQNT1zi9awcTfT2suvEm3NEs2eVfwL7vS0jxQRg/AT1b8zsvqGVUKuKFY6NMq/DRF04xpzpwXoir8OYwFA+qLY475iL+RBcA7rmlyK78ODB9aJz02bL2xgLUlsBb01BB+C8YqUlkIIuE7HHTl6vmNutuTng8bP3lIf7h9jmU+kQQiSAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIf5rETLk3UvVC2Pt9OPQgKDZYcDdG2Swi6RgA3sJC+g7tJ5dMovhtuGYWkT4aym8rg//SWuSC13+qepHHzpbT4zx1eAivXSWR02kt9VHgPr9LjZyBaleYf1kd2ZTOaHeU8sYCVtzchGRJnNozytx1+VAEQzPoOTpB4/wiJFkhNj5G98F9uHw+DF1nwZXXYR8doWXrM9TE4lz9wY+RcakMR9OUeuyocQNb0knK5sMTqDuvHZIM8XCGnqMhaqcHyaV1djzeySXvame5cx5WUww9G2Xacg+Dp0z2P9/D6tvb0Id6MBQFPRHH1dDwB+4g4b+TyhpYZx9v7VDzYTivdozz0XWtHBuKoUgSDlUmq5t0jMR57B2LqHPYMQ+Po52OkK33gwX6RBpbpQdtKInis4ME6cMT+NfVEn2uB4z8Pmztfo537zqvDelYFE9hkPHebo5v3sTauz9AOhbBX1LK01//Mkuvv5Xw8CBIEoHyyqntjm/ehMPtZcM9H8Hh8ZBNJvEVFeXLSBLR8XGSsRzBsmoSoTTz1lUTG0+x6rZWkODSd01Hy+oceinN4Bkb7Utr6TzkZcE1n6K8yc+ZfSmOvBameX4p1W0BkKF1cSmmYTHnklqyyQR7n3pwKoAEIDY+wljnKRFC8kYI1AMgJYdoWbQey7DoORpCUWWW39iM3algcyoQG4Ht3wIsWPBO3IbGPNciXhw7c151hmYSHU9DbRk4fBxoXcv+8EmSzYtYVjKNFbYqtjx0Bsu0kCRYdmMjWU8c8AKg2mUObxpg9e3VlLe0IXlsuBeUkto3lt+BBL61taSNFIMdx1FUG6d2bmPDvZ/i1O5RmheUYHcqdOwaoXZGkGlLy4iO7psKIAEorq0jMRkiGY1QaPOT8pWhOLx4Vbi0SkcdGybtqOHMvlHal1Wg2PKT+c7sG2VlYyseu4Gmq0gSKJLERx44wP+b56P0S3+LVFpKEXD92Bjphs+wvTN03vn55xdO8a0755HVTV7pGBMhJH9gqYMHGfr4J3C2t1M7GaK0PEj3pV7ubL+T5kAzPz3xU4aSQ3x0wUe5dWENdlVGd0awNa5hMutgzCgkWBXm2X/7MgCSJFNYXkKJvxa/z40Zz0/gVYJOJJeKEc3ivbiSbEf4vHbodRKBslqu/ugniY2NcuTVFzn4/EYAomOjzFgdZsk1zfhKHNhtUXLpYRQFpi1fxb6Nj5+txeI37dv4OAuvvp7I6DDZZJLOvbtYesOt7HvmCZoWLBYBJH/Kwr0cMhOMz9hAJHmGhqyf9ul/iWRZGOEIhr+WQFDH7jbRSmfRsmwNpmYw1LGfPS89fF5Vo11n0LUcI2c6KG9qIZfN4uzLUFHfwtenf5e0lORk4hhH9L1suPcG9j06QGwiQ+O8EprmlrD/hV6mr6yiY9cwLQvLKGv0UzMjyCv3d2C3K0xbXoGiSPQeC+H02FhxczNuv51Tu0fz+x9PU1/qQi11o4+lAJDdKu75ZWh9cdauqqRjKMHRnaPMvaSWPc/0UNVaSHljwZt+2oX/uVx/P4Mf+xiZQ4cBsDU0UPPtb6EWFaH4zw8kSEayhEeSKHaZsGeYAqsMu0th0ZUNJKNZxnpixPw+5l91G5YRI5tKUnBVI+GHOuDscNA1u5jcSBL3glIO3/8sTp+f8PAAy266g1Q8TnF1I7mcRNuyCoY7or/ZXAYPh1jzjmqauh7NB39ko7D3h2AaUFAN678ArjcwSGFoPzz7MWhcA73bofu1/PJQJxz9JWQTYPdA/UpQX/87rPDWsgwLaTKDMhCHYje+VVXnhS15V1aRscv0HgvTtrSCw6/0M9oVo3paIaZh4fLbiY+ncQXsLLu+iYmBBOGRJM0LSlHtMpIE3kIHsiLRsXOE9uUV9B2fYMk1jRzfOkT/yUlKqtwsubSYlDdBbsyPXFOLrmuYms5oTyfzLruaHb98AJffz9Ibb8fQdU5sfXUqgAQgk4jTf+ww9oaZ3PNakq9cfQt3XnU94ZFh7B4vabcbazyAvvonOMaeRnUGiC95P4fLmuhAQ9ZGmF3fQHuZk3HjMA92PcVzowGub76eBWULkKQLw9mEPzzLBIfNRWbnuSBeI64he+3Ya3zk+vNhX5JdwbOqii7L4A2MWRKE35uRCGMDUih0ukv5aexeCqUUTWaSV3pCXP1vW3nwfctoKPa81U0VBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEH4vYkQkjeKlobQGWi/lvS8exlX6zFlG3bDybeu9PFXzw9jWmAYBja3Cy2Swb2oHHt9AWZGRw06kZwK0oXzd9ENnYqAxWevmc5PdvRxYjjG6tYSbpxfTbH73ISvrG4Qj2exORRGu2MMnp1gPNGfIFjppqjay6yLqzn0cj/xUIbK1gCzL65GknS8wSCJyCSz1q7DsiyS4TAuv5/MxhfI9fZQeMMN+Mf7MVsbKFGyxDsTZNI6g4bFuKuIqsYqSi/7J9j0D5BLoht2Zq8t4pX7T05N8GycW4zHn0BL50hm4khIeP0S01cUUtHUhN1jI73tMJIsY29qwlZWhup2vyndJ0Cxz8Edi2r5zuYuDvVHuHxmOds7QxzoC/OvN86mRFF54q4l9A/EmVXuhUMTqEVOYrtHUAKOqXrSRyco2FBPxm0juX+MgqsbSe4eIXlwDP+NjeiZLKlslGMdO5A9NiA/saygrJyhUye5+J3vJRGaYPrqS4iODdO1bzeBikqw8m8OWVEwDYPBk8dYeNX1nNq1nfo58ymqruXM3l3M3XA1siJjd7rZ/8zjVLfPpHlRFbI8xGDHaZrmtRDq24nLX4DbX0B8MozdXYanwM6VH6ylY1eKklof6UQOy7KRSbiobHFQ1RrEMi26D/dR1VpAx54YqqLg8tlpmlfERG8hk0M2DE2bOhdGMvHmduL/FTYnXPEvSIk4Ho+Dl350fGrV4KkwV35gNh6/E4ZGYPrVkApDepKUaqMnfRqbQ0XLGudV6T37xOL9NoW/3vJJJtL5yXH+lmJcW2UWXVGPYVgoisTJHSOc8Y4yt206zYFmclmd9hUVIEl4AoXo8SyuuSXYqryYKR2lwIHit5ONTxIdzT99vmnhcrRskJM7hvEVOVnzznYcjgmS4Qm09CT+kjKu+Mh9PPONr+AtLKJpwRJe/sG/59saLOLav/k7VDWDJMvoLh+2ZH7S3qyLqzny6gAjXTEKSl3MvbQG1SGTTOf40AMHuHlBNUVeOyubi1iV68F9801EHs6HARTffDO5vg5KvIWMJ7JT5yae1ekN5Sflp3MG6ZyByy6CI/4QjHic5O7dFFx9FbGNz6BWVVG2/GIu0aPcdeCf+czSz5DVszzf8zz1vjpmFU5j7bRa5NQkQ0Vr+eVXv05p3TCK3TZVp2WZqDYbzz3wDd77me9iRXWslIERy5LtjuBdXI7ptHBeXoG2Kz9OcCwr4szoPtxqCZae5cBzTzE5NIA3WMT0VZdQWt+I0+MlFRsgl8jgKi0lHUtiyDKNCxZT1tSCruXwFgRZefs72ff0Y2RTSSpb26mfOx8jp7H85jsZ6TqNns2y9+nH0LJZZq3dkD8PukZkdBTLNCgoK8dmd7zu+RL+uJxITVKgVRA0JLRMEI/LDak4Y//8z+R6+/D+5IckJgbwFAUpKA7mPx+dMo1zF5IIT9J35ADx0LmJyKloBEmSiU+GME2TZHgS1eFkx+MSkgzzNyyiPj0XuR5WvacFl+LE0k0mh5JUtRZy4MVeiqu9BMrdBEpdJCczLL6qnq0PncHpUdn+SOfUvgZOhrni3lnUzyqivNFPdCKDMa0Q76oqzFgOZAlbpYfk/lHsxW7Sm/poX1ZB213tPPNoJ6pNZqI/TkmtD+VscJvwxy+xZetUAIlSUkLJBz/A6Be+iDYwQMENN1Bw9VXYyssZ64ky0BFByxrIioTT6yUX0CmscLP7qW4m+vLju4mBBDNWN6NnHHjVQoxQjqK3T0cbT4NpYSY17LV+clqaGWvWgQXZdAq7y83epx9j4dV3oqg2RscT2IIXhngEq73UJPYiJ0Yh2g+V82H1J8DUoaAWyme+sScs0psff1fNh83/DLIKl3wG3MWQGIHhg/lAFIcPaha9sW0Rfm96LEty1zBW1kB2qmROhnBNC1JwTSNmSkctcCCXuhkaS6NldGQZLr1rOqHBJBMDcSpbAviCDpLhLO4CB1sfPkMqlg8GmehP0LSghPBYirnrauk/OUlxtZf9L/TSsrCMbEpn5upK5q6tQNGzWH2deFxtZKI5AuXlxCfGMVUbNdNnsW/jE8y77CoKyis4vWs7LUuXk5gMIUkyJfUNSLJMWUMTLctWYhkGz9zsx+WVSMez2DxeIqPDJO0OimpqyX3rOzhuvpG004kmz6cQyOb2s2PyEco9FdRwBfdtvm/qHD3b/SzfX/995pfNf4t66f+WZCSMU/Gi/1rmS2LzAIFrm3AvLsc5owgsC1uVl1+OTFAnF751jRWE12GkIgBkLIXPZWqxWRmecP4dvZOLOdB+H99NRLjjezt54kMrKD37+4IgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCMKfChFC8kbKRLHGO5ic/7dY2Ryhvh5My6S1vIKf39KMkZmgpK6e+tnzkWWZ8COn8a6sQg06SR0ex1HnRwuaxPvHcfl8FFXXcmLiBEktSdpIU1nm4jM3FHK4uxK/y0ZFgZPSAhcAXaNRjg4nmO53Ee+KTwWQ/MrgyTAltX5sdoUFl9VhWaCoMjaXiqFlmBwaYMGV1zHR14uh5WhetBRJlpGdTryXriPy2GMU3nIrwYCb7r4U/Scm6T0SAsDmVHDf1kqRqaBc/Q1ITWKqHrr2jLHq1lZyWR3VppBO5DD1KNGxUfqOHiQZjVA3ay6VbdNxejzY7TJWJoOl6yReexWlKIg6d+6b3Yv/ZxW67axuK2FZZYBcJEtDqY+7ioK4PTbSQ1mKCiXcsk6h04ZrJEnswBjW2hqwwAhnsS3zgAyYEH22B0dTAb6ratm3cyOBaRXYVDtPfv/fSEYmmb5qLXWz5+H2F1A/ez7jfd1c+p4Pkk0mkSSJ+nkLMXI5dj/xS4pr6qba2LlvFwuuuh63P4CvuBiH243qcCDJMp5AIYHyciRZprimnnQsQsviFSg2lZ2P/YLxni5Wv/09PPNvX8ETKKRxwWL2PvUoM1ZfipbbS8f2zRRWVLL0htvQsxHaFpeSjkdRHSniE2NExobwBos4+vKDjHc30zR/MeP9PZTXz+b0ni1gwdIbb2e06zRndu8AScJfWvPWdeifs0gfHH6I7LUPcPQ/e85fZ0H3oQkcdRoVlgmZOMSHweYmZ2h89fSX+PhVX+DkI7GpTaZdXEpFTZAj40c4FDpKKB1iTskcPDYPpVYl3QMJJgbOD5RpyBXQPzJCdKdKUY0XT8CBJBvYXW5Uh43ok124ZhejFrnIdkWwsiaOxSUsu/nDyKoLXQvgCQbQMsNsfeg0N/x1NaHBYfqPHSIyOkz1tBlUtExj9qWXY3M62bfxcQBsThdX/eUniI6M0LFzCxLQftEapPoaokPHMHSJuesrScfKkSQJ1SEjKzKyIvHBNY1sPxNib6/Gt+6Yj2f3MCOf+xzelSsAGP3yVyj/+88wu9rPyyfHp471opZiXjw+iixB0GPnLx88wMcvn0ZjifeN6N3/U4xEAjMcZvJH/wmAraYGbWiI1qVLqM3UcmjsEIPJQQC2DW/n6OQxbqu4hjG5hp2PP4KWSb9uvZlkgqs//EkkSSaxcxilwImt1IWkyGROhfFcXEmsJIz3mnLsONFzOWoL5/Lcj7/OkmtvZt09f4GWToEkk00m6NixBVmWaVmyAj2ZY9OPvsuiq66n5/B+2ldcjGqzERsfIz4+Ttf+PSy/9W04XF5cPj97nnwEyzRZcNV1VLbNZ6Szl8XXvp2amW2U1tWTikbY89Rj7H/mcUzTpHXJSpbd8naKqyrfrG4Q/oe8URvdB/did7ooqa0Hm0qs0Efm+An8v/g5I92dFNfWM9nXS/+xI3iLiqmbNZfY+CjJcIi25asIVtWQjkcpqa1HtTsxtByRsVEmBwdweX1Ex0ZYev1cNv24nyOvDlJW70eWZDKuNBEpzfFtw9TPLiZQ5uLKD8wiFsry0g+OY5r54LKFV9Rx0S0tbH34zHltNw2L0GASy7I4uXOEi25tJWdXsE5N4q7yYWZNwg+fQg040SwJM62DBalHT3PtbdPQZRjuiRMaSlBa638Lzr7wP5E+eGDq32Uf/xuGP/23OBcuwH/FFcRfexUjGqXwQx8hPJpi/wu9ZJM6AJWtAeavryWb1pnoSzBtaRHNC+3IsoGWjHNsy8tctPpOkieHyKlRLCzcM4tBhsTOQZQ1AYpr64iNj1PR2oai2ll87d1kUm4mh2PYatL4A15K6r2M9+THGw63ypw1JXg23gZL7oFXvwipSahemD8Abyn4f4/7ZDYJoVOQTUBhAwSqf/s2v6pfS4MzAEvvgT0/yIeTAPjKYfH74MyL0L8LsKBxzblwlPgIhDrB5oLiVnCIccObQY9lMaJZcr1xUvvHcNQXYMRyGJEskTMRvKuqUAocZIfiWEVOLNNi5S0tDJ2Ocmr3KIGysyF2doUze8Y5unmQVbe3TgWQ/Ern/nHW3z2DyFiKRZcUk0sZNE5zYw94SIR1EpEcQ6ciTA4lqZtVjX48Tv1sifh4lnQ8RklDE3omy/Kb7yAdixKsrMbQc2TjMS664y6S4UlO79qG3e2hacESUtEIDrcHPZdjqOM4oYF+SmrrKWtq4fjmTRx97SVa1l1MeWkJyaEBAqUVqHY7cw+VEDxWRc3M2bjLzw8500yNncM7RQjJmySdTuB2+nHNLkEbyd9HzITG5C9PUfyuGeSCkDUd/GzsUWbVLaTO9zvcpwThTaTF8r+zDmfm0KnZuW78QcrrwgzLPSi9KX7yscXc9B/b+cBP9/Pz9y7FLoLqBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEH4EyJCSN4oegYUG+Eln8LMGEz093Bs88skI2Fal6ygfs58TAmSkQg1M+ZgDYBvRRXaUIJcWsdR70d2qGQyKVKxKFo2Qy6TIePN8ETnE1xedzk+h49wJsysxhxKroxE1sSmyIxG0/SEMwxFMiwMerECDpAA61zz6ucUk0lqeAJ2YqEMsixjdylIEiRycVTVxuEXn0Wx27E7XUTHRmiYtwgjFELr6sSzYiVKYYCJvm5kokxbkmHOmlJSCR+jXRmObh6i9JqVFD64DADjjpspKHUz0BFm4GSYwnI3C66sJjE+wJHXXmLdu+/F0HQM02Dg2FGKagsZOpMh8OSTmNEIwbvvxoxG35q+/D9K0038aQt9JEV1jQtZNQmW2rE77WRHkpinU8guG2ZKI5M1ADBTOkrAgRHJktgxRMHlDeT64lhYSG1Odm99jLKWFoY6TqDaHax627tIRSMoNjuv/Od3mX/FtXRs34zT6yM00EfXvt3MuPhS9j71KEtuuBUsi+joKEtvvJ2mBUtweDyodgebfvgdZq5Zx67HHkK125l/+TU8/fUv4SsqZsGV13Fi6yu4PD4OvfQsy2++k/GeLipaptF39CDpeIx5l1/D9od/hsPtxuHxcGTT8/hLypi2fDXPffurzFq7nsjIMJlkgqLqWo5v3sRFt7+TTT/8Dt5gEcU1dbz0g2+z5IZbefkH3yYdzwda9Bzax5IbbiWbStG6ZD1YJW9ll/7Z6i2qpWvtx5ipK6h25YL1NodCQk/Q5XDTaHPCgZ+AzUOscQmyLENRhovvaiETNXAEZaTSNDvGt/H4mceZWzKX+xbdx/ah7cSyMdrqWhhwnMHQTRpm+ygoVYiMmjSUFjFyOs7Op7u44b65jIxPECjzkY3rWDYT19xSzHiOVO8Y9iovFEhYmJQ2FKLabKg2BUWNMm9DOfufHyGbTnJ40/OsvPVtKIpCdHyMM3t3Mnf9lXQf2kcunQJg9dvfTWJygqe//mWw8jf5U7u2c+3HPo3NYWf7wz8kWFnLjIvvQMva8AacpOM55kyeZtqzj3JrJoPjxps4PBIm2NFByYc/RGrXLgBKPvwhMic7+LtblzAay9I5nmT99DIWNhTyWsc4n76ynZ/s6KU3lMJhU/jKzbNxqBeef+G3S2pJErkEfj2HEgxS8ZUvoxYVEXnwFyT3HyQ5bRUfS30JY8jAaozTE+2hOdDMOs8KTn33EXLTZzPR3w/A0OmTrLrjLvqOHJqqv6VtCbYjFiwycLYXYaV1cgMJbJUeJJsCkkWBFST20y6Sev46Uis9XPv+T3DqyDaSkUn6jh1mxupL2Pj1L03Ve2rnNq7+q0+SioSxgLnrr2Dw5HG2PPBjLNNEVhSW3ng74eEhgpVVbPvF/VPb7nzkQdbeHcBXPJ3aGRX4lQk4+SyDITt6NsPCq2+g+8BeTu3cQqC8liU33ILdIa6vP1bDZ05x6PmnCVRWcWTT80iyzLzLr6GqtZ3Al/6JyckJ0rEogyeOseOXPwcgWFWDlklz6MVnAeg5tB9PYZCr/vITZJMJtFwGAIfbRfvKi9n4ja+w5Lqb8fjyyyNjKVbfUUp8ohc9m8VfWsay60t45ts9rLqjgUwiycEXBpm9thrFpgAWp3aPUlLrR7FJABSUupi9sBRVlrAF7ITHM0RH04x2xTi2ZZBr3zODbG8MoyeGe04JyBKJzYM42wrJ9cSwMgZGX4xcZ5SyMjfy8RAp08IM2PEGXG9+Rwi/F/f8+cSefArJ5UIPh6n66r8Se3kT6SOHcTY345ozm9hkimQ4wsV3uMEycHq82Fxu9FwI1WZxzV/WoKWjKDYVPZNDkVQuWnUH+mgaTAsl4ECyK6QOjqOWunGsLKHv6CFqZ84lMRaic98eWpdeRiJqJxlLE6hzYJZDxBil4Jo41Yki0BVKqwqolAbgks9AKgTrPw+qPf/9zlMCBb/H5PxUCF79Muz+Tv61twxu/wVUzfvvt6uYC6s/Aft/DCv/CpLj5wJIIB8yMtkFE6dgyQfgl3flw0re8QRk49C/Bw7eny8z/y5Y++l8eIrwhtDCGbJnIqCZpI5PgAFGJIta5ib2XA/ei6rAssgcm8ReX4DWWMiOJ7poX1ZB574xCgocrL26HsVrI57SCA0mOfRKP063Dcu0LtifLEmEhpIMnpygtdJN+N3vpOi97yEy/1r2v9BHVVuA03tGWXRlPZ0HxmldVIxkZXB5vfhmzcfl8ZCKRUhGI7h8fizLYu6lV2AYOmNdZ3jtpz+c2teJLa9y3X1/i57L0bF9Mx3bNwPQsX0zlW3tVLS0o9ocOL0+LF2jsLyKVDQKlklpfSO9Rw6y4xc/o3bmHG656AYe6n10qm7TMt/orhHOcnr9kAIjlsN/WT257iiSU8Ve7cU0TXr6kuSyaZ5OPcrCprmUB2xvdZMF4TzZcBjLgqHEKmqdFotGjpKol5AdUXIZgxJN4ss3zeEjDxzgs08d4x+vn/VWN1kQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBOF3JkJI3ihaBnzlSJ4iEsOneO3+H2KdndBy8IWNIEnMXLueZCSCnsuhZGQsw0SPZkGWMBIakk0mFY9g6ho5y0SSJcqVAt7Z/k4mc5N8bf/X2Da4jRJ3CffOvpdSeQFQjG5a2CQLl5FBsSRcfjstC8s4vWd0qnkldX46946RTWs0zS9FQqLz4DgltV6KXeAuCOAvKUW12UECm92B3eXC2dxM+vBh1GAh2RnthAcH6Dt2BMs0UO0OamfOJpNw07KoFJwS2NzQdjmWYeL22Rk6FaaswU8uo+NwQtpm56Jb3sZL3/s2I52nKK1v5KI734VsM3jtZ32sv+pmUt/9OhP/9k2qv/3tt6gz/28aD6col3W0njiENfSkhh5KI7UUkt4/hn9dHbEXe1FLXNirvOT64yT3jlCwoZ7Micn86xPjuC+pIJqdYPDUXlqXr2ByeJiCsnIKSssYPHGUpkXLCQ8NsP59H8aSJC6681043R7ikxO0LFmBotoob27DEyikcf5iJvp70DIZBjuOoah2OvfuRM9mSUbCaJk0M9esY/8zT4BlMWfdFWz+6Q+Zd9nVHH7pOQBMMx+YEiivYPDkMQAMLQeWRVlDM0MdxwFoX7maXY8/hKyqODxeeo8cZOkNt7Hr8Yewu1wko5Gz5S7m5LbNIEk4PT50TTvvPB549kmu+/jnGTglE6xxv0m9939HKB1C0ZqoCukkHBqzVlcxcHJyKnRJUWVqZwTpTR6nyFmEUdKGkpoEOYphaPzTzK+y9z/GOZk6jb/YSfvySuxJN+WBJhb6l1JfWM1HNn0E3dIB0GpMllzTgGWOcPjFn3BsUxeVbTNwG+9iYE+C9uV+uvY9z/EtL1M78z6yqSS4JGSnQq43nb/PT2awVXuRJYl0LILN7mDv048x2nWGiuY2ll9/OxISCy6/hhf+4xtEx0apnzOfJdfdjKFpFFXXUtE6jcjwEOXNLex69BdTASQAWBbHt7zC4mtvYvlNd/L01/6JGasvw+aq4tUHTrLihhZCH7gHzPxnUvLVV1nwzW8iz5+HpGkowSIA1LIy1Or8U+i/vqIQz0AUUx5hUnUw7aJGbvnezqndbjwyzN9c1kZ1objGfxeWZdEV7SKSjhDOhakaNSg+3M/EwUO45s5FCRQy/IlPoodCqP/4PV54aGxqW2WLzNff8y0sn0R05y6iI8MMKip1s+dxYssrWKbJ8S2vsPptdzPYcYKyumbsJ2VyZyLozVlkl0pmIJG/FifSOBoKiEyMoezJgn7uOtKHkjjGNY5seoHq6bNYfO1N7H784d84DpMze7bTumwlyfAkLq+PrQ/ej3X22jINg92PPcSNn/4H9jz5yNR2kiRjYXFqx1YufkcrfmkUfn47WmErUd91nNq1jUwiQevyi2hatJTeg3tpv+gyiqsL3+CeEf6nJvp7KaqpY/vDP5ta9up/fpcr/+LjFM+aiTsapqi6loc++8mp9c2LlrJv4+P4S0qRVYXI8DDJ8CSx8VFe/O43aVq4hOZFy3D5CkhMTnDlRz5GYnKSXDpBUZWHpdcV8PL3/5WJvh4AnF4fl937l1x8p42+w09jYdK2uIXohJ3Dr0aQJIn5G+oAuOiWFmTDwt4bI7tjGAwTx/QiXNPz9z9FlUhFc2x7rpdZqyspqvOT3tSLNpDENasYtdRN/OU+kECSJXK9MZythcRe7MWvypilbkbCWbyFDhFG8kdMKS3Ft2ED9sZGnO3t5Pr6sVdVko3FsNfXo4dCWEYOy+hg5HScZCyCjETz4mWMdp3h0EvPcuVf3IfbX0AqHsUyTEqc1WiDSciZOJoC5IYS5PpiOFsC2Ov8pEeiFO7xEd91hvoF7bRdtpIzHQlKqt3UV+mYLp17dv0Df7HgL3D4FPqlDhRZIWZ4KbYXEgBwB+HY4zB6DGbfAoP7IDoAyz4AnmIonQmyfO5AcykY2AuTnfn1knIugAQgMQov/C3c8SA4fP/1CXP6Ycb1UD4TbB7Y870Ly4Q6oWRG/jsgQCYCJ56Cnd8Gy4Ql90DfDtj/n9B2GbRd/r/tRuF16OEMmeMh0kcnkBQZYzKLo6GAXHeUzMlJ/JfWktwzgqTKuC6qJmaXSSU1pq+sxNBM5s4rJbmxC2NzGstnJ3hZPZGcgWVYpGI5VIdKsNLN5FBqap9Lrm3A5bPT1FJJor8f34+fRAr6OPrgGSpbAvQdm8wXlCRmrLQTGniFUH8at7+A2lnzGOk6w6kdW+g+uJeCsgqKqmtZev3NaNkcB557+rzjMw2d/hPHKG9qoWPnVlw+P7KiAhZDHSdYcOX1pGJRdC3HWHcXuq6RCIU4+uqLyLLCwqtvwOl207lvN+s3fJiHyIeQ2GU7c0vmvkm9JGCCFdNQfHZyA3HMjIGsSCBL4FBQ7S5chTLXK9fzl698hH9b+2/ML5v/VrdaEKZo8Rj7rFbSWg13zS6g+GWTkKJgsyWRDYnTe0a55pYWjg1G+Y/NXcysKuD2xbVvdbMFQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRB+J295CIkkSZcBXwcU4PuWZf3Tb6yXzq6/AkgBd1mWtf9Nb+jvy8jA2AlsM53ExsamAkh+pWP7ZmZdsgGX34/L7cWU0qQOj2Ov9iHZFXJdUezLKxnr66Vq2nSiI8OMdXXiDhRSWFPLj7t/zNbBrQCMpcb43M7P8a1LvgU0EOg+Sfv+AzSe6sCo/xTRiQyti8uobAkw0hWluMaLokoEyt0Mn47yzLePoKgyMy6qxFvgxMwmyKQSFNfUodjsSBIYmo6WyTD+1a8CkDnVgWvFUmLjY5i6xtCpE5TWNRIbH2Pmqja2/HKYlkpgwV3QfhNWEvScQU17EFO3UOwypp7C5fXx1Fe/SDw0kT+Wni6e+cZXuP4Tn0WSJTgbGAFgxGJvRs8JZ7l1DW1vCL0njntdkNiLvbjnl5I5FQbAMvLXtD6exrOwDPl0BDOpEX2mG+fsYvzvbCZpROntO4zbH8AXLKb/+DHKGptQbTZkRaFmxhzCwwMEyisY7TqDzeXCXVDAWHcnJXUNxMfHsLlcaNk0sYlRMokEw6dOMmP1JUSGh5h1yWUYuRzIEuVNLcy69HKqWttxuN0YhoGvuIRF195EYUUVi6+/hVwmTbCqhtXveA+KolLV1o6iqiArrL37HmwOJ7lMmnmXXY2u5Vj1tnfj8vlIRsJc9ZcfJ5tKsvbue7A7nCQiYa7+q08SGxtl/hXX4PL5Gek8xSV334OWy5KYDGGzOzi9ewdd+7ah2pyY+qq3skv/LKVHTdITJhP9CdqWltO5f4zL3z+T/hNhVLtMVWshWlZnSBoipaeQXGW0r/s8SDIVvmqkngCz1ziQZAlfkZNNPzk59XT3mmkttF3i5d9ddzNQaPH96DN4bB7imX52P/KvZJIJAIY6jrHpR99k7V0fJh0fx+Fsw13gZ/DEMYpr6rB0i1x/HEmVsZV6yPXFsJW5UWQVl9fHi9/7FqmzoTbDZzqIhb7B5R/8K575t69gGvl7YM/BfRiaxiV338vpPTuYftEaDN1AUV//afRYFoauk07E8RWVMDEQ5vTeNLPXVKPapakAkl/JbtuGZ9lSBj9239S66OOPU/W1rxLwOhi47V2EE/njdVRVoXzhX8/LPakJunDblT9k1/5Z2zm8k2e6nqG9qJ2KiIzr355iYn9+aJN46SU8ay7GOW8eejjK4c7zh2qGbpI4rlPYEGD4TAcAk4P9tCxeRt2c+fQe2k9kZJhkNIK7IEB96zxyD+ZDyDLHQtgqPEimha3KhzaUwExoFNZXEp48eUE7jbiG3eVGVW3EJ8bxBAoJVlYzOTQwVcYyLTLJBJUtbUiqwtp330tyMsS+Zx7HUxCgefFy7C43C666jvlXXkc2GSc2PobL58NbWEQmEmMw68a6+Huk4zGUyCSXvvsDpJMJUpFJYhPjNC1aRiYVZiylYUwmGDt9mlRokqpp7ZQ3t2F3/p4hD4aWnwyvOn6/7YT/ktPr48SWTRcsP71rG4XX3YKsyETGRjB0DXdBgJW3v5OC0nKq2qbTe+QApfVNyIpCNpXE6fXxzn/5d+wuF6lohPGeTroP7Wes6wyr3343bn+QS96lkApHpgJIADKJOLlMhmQkTGigFy2bwRcsorTGQeU7nDi9fhIRBbtLRpIklPE02S2DU9tnj4VwFzm5+kOzUewyTq+N8b44PUcmCRU5yBW6mLG4gvSWQdJH8uNWz5IK0ifyE+t/NS7KDSawhhKMlLgpbixgqCOKw6Pi9tvxl7pxOFUs08RIpVCcTiQ1/1r69dAI4Q1j6TmkXApDB9npxNHWhmPOHKT6JqxAOfpsN9mlGroNbPYo2VAvis2OZZmEBwdoW3YRqViUwspqrvzIfaRjMYZPnUS22WiZsQR9OA2mBRKkDowhORXcc0rRE1lku0zm+eGptqT3jiEV2RjvehZLq8NW34gnpPODhk+zMXqclmArKXuK/kQ/sVyMsKucwNhxCDZCoBZiQ4AERx7Oh3uc2QR2DyQnwF0CRjYfyjZ6GJ7+6LmTsObTF56YgV0QH/3vQ0jSUXjywzCwG1QnXPTX0PHM+WVqlkCgBnKJc8tyCVBskInCtq/B2r+D/l0wcVqEkLxBcn1xzKSG4negDcQxJjOoi8qRvTZyPTG00RTuBaXotX5OdEUprPQw3p/g2JZBLr+rneQTZzDCWQDMeI7kY6cpvLSexnkldO4f59BLfSy6shGHR0HPmag2hY5dI0SPTFA7vRiXtxYiFkUuA103CFa6aV1UhOLQsIw4I6f7SIYnGe/tom3ZKizToLCikpV33MXKO95Jz6GDlNbVISkqnqCPJTfehpbJ4HC7iY2PYllQWF6BpKhc+eGPISExMdCLrmnUzpiFltMoKC1DS6dIRCMUFAbZ/djD6Nn8MW1/6Kcsv/lOug/up9geZHX1arx2L+tr11PvOfvecheDan8ru/HPmmZo+GyFJLuHkW0KRiSLo74AI5LBTOuYjhzjfXFKlss8e+hZYrkYr/a/KkJIhD8qZjLGf+hXoEgpbr54GSf/ESYVmQKbRk1DAR27R1h6XSN/c9k0jg/H+MwTR2kt87KgLvhWN10QBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBOG3ektDSCRJUoBvAeuAAWCPJElPWpZ1/NeKXQ60nP2zBPj3s3//cdOzMOtmcuk0Lt+Fk7lcBQFMXaeqtR10CUs3cTYHkJ0qlgXOtiBmzqB54VKOvfoiOx99cGrbpgWLueua29g8uHlqmYWFYsikjp8g9K1vkdy2neCHP4wpgwSEh5JYElQ0+1GU/CRHX6GDiN/GvPW1+X16VJxeFUMzMXI5IiPDKKoNWZHRdY3a6bPJnd1f9cMPM9bXzend2xk+/atJ0AMMne5g/T0fYaIvQco+C6vlPViuIHZDwuZQGesLo9oULNOidrqP2MT4VADJr6TjMWLjI8zfUEfuC5+ZWm6rqf4DdY7wu1B1g8TJ8wNHjGgONejEiGRBkpBsMpZmEnupD++KSiSHAn6ZOBH2bXqYuhmzGDx5jJK6RoxcjtGeMxSUlnJq1zaKa+qx2e30Hj3EjFVrGe06gzdYhJHN0rlvF+6CAMOnO1BsNoLVNZzasZVZazcQGRqk+8BeGuYtZPsvf87CK67j1M6txEMTlDU08eL3v8Wym27nyKYX6Dt8gOaFy3jtJ99n4dU30Hv4AAPHjlDZ1s6BZ59kxa1v59CLz+L0eqmZMZt9Gx9n+c13su/px1FtNpoXL+OVH32HZTfdwWsbH0ex2WleuIS9Tz/G0htv4+Uf/DuyrDB99Vpeu/8HzFl3OfufeYKxnq78SZQkLn3PBzj4/NNM9PVyVW0dIK7jPyQrq6BlNU7uGGb6RRWUNxTQd3ySQJkLp9fORH+cYIWH6ysvI/TMkzicSSJWBWpLGxWHhhgvCLD/hT5aF5VxYvvweYEe/SdjTGswqN03SOGLL/LID75PVJVw+9JTASS/svymO+jcu5nKtnZe+O43pu5rsqJyz2d/hJU1sFf7sAwT57Qg2YE49tYAo92dUwEkv5IMTxIZGZoKIJlqz7HDpBMxSusaePbbX2X12+7m+JZXmLZiNWf27mQqFUSSmLbiYuIT4/QfPYivqJhgVRWNuozDY8Nm5fhNtoYGYhufOT+cxDSJP/ccRe3tmIlzx6sPDlJ4YCd+Vx2xtI4qS3z2mpkEPSLQ4XcRSof43I7PcU3TNYQzYdZEa4jsPz9bLfnKq1T84z8SfuY5DP3CkBndlJkcilHZOo2B40cA2PXYQ9TOmsMVH7mPif5eOra9Rmx8nJZZS3D4HRjRbD4Mxy7jaAwgORVkt0r6eAi53o3U4oSD2nn7kYpUxnu7mXfZ1YCEJMuU1DUw65INHNn0AuGhQVqWLAdAy+XQMhk6d28nFY9xxQf/Gt0wUFWVPU/+kuEzHcxas56tD/5kqv66OfNZdPUNTA73Mzk0yN6nHgWgsKKK5kXL2PPkL6fKltS+yrq77+XZ//g64eGhqeXr7/kLZq1Z97ud/GQoP2H+wP3gLYdZN0HlvPyEeeF/xVsYxOUruGC5r7gEp9fHeE8nB57fyJwNV1Le0EwsNI7N4WDj179M2/JVHHvtZfqPHZ7abtXb7qZq2kwsy6CgvIJW90rKGprY+uD9XPLue8lFNZxuN9d/8rM89sW/z++rqBjT0Hjtpz+Yuh+O9XRx0e3vxFVQyKs/+R7Lb74TySpHUb3Y4zr6b7Q3ezSEvTHAlkc6ueSd7XQdGGe0J4bLV8z+TYOMdMdZtLwc/2wLTIvM6QjaYAIkkGz5ICYl4CBzYhLT6yA0mGSiP0FkNElJnZ+G2cWYhpn/Y4FlJvHYdXzZEfSBASS7A2QJxe8n23EKIxLGOWsWckEBEmBZFpIkYUkysqpgq63FVlj4hvTpn5pURwdoGmY2C7KMFYuTOXIYyeHA2d6OmclgplKoNTWYoRBaOo01YxnZS6cRTxk4wxLxkMLpvf14CnIUVYbpP3qQGasvJT4xxv5nnmTlHe9k64M/ZtmNt5PLpMkkgrz8g3/HOvvZ6bMV0uCfhZHUyJyexDmtCMWd/25nc7nRExd+/uonYky/ZC0PffaTVE+fybrrb0PNZLnU3sKWRB/V3mq+vPfLXNt8LTlFgcO/gAXvyn/XbLsM0pF8wIcsQ7QPapfBa1+Bhe+CHd+EZR+GzV85f6fS6wTeVC8C5bcELsSH8gEkAHoGJk7Bknth7/fzwU5z7oDyWTB6LB9+AiAr4KvIB5BMHXQ6/3dx62/vWOG/lT59Oj9GsyywLCy3l2jagSssoSoyuZ4ozvYi9NAwsZf78K2oBFVGKXRwuj/B7u8fY966WpxuG4dfGaBhTjFmLDcVQDJFt5BSGi6fnQWX19F3bBKnR+XY1iEa5hRzeNMAAyfDLLmmkfBIks6hJMuub8IwMyy/1ospJVFIk4rEkGWZfc88TnxinFlr1+P0eLCQMLQcPYf3Eygtp6yxEcs0ySTihIcHcfn8qKrKU//6BXLp/PXj8Hi49N0fIDY+xs5Hf4GWzQCw76nHuPqjn+CJL3+eFbfeye4n8mOJFbe8jVfv//7U+3Wk8zQzVl9COhbn/dPfS4YcLtVFdfdr4CnOX8OBGiif+ab15/8pJpCzUAMOcj0xXNOLsHQTW6UXpciJgcTAyTDPlDzBqfApAFzq7xk6JwhvsP6kxEvmQso92/CV3EzT3/wde/r/mhpbjrrpNnqPaBx5dZB562v55u3zufZbW3n//ft56sMrqCgQ17MgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCH/c3upHbi8GzliW1WVZVg54ELj2N8pcC/zEytsJBCRJqnizG/p7cxWRLFlIOhajuLYef2nZuXWSxIqb70DXcmjZDJl0AlupG8uwiL82QPy1Acysjq3CQ2x8lD1PPXJe1Z37dhOInZ8fI0sy8zxt6IMDJLdtB8A2bSayBE6vimyTUVQJQ7ewOVQkWUJxyFS1FVJS66OswU9JnQ9bsYVkt+Nwe7G73GfbK+MLFmN35P+TvOR0ksmkMQ1jKoDkV2Ljo2STCYqqPNidEsbAAOmxKJIs4yt2Uj2tkLIGP+0rS8mkkzjcntd98rvD7aGqLYA+OgpA8K534mwVE9XeTJYKSvHZPlfzaTbZrgjO6UVINpnkrmH8G+pRipxYmknqdIiEK8bzz36HB7/8CYoqKnn2W/9KZGSEwRPH2PLAj6lobOG5b3+NycEBkuEQWx74MdXTpvPS97/NwImjKKrKaz/9IdXTZrDl5/9Jx66teINFvPrj71HR3MrORx7kzN6dFNfWseXnP6Zm2kz2PfME/SeOUj1tBrsff5j62fM49upLhPp7aVmygm0P3U9RdS19xw4z1nWGxvmL2Pf0YxTX1jNy5hSx8VGaFy1j38bH8ReXEhsfJTY+Suuylex96jFcPj+ZZIJ4aILWpSs4+PxGFFXFNAxS0Qity1Zy6IX8U+A9geC5ABIAy2LnIw9S0TINgNBg35vej3/uZBl6Dk+Qjmvouo5ilymp9SArMvFQGneBA4fXTmLTJtIP/JLsgYOE7v855uAgky9tZvfGXgzNxF1gJz6ZuaD+bNrEVlODmUyhdXWjqBIOj+e8MlXTZpCKhDm9axvR0eHzgpVMQ0eXNJwzikjuGyW+qZ/MiUm8C8rImWlkWQZJOq8+SZKxu8/fB4DL50ex2ZFVFVmWyaXT7H3yEQrKyrn6o5+gedEymhct4+qPfhKH20tJXQNVbdNZdtOd2OyFVLYG8PoV7P0nQD3/M8SzYD6Wrl2wTzOXQ+vuuWC50dvDD9+5iG/cPpcnPrSClc3F/20/CefEc3EGEgOYlokiK8i68brlJLeb7O6dtDf/RgiJBDV1droOxQmU19GyeHk+FEqS8RYG6T92hN2PPURsfJyFV1/P9qcewHFpST6RDMh2Rsl2RtD648Re6MVMaCTTYYakLuyzC0EC2a2iXBwgZoUpa2zG0DWe/da/cOTl5+nYsYXXfvpDFl51PVf/1SdxuH10HdiDLMs8+2//TPfBfQQrqhjsOE4mHmWw4zgnt73G3PVXsvPRX5x3KL2H9hOfnMDlK2DfxsenlrcsXsb+Z584r+x4Xw/hkaHzAkgANv/0hyQmQ7/byT/yEDz5IejfBSeegMfeB12vQDbx27cV/ltOXwEz16xDtZ8LI7K7XDTMXYCWTePweBjv6aJ9+cXsf+4pnF7v1MTwoqqa8wJIAHY+8gDjvV3o2RxaJos3UMh4TzfBqhpyqRS6liOXyeDyeqmfuzC/P7eHka4z5wKZzurYuZVULExFyzRGOk/RdWAvmeQkatHrTP4sdpJM5LAsmOhPUD+7iIn+BLKSfwMNdcd44menGNZM0r0xcj1RZK8N//p6UgfHkP12FK8dqrycOTGJnjPwFzsZ6Yrh8to4tWuEiYEE6YRGdDRNZCTN8IBGRC4FRSW9fx8SMPyJTzL2T/9E6Dv/weAHP4TW24sxGcaMx9HHx7ESCfSJCXJdXVjahffu/2vSJ09ipdMYkQhWLIY5EWLg3nuZ+Oa3GP+Xf2Xwo3+FmUwiFwQwQyGGP/23KDMWkEpDaDCJokqM98eJhTKM9sRQ1W6yyTgF5RUoNhsntr5K67KVHHx+I4UVVQyePE5xbQOnd++YCjQA6Dy+F0u20EIpvMuryPVEib3UR/rIOFaJjJG6cJxhBGHw5DE2vP/D9B05SCSbRrIs7Dq4FBe9sV50UyfgCGAaGigOsHvAd/a7pb8qHyDStxNKZ+TDPuLD0PUqzH8HxAYgMXr+Tvu2w9IPnAsjCdTmg018Zfy37F5w/VrozZGH86Ek73gSbvoxlM+G6ABULcwHPjWtgSv+Gfb95/n1KA5Y/D6oXvC7dbDwujK9vRgTIcxUGn1sHGw2xkMKW58dQyp2YUSyKP58sJFrTgmYFoldw6R1k7BDZf/WYfScSXwyQy5jYJkWkgRZw0KyKxfszxl0cnTzIMe2DrH46noSk1miY2lyKZ2Bs8GVumbgK3KRTWrk0jpGbpxcOoWRy4IE3ft3k47HiE+MY3e5cQcKKaioxDR0nv3Wv5KJx1HtdlLhSSYHB5gc6CcdixCfDNG5f89UAAlANpmk7+hhctnMVAAJgGWZHHzxGVqXrWDv04/RvnI1mUScE9tepXHewqlywapq2pav4oVvfw1fzk6Z4mLmiefhuU/AyGFIjsHIEYiI729vBEmSMCVjKnQktqmPxNZBzLSOXGDn6LYoBU0KHfETADgUBxdVX/QWt1oQzvdophkFkyrnPgCCd9xBCCdBScfpmKB2epB9z3WTjucocNv43jsWktEM7vjeLkZjF44JBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQ/pi81SEkVUD/r70eOLvs9y3zx6e0DU3TAItcLsuVH76Pde//CKvefjc3fupzlDa2oNoc6DmN8NgAps3C3uAncHMrwVtbsbcGsFwSWi6L8TqTC3OZNIp0bnLQv6/8BoosnzcRLr7xSSRJRrUpFJS4cHhs+IJO3AE7qXgWVVGIekZJOsJkvBHcVRJJM07KbeIq8BMor8Tp9VJQWkZRTR3Ww/nJoqVf+TKGoaE6nRdMngfQNTsX3dKKXTFJbHqF7Gsvkk3pqDaZQImLYIUbf9CBZIE7EGDBldeft/3cDVfiKapBUSUqvvhFqv/92/iuvgbF5/sDdY7wu0hlYpgL7aDKpPaNUnBZPYrPTvyVfvxX1ONdV81kchhjtZPsBpX9uU30DByh78hB/CVljPf1YBoGNTNmcXrPDmwOJ6l4DNPQaV64hONbXgHAsiy0bIamBYs5seVVAFS7nXQsSs30WXTu2wWAw+MlHhqnpL6RwY78ZCRvsIjIyBC+s38DBCoqGe/tRpJlcukUABUtbfQfzU9w1rX8U+iLqmsZ6ToNgKHlwLIIlJUTGuifapdp6Lj8fpLh/AR3WVbQtRyyok69L1WbDS2bf1q4aVwYJpCMhHH5/FPtFf6wZEUhNJgEYM9TvdgcEt5CJw63SqDMTUGxE9UuEfrGv+Fds4bIY49T+S//zOTPfoZc08jkSP4aGe6MUtMePL9yCTxGBM7eVy3DQLErOD2lzF1/zVSxoupasukUqt1BMhq9oI17X3kc2WOj4LI6gne04V1VBU6Znq5DdO7fw6y1688rv+jaGyksr6Ru1tzzlq+47R3YPR5sDicLr7qBU7u2sfrt7+bxL/8DdreXZbe8jeW3vA13QYBcOoG/tIyqxhb8sg+nquPRw+hP/oQxfxFln/ok/iuvwLduHeVf+AJSsAj/lVdd0Hb/lVeSPXP6guXuhQtYWB/kmjlVzKgsQJEv/CwQXl+xq5h5JfOIa3HSeppEqQ9HW9t5ZVzz56MUF1H68b+hMHaK9XfUUdlSQG2bjw0bnFjf/UdmrAgy2uOhomUOK255GyvveCczly+ndemlXPKej3P9J/+J2tkLaV26ktFMP8nlOr7r6ii8tRW10kPq4Di2Sg/+q+rZ8uxPefXRHzJSMkB8lUZseY7HH/0SsfBYfvuuM+eNL7AsOvfuwu7xMnT6BHpWIzTQh6HrABSUlaPYbMiySt+RgwAoNht6LnvB+cgmk5imcV79kqJM1fXrXm/7XDqN8ToBOhdIjMG2r52/TEtDpBfCPb99e+G/JUngCRZzwyf/H2vuej9r33UP13zsb5FkmXQ0grcwyJz1VzAx0IOq2tBzGtrZyeSv99mZS6cxNA3T0MGy0HM5ambNpqiqBkmWyaZTGIaOaVrMXH0JSBJljc24vBeOFe0uN8lQiEwyQSoaYfDkcSzTxCyVUSrOBT5JLpVsY4BkXEO1y+TSOrFQhpoZQWIT6fPqnEzoHMiYRC+qxnF9M1KBDfecEtxzS0gNxAkVurCA8EgK07DOHqfFWF8c1S6DJWEBhmGSjOY4fSSKUlCA7Hajj49PhfD9SujfvwMSWJkMlqahnQ3jMaJR0oOD/5uu+7NgaRpWOo2VzoBqI/yzn019dgOY8Ti5nh4kh4PsyQ7K/+mLaLKLyeEU6XgOQzdJxzUM3aK0zk3PoW2AhJbJ0HPkADaHE19xCZGRIQLllUwM9GFZJlrm/Oti8PQxUv4UtnofVs7As6KK4N3TGZs2Qd/ECai0IwftU+Ulr42oL8z2h3+GO5AP99B1HdMwMAydIlcRFhbr6tZR7i6nomcbzHsbFDbmwz5KZ4CuQWk7FLeCtxSOPQqzboYzL+Z3Eh2Eab/x+d71KrRcBms+Bavug0Xvhcp5oDr4bwVq8qEiv66oGUqmQc3i/J8ZN0D7lXDLT+D2X+Tblhw/V37WLdCyHi79LHhKfpfuFf4LZjSKmUxiJRJINhum6mTvayHqZxUTyhpQ7sZW5UXx2LA3F1D03plEl1fy3GtDjPTFWXJtA8XVXk7uGMHuVAlWeug7NknGsnCtr5sKLwNQFpYhFzm4+iNzWH1bK3aXSjKaRbXL6LqFzZH/XUK1KSQmM6h2mYJSmUw8hmnq6Nkc2XSS6PgY0tnfD0rqG0iEJ5GQiI2NkpgM4fL5yaVSIEk43C5Gu89QWFHNyOmTREeHLzgHickQsnxhYEo2lcJbWEQqGpkK9hvv6aKwshoAd0GAmhmzefJf/hGnz4czPUHtT28BPQW5BBg5iA2CZUK49w/ddcJZcTOGKRnY630U3txK4S2t2KcF6Bk4zHhPlJblZSwoW8zdM97Lf172n8womvFWN1kQpkTTGs8Y01mt7MKvnAsUSUp+ZAkyfYdYtSrGXb6bGP7hJwFoKfPx47sXMR7PcvN3dnBs6MLfDgRBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEP5YqG/x/l9v1rL1PyiTLyhJ7wPeB1BbW/u/a9n/lqxg6TqKakPP5UiGJ7E7XPiLirE7XeTSClouhctbhqyoDIyeoNBfiVv2giJBgUJOzuILBimtb2Ssp2uqarvLTbCiim82fpPBxCBeu5dZwXaMeBJbVRX2piZynZ2kntmI7X0fw+5ykk3q2J0KdqeCzSGDpSLbIUMCl8eFKckY6GimhqFaZApVCuxl+IqKURUVV1ZDXrcOWZJRCgJomoHN6WbWmvUc2fT8VNtqZ82lqLqWQp8JuoURj5N4/FsUrLmabFrF0EwkSUJWJWx2J6HBXlqXLqeipZVEKIQnGKSwooZMXCHojaEpCpLNjrOt9a3oxTfNH9W1O8XimV98jWXrbqGsuJGJ5CD2DV5io6Ps2vR9Slua2PnIg1S1z0CWZfqPHWFZ7e0ASLKEZZ3/NrUsC1nO5x6ZhvFrQR75t7iuaaj2/MTMX01O03M5bE7X2WX5bTOJOEVVNVP1yopKOhHHc3byZjaRwBMoJBkJo9ry9UVGhimuqWWsp2tq2XhvN5Wt7XRMjKOoKrKiMN7bTftFaxg+fRI9l8Pp9REeHmLaios5tXMbEwO9lDe3MnLmFG5/AZIk07V/D9NWrOL45k0oNhvSb4QBtSxeRu+Rg1RPn0Vh2bl2/zl5K69fj1+hYU4xh17up2v/BHPX1qCoCqpDQZJAcchImRT68DCSooCmgWlixuKYe7dSO+Pd9J1OMXQqwpJrG7EMi4GOMG6/nSVLHchPP4DRWAeAvb6B1Gg/arCKaSuvpnbWAlLRSQorqxjvOUVBaRnljc0XtNER8JLz62SHonjcfnApJMwIRdW1xMZGcbo9rLj17ehajpLaBk5se5UWJJbdfCfTV19CJh4nUF6B0+fDZrPjKyqmacFifMXF+MrK2XDPR4gMDzA52MfQyWMU19RT2TadXE8vjo4zWJaFt7SU8GOP4r77/WQDJUwGAnhb2pFdTvodBdT7A6g11VR+5StEH38MJImC665DralBCQQouPFGYk8+ieRwELjtVpzT/zwmAb4V167X7uXTSz/NZ7Z9hnX16zjhSLHi439N5uXXyB47hmvBAjyrVmFGoyjBIL66OvxWBn/RUZIvb0L/ZSeZe+/gOftD3LDonUjRUty+LL74QQLP3szhpZvZ/bTJpXeX8PKPThAoq6Z9mY1nfvZ3FFfXcem7PoivLYi3qg7LKfH8o/9OeWMLfYcPkopH2PnYQ6y4+U5S0Qiv3f9Drv7oxwkN9F1wHLqu0b1vF4rNhmFoyMq5IWX+fp0PmCqpa2C06wzDp05O3T9/xeZ0UVhRhZbJ4CkMkgxPAjBw/ChNC5bQuXfnVFmnx0vA6UG12afCpABmX3oZ3mDxbz/xkgyK/fWXv06g2h+7P7Zxg2noaJk0lgTBqmpS0RjZRALVYceqKMDK6UxfeTG5bBbTMMilUszdcBWv/uR7SLKEanecFzJTM30W0bFhAhUVgAWWhWWYlDe3kYxGsEwTxWbDMgwKq6q56W8/D1Z+fGFzONGyZyeDShKtS5ZjmiYHX3yGS959Lye2vIKha8RSEezr6rBFsmSiOWK6SWQ0TWGlh9GuGPWziglWeimu9mKZ+bFNYjJDy6IywiMp+k9F0HQTW4kLp8+Gw2kjl9A4M5FBSWjMXlvNqz/tYNGVDQCoNpmGOcVERlMEK7xgWZi6hWGY5LIWyDJIMlYud+H5TSTO/uPs2MowkOx2rGwO6XUCe/7Y/cGvX9M8e43oSFgYkciFReJxJMsEVUUtKCAHGLqZv7xMC7tTwTQtEpMahRX1KDaV07t3MHPtOmavv5zBE8cprW9kpPMUNdNnoedyNMxdyNDZYDyAXDpFJDPGiZ2baGpfRLw3xMEtzzBj9SVYJgRKyxit7aPl0qX5gJO+w2x74KeYuoFpmgSraigIBJHCEXIeG6fCJ2kNtFLsLCZo8xJwhqG0HHJJmHkTjJ+C+hWQjUJyAg49AC0boLA+X2b//bD0Xpg4DTNvhI5noaAaLv17aFgJRQ2gpcBfDQ7v73aup10N730FQp3gKYby2eA+G+LmKztX7lf11S6D970Gk53gLICSdnAV/I+6+Y/BH9O918ppYJlYhgGKjGVBJp0PdUqndEadKoWthSgyTISypE5FKazwUjs9SPfhCWavrWbhlfUoqozqkFlyTSMntg3RdzKMc2EZxbdPg4QGbhXTZ6frWIjuwxMsuqoB07TwBp2M98ZpXlDKrIur2f98L50HxqifWUztjCCpaCb/vjQtZEXB1E1qps9ClmWq22cQHR+jtK7xbJlffXey8l8NJQnLOhtgpuVIJ+K0LbuIvqOHzjsHNTPnIL3OT0Oz165n20P3U1hRRXwiH4JTO3MOlS3T2HDPX2CaBn1HD5FLp7jixpsoH98NqYn8OEGSQbblxwaKLf/nz8Qf1fVrWaguGwc2b6Rp2lLcspdMLslrD/2YJTfewuo76sk53PxN3d/gsL3VP1sKwoV+trOHNHbW2l9ii3Qu1M5QS4Eecj27KYg/BVKO+skfMnr0A5TNbGJBXZD7372Ye366j+u/vZ0PrWnmrhX1+J1/PvcaQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRD+PEi/GRTwpu5ckpYB/8+yrA1nX38SwLKsL/5amf8AXrUs64GzrzuAiy3LuvBRuL9m4cKF1t69e9+wtv+uRrs6Mc8+KdvQdewuL+5AIXrWSTqewOFOY5omqfAkmUQcWVXxBgpxFxYjKzJaLkMukWD3k4/Qd+QgJbX1LL/17cTKFRRZIWtkcakuip3F+MLZ/FPAo1FizzxDeu8+PGsuRrn2HeiSDUmRkKT8RDssiPnHsCQLm2TDsiwUWUEzNTRTw21zU5a0Yek6ViwGuo6ZSqMEC0FVSbqcZOIxFEVmcmiQkc7TlNTVU9Y0Dbvdj5MMkmWRO3yEwY9+FLWyCtenvohUVo3ssJFwxykwJYxcjlQ0ip7LoNgdeAIBsqkgTq+Kb/wUkseDs8SBXHrhxP4/En/wmct/LNduJplk+0M/5cBzT7H85jvY/eQjqDY7sy+9jN2PP8z8K67l1M6tpKIRVtzyNrY88GOmLV9FZHSEkc5TrLrzXWz5+Y+paGnFGyzm1M6trLj1bex58hGcXh/Ni5ax/5knWHztTRzbvAktk2HRNTey7Rf3M2f9FfQfO0x4eIhVd97Naz/9AY3zFmKaJj0H97Hqznex45EH8RUVUz9n/lQ9p3dvJxWLsvT6W9nywI+ZdckGhk93MNHXy+q33c3WX9xPWWMzRVXVHHrpOVbccicd27eg53LMuPhSdj/xS2asvoR4aJyBE0dZftOd7N34GCW19RRV13LopWdZfO3NjHV3Mjk8yNz1V3Ds1ZeoaZ+FYrMxeOoE05ZdxN6NjxMbH2PaytU0LVhKOh7HV1hOUFHwz5r2Vnftr7whs+7f7Ot3KDqEEvaw+6lu+o5N4vTZ2PCeGfmwJcNCP3kYe98JQt/5Dq7585FUFSObpWD9ekY//3ncn/kKe7qLGO1PY3MqrL2lDg8JjOOHUCcHUQOFjH/jG7gWzKf0U5/CnJwkWdqCgYIs5cN2JNNA69rDhGWAZSLbbOx69CGyqSSzL9lAy9KLeOAzH6Np/iJK65tonb4QwxPEyI5jGDrDpztIRSNUT59Fz6H9jHd3su59HyabSZGORjEMDbvLjaegkFw6g2Xq2LI5Mr98lNz0NgovXksyNE4qmn+SsitQgLeoBE8ojJVOIwWDGJrOsLeEkYyFLEHAZcOuSugm+F0q1YUeksePg6YhnQ0DsnI5sNtx1deTOXGCXP8AksOOo7EJZ2vLm9bH/4U/+XtvJBNhJDWC3+anwFmAIxJDy+Qgp6OHQkiSlD9Imw3J7WYgYGCNjfPy6BYeHH+epRVL+eCsD1D3yLth/edBS0NqghH7ch79xmmcXpVl1zWz84lOHG6ZhpkT7HrsR6y47R0Eyio4vvllRs6cYv7l1zDW00mgvJKRM6dpW34RAyePUVxdy8ntWyiqqqZp4VKe+uoX4dfGjJd/6K+ZHB6kun0m/ceOUN7YxAvf+QaZZIKaGbMpbWzCGyjC4Xaz7aGfkopE2HDvX3B693Z6Du2nqLqWJdffimxTSU6G8PgD7HjkAcZ6uiiuqWPNu95P/6EDnNq7g9LaBmbUNGHbs5fcmtXs2f4K0dAE05evYta6K/CXlP5uJ/3Az+CJD5x77QzA5V+C6deBzfkH7N3/1p/8tftfGek8jYVFLpXGMA0cTheOAh9D8iQJLUFtyom/e5hIZTXdB/dR1TaD+OQ4p3ZsZfqqtRx8YSOhgT6a5i9m+uq1xMbHKaqqwZIl7A4n0bFR7G43WjqN3e3G7fOjaRkSBQqluptsOoNp6KTiMcZ7usilUpQ1t6Lnshzf/AptS1fSfWgfc9ddSTqZoKRuOrkUxCd1IqMpXD4bdpfK6b1jlNb7Ka3zEQtlKK7ykoykObV7jMVXN3By1wi17cH8e1SGZDhHKpYlUO7GMsHptTHSGeHU7jFmrKqkY+cwTfPKSMWzNMwpYXIwiWma6Jp1NndEwu+1KImeIHP0KM7pMxj6+Mfh18JFgu97H4729nzIm6ZhmSaSzYbq82NvbcVeWvJmdfMf5fWbPnkSMx7HSCSwDANjdJTRf/j8eWUqvvhF5IICJFli/Dv/QeHn/5nJuI3oaAqX347DrZKIZEnHNGyOSQ6/+COWXn8rPYf3UTVtBopqQ1FtbPn5j2hbvoro6CjTV68h1N/Hye2bUWw22pZdhKHreAoCbH/4Z2QScdqWr6J+znxM3SCbSTF8uoOGOfMZPHmcA889BcD0VWuZuXY9RjZLiWInq1oc9k7iVt0EbH4CFpTGR0HPQioEyXE4+DMYPwmtl+XvZZIEbVdC6DR0b4amNfDal2DpByHYkA8l8VWAvwIq5/9Jhi/9L/1RXrv/G6kTJzDGx/OBRtkstspKTp6yGB2DqrYADreNXEbHU+BAsckMnQ5TUOLC4VZxeu2k4xqqTUJRZYY7o0wOJaifU4LbZyc6kaLr4AROl8q0ZRWY5v9n767D5Crv94+/j42te7JxFyJIcHfXQqE4VGlLqbfflhptqQulQml/FFqkLW2B4lbcgwUSEuKerNv4kd8fZ5MlJEBks7ObvV/XNdfMeXbmnM+ZeebIJM99AqIJm2zSJdmRo7gsSqzYoWF5B6vmtzB6ejVuzifVmaOqPk6Rk8csLiKTXInr5bEtG9O2SbW10t64nvIhw+hsaiBRVk6suAR8n3uv/RnTjziWoRMmkU2nyGcyWLZNsr2d2lGjyaXTdDQ18Or9d2OYJtMOO5rRM/Zgxdw5xEtLWfDc07jZDDOOPh4v7/LK/Xex5/Gn8OQtf6GsdghHf/yzYWBWLocBNK9bwdCKOHUr/41TWgONCyBeAcW1kGqF4bMgXgljD4OiqoJ9zt12uf4L0LR6JdmuLl594G4Wv/Q85UPrOfDD51MzbiJ5J051Sbyg9Umv2Bk7m8L9iN0tk/c44EePMjX9GmeXXsMj0alc88WHAfjzHz7Bhxpvp6I7XGlJxWGMbX2cV4xPMPPKn2BZYbhuc1eWK+98k/vfXEfENtlndCW71ZcyrraYsdVFjK0pprJoCwGK0lcG3YGSiIiIiIiIiIiIiIiIiIiIiIiIiIjIuxU6hMQG3gaOBFYDLwHnBkEw9x3PORH4LHACsC/wmyAI9vmgefeHQRUbNK1cQT6bwTRNyktskl4Fbg4s2ySf8TCtDuxoBNuxMQwTy/dJpZKYjoPneViWBUFALpMiGi8iGffJ+BnSbhrbshmWGEZpvJT0ypXhgPNoNLxCOhDEYiSLy4g4NvgesUwrTraNjB1hvWOCYTCkuJ6YHePtpreJWlFyQQ7P96jySihK5wlcFyOTIcjnMUyTIBIhX15CNpPFzedwc3mi8Tim4xCxyyn3F4HnkvLrIJcnP/8tmm+8CVyXigsvIHLYIXQloMQpwUtmyCa7IAAnVkQu62A7JhXL/g4dK2D0wTDiAIgVfeD7XCC75ICgDdYvXsLbLz7FktkvsueJp7Lg+acpqaymcthw5j/9BDOPOZ51ixaSz2YYOW0Gc5/8HxP3PYh0ZzvNq1Ywcd8DWfjiswwZNxHTdlg9fy6TDjiEhiULiRaXUFRWzsp5cxi7xz60rl0dXgV+6DBWzHuDkbvNoLOliXR7G0MmTGLt2/OpHjGagICOxkaGjBtPe0MDseIi7EiMzoYmKkcMJ9XWhoFJUWUZHesaKBtSS6qtDS/vUlpbQ1djM/Hycix8su1tlAytJ93YSKS0FBODTGsLxUPrybS2YjkOTjRCurWVoupa3I4OAsskYjvkOtqJlpXhtbcTOBEirkums5NodRXZjk6CRBzHjpNsSmHgU1oZpWjPPTAsq9Af6wa7RAgJQPP6DgzfJN2ZxzAMYqUWhu+T+dM1JJ94ksT++1N67DE033ADib1m4aeSFB11FNm5c2n7+z+IHnYM1uHHYybbiXauIzpmDNFx48BxcFetwutopqO2nLgfxUllwou0OzZBUSnuiqV4y5YRGT0Kb2gN7e2dROIJTNPEy7tES4pZu3AB2c5OiiorqaioJt6VJPXqaxjHHI+b6SDd1Ung+yRKy7AcB3v5CqL1wwgsi4xjbbyCfGmiCDORIDNnDh0PPIgRj1N69FHEpk+nZfUqIkVFgEEu2UW7XUplaQzP9WlJ5agti9OZzuEHYbBF3DGoLk1s8j56qRTZpUvD8BHAiESIjhmDlUhs/qYX3i677e1asxazuRl8H0wDTJNMfQkNXop5rfNY0LKASZWTmFg+kanLXsCIlWGUjwLPBcPC9/Ks7BrHS/etJJfx2OeUMViWQVdrBs9txYnmKauuwnJcUm0teJ5LcUUVbj5HJB5+1rbj4HseZsQhcD3AoKullTmP3Ivr5pl+2NGU1tRiR2JkUgliiRRtDWsxgNXz55HuamfifofgZ9IEpoEdiZDq6IAgoKJ+BL6XJ5/NkU52UlZVgxEEuL6P7dh4eRc74hBtbsNYvRpGjiT/xhvkcymKZu5O641/JTJ+PJEjDqNyyjTM2DaEh2Q6YOkTMPcuKK6GsUfAkBlQOmSnfJbvYZftuwBNq5aTz2QxLQujyCYb8XH9PEN8g3iqNQxRiJfR7JaQ6ujEjsVxolECz8ewLDKdHfi+Rz6bIVpUCgSYpoEViRL4PrlUikg0SrSoiEwqSVuxS1O+mXKzmOHLusg88zzp3WewYP4bjN//ICLRGBgGth2hq7WJoooqvHyOWCRGfNly8uvW4wwfDjVDyVcMJZ308H2IJmx838eyLEwvQ+vKNlwjQmldKRiw9PVmFr/SQGV9EZP3G4KbdympjNO8vJ3SuiKsiIkTs8EwCNyAIAArYtLVlAHTwMt7BD4EgU80ElBptGA0rCbI5TGKijCAlptuwl2/nvIzziC2+8wwEBAwslmMRALDsjASCRLTpvXlR9wv+29q+XLo7IRsDj+fA9Mi9/bbtN5yC2ZxMZUXX4QRieJ7LnZVFaTTGNXVpGJD6OryyabyxBI2TszC94EgwDA66GxZQ0llJYGXx/N9YiUlBJ5PtqsTJ15OPpMiXhLHME1M0yaXTgIGPh6+5+Flc5i2hWlaeJ5PV3MTpbW1EAS8+dgjNK9azvi992fE1OkkIy6ZooDa2BhcIw0uDOlcit22DOON22HyCeB7YDmw+HGYcDR4OehYA2MPB3zIdoVBI7lOyHSCEw9fUz0O7AREiyBa0guf2oDUL/vujvByOTILFhBks5DLg22TL6pk2aIsZkkJpZUxDBP8AGzHxHLM8LDCMjFtE9/z8F2Y+9RqVr/dxpDxZUzYqw7DgkxnnliRg+/7rFvUxujdKjAcGydi43o+Xt7DMnycmEM66UEQEC91sJNtmKuXYBhgxBNkHYtkPAq+j2nbGIaB73lhiJ9p4kRipNtacIqLyaVSvPXUYwybOo2ymjqCwMc0TPLZbPdrwbIdDMMgANxsFt/3KKmuxQBMywYCfN8j2dZKSXEc8ilcz8QpqybT1RmGCZXYGPlGalwPMq3gJCCfBMzwu+O5EC+DaBlUjYOyYQX7jN9hl+u/AG2Njbj5DKZpkU+nsSMRIoliSrwOqB5V0Nqk1+ySISQ3PL2Uq+6Zx9+d7zO/ZDVznWn87Av3A3D/375PU+MfuaCjk9eiES4vHs4/V3cQteKs2eePTBu5DKaeCtFiAN5Y1c6/X1nF7OUtLFjXSd7rWb1h5XHO2HMYF+w/itqSPgtMlJBCSEREREREREREREREREREREREREREZNAraAgJgGEYJwC/BizghiAIfmgYxqcAgiC4zjAMA/gtcByQAi4JguADR0v0h0EV76ltDQEWGasMM2ISjW56dUs3k8FLpjCLi3CiUdKdnVjRKJHIB18FMwgCss3NmKaJEYvhbGnwePNiSLdCtBRqJn7gPLNNTRCJYPg+ViyG9Y4Bv24uR2AYOI7T84JcOhzwFism294eDuTbMNCorGyLy/D9gExXFjNiETN96FoLdgRK6z+wvgLbJQcEvZObzdLR0EwkGgUC8rk8iYoScp0pTMPCsg1yeZdoIoqXy2MEBpZt4Oc9TMsIB9IHQDQC6TTYThhsAxglJQTt7WB1j5BzXczqarymJgzbBseBdDpsa2vDMAwoLibo7MSuqMBPJgGwSkrwk0mMWIzA9Qg8F6e8nHxbG1YiEQ52y+dxSkvJt7VhRKNY0Sh+JoOVSODl85iGgWHb+LkcZiRC4HlgGBimiZ9NY0Zi+KmwP5uxBEEujWE5YV8PPIiXb7yqvJ9O4+fz2KWleMkkWDZWLFqoj/C97DIhJABkk2R8B4OAaHo9QS5JNjEMM5MhiMWwPA8zFsNPp7ErK/FaGnGjcYxUmsCycIqK8Do6MGMxrJItDNDtWBcOULSLybg2pmURRCJE3yOgI5NOYRgmbj4g5hibbDffzc3n8V03DI+yLJx4z1W/gyAA3+/T8JoNxwWG0a/H3ezy297A9zFMc7tfn03nySTzxKM+ZusKgmgxQelQPDfAiVvYdhhwRroVIsVgmGBauK6P53pEzYCs74cDm02DvGti4WL4LmYujxmP4+WyBLkcVmUlfldXuN0uKsIGsCz8fB6vsxPDsjDjUYKuJESi5AgwTAuzsxMzkcApKQm3v5kMfi4XbsszXZi+j2lkCWKVGNHwexF4HkB/CnTaVrt8392E50KuKww+MN//M8vnsuRSKeKlpRiGSS6dJvB9YsXFH7ycTDt0tYJtgxXBw8DHwYomwu9REOATbm9Nz4N8DgIXP+NC4GGaEGAQmA544YD6IJ8n8DzMigoiFRXg+7gtLfjROHkX/JyL44CZSWKVlwNgRiLhMYTrEnheuG23LPyci5fP4wcmgWGBaXcHAgRE4k4Y8uf7BEEQfg+y4XcryGTBsSGfD2tyXejeLphVVUS7l9uH+nX/Ta9cCfl8eIzpdJ832RZWURFmURFG93Fd0B2CYNo22fXr8SIJMCwwwbQNfDcgyLkYjo2byhKLgevEsW1wIpGN2+ZsZxrSrURph+I6SFRurCWbSuK5LonS8NwnCAIC38fs3nZlU0ly6TTRoiIisTjvKZ+GfBYicbD73bHkQNKv++6OyKxeTZBOhxOGgRGLkXMtrHgE37CxIg4YBk7UItvSjm24gIkZj2JGIvieTz5vEE04eC1NBJkMQWkl+ZxPcU0Jpm0DhGGMqRRWVRWB65Jbtw4It3sYBoEfnvNjWxgEYEKQzUMkglmc6I4NMDCDPEY2iRGN4cXKsbpf6wY+2XQKy7RIFBfhuzkM38PwcxD4YDqQKCfd3orhZolV1HzgfmUXssv233w+j5fPY1gmRj4gUvw+20MZiHa5EJKGzgxH/uIJxtg+/3XP4aqKSnx/Jt/9wp0AvH3vbZzd+EMOSaWZHYvRYZn8pnEMh3c9sXEe7tjjyJ/xN6JxG9PqOd9zPZ9VrWmWNHWxpDHJkwubeHphIzHH4mMHj+WTh4ylKGr39SoPVv36xxAREREREREREREREREREREREREREZG+UPAQkp2lvwyqkF3eLjsgSHZ5u1YIiQw22vbKQKW+KwOZ+q8MVOq7MpCp/8pAtUuFkPh+wCdvfpknFjTyPWc1H+FLXDKklunpvfjiF/4OQPLFF7nw4Ut5e7jBWRPP4va3b+eTEy9h37//laL4aFwvxoyi+7mj5fs0GjMZMbWS8XvWMmJKJaZl4PsB0biNYYZv3dKmJD9/aAH3zllLTUmULx8zkTP3GoFlKiNjJ9MbLCIiIiIiIiIiIiIiIiIiIiIiIiIig54uoSgiIiIiIiIiIiIi8i6pnMt3/zuXh+et50sHj2Pko/dDKSx1HA7Kl2x8nl1VxVf/5bH+mxdy/L5f5vXG13m5/Q1m7vcFnrzzX8QS1YyLl7Bv4vc8EfsR6xZbLHm1cZNlxUscdjt4GLsfPZIx1UX87tw9+ehBrfzgnnl87d9vcP2TSzhv31EcPbWOEZWJ7VqfTN6jJZmjJZkDIOaYlMQcqoujCjgRERERERERERERERERERERERERERERQCEkIiIiIiIiIiIiIjIA5RtSBFmPIAggADbe09PGFtre8TyCgMAPCFyf9lSeec1J1iazLOxIc//aNhqzLudWlVHzxBomxp9kvRun2baoWRmj6S9vEngBfi5Hadam5rbVNK14nX3ik7klfjdnNB3E/iNOpzJaS9YspT72O8Y0fYvV6bHUF52GZ9QSs4qwTJPmrMfs+5bx2v3LGBU1qXJMHAO+bwa8bCX4e1OGq+6Zx1X3zKMCg3rTosI0iGMQM0wsAwIDfMJbBmgPfDp8n/bAp90PyATBFt9HC6gxTWotixrToto0qTZNqkyTuGEQJbxFDDACMLrv602LMsPofp8By8CwTAzLwLDNcNoOp7FMDLv77+/8m/2uv1kG2Gb3897xN9PoXnD3st7x2Njw2NhCO/S8pptTXxzOT0RERERERERERERERERERERERERERDajEBIRERERERERERERGXBa//U2uRWdvTa/53H5Mikg/OF8Tyy+bSRYtThHSfRlquLL+IM1jkTeZ7K1J14yj2GZmLaDM2o62XmPUXzshZxcdBx3uo/yjZpf8/DY24haMQjGk1u+kP14CFjJbxdkyfoOJx37eepqR4EBLR1Z5i7sYMnaJIszPgClxQ6nHDKCU3xYkszwfGuShaksqzM5GlyftOeT9vMEfpi1YRlgYBA1Dcosk7qIw0TLpNyyKO++L7NMDMMgFwR0eD7r8y7rXJeGvMci1+P5bJakv+XAknf60eg6jq0o2RjyEfgBeGGgS+AF4PoEeR8/7YPnE7gBQff9O6fxPnhZvan+ewdgRK0+XaaIiIiIiIiIiIiIiIiIiIiIiIiIiMhAYQTvcfXDgc4wjEZgeaHr6FYNNBW6iJ1A6wVNQRAc15sL346+uyt9DlqXvtPrfRcKvu3tb+95f6qnP9UCO15Pf9j2wq73vu4MqmlT/aXv7kz98TPfUVqnUH/rv/3xc1FNW6eva+pvfXdL+uPntLMMlnXtrfUcCP13Rw3kPjGQa4edW//O6LsPENbc3wz0frA1BtM67pTfy0RERERERERERERERERERERERERERAaSXTaEpD8xDGN2EASzCl1Hb9N69Q8Drd73o3WRHdHf3vP+VE9/qgX6Xz3bq7+tR3+rB1TTYLQrvr9ap/6pP66Dato6/bGmQhtM78lgWdfBsp69YSC/VwO5dhj49fcXg+F91DqKiIiIiIiIiIiIiIiIiIiIiIiIiIgMLmahCxAREREREREREREREREREREREREREREREREREREREREREZHCUgiJiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjIIKcQkr5xfaEL2Em0Xv3DQKv3/WhdZEf0t/e8P9XTn2qB/lfP9upv69Hf6gHVNBjtiu+v1ql/6o/roJq2Tn+sqdAG03syWNZ1sKxnbxjI79VArh0Gfv39xWB4H7WOIiIiIiIiIiIiIiIiIiIiIiIiIiIig4gRBEGhaxAREREREREREREREREREREREREREREREREREREREREREZECMgtdgIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiBSWQkhEREREREREREREREREREREREREREREREREREREREREREQGOYWQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIDHIKIRERERERERERERERERERERERERERERERERERERERERERERnkFEIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyyCmEREREREREREREREREREREREREREREREREREREREREREREZJBTCImIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMggpxASERERERERERERERERERERERERERERERERERERERERERGRQU4hJCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIoPcLhtCctxxxwWAbrrt7FuvU9/VrY9uO4X6r259dOt16ru69dGt16nv6taHt16n/qtbH916nfqubn1463Xqv7r10a3Xqe/q1ke3naHQ66Tb4LmJiIiIiIiIiIiIiIiIiIiIiIiIiPSKXTaEpKmpqdAliGwX9V0ZyNR/ZaBS35WBSn1XBjL1Xxmo1HdlIFP/lYFKfVdERERERERERERERERERERERERERESkb+yyISQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKydRRCIv1LLgmdDeD7ha5ENggC6GqAbGehKxF5f+l26GosdBUi0luyneH+R2RHeXnoXA/5dKErkYEm26lzE5Hekm6DZHOhq5D+JtUS3kQGqq5GyOj3MhEREREREREREREREREREREREREREdm12IUuQGSjFc/D/74PTW/DjHNg1kehcnShqxrc2lbCq3+DV26CspFw5Ldh1IFgKr9I+hE3B0seg0e/D+kW2O8ymP5hKKkrdGUisj18D5Y9BY9eBR2rYa9LYY/zoGx4oSuTgajpbXj2t7DgPhg2Cw77OtTvXuiqpL8LAljxXHhs0bIYdj8X9roEKkYVujKRgSeXhIWPwGM/BDcNB34Bpp4KRVWFrkwKKd0Bb98LT/w0nD70qzDxRIiXFrYuka3VsRZevw1e+hMkauCo78CYQ8ByCl2ZiIiIiIiIiIiIiIiIiIiIiIiIiIiIyA5TCIn0Dw3z4K+ngpsJp5/9DSQb4eRrwI4WtrbByvfghT/Cc9eG053r4ObT4WOPwtCZha1N5J3WvAK3nR0OGAZ46EowLNj/04WtS0S2z9rX4eYzwv0QwONXg5eDI64EwyhsbTKwpNvhrsth5fPh9Nv3w6oX4eOPKUxC3t/6ufC308DNhtNP/woy7XDcT8HW4GKRbbLyJbj9wp7pe78AkQTMPKdwNUnhLX8K7vhUz/Qdn4JzboXJJxauJpFt8dqt8L+rwscda+CWM+HSh2DE3oWtS0RERERERERERERERERERERERERERKQXKIRE+oeGBZCogulngWVDsgnm3A6HfAWqxhW6usGpY214RdcNykfBtA9B2wooqYfimsLVJvJOK56Humkw8bgwoKBhfhigM+NsXWFdZCBaPxcqx8GUU8LvdOtSmH0D7P1RKK0vdHUykLQt6wkg2SDVDM0Ltz+ExM1A85IwJKdiNMRKdrRK6S9yqXB7ExAeB28IINnglb/CgVeEn7vIYODloWVJuN0rHwXx8u2bz/x7N2974Y8w9TRwYjtSoQxkr/wNRh8Mow4Ip5c/G7YphEQGgq6G8Pey3U6H6glhIOr8e8OAVIWQSD/w8vIW1rZnOGmGzp9FRERERERERERERERERERERERERGT7KIRE+od4Bex1ETzzG8h1QflIOPp7YEULXdngZUcgURle0XXo7jDhKHjud5BPh4PDz7oRhs4odJUiYX+s3xOe/mU4KHz4LNj/M2Br+yEyIBXXwfgj4dnfgJcLQ4YO+7qOCWTbWVEwbfDdTdudxPbNr3M9PPkzmP3/IPBhwrFw/E+gcsyO1yqF1b4KHv0+zPl7OD31dNj7Y/DSn3ueEyvTdkgGj1QrvHAdPPXzcBs6fG849XdQM2nb51UyZPO20uHh9lkGr0nHw9v3wxM/6Z4+ASYeX9iaRLaWFYWDvgiv3wpz7wi3Z3tdDGUjCl2ZCAAf+sNzAJw4fSiGYRS4GhERERERERERERERERERERERERERGYjMQhcgAoATh8d/FAaQALStCAc96crIhVNcC8deHT6eego8+fMwgASgZTHc+0VItxeuPpGNfHjlpjCABGDVbGiYB7a2HyIDkgE8/4cwgARg/Zuw9EmIFBW0LBmAqsbBwV/etG3S8VAzefvmt/zp8Kr3gR9OL3wQ3vjXjtUo/cOiR3oCSADm3RGGjsQretqO+SGUDu372kQKYc0r8MSPe0KcVr0Ez14Lbn7b5zXx2PD7tIHlwAGfAUshJINaPgUL7u+ZXnAf5JOFq0dkWzgJWD0b1rwaTvvupsFlIv1ER8b94CeJiIiIiIiIiIiIiIiIiIiIiIiIiIhsgUZ9SP/Q1QBBEP5H/uI66FgFzYugcx0UVRe6usFr4nFw8X3hAPB361wHqWaIl23+N5G+1LAgvE9UglME7SvDgeHpb0JxTWFrE5Ft1746vI9XQLQU2pbD4kch1QRlwwtbmwwslgP7fgpG7A3r3oTKsTB8Vri/2B5LnwrvS+vDwaZdDTDvTtj/0wrJGejm3xfelw4LA5CSjbDyRfjQDeFxcP0eMGzPwtYo0pca5oX3kSIoqg2PrxfcB0dcCSVDtm1eQ6bBJQ+EQSZuNtwmD5nZ+zXLwPL2g+H9hv7UuQ4WPgT7XVa4mkS2VroZFj0cPi4fCdlOSLdCy5LC1iXyLk1dWcriTqHLEBERERERERERERERERERERERERGRAUghJNI/lNSFg03seDjYeMaHw4Gd2ztIVHqHE4fRB0Lg97QNmQHTPgRNC2HuHTDxGBgyvXA1itRNgSO+BV3rw8E/tVPC7UestNCVicj2KBsOh38jHMiXaoa9Lob2VRBV6JVsh0QFjD8qvO2oUQdC5ZjwGMiKhINOMx3h8asMbBOPg9EHQdMCsGPhdsh0YPwR4U1ksKkYDft/NgxzalsBM88GzO3fF9dNDW8iG0w8HsYd3hPaUDlW+1MZOKJlMP0sKKmHxrcgVh4GKFeOK3RlIvh+sPFxMusWsBIRERERERERERERERERERERERERERnIFEIi/UNJPax6GVa92NM261JIVBWuJukxZDrs+2l4+QaYfiY8/O2evz17DVxyP9TtVrj6ZHArqYe7PgOZ9p62s24EO1qwkkRkBxTXwYt/gmRjT9vJ10KspHA1iUAYjnfHJ3rC2ewYnPcvMM3C1iU7rmw43PZhCLoHbTpxOO8/ha1JpJBqp8ATP4V1c3ra9v8MmPoJSXpJ7WS4+QzwuwfImzacr+2uDBCRONTvFR4XbhCvgAvuLFhJIhuk897Gx10KIRERERERERERERERERERERERERERke2kEXPSP7Qu3TSABODlG3uuiiuFFS+HI74BH30I3vz3pn/LtMPyZwtSlggAa17dNIAE4KlfQqazMPWIyI5pWrBpAAnAkz+BrsYtP1+kL3huGI6zIYAEwM3AkscKV5P0Di8HL1zXE0ACkE/DsqcKV5NIobWv2jSABOCFP0Krzs+ll7zxz54AEggfv/GvwtUjsi1SrfDkzzZtS7fC+jcLU4/IO2TeEUKSynrv80wREREREREREREREREREREREREREZH3phAS6R/8/OZtgR8O+JT+IVoC5aMgl9z8b/lU39cjsoGb3rwt17XpoDYRGTjc3OZt+ZS+01Jg/uaBVwCZjr4vRXqX70N2C59jTmFmMoh5Wzg/913ti6X3bGn/uaX9rEh/5Ltb/h3MzfR9LSLvknF7QhOTOe23RURERERERERERERERERERERERERk+9iFLmBrGYbxBeBjQAC8AVwSBIH+d/euonoi7HYm1E0O/9O+HQ+vLFoxutCVyTvFy+HAK+C/l4fTk46H+r0gVg4rXoAh0yGSKGSFMhiN3B/2vSzsn4EHpg3loyFRUejKRGR7DJ0Oe38MimvDQdBWBOIVUDq00JXJYGZF4IDLYdQBYFpgGGH/HHdkoSuTHeXE4PBvwMoXIAjCIMQXroNJJxS6MpHCqZkE1ZNh2unhd8IwIVGt83PpPbMugeoJ4bmbYYQBtKMPLHRVIlunuAYO/Tp0rQtDlQ0LUi3h72MiBZbNexsfJ7Pe+zxTRERERERERERERERERERERERERETkvQ2IEBLDMIYBnwOmBkGQNgzjn8A5wI0FLUx6T7wSyofB/37Q03bEt8GOFa4m2bLJJ4LpwKoXIdsJj73jMzvp17DXxeEgIpG+kqiEJY9D41vhtB2D8/9T0JJEZAfEKmDNa7B6djhtOfCRfxS0JBEASuvhv5+FVHM4XVQNU08raEnSCxregjsuCwcSQxh6dPbNMGxWYesSKaTykXDiz+G2syGXDNsqxsLYw6B6fEFLk11EogpevvFd+9RTClqSyDapGAX3fQm8XDg9ZAbs/5nC1iQCZPL+xsepnFvASkREREREREREREREREREREREREREZCAzC13ANrCBuGEYNpAA1hS4HulNTW/DM9ds2vb41dCysDD1yHtLVMHuH4HpH4Y3bt/0bw9+A1qWFKYuGbxWvNATQALgZuDJn0EuVbiaRGT7rX2tJ4AEwMvDw9+GdFuhKhIJvfnvnsHSAMkmmHdnwcqRXjL/3p4AEoB0K7x1D9iRwtUkUmhuDl68vieABKB1CSx7snA1ya5l7h2b71Pn3lG4ekS2RbYzDFHeEEACsG7OpucwIgWSdb13PPbf55kiIiIiIiIiIiIiIiIiIiIiIiIiIiLvbUCEkARBsBr4ObACWAu0B0HwUGGrkl6Vad+8zXch09H3tcjW2dJnlk+FgzFE+lLH6s3bmhdCPrl5u4j0f6mWzdtal246EFqkENa9sYW2N/u+Duld69/avG3dHPB05XgZxNwMNM7fvF2Bk9Jb1s7ZvG1L+1mR/iiXCoOZ3q1zfd/XIvIumXxP8IhCSEREREREREREREREREREREREREREZHsNiBASwzAqgFOBMUA9UGQYxvlbeN4nDMOYbRjG7MbGxr4uU3ZExWiIV0C0FIbuDrFyKB0G5aMKXFjfGJB9t2ocOIlN2yadOGg+M+lR8P47fO/wvnwU1E0Dw4SZ50Kiuu9rkQGl4H1XtqxmUnhfNhyGTAfThulnQ3FtYevqR9R3C2TmOWAYUDsVKseGbdPPLGxNA1C/679TTwY7CkNnQnFd2LbH+WDZha1L+p1+13d3plhp+D2A7v3xjHB/PObQwtYl263f9d+Z54T31RPDG8CMcwpXj/Rb/a7vAhTVwIyPgGmF5yvlI8P2oTMKW5cIkHG9jY+z73gsIiIiIiIiIiIiIiIiIiIiIiIiIiKyLYwgCApdwwcyDOMs4LggCD7aPX0hsF8QBJ9+r9fMmjUrmD17dl+VKL1h5Quw8GFY82o4CHDS8T3hAv2X0dszHFB9d9nTcPfnw0Ga+3wSVr4YBpPM+DAMnxUOyJD+qtf7LhSo/2a7YNlTMP8eSLfChGNhzCFQOaZv65C+NLi3vbu6fCbcv8y/BzrXwvgjYczhUDOh0JX1BvXdgax9DSx7MuybTlF4rDr6ICgaNKFXu2b/bV0OCx8Kb+Wjws905P5QUlfYuqQ37Zp9d2drXw0rnoelT0DXephyEow9CsqGFrqywWbX7L+d62H50/DWPeH0lJNg1EHa9u5ads2+u0HzIlj6ZPg7ZlE1TD4JRh8MkcQHv1b6u53xe1mf/QPMA2+u41M3vwzAxQeM5run7NZXi5b+Yaf83isiIiIiIiIiIiIiIiIiIiIiIiIig89AubzzCmA/wzASQBo4Eugn/+tcekWmA57+NSy4L5xe9EgYaPHhv0KisqClyfsYfRBc+iCsfhluPaun/ZUb4eL7YOS+BStNBpGWJfCvSyGfCqfn3wtn3qgQEpGBqm0Z/PujkGkLp99+AE6+ZlcJIZGBbN0cuOOTPdPz7oBLHhhMISS7Ht+H1/8Oj1/d0zb/Xrj0/sLVJNJfZDvh3i9uuj8+6RqYdXEhq5JdRfMi+PfHIPDD6Xl3wkX3KIREBo5Vs+GeL/RMz70j/H2sToEPUlhZ13vHY7+AlYiIiIiIiIiIiIiIiIiIiIiIiIiIyEBmFrqArREEwQvAv4BXgDcI676+oEVJ72pe1BNAssGyp6BpYWHqka0XL4fZN2za5rsw9z8FKUcGoRXP9QSQbPDkTyHdXph6RGTHrHm9Z8DzBo//CLoaClKOCBDuZ57+1aZtbhYWPVqYeqR3tK2Ap3+5aVvnGlg/tzD1iPQna7ewP35C+2PpJa/8tSeABMLHr/6tcPWIbItUCzz+403bsp1hMIlIgWXzPdvWnEJIRERERERERERERERERERERERERERkO9mFLmBrBUHwHeA7ha5DdpbgPZr1n6UHBC+/hTa37+uQwcn3ttCW5z23KyLSv21p3+97EOg7LQUU0L1veXf7FvZBMoAEW97m6BxE5D32x672x9I7/C38XrCl3xVE+qPA3/IxoI4fpB/IumHfLI3ZGx+LiIiIiIiIiIiIiIiIiIiIiIiIiIhsK7PQBYgAUDkOxhwKlgOVY8FJwLC9oHpCoSuTD2JasN+nNm2zY7D7uYWpRwafUfuDFYFEJZSPDNsO/jLEywtalohsp6EzIFIM8QooHxW2HfxlKKkrbF0yuEUScNg3IFLU02baMO7IwtUkO65sBOx3Wc90cS3UTYPa3QpXk0h/sWF/vEHZCDj8Su2PpXfseWF4XzIkvL2zTaS/K6oOz08gPF+JV4ATh2F7FrYuESDnhWFhJTGHrKtgHBERERERERERERERERERERERERER2T52oQsQAcKwgON/DMuegVUvhYP/xh4a/sd+6f9GHwTn/Rte+hOMOQScInjtZmheCKMPhrJhha5QdmVDd4cL7oCFD0O6BcYfDSMPKHRVIrK96naD8/4Fix6BzrUw7tv6TkthdTXC8mdh6ePhAHzDhFUvwz6XarDpQGfZsN+nw3OPXBKaF4MBdK2H8hFhQKLIYFW3G1x8Dyx4EEpqoWE+JJtg7ZwwoERkRwzfNzzeW/hQOD3h2LBNZKCYcBycdVN4zlJUA+OPgqEzC12VCK4XBo8kIpZCSEREREREREREREREREREREREREREZLsphET6BzcLL/0FXrq+u+EfMOpA+PBfFUQyEDhxmHAUjNgH7v4czL0jbJ99A0w5BU79LcTKCluj7Loa3oK/nwuZ9nD6lb/CObfB5BMKW5eIbJ+mhfDPCyDZGE6/dgucfj3MPLuwdcng5Obhud/BM7/qaRu6B5z7DyipK1xd0ntKhkDVePjL8eBmwrbnfgcX/hfGHFzY2kQKrX6PMIjp1rN62p69Bi55AIZMK1xdMvCtex1uOwd8N5yefQNcch+MUBCJDBArn4PbL+qZfvkvcOkDUDO5cDWJAK4fAFAUtcm5XoGrERERERERERERERERERERERERERGRgcosdAEiADQvgtl/3rRt+TPQuKAw9cj2aV7UE0CywVv/haZFhalHBoflz/YEkGzw+I8g01mYekRkx6x9rSeAZIP/XRUOghbpa61L4blrN21b+yo0zCtMPbJzzL+vJ4AEIPDh2WvBcwtXk0h/kO2CJ37yrrZOWPZUYeqRXcerf+0JIIHw8Ss3F64ekW2RaoX//WDTtnQrrJpdmHpE3iHv+QAUR22yrl/gakREREREREREREREREREREREREREZKBSCIn0D74bDvZ7Ny/X97XI9vPy79Guz1F2Ije9eVuua9NBbSIycLhb2GfkU/pOS2H47pb73nsd88jAlNtCcFm2c8vnJyKDSeCHx9Xvlkv1fS2ya8l0bKGtffM2kf7Id8Pzk3d7Z6CZSIG4XoBpQMwxyeZ1LCsiIiIiIiIiIiIiIiIiIiIiIiIiIttHISTSP1SOhXFHhY8NI7wvHwXVEwtXk2y7qnEwZOambXXToWp8YeqRwWHk/mDa4eMN248DPw+JioKVJCI7YOh0sGPh4w3f6QOugNKhhatJBq+K0TD19E3bimuhZlJBypGdZMrJPdubDfa7DOxIYeoR6S9ipXDA5zZtMy0Yc3Bh6pFdx14XbaHtwr6vQ2R7FNfAgVeEjzccP1gO1O9VuJpEuuV9H9syidoWWdcrdDkiIiIiIiIiIiIiIiIiIiIiIiIiIjJA2YUuQASAaAkc/xNYNRtal0BxHQzbG8qGFboy2RZF1XDm/4NX/grLnoL9PwNuDhY9DENmwJBpha5QdkX1e8BFd8PqlyGfCfvZiH0LXZWIbK+66eF3etVsyHZC7VQYsXehq5LBqGMNrHk1HGw/+USYfw/Ey2Hvj0PFqEJXJ71p2N5w4d3Q9DakWyFRGYYkighMOgHOuB7m3A5TTgLPhbaVkKiGKn1PZDuNPAAuuDPcz0J4TqdzOBlIdjsdyobD+jchUgzDZsHQmR/8OpGdzPUCHNMgapvkPL/Q5YiIiIiIiIiIiIiIiIiIiIiIiIiIyAClEBLpH4IAljwG9325p23S8XDKb8NgCxk4qifA0VfBujnwt9Mg1RK2O4lwUPnwWQUtT3ZBrUvhjk9B2/Jw2rTDAW26QrvIwNS2Av77OWh8K5w2TPjIP6BkSGHrksGlcz3855Ow7MmettP+ADPOBtMqXF2yczhRcOLw0JWQT4VticowmEQhejLYJSrCbV/JULj5DPDyYXvZCLjgjvD8T2RbNS2Ef14I2Y5wOloKF9+jEAcZONbPhX9eAL4XTleOg/P/pRAzKTjX87Etk6hjks0rhERERERERERERERERERERERERERERLaPWegCRABoXQYPf3vTtgX3Q8O8gpQjO8gw4O2HegJIIBzQ+eKfwNd/gJdetvy5ngASAN+Fx66GXLJwNYnI9lvzak8ACUDgw0Pf3HSfIrKzrX9j0wASgAe/AZ1rC1OP7Fy+Dy/9qSeABMJtzoL7CleTSH+SS8ETP+0JIAFoXwnLnylcTTKwzflHTwAJhI9f/0fh6hHZFplOeOS7PQEkAC2LYdXsgpUkskHeD3Asg4hlknX1G6yIiIiIiIiIiIiIiIiIiIiIiIiIiGwfhZBI/5BPbTrob4NsZ9/XIr2jddnmbS2Lw4AIkd7U1bB5W8cqyGf6vhYR2XHvHJC6Qedafaelb2W20A8zbeqHu6rAheYlm7e3Lt+8TWQwcjNh6Mi7dTX2fS2ya2heuIW2xX1fh8j2yKegY/Xm7QpNlH7A9Xxs0yRim+QUQiIiIiIiIiIiIiIiIiIiIiIiIiIiIttJISTSP5SPhNEHhY/tGBgGRIqgakJh65LtN+WUzdtmfRTsSN/XIru2kfuG96YFVnf/2utSKKoqXE0isv1qp4Bhhjc7GrbteRGU1BW2LhlcqieDHd+0bdJJUDqsMPXIzmVFYK9LeqbtaHhcMeXkwtUk0p8kKmHWpZu2mTaM3L8w9cjAt/u54b3lhDeA3T9SuHpEtkVxbc9xw4ZjBoChMwtXk0g31wuwLSMMIfEUQiIiIiIiIiIiIiIiIiIiIiIiIiIiItvHLnQBIgBES+DEX8KK56F5ESSqYdR+UDOx0JXJ9hq1P5z6e/jf92H4LJh8EiSb4K27YdgsKB1a6AplVzFsL7jgDlg1O7wi8ZDpGhApMpANmQkX3gUrX4RsZxhKMuKAnsF9IjuT78Ha12D1q3Dcj8L9ypM/h8knwkFfgEj8A2chA9TEo+Gk30CuA1ItUFoPFaMKXZVI/zHtLMin4bVbYZ+PhyEkzQshXhYef4tsizGHwzm3wppXAQPq94CRBxS6KpGtYxiwx4Xhb5br54KTgBH7QP2eha5MhLwf4FgmEcvC8wM8P8AyjUKXJSIiIiIiIiIiIiIiIiIiIiIiIiIiA4xCSKT/WPEC3P25nunh+8CH/6qwioEqVgp7nAfjj4ZX/wZ3fLLnbxOOg9N/D4mqwtUnu472VXDnZ6BzTThtWnD+f8LBwyIy8HSugbu/AC2LwmnDgLNvhcqRha1LBoflz8LfTg3DSCC80v0F/4HaaWA7ha1Ndq6iGigfDrecCUH3VeNLh4ehSNXjC1ubSH9QVg+Hfg3GHQ5/OwNyXWG7k4CL7wmDAUW2VttS+M/HIZcMpyNFcPG9kNijsHWJbK2mBfCfT/QcM5SPCsNRq8YVti4Z9FzPxzYNIrYJQM71iUcU6CkiIiIiIiIiIiIiIiIiIiIiIiIiItvGLHQBIgC0rYSHv7Vp26oXYf2bhalHek+2HZ748aZtCx+AhrcKU4/sepY/2xNAAuHA8f/9ELJdhatJRLbf6ld7AkgAggAeuhJSLYWrSQaHXAoe/0lPAAlAVwOsfkUBJINBphP+9/2ewcQAHavCcxIRCRkGzLu7J4AEIJ+CV24uXE0yML12W08ACYSPX/t74eoR2Rbpdnjku5seM7Qth1WzC1aSyAZ5L8C2zE1CSERERERERERERERERERERERERERERLaVQkikf3BzYVjFu+UUIjDguRnwcpu351J9X4vsmtJbCCboWg9utu9rEZEdl+vcvC3ZGO5PRHYmLwddazZvTzb1fS3S99xMePzwbunWvq9FpD9rW751bSLvp1X9SAYwNwvJhs3bM219XorIu7m+j2MZRCwDgKznfcArRERERERERERERERERERERERERERENqcQEukfyobBbmdA+SjY43yYcAw4RVAzqdCVyY4qHwWjDuqZtqMw/cNQMapwNcmuZcS+Yb+acjLsfi4U18E+H4eiqkJXJiLbo24a2DGYdDzsfh6UDYe9PwrFQwpdmezqLAeO/B5Ujdu0ffRBW36+7FqKa2Dvj4ePY+Uw/SyY9iEYvk9ByxLpd2ae0/PYjsHUU+HAKwpXjwxMe54P0VKYfmZ4i5bCHucVuiqRrVNSC3t/DEqGhucrk08Mf5Oo373QlYngegG2aRCxw3/2yXtBgSsSEREREREREREREREREREREREREZGBSCEk0j84MTjoCzD5JFj0aHjF8bNuhJopha5MdlSsFE7+FexxAQzbG477MTTOh5s/BM/+Fjq3cMV5kW1RNwNOuw5al8HSp2DWR2Hi8YWuSkS2V+1ucOYN4f5hyWMw7UyYfjaYOmyVnWjlC3DbuXD/l2HckXDo/0H1JDjnVhi2V6Grk74y8xw49Xew32Ww+mVYNwfalkO2s9CVifQfow6G034P446CI78DzYvhv5+FF/4IXQ2Frk4GimGz4KjvwJpXw9tR34Hhexe6KpGtN/W08HeupU9A+yo4489QN73QVYmQ93xsy9wYQpJz/QJXJCIiIiIiIiIiIiIiIiIiIiIiIiIiA5Fd6AJEAPBceOVmePG6cLpzLfzjPPjYozB0RmFrkx1XPRFO/BWsfA7+egoE3VfhfOib4f0Bny1cbTLwNbwJ/760p189fjVES6B6fGHrEpHt0zAP/nkB+F44/cyvwXKg9ptgGAUtTXZRDW/BX0+FfDqcfvF6mHE2XHI/FFUVtjbpW6X1ECmGx3/U0/bvj8J5t8OEYwpXl0h/Ei+F3c+D0mHhtnOD+78KpgN7X1q42mTgWPMK3Pulnul7vwQlQ2HyiYWrSWRbzL8Hnvxp+Lh9Fdx+EVz6IIzYp7B1yaDn+gExxyRiWYBCSEREREREREREREREREREREREREREZPvokvLSP3SuhZdv2LTNy4WDQmXXYDvhFY43BEVs8OIfIdlcmJpk17DiefUrkV3J+jd7Akg2ePH68FhBZGdoXNATQLLBG7dDpq0g5UgB+R689P82b3/j9r6vRaS/W/bs5m0v/AHS7X1fiww8r/xt87ZXb+77OkS2R1cDvHDdpm2BH/7mJVJgec/HNk0idvjPPgohERERERERERERERERERERERERERGR7aEQEukfrAjEy8G0oG5aeFVlgEiioGVJL4uVbzptRWDEfmBHC1KO7CLi5eF9+Uio2w0MExLV6lciA1WkKLwvrQ+PCUwL4pVg6TstO0lRLZSP2rQtWhIep8jgYlpQ1n0eYtowZDqUDIWS+sLWJdIfFVW9a7oGRh6gbadsnbLh4X31hPAGPb8DifR3VjT8zcEww98gNvTnWGlh6xIB8l6AYxk9ISSe9wGvEBERERERERERERERERERERERERER2Zxd6AKk8LyODjILFuA2NOIMH0Zs4kTMeHyT5+TXrye3fDlmIkFk7Fgsy4dcEoqqw8F6O6qkDk6+Fta/CatfhmF7Qt10GLL7js9btlvg+2Teeovc8uVYJaVEJ4zHGTIk/GNXQ094zNYauT8U10HXeph8Ujiws2EePP8HmHIS1E7ZKeshu7hRB8GxPwq3H5l2mH4WDNsbosXh37enr+4E+bVrySxYQJDNEh0/nui4cQWtZ0vc1lZyS5cCEBkzBruiosAVDU6Z+fPJLl2KmSgiMm4s0eHdg9qSTWBYkNjFP5f6PeG4H0Pj/HB/sdtpYdu7BzvLwJVLQ7Yd4lVgOwWsIwkrXyS/ehm5iV/FLC4jsupfWG/fCcdcTbbVJfviQxi2Q3TyJCL1CqIY8NLt4GahpHZjU2bxYnKLFmGWlmIVF+NXfgj7kP2JxDoxlj8Nw2bBxGMLWLRI/5Nft55s52j8cVcRLXOJVtn4uQw5fxjekw/jjJlEZNykQpe59ZLN4b2ONfrOnheSGf4hcmtbwYDoIeVEaxRCKwNEvAyO+xFuR5JcSw5Mk0i5iT18aqErE8H1fGzTJGKFISRZ1y9wRSIiIiIiIiIiIiIiIiIiIiIiIiIiMhAphKSfCYKA3LJluA0N2DU1REaPxjDNnbY8L5mk6Y9/pOuJJ4lOmEB2/nwqL76I8rPO2rjczIIFdD72GOTyYJlYb7xBSclCnLdugulnwz4fh8oxm83bbW4h89Y83KYmIqNGEZs8ebNwk00kG+F/3++ZjpXBmIN7e5VlGyRfeIE1X/kqXlMTxYcfRvGRR2LGYkRrIkRfuxrDy8KR34axR4AT/eAZ1kyEi++FtW+E4SOP/yhsn3cXvPRnuPSBLfYlkfflpsJtRz4VTs+/B+/sf5F54TnySxZgR9LEjCXYux2x9X21l+VWrGDl567AqarETBSRnvM6w6+9lviMGUC4Lc4tXYqfyRAZNRqnprrva1y+gs5HHsZPpwFIv/YaJUcdRWTkyD6vZTBLvvIK6dmzCdIZsG1ySxYTHLA/sY5n4OlfgR2Bw6+ECcf0BO3sZF57O7llywh8n8jo0VsdTpNZtIjs229jWBaxyZOJjBq1lQvMwhM/gXRrOL3gfjj5mu2sXvqdNa/D41fDqpdgwrFw0Oehpu8HqmcXLyYz52Ws8nLWXX0b+ZUrASg9+QRqP/ognptgzVe+SukRR+DncuSWL6fo0EOIjR2Ll86A72EVFQHhNj6/di1WZSXR0aMxnAIGq8iWeXlY+iQ88l1INsDeH4PdzyW9rJlVl19O2Yc/jFVUTMPPfw6+T8X55xOfNhUjfgoxbxGRf10CF90D1RMKvSYiO0UQBORXrCC/bh1WdTXRUaMwbBuvsxMjFsPv6CT79tv4qSR2XR0Nv/wVqWefBcCIxRj56++SmfsW6//wY/A8zOJihv7wB5jFxcQmTcKu7vtj262S7oAFd8MTPw2nD/1qGJYZKytsXYNAal2e1MtzCbrPPXIr4vh770F8aIELE9lK2VSCjpfehkwGDINUIkFpYiQRZRlJgbl+gGObROzwd/2cQkhERERERERERERERERERERERERERGQ7KISkHwmCgM5HHmHtt79DZOhQ8k1N1H39a5QeeyyGZe2UZeYWLcYqKSE2aSLpN+cSnzkTt7WV3PIVRMeMxk+nyS5egmGYEHHAMPCTKXJ1e+EkfwHP/w661sOpvwMntnG+blsb66++mo777sOIxwlSKYZe/UPKzzhjy4V0NfYEUmyQaYc1r0HtlJ2y7vL+8g0NNF772zCA5MgjMQyDdVd+C7umhorzziVd9gksv4nYvOeJJKpgxD5bN+PqCWBH4b+f2bS9ax2sf1MhJLLtlj/XE0ACBBVjaX/iTdb/+nqMRIIgnab85KOpbbseK1YGow7o8xLTc+dSftqpdD74EG5jI2Wnn0Hnk08SnTQJv6ODhl/9mtQLz2MWFeN7LsN//WtiE/p2oHNmyWICz8ewbTAMAs8js3ixQkj6kNfRQX7tWiAc0BsEAbge+XXriT37Xch2hE/81yVw3r9hwlE7vabcqlWs++53ya9aDaaJVVbG0B9dTXT06Pd9XfrNN1lx8SUEuRxBEGBXVDDyhv9HdPz4D17omtd6Akg2ePxHMPEEKKnd7nWRfqBtBdxyBiSbwunXb4XG+XD+fyCxdeE2vSE9dy4rLr6EyKhR2HV1GwNIADruvo+So48jNfsxKs89F7exMfw+ZjN0PfkUXmMjDdf8hiCVouoTn8AqLWH157+A39UFts2QK79J2emnY0b7PvBK3seaV+GWMyHoHoT5vx/gx4fQ+WInNV/+CoZjs+aLXwLfp/rzV2DGYuTXrSfwPLJuBSWTP0ds7RyFkMguq+vJJ1nzxS/iJ1MYjkPdt79NkM/T+re/UvbhD+M2NGKXlhIYBsaSpZSdfjqpF14AzyPIZMis7mL9b2/aOD+/q4v1V/+I4sMPp/XWWxl61VXYVf1wZP6yJ+HOT/dM3/lpOLsUppxcuJoGgcB1cVvaCHwfIxKBIIAgIN/cxvvE1or0H+l2MmvbMACiUQLPg7xLdlUTkYmFLk4Gu7zn45gGUYWQiIiIiIiIiIiIiIiIiIiIiIiIiIjIDlAIST+SW76c5HPPU/Hhs8jOX0Bin73Jzl9AbvJkomPH7pRl+vkc6TfeID5tOs7wEZixGJlFi/EPCQf0u+3t4SBo28IwHbAs8Hx8uwScIsgnYe5/4LBvQPW4jfPNvv02RsSh+rOfxe/owCovp/Oxx0nssw+R4cM3LyQIwHehbARMOCYcaL3gvp6BgtLn3OZmMq+/DkB82jQar7kGTJOqj3+Mhp//giCXo/zss8nV1+P/6ynie6SI77UXdknJB888CDb9bK0ITDoeoqXge2DunNAd2UX5HkSKYNIJEC0lZ4yk696F1FxxBV5bG2ZpKfmVK8iWHUJi+XMFCSHB98ktWUrRgQeAHxB4HmYkgp/JkJ47l+i4cBvvd3QQmzmTzgceJDp6NIbj9FmJQd7FcGwwupcZBOC6fbZ8gXxLK/g+2A6GAQZG+DkAFFX3hJAAzL+nT0JIki/NpujAA8nMnUvgesRnziD5zLPvG0IS+D5td99N1cc/FgYzmCZGJErXM89sXQjJlvb9vgsE270em8glw6CzRFUYiiV9p3lRTwDJBmtegdalfRpC0nb77fidnUQnTiD59DOb/T27cCGxadPwO9oxYlFwPQh8TMchu2QJxfvt1/3dDFjz9f8L+zmA67Lue1cRmz6D+G5TN84vcF3Sr79O+z33EHgeZSefTGLmzHDgtfSNta9vtm1xkz52WSmdDz1I0X77ge9jlpYSmzSZ7PJlGI6DYdv4pkXWrSEaLQoHG4sMMF4qRWr2bDruuQe7ppbSE44nvttuG/+eW7WaNV/5Kn4y/A0gyOdZ993vMuznP6fk2ONw6oaEu2DPxTBMTMchyKSpOP98Wm8Kg0e81tbNluuuX49VXk7b3/9O5rzzKD6gAMfgH+TVWzZve+VmhZDsZH4uG25zne7fmFwXMAh8/f4jA0OurRPDtCASxfA9DMchyObws7lClyaC6wXYlkFkQwiJp22riIiIiIiIiIiIiIiIiIiIiIiIiIhsO7PQBUgPr6MDr7GRzLy3iE6ZgtfWRvK55/Da2nfaMgPPJzppErmVK8EwcFtacOqH4mezABimiWEamEXFYFoYpoVRXBQOki6uCWcSLQU78q75ergNDbTc8P9IPvssTddfj11VtXG+mymphRN+DpOOg7fuCq9WfujXoX6Pnbbu8v4MxyE+YwbAxs+taL/96HjoYYJcjtKTTiS7cCGNv/o1yaeeJbdkCWu/9jVWf/krpOa8QW7VKrxkcsszLxsO+382fFw+Eo7+HjQvhtsvgoe/A63L+2IVZVcx+iA49GvhduOtu/ArJhKpq6PpuutIPvssLX/5C25jI0FRfRg6UABBAPnmZoJ8GOqRWbQIq7QMw7IIMhlabr0NsyhBZOwYOu67DyMWw+vs7NMaDcPALCoKBwJaFmZxMRgabt2XjMCHIMCMRcF2wLYxEvEwmMn3Nn1yydC+Kcr3ab3lVuzaWpxhw2j7xz/BMsOrfb+HwHWJjR9P0+9+T8eDD9F+9z203HgjVvlWhkwMnREGC73TwV+GkrodWJFuq1+Bv58Hv9sX7vosNM7f8XnK1nMSm7eZFjjxPishcF2yCxYAkJ2/gPjuMzd7TmTkCOzaGoxYDCMSwXDsMDDEMIiMHk3LbbfhNjUR5PN4Te8KVQkC3PXrNmlKvz6H1V/6MoYTwS4vZ/33f0Dq1Vd32jrKFkRLN5kMqibR9vI60q+8SnTceAwnApZF8dFHEWTSWMXF4YBi38dMxCGWIDATkM8UaAVEtl/yySdp/PkvcIYMIcjnWf2FL5J5662Nf3ebm/A7OjZ9kefhtrSAYWAkEpiRMJTHMM1w2xiNUnTIwRufHhlev9ly7fp6vNYWAPz2js3+3i+Uj9i6NulVQRAQmCZGUTGYZhjAV1SE0Ut5cyI7nemAmw8fGyZGPI5VVdkTIClSQK7vY1smEas7hMRVCImIiIiIiIiIiIiIiIiIiIiIiIiIiGw7hZD0I4HrEt99Jn46TfN115F69TVKTzwRL5Peacs0HQe7ogLfzUMQEOTz2JU9/3HeKCoKBxtZFgQ+gZvHNM3wSsjF3QOgj75q84E6rkdk7FgqPnIuzsiRVF54QTiY3XXfu5iWxfDinyDZBM2L4OFvQaZtp6y3fDC7ppbyM8/EHjo0HBAPWNXVuGvXABAZM4b0K6+AZVF64gk0/PRnJJ99jvj06TT86GoWH3sczX/6M11PPUXypZdwm5t7Zm5asM8n4JTfwgGfg4e+BevfhHQrPHctPPMb8PKFWG0ZiHKd8PC3w+1Gsgkynfi5LFWXXkpk1CjKP/xhopMmExhRGLX/xpcF+Ty5lSvJrVm782s0IDZ2LH4mTZDPYVdWEHgege/jJVOUn3YqHXffQ/MNfyE6bhxBLhcOyOtLBmBZBK4Hnhd+T1EISV8KDANjwxXh8+F+2Ywnwn2w9Y6wr1gZTD6hT2ry2tooOfJI2v7xD1pvuYXEfvvhtbdv1j/za9eSW7mSwHUxLIvM/PnUfP4K4jNmULTfvlR/6pPk3xXM8J7qdoOL7oE9L4JRB8EZf4LpZ+34yrQshZvPgCWPQbYD3vgn3HEZpFp3fN6ydWomw9TTNm076ItQOb7PSjBaF1F2xCwAMvPmEZs2jcjYsRv/Xnb22bjt7WAYmCUl4Pv4mSy4LkYkQmDZ1Fz2KdJvvhkeN9fUvGsBBvaQIZs0JV9+mbJTT6H9zjtp/suNxKZOIdMdhPJe8g0N5FaswM/lemfFB5nAdcmtWkV+bfc+fvgsKBu58e/5sWdh19aTXbSQ5uuuo+upJ6n7v/8jvvc+GEVh4GKQyWCYFmY0iuE4mA9/DVa/XKA1Etk6XipFdvnyjedeXkcHmQVvE5s6hea/3Ej7XXdRevzxZBcv3vgau6oKs6xs0xltCKTzfcx4HAwTP+8S5HN4XZ3h9yIWA9Ok9MQTie02iSHf/Ao4Tvjy8nKqLrqI9rvvAdsmMnZMn70H22TmOZsGZDkJ2P3cwtUzWNgOBuFPREE+D4GPads69ZABw89m8Tq7sOJxAs8lyOXCY4XSkkKXJkLeC3BMA8c2u6cVQiIiIiIiIiIiIiIiIiIiIiIiIiIiItvOLnQB0sNMJOh87HHiM2eSmDULw7HpuP8+aqZ8cScuNBzlYVdWgWFglZViRmMEfoDX1UVu2TIwLQzHwaqogCAIB0jbFhzyFbAjUL/HZrM14jEy894iv3YtsYkT6bjvfiJjxhC811VBk80w+4bN21e+AKMO6MUVlq1ll5XijBpJ6Ykn4oweTd2VV9L6j39QfOhhtN56K3T/J/b4zJl0Pf0MAOUfOoPmG27AXb+eigsuIPXiizRfdx0lxx5DbMZM3HXriO+xB9Hx4zAiUZxpZ2O+9R/w3xVO8+pNcNAVUD7y3WWJbG7Fi5tO+zn8VJq2f/+b2NSpdD32P4xYnOLDDoaaSUAYmND15FO4DQ0Yjo0zchTFBx+EVbJzBg0ZholVXg7tbWBa2NXVYeBH9zY1/focKi44Hzwfr7UVt7U1DJ7oS7aDEYlgV5QDBlhmuK2XvhMEmNEoQTwRDuK1nTDsI/Dh7L/C8ufAcmD43lA7pU9KMiIO+YYGKi+5BIKA/KrV4AdhWJlh4HV00PXUU+RXrCTwXOwhQyja/wBiEyaw/uofkZg1iyCfp+Pe+xj6/e9v/YKH7Rne/A2BOL2geXEYdvVOa16BtuWQqOidZcj7i5fD8T+BGR+GliVhP67fK+zrfaXhLUoSC3AvPZvm2/5L8/+7gSFXfgW7ug6rrBKruobWf/wDbBsDMGMxjGgUvysJvocZidD5xBOUHn8cmbfmU/vVr7LuqqvwOzvBcRhy5TeJjt80VMUuLaXtwQeovOhCgnw4UNVra99ieX4mQ+ejj7L+h1fjtbVRetJJ1Hz2M0RGjgyDNZYvx2tvx6mvx3lX2Mlg5nV1kVu6LNx9lZfTcvPNtN5yK2Y0Ss3nP0/ZKSdjXXgnrHoJMu14iX3ouus6So49jpIjj8JPpWh/+GHqvv513HVrw9AF0wqDlaLRcFvcMBeWPwujDyz06ooAEHheuE1oa8MeOpQglWL9j39C8qmncIbVM+S73yM2cwZBLotZUkL1xz4GtkXqhRc2CQWJDB/OsJ/9lNVf+CJ+MokRiVD71a+QXbECwzAwIhEgwIxGwoCmaJQgm8XwfWqv+h7xyZOJTtiNiN1BvOQ83Oh4cs1JGq/9PXZVJUO++TWiEyYU7o16P8P2hI8+DKtnh8c2w2fBkOmFrmrXl8tjWDZGLIbtRMLwEdPEiMbC8yNDaSTSvwWZDFZ5GX46jR2LgusRmCZGb523iOwA1/OxLZOIFYaQ5FyFkIiIiIiIiIiIiIiIiIiIiIiIiIiIyLbr0xASIxxJcB4wNgiCqwzDGAkMCYLgxQ946eDg+5QeczSNv/s9fns7mCYV55xDkM3uvGWaJkYkglmUwPADiMQILBM8NwwRaW3BShRhJOJhAIltQwDYNl7NZKySCrC20I3yeRKzZuG1tpCe8waJvffGGVZPkMlsuQ47CjVTYM8LwcuBYYKXh6Lanbfu8r78fJ6Ou+7CiEQwbJvW226j8qILMYuL8fN5/FwOq7Iy7BfdYTZWaRnu+vWYRQnMaJT0K68QnTwZq6SUxp/9jMTee2Mm4qz79reJjBpFxbnn4qdyONN+jlNdgpFuwq6pxso1wppXw4FgFaMK/E5Iv1dUDQdeEV65PPAgMRxn6Drso44i9eqrxGfMJDJmNEE2v/El6Xlv4ba0QBAQeB751avIzJtH0b777pwabQssKxzI7Afh9yoaCS/27bkYjkPTtb8FwBk+nIoLw4HqfcmIOOD7uI2NYcl1dRiRKBBe6Tm/di2G4xAZNqxP6xpMgiAAx8GMRcEuIvB9TNMC24ZEHcw4G6LFfVqTYZq469bRef/9AESnTCG6227h9hlIz5tHfvUaAs+FIMBtaiKzYD65deuo/uxn6HrscYxIhJrPX0Fu7dptL6A3B/LFyuHIb0M+HYa5ZNrhpT+DE++9ZcgHKxkCow+G4ftAcU3fLdfLw9rXIdmEPXwc1cMCyieeAoGHs+JncPDtUD6CfGMj9pjR4TbbdiAShUwaq7IiDOYjoOjAA7ErK8G2ya9dS+3//R9mLEp04kSio0eHx8vv4GcyxGfuTtNvfxeGDZWVUfvFL+B1dGCVlm7y3Mzcuaz50pc3Tnf8979YxcXUfPELdD76KPk1awiyOcxEnMT+B5CYPq0v3r1+Lbd6NR33P0CQSYfHpfE4Bgbk8/j5POt/8AMiI4ZTfOihYf/LduEvWIldWUHTtdcCYFVXU/3xjxPkw4HxRKOQy4WfpWmG94dfCdUTC7y2IiE/m6Xr8cfJLl4C+TyYBvbIkaReegmA/Oo1rLzsMsbceQeRESNp+v3vcRsaACg98QT8VHqT+RUfcghj7vgP+XXrwA/IrVpF5s2niU+eHAaXWnb4PbAs/GQKw3Hw2ttJTJ2K29wMgOHYxF67OqxvwkkU//bbmCSxo83QugSqNg1o6jeGTAvPOfNpKNbvD33B91ywLQwnghm3CAwTwzQwHFsBJDIgGLEYRiKBkc6A42AURzEcG9/zCl2aCHk/wLYMInYYQpJVCImIiIiIiIiIiIiIiIiIiIiIiIiIiGyHPg0hAX4P+MARwFVAJ/BvYO8+rqNfMmMx2h95lKqLLybI5cKBjStXbBzouzMYto1hOwTZXHiF48AnSKYwTBOvvR2vsRF7QjlGEINcDnwfwwlDKbwVc7FW3gP7XQZV4zadb6KI5LPPgmGQ2GtPcsuWkZ4zh6JDD91yIdFi2OcT8I9zwe0OXSkZAmffstPWXd6f29hI2513QT6PXV9P2Wmn4bW00nHPPbgtrdR84fPEpk6l4+7/ktj/AJLPPAuWCYaBUz+M7NKlABQfdhjNf/4zAEUHH0TjL3+FVVFByZFHsvab3yQ6aRKlxx3H2l/8BmdYPeUf+hC5Ze04Q1cRGx8FZxXOqHE4Q4YU8u2Q/qxuGjz5U2hbEU4f9kcy8+bhp1Ik9tyD3LJlpF6ezdA9ZkA+i5vJEWTS4UDj7jFufjqN19m500o0TBO/e3A0QYCfSWNbNvg+gefhDKun+vLPgueDZZKZP5+yM07fafVsSZANB1tHx48Pa0ynCbJZcqtW0XTtb2m/+27MeJyaL36BslNOwSop6dP6BgsjEiHwPHBdDKs7gMS04N+XgOfCkd+CUfv3WT1eMkVir70oOvCAMLTH9fCamsLaAL+jEz+TwbDM8O/5PEE6TXz3PVj3zW9ScsQRBG6epj9cR903v9FndW9RphUe+yH43YMDq8bD6X+EynHv/zrpPW4Olj4Bj14FqSbY91Mw/Wwo7YN9/OL/wW3nQBAOwjNqp+CMPhhevB5O+S2UjwDAd12iw4aRX7ECr6k57OsRByIRrHgMs6KC2NSp5FeuxGtspO0/dxCdMIHigw4iOnHiZgEkAHZdLdmFC6n+7GfD73YkQvvd91B8xBG8O2Ynu2jxZq9vv+suSk85GT/dHbJhGPjpDOk5r+MMH4ZTUdHrb9dAkl2wAL+rC8Pu3i6lM8SmT8MqL8drawOg69lnKZ5QBg9/Bxrn4U3/DWZpWc++1zSwR42EbIbAzYfHCPE4QS6HYZrY5WXwr+/DwV+CCceCEy3gGotAduFC0q+9hlVSih8EmNEYBlB68sm03357+CTXxevooPOxxyg744xwX929/bCKizabZ2TkSCIjR9L55FN0PfEE0ZEjabnhBgLPJT5jBm5jU/idMCC/Zi3BylW469Zh19WRWbSIyJAJmLufB6/fijnuQCIPXvyOc/uhcOFdUDOpz96jreJ7sOypcL/UsQZmXQq7nwdlCt3bmQzAMMOAxsBzwTDAdsKbyEAQi4WhcBUVBOlUeHzp+1jFJfi5HGYkUugKZRBzPR/HNIl2h5DkPIWQiIiIiIiIiIiIiIiIiIiIiIiIiIjItuvrEJJ9gyDY0zCMVwGCIGg1DGOr/me2YRjlwJ+BaUAAXBoEwXM7rdJCMAxKDz+Mhl/8EmfoUPzOTiLjxu3cK8HaNkHg43d1YlZWhYOLuzrxXRcAr6MTw3EgkyFwPQI3j2kYGK6HV1YLL/0JWpfDWX8Jg0S6BfkcJUcdSfr1ObT/926ikyZRcdZZ4eDJLXFzMPuGnkFKAJ3rYNVsGD5r562/vCczHscZNoz8smX4nV00X3cdxccfT/k5H2Ht//0fmTlv0PzHPxLfY3ciI0dQ953vkF++nPKPfIT2O+6g+IjD6Xr0UQzTBNfFLC3FXb8egNITjqfl5ps3Pm781a8xixKUHHMs6753FdFJkyg+/HBWfPprRKdMofS4Y0m99BKRMWMoPuRQgnweZ+gQnBEjMDZcnV4Gr/VzewJIALI5ig7Yn/Rrr2/c/lRecCF+80pYYxCUTCLoHsTdE/JkwM68arHv43UlMW07DHnq6ATfDweNxuMkn30Or60NM5HAbWqi9mtfDYOf+pAZi+EHAflVqwGwhw3Disdp++fttN93H4m9ZxHkcqz/wQ+JjB5N8YEH9ml9g4rrbgwjM0wzDEHIdsKaV+Bvp8HHH4O6qX1Sil1ZQdPNfwMMME2CTIaaL3yewPMwLIvA98KRpN3fJQODwA/AMCg//XTa//tfjEiEyosuIr9ufZ/UvEXpNnjkuz0BJADNi8JpS/uQPrP2Vbj1rJ5t78PfBgw48HM7d7nJZnjg6xsDSABoeCsMQbn4PqjfHT+dJvP222SXLiXo6iLI53Hb2vDaWomOHRsGRtUNwV23nvRLs3Gbmuh66imqL7uMpj/8Aae+nnjbXltcvBmPk337bbqeeAIzGiHf0Ejtl76E39UFNTWbPNfaQqCIM2oUfiZDkEyF751hgO9hmCbumjWDPoTET216fmGYYdBCfK+96Hr0UQAiw+vDvtfVED7HjtD1xBME2WwYoGRbDD/8cLyGRtzGxjDQIeJgFhWDZWHSFc786V/BtA9B3W59uo4i75ZvaMAsLSPwu7drfkDg+yT23acnhIRwF1203340XXcdhmliFhcT5HIk9trzPecdZNLEd5tK46+voepTnyL1wguUHHkUfjZLbvFivM4usvPmUnzIoWTemk+ks4vckiWYZWVEx15MdK/PYT991bvO7dfCksf7XwjJ2tfh5jN6jg8e+yF4eTj8G7Azf4cZ5AIIA0wDHwwjDHFwI5iJeKFLE9kqRi6HmYjjdXZBLkeAgZkwCXwfL5lUCIkUjO8H+AHYlkHE6g4hcRVCIiIiIiIiIiIiIiIiIiIiIiIiIiIi266vR13mDcOw6B5zYBhGDbC1/xP2GuCBIAjO7A4uSeykGgvGiEZJvfoaNZ/7HNm338aqqMCurMRLZ3beMp0IuB5eczN4Pr6bxyotJchkwLIwDCO8Qm0Q4PseVkkJZiQKEYfA6x6Us+ghaF8BtT0Dos3iYjoffgS7ppqK887Da26m+aabGPaLX2y5EC8LrUth6mnhwCTDgNUvQ/vKnbbu8v7sigqGXPlNVn7yU5ix8GrvVjRC85//TNUnPkF00kQwTbz2DjruuYeOe+4Nw2bOP4/4tCsxYjGKjzmG9Jw5JPbfn9RLL2GVlQNgFpdsvDK9392/iw8/gvb//AeA0uOPo/HX12A4DiVHHUXDT35KZMxoYpOnsOKjHyU6fjxlp5xC1zPPYFVVUn7GGZDLY9fVEh07Nhy43w8GjQWeh9vYiBGJYlcO7gHCO1XXehi5H4w9DAIfo3g4nX/6A079UCrOOw+3YT3Nf/kL9d/9Mix/FmZMCbdtjg0YBAYY5s4dlBH4PmSz4YDRWBSvozO86rfv46dSlJ12Ku769XidXcQmTiQzdx5lJ5+8U2varEY3T27RQqKTwsGh2QULiE2Zgu/mqbn8s3Q9+RRmURG1X/sa2SVLFELSy7xkEnK58Irwra0Eno9heASe1x3Y0L1NczPQ+FafhZC4Tc1UXnQRueXLCVyP6Phx5FatDoNRAAwjDIIyjO5gH8A0MR2briefoOy0Uwlcj/Y77qD6ssu2YcEZaFoEuS6oGAMldTu2Ivk0dKzevD3dtmPzlW2zavY7wp+6vXAdzPwIFNds+TU7Kp+GlsUw4+zwuPL1v4Mfhu1h2jD6QLIrV4bb4LY2yGQJ8nmMSDgY2orHSb/5JolZs7CKi+i4735SL7+M29BA1SUX03LjTZSfdhodDz5I5SUXb7GEIJul5JhjyK9eTZBJE50wgeQLL1By7DGbPTc2bTdiu+9O5rXXwgbHoe6rXyHwPALfC7cR2SyGZWHYDkF3cOCgZhBuh2wLwzC720zMqioAnJEjKZo+BhaHASQ4CfItLVSc/WFyS5ZCNEr5GWeQW7cWw/Owa2rxWpoJXBerMoZVWoLlroVDvhIec+RSBVpRkXewLMySYoLOLohEwLIIkimccWM3PqXk+OOwR40ie8cdVF1yMW5TE157B9GJE8mvb3jPWTtDhpJ+9VWcESNwGxspPvxwWm67lfLTTiO/YiV2RTnpVJrsihVE6ofiZzKk586l+JBDyC1bjt9VjTn6cuyxZxN94otQVAVTTwUnDu2roWxYX7xDW2fdm5sGlAG8eD3MugRK6wtT0yBgRqPd+zEbbDsMQ/R8/FSafGcnTklJoUsUeV+B72MYJkGyC6+ri8jIkWDb4Hu4bW2DPiBOCiffHU7mWCamaWCbhkJIRERERERERERERERERERERERERERku/R1CMlvgDuAWsMwfgicCXzrg15kGEYpcAhwMUAQBDkgt/PKLJAgIDZpIm3/+DtFBx+C19pC5yOPMOS73+nd5bjZcEBmvBwz4uAnkzgjRtL50EMQi1F+8kkEuRxmPI6ZSBAQEBgGVkkJzdf/icxrrxHfe2+qP30Z2FEwTLBiPfP3XIKuNkpPPIHOBx+i+brrsOvrqbzwArxUcss1RUvgiG/BM7+CeXeGbeOPhInH9+66yzYp2m8/Rt9yA25jE5UfvZTkk08RnTSRpmuvpeZrX6X6sk+RmjOH/KpwYLddV0fngw+RfPppAIqPOoqyM87AqqwgtttuYFlEJ00i9eILFB92GF3/+9/GK8SaiTh+d//wM+FVsxP77rPxCvalJ5xA03V/BN+n7JSTafjpT7Fraqi48EJWfeKTWFVVVF58EU3X/ZHA96g891wC38euqiIyZgym4/Tpe5dbs5bWm/9G69//gV1RQe3Xv0bxoYfqirg7w6gDoWEuPP5jAPwD/0DpSSfRcfd/6XzoYZyRI6m65BKCZBfUVQJg2E4YitP9eQSZdDhoaCcJXBe7tpbk00/hdyUpOf54/HS6ewBpCY2/uZaifffFLC6m5ZZbqLzwAoJcfqfVsyWGaYFhsvbr/wdA5cc+CpZFbOLEjW0AyWefY9g11/Rpbbu69JtzafjZz0i/8grxvfem6tJLyTc1hVeHj0TCEJvgHf3BKeqz2pzaWhp+8QuKDz0Ew7RovuEv1Fx+OYHnhUEklo1VWooRi4dXss9lMWwb33WJTZtG819uxLBtys86C7etdesWmmqFZ68NjwkCPwwhOftmGDJt+1ekuA72vAie+XVPm2HAkOnbP0/5YL4fhuY0LYRYeXi8925FNWDHNm//IG42DINIvM8gz2QTPPlzePG6MPykeiIccSU88l0wTIK66aTfeIPOe+6l7V+3Y5aVU/Xxj2HEE/gd7fjt7VhVVZiRCM7QobTdcw8dd9xJYs89KTnySJr+3w2UHnM0RjxObMpknBEjtliG4Ti03X47RfvvBwE0/f4PVF/+WbYUlxapr2f4Nb8m89Z8/GQX0bHjiE6aSPL553GGD8dPJvE6u/Db2zAsC7O4eNvfu12NbWMk4lglJRiWhe/7GBGHirPOpPSQQ4hOnkQk83bP82NlWPE4Db//A8WHH0bpySeRW7sG0zRpuuEvZOfOJbHfflR85ByIRDASCYzX/w0L7oDKsTDr0veuxctDtgtipdC8CBoXgJOAut2gdOhOfytk8DBjMQLXCwMcEgms4mLMeByiMep/9lOsykpik6dglZYQmziRxt/9ntjkyUTHj6PrsccoPf6495x3dOIEnLdGE5s6BT+bJfXSSyT22hOvswtnxHDyK1YSmzoFoyhB1zPPEJ+6GyWHHxYex970V3KLFlF08MGUn3UmmanXEBtZTfSxT4Who8V1cM6tMHxWuLBMO1iRMKCkN+TTsH4etK0IA8zqpoXfx/dc2S1sQxNV27dfkq3muy6GaeKuXk3TH67DT6epOP88ig44EK+xUSEk0u8FXV0EgU/geeRXrqLpl7/ELCun+jOfJgC8uiFYiV7arolsA9cLAx9tMzzTiNimQkhERERERERERERERERERERERERERGS79GkISRAEtxiG8TJwJGAApwVB8NZWvHQs0Aj8xTCMmcDLwBVBELxHosXAZDgOXkcnxYcdRsf9D2CVlVJ53nn4qV682vaq2eFgzKYFsPt5MO5DBIGPVVYaDkQyDPADyOcxSkqw64eGg+TjCdZ+6Uu4jY0ApJ59lrUrVjDqk+fj1I+GitE9y1j8KGaqnfb77ic2dixF+++Pn8vRevMtDL366veubf2bsPLFnulFj8KUU2DMQb23/rJNjGVPEL/7HPByFNXNovTyC/HLJ5NbuYoglaL93vuouvQSvI5O0q+9RnzGdJp++zsAEnvvTWzKFBp/8QuCfI6KSy8l8H1qrrgCr6Mdq6wcq7qK3Lp1JPbdl67Hn6D0hBNoufEmzGh0s1oC1wPPIzp5MqnZLwNQevLJNP/xjwS5HJXnnUfDz36OYdvUfvELrL7i8/i5HNWf+DheKo3X1kbZySdhlZVjlhQTGbrzBoIGQUDb7f+k5Ya/AJBPpVh9+ecYdcvNJPbaa6ctd9BKroc3/71x0ozFaL/jn8RnzqDo4EMI0mmab7qJYd/5MozaHcMwMGJR7KFDCPJhsINRPzQczOn7GKbZ+zW6Lo2/+MXGySCbwTBMOu66C6uoiKqLLqLt3//Ca++g9ITjcVvbwOnbnLD8unWb1Nj4s58z9Oof0vHo/zZ9ou+TfvllSo8+qk/r21Xl1qxl1WWXbbJ/za9YQd23rsRraYF8HgjAc8MXDJnZp8EZbksLFR85h7b/3AGuS/kZp4e1mibkUpgRB6OqGj+bAcPAjJVixKIYGORXr6H64x+HIKDrqacoP+vMrVvo2tfg6Z6+SOtS+N/34cwbIbKdg/lME/b+KPguvPwXKKqFY38I9Xts3/xk6yx7Cm45E7zu7MATfwkl9dC5Jpw2DDjyWxDbxsHGq1+Bp38N69+AaWfBHudBxajNn7dqNrzwh57pprdh1Usw+WTye3+Z3IoMyeefo+WmmwDwkynWf+8q6n/6U4JkF1ZVNXZlBWZpKYZlYdkOVZdeQuvNt5BfvYqifffFKg0Ht1dfdhn2ewSCeB2dlBxxOO333IsZj1P1iY+TXbQI86yztvh8p64Op65u47TveVjl5WAY+NkcZsQhiMXwsxnM8vcJYRkkjO6QGBwHM5HAicUIPI/I6NHY07u3l502DN8XVr0AiSrc9espPfYYio88CnfdesxYlDVf/Rp+RwcAyaeeIr9uHfU//hGZOXOId3X32ZYl8NQv4Yw/gfOukIL1c+H538OyZ8N+/d/LIdcV/m3YXuE2rGJk37wpssszDAMzEccwi4EAs6QEs6QYqyhB8cknb/LcwDSp/tSn6HrsMdrvvIv4HnuAZeG2t2OXlW02bzMWo+iAA8i8NZ/41Ckkn3uezkcexU/eRe13v4cZi5F+Yw6JMWPxhrWSW7eO2IwZrPvu9wjSaQC6Hn0Ur7WVsjNOp+PJl4nu9jMikXZiz30Z7vosfOTv8Pb98NKfoWw4HPJVGLl/uL/eXr4Pc26Huy/vaTv4S3DQlyD6HgFu9btD+ShoW97Tdsz3IVG5/XXIBzINg3xjE+u+d9XGtuY/XIdVWkrxsccWsDKRrRSAYZrkly2n9cYbgfA4ct03r6T+Zz8ju2A+iT10niF9b2MIiRXuTyO2Sc5TCImIiIiIiIiIiIiIiIiIiIiIiIiIiGy7Ph3hbBjG34IguACYv4W292MDewKXB0HwgmEY1wBfB771rvl/AvgEwMiRA3CAl2mCZYHvhVfeNk0yb82jZOSWr6q+zRreCgfDTToBhuwGXesxG9/AKimh5eZbKDn0UCCg45FHqLrkYojHsZ0Ipm2Ta27aOEB6g/yqVeSHfBVnz316Bgt1roO7r4ApV1B24om03X477XfdhVmUoPKSS/DT7xGo4uXCQUixMph4LGDA2w/Ckqdgr4t7Z/37sX7Zd5PN8OA3Ng4cNtfPJt40Bz70/xhx7U/JNyeJjBxJ47W/perjH6P8zDMJs4UAw6Do4INo/OWvALCHDCHo7MQeWk9u9Wo67rqTkmOPw89kcGrrKPnEJ8gvX4ZVWkbt175K+o03qbz0Ulpvvpmaz32O9GuvYVgWhuMQuC6G44SLiUTwu7qwKivJr14NrkvpKSfT8reb8draqP7Mp2m58abw8eWfpfm660g+8ywlxx5L6QnHg2kSHT2ayLhxvRo84TY10fbP2zdrz8ybt0uGkBS8/659Pbxa+cRjIVpKkOyg/ENn0HzjTbhr1mCVl1P1sY/hEYOaSQTt7RjxBEEqGX7ujgO5HA3X/Ib49GmUn3UWsYkTe7XE7KJF2EOGUHr8cUTGTyD18mzWf//7mEVF1H3n26TnvE7pSSeB5+OnkkAAQdCrNXyQzgce2Kyt44EHKD3tdJKPPrpJuxHfNa7qXPC+C+RXrNji/tVracFtaMAeMiQcVLvfZ8COwoh9oGxYn9VnFiXILFlM+RmnQxDgNjUTGTWS5IsvUjS6AiMWw3QiGNkoBAFGLEZgmuHg6FiMpt//HkyT0pNOwktntm6hrcvAcmDCMZCogsWPwdInIN0MkeHbvzLlI+Hoq2C/y8COQ1HV9s+rwPpD3/1AqRa47ys9ASQAD34TzvsntK2ETHsYzDBsz22bb9Mi+Ntp4esBnvxJOHj8lN+E35GWZdC1HkqGgpuBUQfA8md7Xr9qNpmT7iT1wmuYxcW0//fuzRaRXbKY6OTJBJ4fhox0dZKa/TL5devouu0xqi+/nIYf/5iSY48ltts0ohMnEKmvf8+SvWQXZqKI8jPPxHAcOh9+mLLTTgtDWD5AZtEi8mvW4nd04Hsuhm1hJBLh9y0axSrZcvBJf9bb/deIRDCdCIHn4nd2QTaLVV6Om0xil3QH3JQMgTP/HIbQYOA/00h02jS8tjbyS5diVVVuDCDZILdwIW5zM15XJ0bHCph0fBhgtPIlSLdAPg6tS8CKQKwcbvtI2BfHHAqzb+gJIAFY/TKsfEEhJANcf9r2GhtCjywTq7ycIJnEy6TxOzoIslliu+2GaVkAOEOH0vCTn1By5JHhuYhlkV2wAO/gg7cYQgIQHTGC2is+R27FSvxkiq5HHqHmS19k3Te/QdUnP0nlRRfhtbVjODZuWzt+V+fGAJIN0q+8QsUF55OeNw8Cn6xlkZv5exIlLdjtq+CBr4dPbF4Ey5+Bjz68Y+FgrUvgga9u2vbUL2DySe+9r6kcCxfcEW4bks0wfE8Yuo37pQGiP/XfwLJIv/TiZu3td9xB8YknFqAi6c/6U9/dyDIxolHa77prsz/lli4Jjyvyeczu365E+kreDwNHHCs8z4hYJjlXISQiIiIiIiIiIiIiIiIiIiIiIiIiIrLt+jSEBNjtnROGYVjA1ozIXwWsCoLghe7pfxGGkGwiCILrgesBZs2a1bejt3uBYVo4dXW0/O1vRIYOJXDzYJgErts7C2hdHgaQPHctuFkoGYI1/jjctnUU7b03LTffDL5P2Wmn4nUlcWIx/NZWsEoxtzTg3DQxa0dC/B0Dl7KdUDYMY/h0Om/+PUX77kvpMceAZdL1v8eI7777lmuzIrDHheGg0Tn/CAff7/tJqJnaO+vez/XLvptsCAeEQThIdv/PhkEPq17CbluOPfpg4iefTHyPPXCbW8guXUZs95lEp04lyGTIzJ0HgFVeTuUF59N4zW8IcjkwDGq+/nVw83Q9+BBVn/g4qy+/HHyfms9dTtP1f6Ls1FOITp1K1ac+hVlWSv2vf0XXM89Q+/Wv0fjLX1L+oTPofOQRgnQaq7w8HKBvhv/B3q6tI79y5caBvV5bG7EZM8jMe4vkM89SfPjhAKy+4vM4w+opP/tszOeex6ooJzZjBtFRo3b4rTPjceyhQ/Gamzdptyp2zStqF7z/1k2HI66E126FTBvGoWfR+odfUnrccRiRCACt//oXQ3/w/fD5vo9BgNvZRZBMgmVilpRQccnFrL3i83Q++j9G3XIzkaFDe63E2OTJGIZB621/x0+nKTnmGCouOJ/2u/4Lno/X0UnXY49jFheTX7mSyksvBc/rteVvDWfE5oFXzogRxGfO2KTNiEQo6f4eDXQF77uAWbyFAAHTxIzHCQwDr60Nu6oK9vowBB44OykAJtUCK1+ENa9BzQQYsS+UDQ8D0eYvILd0GVgWXlsr0YkTaP7j9Vif/wwGBn4uR+cjj+C1tFJ0wP5EJk0iMAzMoiKqP/NpMAzSc97AdLbysLNyLBz1vfB4YNXscPBw+UiIV+z4eppWuF4DXH/oux8o2wXNC3ump58J1RPDPla/B9RMgeLqbZ9v4/yeAJIN3vgnHPrV8LjlP5+EAz8Hq1+Blc/BkBlw7A/hfz8gSNSSPuB3JP/3eLiNtR2c2lrcNWs2mZ1VVY1dV0fg+XiNjTT/7vfkFi0iMn48NVd8jtTrc4jtthuxqVMpOuhATPu9+7aXSmEEAZ1PPI5dVk769dcpO+MM/GQKK5F431VNvvIKuSVL8bu6wywsC9O2MeIxsCzsqiqs7v3cQNLb/deMxfA7O8Pwpo5OTNvC6+wkAjBkSM8Ty0dCST1eZzt22cPYlZWs/8EPiYwaRekJJ2w+Y8chM+8tYmOGw/QvwOu3hf1q6qlhWMG9X4JV3aene5wPI/cNQ0jKhsOSxzafX9tyaFsRbmubFoVhB8P2hkQvbNukT/Snba9TN4T8smUYTgR33TqMWJwgkwHPxWtpwU8mSeyxB2YsBgFUfORcmq+/Hqd+KGZxCWZREV5LC2zh+G8Du6ICu6KCIJuh6PDDyS1dRukxx9L445/gZ7NUfvSjFB9xOH5bWxgA9C5GIoHX1ETxgQfgtbSCH5BbuRqvspLi3Byc06+Huz8XBkZ5eVj7xtaFkAQBrH0Nlj0bnvONPhCGzoR0G+TTmz8/1bx52ztVjQtvbq77WCv6wTUMQP2p/xqAXVe3WbszbDimbfV9QdKv9ae+28PA60piDx3y/9m76zg56vuP46+x9d3bPb+c5eJ3sYs7JIEgwS04lOBWihbqpQVKoXiRtkDxQou7a0JC3N1Oc363LiO/PyZcSBNIgBDoj+/z8cgj2bmZ73znu7OzM5P7vAe9qWmHn1iWRd0ll1Jyxx349pv0PfVP+LHSDfsjom4LWnaoIoREEARBEARBEARBEARBEARBEARBEARBEARBEARBEARB+Gb2SQiJJEnXAb8A3JIkhbFrDgDSbPtF8q9iWdZWSZJqJUnqb1nWGuAAYOV31uHvi6ait7USOvFEYp98jOzx4J00yQ5u2BskGZpXwqSrwEiB6oa6OUhKAZJDI+fcc+wwB9PCiseQACMSQQqFQFEIzjiBzmf+3d1c9k/OxJFYDm0a5PSyJ/qLoPJIqJ1P4OBDaPvnP9EbG0GWCc6YgZnOfHn/3CF45WfbX394Mxz/8N7ZduHrc4Wgz4Gw9g0YdQ6sewta1mz/+YG/h2gLjvwBOPIqcLlHkW5sIv+aq4l+9BFs228DRx5J29//gZVOo+bnkz1zJvE5n2KEI+Scdx5qXh5WPE5g+nQ6n30Os7MTNRii8aqrkDSNvJ9dxtbf/BbPqFHI48aTfc65mIZJ8Z13El+0iPxrr6X5T39CLSxCcjjQm5vRSkvJ1NeDaf/yvWfUKDoefRQA95AhtNx5JygK2WecSdMtt4CuEzrlFNKbt2DGYrhHjsA9bBhaTs43GjrF5yP/iiuoPffc7iAJrVevncIchL3E6Ye3f20XJALW1tUEjzmG1nvvwwyHkZxO8q+5GiuZIDZvHnJODkYsTvSdt4m8/Q6S00nwhBPw7r8fciiE3tBAesPGvRpCIrncmIkE2Wf9BAwTJDDiCZTsEJZl4ps8mfjcORhdYbKOPZZMczMo+7YAzz9lCuGXX8aMRACQ/X58kydjZXTKHn2EyBtvInk8BA6ahmvwoH3at//PHL0qCJ1+Gh2PPd49Lfv000lvqcGMRpEVBcu04KVL7UCHsRdD3wPBlfUVrX5Negbm3Asf3bJ9Wu8D4LiHsNIZso48gthHH2MZOsGjj8GMxQmeOIOtN9xMcslS/NOnEzjwANJ19SSXLUdyunAOHUq6tpbwa68B4J04cc/7ozrtz7S5LYhn/oMw4Wf2eYvwv8OXb58TrnwBek4CJFCc0LDIDnPoPRWGnwl5/b5eu+ouisNVlx16svQZmH4LzLoTmpbZP9vwHjSvRN/vBmId2cReeQs1OwczHqPz2efIv/oqEitWQMY+R9VKSnBVVVJ7wYXkXnghpFJ2oN6hh2C0ttH6wN/IOessPKNG4hkz5isDSMAOQ8OycJSUojc1ETrtNDJ1tbiHf3WhfXzpUpLLlmF2hTEiEbQeRWDJmOk0pFM4evVCzf7/GW72tckKmCZGRydGZyempkJbu30N5fXi6bdtH6tfAJ/ei5XVl3STB6OzE9nnQwkFCb/1Fv7DDiPy6qvdzWafdipKKIiRMeCtX9ohCQCf/Q1MHaqOhN6T7WmmAQ43yCrUfAq9psDiJ3bsZ9l4eP582DJ7+7QDfmMf32RRdC98PVpONkpWFnpHB7LTiWWZWKkUZiSMZZiYySSSy4V32DC0HkV0vfgiOeedR3zuXDukcdRIjM6u3a8I8Awfjt7UTKapiY4nniDn7LOxMhlQVTIbNtLx9NPk//IX+KZOJfree93L5V5wPqmaWhzFxegdHaihIOnNm3EYJqnCwWjvnAcjZ9rnIGB/hvZE3Xx45DA7WBXsgLafvApZpXbYUGfNFwbKA6GKr27PyNify1l3QKLDDt/scyC4g3vWH+FrsyQJ16DBqPl56M0tAPY12YwZmKm9dP9LEL5DlgTxOXPIOfts6i+/AraFR9shntW4By5g6x/+QM+n/yXO14R9KmPYgSOqYv/Xi0OVSRsihEQQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEH4+vZJCIllWTcBN0mSdJNlWdd9w2YuBZ6QJMkBbATO2msd/IGQAC0vn8zWrXhGjgTAaG1DyfkGT4nfJQsUh13kEyyD9o1IB12P4vPS+dS/kP1+ZI+H1KZN5F95BSgqpNKY7e12odKwYRQNGYre1Ymak4uSm4O84i5Y9iTM+KcdBJCOwaf3IFdfTvi99whMn47sdIIik1y2fHtB8a4s+4/9t8NnF+Ilu2DxkzDo2L20/cLXEiiE0edDOgrBcuis3fHnn9xmP/V91h0w5jzkZ87ABRilk1Em/xTT8tP18ivIHrvIEyB02mm0/OUvWJkMWkkJkixj6Rlyf3YZstdLbK79NHkzZReUeUaPIvLOO2BZuKuH0vTrX2PGYmSfeSYdH35IprGR1IoVFN5wA5JDo+SB+4l9/DG5l1xM801/QnI6kJxO9K2NaGVlpDds6G7bO24c4TfeAF0ncPjhJFetJLFoMWpeHkp2iNS6dajBIO7hw3H17/+1h887ZjQ9n/4XqXXrkD0eXAMH4igp+abvhvBV6ufbASSaB1Qnkj+Pzn88Tvbpp2EZJmpREekN62m64UYwTdT8fAp+cR2Sy03uBReALBGfvwBnn964qqqIz5qFtJefwG2lksRmzUZyOpE9HpLr15N/+eV4R49GdjhJ1q7ANWiQHVBiGih+f3eAzb5iJJMU3fBHMo1bkSQJtbAAM5VCDWbh6lmOd/TofdqfHwvF4yH3oovw7T+ZdG0Nis+PpeukVq9G9ngwkyk7IKzuM2jfCM/OhOMfgkHH7b1OdGyET27fcdqGd6FzE7LHQ3rDejswwbIwujpRexQTm/0pySVLCZ16Ko7ycpJr1yFJEnokgpVKQjpFctmy7uZin3yCd9IePgm8dZ19vqC67MLiRAcseBjGnA+BHntvu4XvluaCKb+EVARKR9vnE3Puhei2p8U3r4SOzXDs38Hh2fN2CwZC4WDYun3/YvIvwEiCnoDoVujYtMMiRsFIYp15JFeuRM3JBcsisWw53rFjSNfWUfCL68AwkNwetJJi4p99Rs5pp+EsLaH+qquxkklcgwbhqKok65ijUQsL8E6ciOLZfb/1lhaMSBRn7944+/VFb25Byc5Gcu4iTGWbZF0dqdWrMTo6sTIZkitWoOblIskySiCApesofj/ugQP3fNz+H1P8foz2NixVQfF6SK7fgLO8DKOlBaO+Hvr1g5bV8MiR9n5YdTrOntOIfvgRvv0m0frXe8m7/Gco2dn4xo1D7+y0zwdzc2j68y0U/era7QEkqhMcXvsaZcLP7JAdU7ePUwf/CfyF9rG68kgYcASseQU0L0z9tb1/fjGABOzQxaqjIKfPvh424f8BrbzcDh7RNKx4HEyD1OYtOHuWY0Yi6I1bMfrFUPx+PNVDCb/2Gv6pUzCTKSRZwejs2KP1SKqKb8pkWv/xD3LPO4/wW2+SqakhU9+Ae1g1uRddSPTtt8m/+iqCJxxPprERNScXvbMTTdWQFAW9sRFJVdEKC9Hb28nUNZAe8TMcStReib8Iir46nKnbgn9uDyAByCRg6X/g0JvghEfghYuhZSVklcBRf4Wc3l/dXv18eOyo7kBDnj0bjv0HDDlhz/ojfH2GgZlIUPDLX2GEu+zA0txcjFQKSdr94oLwQ6CVltD5/AsU33YbmYYGJIcD2eMmuWoVWcceQ/Otf8EIR0QIibBP6dvCmLXPQ0gUmbQuQkgEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCEr2+fhJB8zrKs6yRJCgF9AdcXpn+0B8suBkZ+d737/kkuF5LDQaahHmfv3mBZ6B0dOKw9+2VhM50muWwZsTlzkAMBvGPG4Or3hafLS7IdPlJ9KrSusZ9AH+vAiEuETj+NxPwFGNEoOWeeQaaxEc+4cZh6BmNrE7Kqkl67FtnnA8Mkk6xDUhQsTz7SksfsYruioXaBnubFCvUicICP1nvv6w6g8E2ejPRVFSW5fe0CvWSnXcznK7SDSITvjZFXTbLicmKz56PlXI6n0MC58TEYcab9HgV7QsVkmH1X9zJK7Qe4az8gfdCD5F1yCWqPItSiIoyuLoyOdqxMBtnnI3TKyTT/5TYwDJz9+xM8cQYFv7iO+GfzkL3binolGWvbL9AjK5ixGHJWlh0aMW8eSjCIb/L+NF57LWYkgnPwYAquvhq1IJ/yfz2F3tZGSXU1nc8/T/YZZ7D1j39EdtuHHiWYRbrGfkq2o6In4VdeASD77Jm03HY7VjqNe9gwjEiUxPLlOCsqcPbrh+Lz7dHYSYqCe9Ag3IMGffs3QvhqnmyYfC1kkpCJY6k+sqZPp/Wuu7AyGfIuu4yORx/rnl1vbkZvaUFy2fuCpRs4+/bF0nXMcBjX0CE4++y6GDhdU0N8/nzSW2rwjByBu7raDgzZDSMWI3jijO7jbO7Ms8hs2YJSXIISCqIVFmJGo3S99DJGVxcFP79m74zNl0itX09q/XoklwtXZSVaQQFWLErj736PlUwCILndFP72t6jB4HfaFwHUUAjfxAkkN/YgNudTrM4uZI8HVBUjEgHL3LHYdvY90O/QPQpuSNfUoLe2oubm4Sgr3fVMegoKBkHlEXaRvOKEphWQSSPJMuHXXkffuhUAJTubvMsvR9/aiBwI4Bk1kvZ//QtP9TBQZHwTJ2J0dJBYuXKn1SRXLLf7VF9PYv4CkhvW46muxj1sGGootH1Gpw8mXWVvdypsFxHXfmb3S/jfktcPZjxqv39t67YHkHxu9St2YEjB1wjTCPSAGY/BllnQtgHKx0HJaHufHTwDWtbCiJ/Y3w2f3gOZBImyc0gvW4Xs9iBpGl3vvIN/4gQ7qKyxASwTz/jxKIEALX/+M9H3P8A7fjyx+fNxlJSQPfMszGSS1Lr1qDm5aCUle3xsNGIxLEMH08SMRkmuWYNnxHC0wqJdzm8ZBuk1a9FbWsAyic35lKzDDye1fgOuwYORgyFklxNHr90U1f+IuPr0xoxGMFpbybS1YXZ2YASzkH1+0hs3kujRA3dmpR1AAkiKiRlP4BkxHKOtHSUrCzUvn/jixTgKCjDTaXQslKwAwSMOJ/bxbKj+E+5sHTnTaQeOZFdAXqV9TSUrUFQNLWvgsNug5lPoMRzGXAAH/tYOgQyWwdo37HClcReDotmBB5Zpn1MLwjfgLCnBDEcwYva+nd64ESuZxIzFkFxu0ls2k1rfA1dVFSgq3nHjSCxbjmtAfyxDR29rI93SgiMv7yvXk9q0ifjcuaDrSG4XWnEJWo9iHCUltNx7L+mNG4m89Tb+qVPxT5kCYAcorV2L3tmF3tiIVlhIctlSvOPGo/h86K2tJHMH4PDVwclPQXZvyN2DMB7Lsu85TP016En7sxRpsgOoAIqHw1mvQrQZ3CHwF+y+zfXvbg8g+dzsu2HAdDt0SNjrJFkCLMx0ivSWGmSnAzU3FwkLSdO+7+4Jwu6ZJlqPHnhGjiCxaBFqYQFKVhbpTRtpf+xxvGNGk3/NNai5Od93T4UfGd2w/w9BlWUAHKpM2hAhJIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCMLXt09DSCRJOge4DCgBFgNjgU+BqfuyHz9UkiSBJKHm5oFpgQRKYPfF7Z+Lz51L7Xnn4ygvx0ylaL3nr5Q/9hiufn3tGRQnbP4YHD672LN+IYTKUXP60HLPXwlMm4bT6SQ2Zy6+SRORfX67YDIcQc7LJV1Xj6uv3VamqRm1Rw9MRwBFkkHeVigSKIIpv0CqX0x0bh3ZZ5yBpeugyJjhCOa24vZd6j0Vnjqpu0AQWYWTn/4mQynsJdEPPqbh59d2v3YM6EfZH/+G9tKJ29+nAUfY+9R/cUSX4j/4EjL19eScey7tDz9kF2kCgemH0v7oY2AYeMaMwdm3D0033AiWRfDEE3FU9KLHrbfS9vBDBA45lOSSJUiaCpKEo2dPkqvXAJB19FG03f8AZiSCf9o0lGCQmpkzQdfJnjmTrKOORC0spHjUKMx0Gnf1UDINDRRe/3vaHnwI/9QptP/zEdj2C/nOAQNIzF+AlU7j7NcX1+DBtNx2GwCeMWMInjgDraQEZ0XFHgVPCPtIwWB4+lSItwEgTR1L7NNPyb34IvtJ7x73DrM7Knoiudx2kWYijplKI6kqak4uwZNOxDt6NGpu7k6ryWzdSt0ll6I3N6Pk5tL20EPkX3Ul2Wec8dUBS4Di9dLy8MMEDjgQSdOIfvwJ/mnTcPaqoOHKq1ALC/EfeCD5V11FfN48wq+/gf+gg/beGH1BfNEiamaejZVIAOCsqqLkrjuJz5mDZ/Ro3IMHgSSRWLqM+Ny5BI8+6jvph7ALEkimRfN990EmA4B7WDWe0aPRex9NOhNEViUcUiPytuPpl7Esi+h779Fwzc/tACefjx63/HnXgWChCqg8Et67fvvy1aeRSfmBBnwTJ9L18stYqRRGezuJJYtRQiGCxx9HbO5nuPr2w4yEkTSNdF09/gMP6A75+SLPqFHobW00/PxaUmvXoubn0/HPR8ieeRZ5F1+8veg0uxe8+UsI129f+Kh7wSuK+P4nOX0Q6gktq3f+maxuP4f8OrIr7D9flInDc+eCkbZfax6YfC3JpI/4omXd59nh998ndPRRGOEIWBauwYNxDxmKq19fIp98gnvoUJz9B+Ds25fmW24h9+KL0Nvbabnl1u5VaaWllD38EI6Skq/sZiYcxuzoQML+TMaXLCF42HSMZBIlFNzlMonVq0mtWY0ZixOdPRvvqFHE5i/AXVmJVlqC4vPhHjQISfnqY8CPjdqjB6ktW7AkCe+ECXS98CKe0aMA0Bu3QvH28zY50Q6yCbKC2qMIraICyenESsQxohEkhxOtsIjG636BpGnIXi9tmzdT8ser8K/6ox18ADD1V5BVBrFm+PhWOOD30Pcg6HfwrjuZ2x8mX2eH48Ra7GmuIPSf/h2OjPD/nbuqkuSGDVjhCLLbg3voUCKffIJ3yBCMZBK9vR1Z09BKitGbm7F0HSMcRvb6kBUFvXHrV4aQpGtqqJl5NnpjY/e0/F/+AisWJ1VbS/app4IFWUceQbqmFs+wYQBImoZ74EAsXSfy/vsouTn4Dz6Y8Ouv45swEXSDxKLFaKN74f7kWjjjpT3bYEmCysPhP2dtD/ApHAwH37h9Hk+2/WdPabsIGnH6QRLH2e+KZQG6TuPPr0UrLcVKpzHDXfT4y1/s635B+IGTFIXksmW0/vVeHOXlGK2tKLm5BKYfSt5FF9F0000ETz55j0NsBWFvyRh2qJam2Nf8DkUmrYsQEkEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBOHr29e/3X8ZMAqYY1nWFEmSBgC/38d9+MGSVBXJ6UTJDtkhJIaBEgyCJO92WSMep+OFF8i/+iqSK1ciOV04e1UQX7Z0ewiJZcDg42HdO7DocSgeAYXVmBsTZB16CB1PPIEZi+OdOAEkGdmhgSQhqQpYFmpuDokVy1FzckjXN+AZMhhL9cPIsyGnl72OdAw+/SvkTcQ3bhzNt96Ks1cvzGQSyzC6iwF3afMn24MtwC4qWvAI9JoMiihE2dcyLS003/oX+4WmkXveuRjhCM0PP49/2DV4jM9QN70Ca16B4x6EdW/aT3MHUJ0w4DC0/Hy0/HzMVIrAoYfiqh6G/K+nULJz0LfaT6v2jhtLyx13ApB3+c+IvP02nU89haNfP3LPPw9khdK/PUB07mdkn3EGXS++SNZRRxH/9FMklxujsxMUBVdlJS133QWSRN7PLiOxeAmbjjseR+9eFF1/Pc7evXH174+rf397vePHY3R1IWdlYaXSqPl5KAE/emcnAP5p02i9/wEAso47DqOzk4YrrgQgcOyx5M48C620FNnp3DdviPDlGhd3B5AAWPEOe3+4405QFPKvuXqH2d1jxmIZOkogQLqxESxw9euLaeiEjjvuS1eTXLsW/4EHYkSj6I2NBA49hOSyZWTq63dbiG4mEvj335/2J57ASibxTZmCWlhIw7XX4R4+HPewaqxMhvSGDaj5+WilJWB9ZZPfiJFI0HLPX7sDSABSK1eSWLgQZ1UVxpy5pDdvBkDSVJwDBuz9TghfTpZJbdpE4NBDiX30EUZnJ4lFi5E0lZqHVpBasxZkmezTTiFnvzhq6MuPP+nNm6m/8iqsbeFfZjRKw1VXUfHcczjKy3ecOd4Gs27vfmnmDCTc3petJ52OFY+TNeMEetx0E6mNG0hv2kR6w0ZyLryAxLJlOHv2xGhuIbl+PRIWWn4BRlcXrvJynFVVpFauBMA7aRLe8eNJrVuHZ1g17sGDyNTWETjoINKNDaRra3H22nYu0bZhxwASgA9vtgv7vTsHBAn/Azo2QcsqKBgETcu3Tx97kR2C821ZFix+ansACdihJB016MFj0FveBNMg/tk8vBPGk9q0GdfAgWg9inANGowa8GOZJqlVq+h68SVkt5vwa68ROnsman4BLbfdvsPqMrW1pGtqiM+ZQ2zuZ3hGj8Y7YTyOHj12nG/zZjKNW0GWiS9ZiHf4MOILF+I74AAcRUU7bYYRjZJat45M41YSy5fjrhwApoFn8CBkfwBHr944iwq//Xj9P+TIz8fo1x+zs4v4woV4J0wgOmsWrgH9kQN+HCXjcQbLoXMLUqQOqciD0dqGBeRdcjFGNIprwADC776Ls09f5KatZM+cSaa2BiMSJXjccXTNWo5n+CEo61+wVzrnXhh2OtR8CuMuhWizHZDwZXJ62dc6nweQACQ7YfGTUDz8Oxwd4f87V+/eSKpGpmkrenMzWk4OybVrUfPz0LduJdPWhhIIgKriHTeOyHvv4R09CjMawwh3ocfjJBcvJvzGm6g5OfinHYi7qgqA5IoVOwSQALQ/+BC+SZNIr1uH/7xzydTUEP3oY5AkPMOqcZSVdc8rqSquQYMwurpIb96Me/BgOp9/Hu/YMaBq6EaWfR7SuBiye+5+YxOd8MHN2wNIALYug3jHNx/APlPh41vsexlgf44nXQHazoFqwl5iGEQ+/oT8q67c4f5Vcs0a3EMHf9+9E4TdslIpUmvXkXvBBaRWr0YdPx7F50PNyyO1Zg3uESOQdhMaKQjfBd2078uqsv1/CQ5VhJAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAI38y+TnZIWpaVlCQJSZKclmWtliSp/z7uww+apGnILhdWRkdyu+0QEJcTI5VC+aqwA13HO2w4Lbffjne//bCiEVr/9neKrv9CxovDB4v/BRWToGQEGDqsfB45+zzSW2rInnk26DrIEum6OizTRFJVZM0BioKzZwWptetI1DfgGTECPRLFGnoQFJaAuq1AJx2Djs3IFYeSWLSa3IsvJrFwAbLbg2vwIPRw+Mu3IdYCVUdDfqUdmJLsgq0rtgdbCPuWrmNE7VCY7NNPo/O557sL0GKzQxRccxm+IXkomRaQnXD4HVA/H2QNysdDMgwdWyBUjm/sWBxFRSRWryHn/AuQ3C4848cRn/0pZjIFgGvwYJLLlpNcvgLXwCo8Y8fR8PNrkZ0Osmeejat3b8xkgoLf/85+svaqVVjpFLLfj+z1kq7ZAtiF7rFZs4nPm4ean0/o2ONoueNOjNZWgqecQuCQg1FDITs0oqQE98CBZFpa8B94AB3/ehpHWSmJ+fO7g4AkpxM1L4+uZ58FIPeii7Y9EXwmroGDyL3kYtwDB+7790fYLtll/503AAYdC56+dDz2U9wjRuDbfz+M5hZCp51KxxNP2oFKhQXIfj9qXi7xefOQvV7kYdXbHsn95SRVo+ulF9GKS9CKCul6/gV8++2HtZvlACRZpv2xxwmdcjJKVhZYFpIkYXR0EDj4YPSuzm3HewWw0IqLkQP+bz00/81KJEivX2/3ye1GkiXMWBy9vR05KwvXwCrCr7wCQODwI76TPgi7ZiaTpFevITHvM8x4guAJx5OuqcXo7CD88it2AAmAadL+6ON4xk/AP3nyl7anNzV1B5B0ryMWJ9PcsnMISToKsgrDzwTVQcrsQ+NVdjhU6PTTSK1ZQ/0z/wbsY3XOeeeSWLYMz/DhGK2tWF4PRlMTZjKJs28/rEwGNRSi7KEHSW3ajCSBo1cv0HVMXSfy/gcogQCOslLCr7+Oa9AgLPML3/XpOPQ+AMrGgJEBPQXLn90xYEL435LogFUvw4HXQ7wFmldBTh+oPBJU7du3b1lQOgpye9v7cs0c2PAeRqAcvaUFfWsjqU2b8U2cgKRpqAWFOCp64h0xoruJVG0dkqbhqqrEjMfxH3oozt59sKIRzFhsh9X5pkyh/dHHsBIJtKJC2h54gMg771B8y5/tQn8gsXw5sbmfQTpNpqERLSebTEMjrqpK1FBol5uRXLWK9MaNpDduxNW7F46ePcls3YplWbiGDBYBJLvh6tuH5PJluPv3x0zE8QwdQnLtWpSsIJn6JpynPQcb3oGOOhTJjSlL6E3NJDIZXFVVWKaJlpdHcsVyvGPHsPV3v8czYgSyx0PbP/5BznnnYfWIQGmVfWwyM1A8CjS3HcY37Izdd7KzDoaeBO4QrHsb2tbbwTymAaJYWfgWnOVlJJZnYW7ejHvwIKKffIIZiWKGI6Q3b8bRuw/K8uVEZ8/G1b8/eksran4eqbXrMDs6afzVr/Dttx+ZWJS6iy6i9L77cVUOwEyldlqXGYngnTAeNS+PTEMjcn4BiteLc0B/0jU1O4SQADiKijD698WMRkmtXYt/2jQi772Ho6QYvbkF/Yi/o1p7+B2fjkHnZvvf5eOhx3D7c/T59cA3UTQUznrd/kwmu6D/oVA88pu3J+yeaeKuHEDLHXfimzQRMxym9f4HKPj1r7DS4nxP+B+gKKihIF3PP49n5AhSa9aQrquj4Je/wFVdDRY4epbvthlB2Nsyhn1/SlXsYDyHKhNN6V+1iCAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDs0r4OIamTJCkIvAC8LUlSB9Cwj/vwgyY5HUiqitqjGDJpzFQKxefHSiTgK0JIJJeLVM0Wss/6CeHXXkdSFHLO+gmZ5i88adtIQ/XJ8PFf7KcNO3yw/zXQIWNGIrQ/+ihqdohMcwv5V1xhFy/LCkY0giMnm9TWRryjR2N0daEW5GPF41j+YvAXbF+HLx+mXY+VBFdviH/6Ka5BdkBD9KOPCc6Y8eUbP+g4+OBG+OAm+3VWCRxxF6iObzOkwjekFhSQfeYZtN3/ALLH0x1AEpg+HbWoiJZ7H6Q9mEXe6Ufi/eBmpKwCGHCEXUT5yR0QLLELLaf+CvodjKO8HDkQoH3FcmRFwX/AgZDRkbft1+7Bg+l69VUA/AdOo+VOuwA+98oraL3/AcyuLoInnYSa14He3Iz/4INw9OyJZ/Rotl5/PWqBXZjrHjiQ1vvuAyB06ik033orViaDd/x4zHAXTTfehLNPb3xTpuLq1xcALS8PLS8P12/7k2ltRc3JIdPQiJKTgxIMkt64AQDf5MnE580jPm8ektOJq7KSyFtvE/vkEzyjRuEeNAjJsX1/TdfUkNq0Cdnjwdmnz5cWHQvfUulYO4BkwOHwwZ9g8K+Rs7Ls8IRVq0GSSNfUknvpJbiHDUNvbib67ntoBfn4Jk2k7R8Pkli4kKKb//SVq7EScbJPP4PIe+8Rm/sZvgkTkDweMIzddjG1cRO5559PdNYsjPZ2MrW15F58MVpZGVJWAM3twugKY6VSqIUF9rG3qwvL5cJKp/favqOEQgRnzECSZTtkyNBRsnPwjB5NfP4CWu++p3ve1rvvpuBXv9wr6xV2L7FkKfWXXdb9Wm9rwzNmDJm6OpRgFv6DphF56+3un6fWrvvKEBI1Lw9J07Ayme5pksuFmpO988zeXBh/GUbDWlJaX9JWDjkXXEDXyy8jOZzEP5vXPWty2TISi5cQfukloh98SPYpp9D851u6Q3xSq1ZRdPOfSMxfQPTDD3ENHoSzXz86Hn8CIxzGM2IEweOPJ/bJJ8TmzMUzejSOkmKsL36OCqpg3Rvw/o32a082HH4n+Iu+5qgKPxi5/WDM+TD/H5A/EMrGQiIMoYq9037jIvjozxBvt19XHglVR5Pyj0VfsxRHeTnuwUMwUymM9nak3l7cgwbt0ITe2oLR0YlWWoYZCdP+yD8p+MUv6XzheQJHHE7Xs891z+seNRJZUYm89679fTBxIrLXQ2LpUhz9+5Nes4bEsmX2ubJpkVq7FtfAgWgFBVipNI6ePXfahExzC6m1a9Hr6nH26YNWXEz49dfxjhuHa+BAPIMH752x+n9MUlXcgwbT9frrpFavwTNsGJLDgd60leS6dWg9e+IcdR5k4jB7HpZhImkqkqIgOR3o7e04+/ZDzS/A6AqTe/55hF95FSMaJXj88WS2bkXqkwXvfeHYFOoFH90KA4+xQ0m+SiYFfabA+zfZ12JVR0GvydBjmAggEfYKragHRlMzemsLrgGVZGprsZJJjM5OtOwQalYW3mHD0NvbSK5ejS8/HyXgJ/LuO+RecgnhV17BSqXIOvoYkhs34qocgLN//53OJ7KOOZrmv9yGlU6jhEL2OfbQoQAkVq7EXV2N4vPt0De3u42kx4Ps8djXdcccjd7SitHWTjpSgOrptM8lJOmrN9JXANWnQlYxbPoQ5j8I+VUQKvvq5XanaKj9J5MESdk7AVnCl5Nl0nV1hE45hcgbbyA5neScdx56ayuWvvtrK0H43pkmksuNd+xYorNm4SgrJeeMM7ASCSRVJXjqKWgFBbtvRxD2soxhh3tqigyAQ5FJ6yLcWxAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQfj69mkIiWVZx2z75+8kSXofyALe2Jd9+KGTnE7UkhI6//U0sQ8/BEAOBCi5917UkSO+fEHTxNGjB8nlKwhMPxSwAxA8I7/wBF/NAytfhrEXgp4ExQk1c8FbgXtYNc6+fck0N5N15FGk1q3Df8jBSJqK0doGvXqTrqvDNaASORDAjMeRPF6s1g2gRe2n2YNdQBdrQW5ah6WPwjRNJLcbSVXxTZmM7PN++TZ01cLmT77wug5WPAcV+4EiioD2NUmWCZ10EkoggJW2i86UUAitRw/a/vEPAGSXi0RtGLP0MpzmOhwrXkQachz0qIaGhVA2HowULH8WegxDzelN7qWXkq6ppeWO25FcLlwDqwjOmEF6y2Zc/foRnzev+4nbroFVJBYsxOzqwn/QNNIbN9D5r3/hGTUKrbCI5htvQnI6KfzNb0BRyfN6MONxex+NRjETSaxMBiUnB9eQIbTcfgcAWccdR/S990gsWoSzX19clZXILheSpuEoKsJx3HHonZ14x49j659vwVFmP8HWVVVF6733ApBz9tl0PPMMRmsrst+PEY6gNzWhlZXhqOhFesN6as45F7PLfiq3b8oUCn/3W1GI8l3wCP+HsAABAABJREFUF8GUX8J/fgKWiWbVU/ibX5NpbET2+5EcGv7yMiLvvofe2kbnk0+i5OaSfcopWBIUXHcdekszqbVrv3I1kstN11tv4Rs/HvewYWDo6M3NIMu77aJ76BCsZApXeztmLEbopBOJfvIJeZdeiux00vrggyTmzQfsz1nRn/5EcsUKWu+9D6OlmdBppxE47HC0/LxvNVSSJOEdP47ac87FjMXsiZqGe/gwYrNnk3XUUWilJQBkauuIfjKL7NNO+1brFPZM9OOPuv/tmzoVS5Ix2tuQHA6sRBKlsBDXkCEkly4Fdv9kbUfPnhTd8EcafvkryGSQNI2iG/64y/ADmlagb15G0zyN8Fv2MU72esj/+bVE3n9/p9kTCxfg6NULvb2dxLJl3QEkAFknnICVTpPctBnXkMFohYXE587F6ApvC9OxiLz9Nt7x43BtC4FIrlmLf/r07SuItcCql7e/jrfDZw9A7yng9O9mJIUfpGgzJLugdAxsmW2fGwyeAQ73t287GYG3fr09gARg1Utw3ENk6tqw0imUrCBGRwex+fPxjhuHs1cvjLY2YuvXI8kyjl69Sa9fT3L9eoyWFvSmJopuvJHM5i2owRDuwUNQ8wuIvP46akkJ7spKOl96Cc+oUXhGjiSztYlMYyPpujrin32Gkl9ActkyvGPH0vX6G7gGDkTNy0Xv6MA9cgSOkpKdNiO1ZjVGRweOip4gy3Q89RShGTOQnA6cvXt/+3H6kXD07oWzVwUSFl0vvUTg8MNQgkGsRILU2rU4N/4LVr+EOuovGAE/ViaDmpsLloUVT4DHgxIMovh9dL72Kr4DDgBdx0wmULJD0LRi+8ri7bD4STj8dog2gTvnqzvXuBieP3/7MXPRYzD2Yuh78Hc2HsKPi1ZSQmrlCpJr1+EdMQK5b19QZPTWVvRoFGefvqTr6lACAXzjx5NYsZxA2XQ8I0bQ9fIr+A86CHQdK5PBikYwk0lcAwZQ9s+Hab3/ATK1tfgPOwwzEiF00omkN23GMnSMzi6SjQ1Ybe1IXg+pjRtR3G7SW2pQQiGc/fqiuLNwlGrEPvsMKxrDWN6Fa9AgLF0nVdeMK/ECcsloyO3z1RupqDD6XPj3T6BpuT2tfoH9+tz3IfsbhlvFO2D92zD3fvDmwfhLoWycCAj6rkgSaiibTG0NgcOmgyyTXL4c3+TJmBn9++6dIOyeoqK3tqLl5ZF11FFImkbX22+Te/ZMEsuW49tvv++7h8KPlG7Y55mqbId6OVQRQiIIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiB8M/sshESSJBlYalnWIADLsj7cV+v+nyLJdlHkh9uHxwyHab3nHlz33Yvi3nWxpuRwgCSTrq1Bb2pCcmgYXWG7UH57S9uevn0jZJXaT+DuOw3F76PlhRdx9euHVlJM5O238YwdA7qOpKjoHR1YpoGjpJTUmtU4ystJrluHd/wErM4mmPM7OOUZ8IQg1goLH4HCUSBJhGacQMttt5PetAkAtagI7W8P4Orbd+eNaF5p92vg0aC6oGaOXaiajoE7uNeGWNhzWmEhOTNnkly7lraHH8Y7YQLht94CwDlgAL6JE2m99z4wDDss57YbcNe9jbzkSTANu1Bzyyw7lOSNa+HMl5HzK3H17kX+FVfScvddJJYsIbVhA/6DD0IrLiG1bh2y0wmAWlhEessWe339+tF6z19BUfBOnEDL7Xcge73kXfZTtl7/B/SGBrJOOAHfhPEU/OpXpFasQHLY4TWBgw6i89//Buwnd6c3bqTr2WeRNI3giTPINDSi9eiBs3cvlEDAXncwiDpiBGUP3E+moYH4smVYhoGk2W1ahoHR2oqkaeRdegktf72X9gcfRHK7KfjNr5EdDhwlxSS3hZBE33+f+JFHkHXoofvs/fvRaFkNnVvsQl7ViaUWo9e20Pynm8G0iy2yzzmb4IwTqL/0pyBJ5J53HumaGpRAACMSRm/vwLf/VxcKWekUrr59ibz1FlpREakNG8g+/TQwdv+0bjUvj7pLL8U7bjyKz0f740+QfdqpRBcswN2/X3cACYDR0UHnM89gdHSQXLwYgOab/4xlmOSec/Y3H6dtYp/M2h5AApDJ0PH4E2QdfRRdzz6LHLBDHvT2th2DIYTvlHvwYLLPPpv4nE/xjB+H0dJK6333g2EgOZ3kXXop2kHTSC5dSmDqRNylvq9sT1IUAtOn46ysQm9pRs3Px1lRgbSr0Jw1b5DUqgm/dV/3JDMWp+PJJwmdcgqx/woicY8cRXLpUoy2NmSPp3u6Z8wYsCxSq1bjrKokvWEDTTfeBJkM3v33x1VVCRa4hw4l/NrrOEpLSW3cQOjkk7H0LxSchhvscKFBx9oBavULoOZTO8RChJD8b2peCe2bYO227MGm5bD5Y8h9A3K+ZcBGqsveX/a7ChKdsOw/kOwkqefR9exjxD6xA+60khKCM2aAaaIEAmw+9TT0xkYAPPvvT+7ZMzGjMZLLl5N1xBFY8TjJ1auR3S5a7rkH2eMm97LLkBQF0zDQGxoxWtswoxHMeBz/AQcgaw7a/vZ38q/9OZ4RI2h78imChx8GkoQZi+HoVYFnxM6hgonVq0muWgWGiZmI0/XKK+ScfTZ6axueypE4iou/3Rj9iMgOB1ppKZm6eoLHH0/bY4+RNf1QZJeb9MZNJIaOxt10I45NT5LJOgwpmUJyOTETCdTcXDuoZEB/jEgUNT+f2McfI/v9pDZvIvfcc+3gsepTINADwo2w+lVoWw8rX7TDRPocAM4vOT43Lt4htAmAZc/AhMu+83ERfhwc+XlIHg/+/SYRfvc9vKNHg65jtLeT3rQZ96CBpJubSMydi5KVhad6GHpjAxbgqhxA53/+g2tAf9LNLWSXn0i6vh5X7954Royg5O670Ds66HzyKbTCAppvvwPF68V/4IEklywmePAhtP7972RNm4YZi9H8l9tIzJ0LQPCUk8k/60Q0dwfuqipS6zegNzfTevc9AGjFxTj/+Gs88VZgNyEkAKnI9gCSzyU6oG3DNw8hWfMavHjR9tfr34aZb0LJqG/WnvDVDAPZ4yaxeAlqXi5WOoMFWHoGaQ+urQThe2cauAdW0f7oYzj79CHT2Ii7uhoznkB2uUg3NeGu+IbHI0H4FjLb7oGpin3d71BlUiKERBAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQfgGdlGJ+t2wLMsElkiSVLav1vm/SJJl9JbWnaYnV67EDIe/cjnLNAkceijIMmYsjv/AAzATie0zyRo0rYTJ10HZGBh1NoR6YiUS5Jx5BnpLM13Pv4BWWoLiD2Cm04CFkhVAkhW0HkVk6utpe/ifWMkUkiyBpEHdZ9C+wV6H5oZACVJWIUpBAak1a7oDSAD0xka6nn9h1xtRsT8MPh7mPwwf3wqaC6b8ApyBbzCSwt7k6tePsocfwjVkcHdIR+Cgg2h78EEwDNzDhpFzzjm0/PUh6p5tIjroT5i5VfbCzSuheASMOgdm3wMLH4X2zTgretLjppvwTZlCav16Mpu3sPWPfyR4/PGopaXkXvZTEsuX4xk71m7HtIs2XZUDSCxcBNiBIm1//wd6QwOhU08hvX4d9T+7nLYHHkAtKEDNzSPvyitw9O2LlU4D4CgtI7HIXj7vssuIvPc+DVdeSc3MmXQ++yyJVaswIpHubVeDQdxVVZTccTu+Aw8g+7xzQdOwUkkA/NOm0fnv/2B2daGVlpL300tpu/8BGq+9Dq2klJzzz+tuK7Ol5rt7k37MfLmgJ2H+g/DxrWRcfWm57bbuABKA9n88iOxwgmniHj4cye0iXVeHZeiYsRhmIoH134XB/02ScZSV4ujZk0xTE/4DpqK3tsJuFgNIbdxE9umnk1i0iPAbb+A/YCpGOEzggKnojVt3mj+5fDnuYdU7TOt49FH0trY9GZGvlGlp2WmamUxiJRI4+/aj66WX6XrpZZx9+yFJ0rden/DVLF0n+tHHNN9+Bx2PPopWUoqzooK2v/+9O+DGSqVo/dvfcFf1o/yGcykcHUFbcu9u25ZUFVffPvjGj8fVpw+Soux6xlAZejSz0+TUunWohYUU/O63qAUFALgGD0bNzsa3/34Y7e0oWVlIHg9KdjbBk07Et/9+6B3tGC2ttD/8T8jY7br696P94X9iGQZKMIircgCZrVvxTZqEsS2sqVtuPxh+Oix6HD66xQ61OvQW8OTs+cAKPyyhclj35o7Tos3Quu7btx1rs9v56FZY9bIdRpLdi+TGrd0BJACZujqSK1aglpUSeeed7gCS4IknouXmUnv+BXQ8+ije0aMxsUjX1BL79FMi775H1pFH4uo/gOTixWilpRjt7XhGjsDo7ET2esk65liMdIZMk308l90e2h56mOBh0+3zF9PEUdEL38RJaDk77sd6ezuJhQsx4wmSK1fS9epr5J57Homly1AL8nFXV3/7MfqRcQ8ZgtqjB6n168k9eyZdL79CcvVqzESCxIYG9KHno654BC0/F1nTMLcdg5SsLDL1dbT/8xFknxf34CHIfj9GRwfBw48gtWkTZuUxdlDiR7fa4UgH3wB5A6BkJLj80LzqyzsWKIEJP4P9roaycfY0fw/7+kkQ9hKtRzHpunq0wkISixZiWRZmVxijox1JlvGOHIlr4ECUUDaptWuxEkm0/HwUfwDf/vuTadyKq3dvzERih9A62eXCUVSEZ9xYUmvW4h0zhuBJJxH9+GOiH3xIavNmcs89h84nn6T23POQNY28Ky4HWabzyadI1mzFsfU91KICtB5FOx6f6+vpeP5lLGMPi6QdXlA0O6hs2On2cX/YaeD6hvcNkl0w644dp5kGbJ71zdoTdk/TMKIxAtMPxYzFQZbxHzAVy7KwxPWH8D9BItPSgn/agWSamnD0LMdRXobkdND16itYX3H/VhC+S7ph36DSFPtY6lRl0nv6/SoIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIX6Du4/UVASskSfoM6K5osSzryH3cjx8syzLRigp3mu6bvD9qdvZXLqv4/aQ3bsAzerRduGxa4HBgmSaSLNshJIEimHsflI6xn9qd7EIaMpn2e+8na9o0rIkWVjpNYvlyAodNx5JltOJiJEWm5a67MbeFM8TnzEFvbcX9iwvtlcvbdiWHFw74DdLmT1DlLGJr1+7Uz/iCBVi6jqT+1+5nWfDJ7dtfr3sbsnvBwGP3fACF74y7qgpXZSXOigpqzzsfM5Wy3zNNwzdlih36AHgnjCfZbJLJOh9HQRhX5CMUxQEf3GQ3tOQJGP4T2P/nyIEi3IMGUf7Ph4ktWIBz40ba/vEPci++iMhbbxM64XhcgwdjhMPoHe1oZWUYkSiOil4AyD4/eksLksOB7A+QWLQYJTubrCOPoPmWWwC7uNjRq4K8y3+G2dkFTicArkEDSSxejN7QgJKTQ+5559H++OM03/xn3MOHU/SHP9hFJNv2UzUUQg2FcJaV4amuxohEQZbRysoIv/aava7jj6P51r90BwdE3nwT/0HTcA0eTHLZMtSion31dv24JKPw/g3dL81Eeoeiyc9Zuk5g+sEoOXlY6TRKVhaJpUuxDBP34MFYmZ1DGL5IUmRSW2pw9e+Po6IC2e3CCEdA3n2hnKxphGfNIuvoo8A0yTQ0IHs8OHv3xuzo3Gl+35TJO+0vSiiEpGm7XdfuBA45mK5nntlhWvaZZ5BasZL2hx7qntb+0EPkX331t16f8NWSa9ZQe+GFOxw3JE3DNWgQyaVLu+czw2Ho3IJn2e/tCdWn7r1O9DkQR/OHO032jB5N+4MPkli4kB633oKZSpPesIH4nDn4px9K3q9+hXtgFeWPPkpiyRKarv8DRjhMwa9/BYZB7kUXgaLQ9dprmMkUqTVrkDSVdE0Nzt590EpKkT1uMs0t8MWCU9OAD/+8/fWmD+3zlyEn7b1tFvYtXyFIClj6jtNVx7drN94BL10MW5fZr6NN8M7v4LgHSb26cxhEcsUKci64gI5HHrVXX1SEpCp0Pv00AHoiwdbf/57iu+8itXIVWUcdhZVO0fnss2SfdiqmYeLq25fUipVIDgfeCROQXU4SixbiGTuO+Lx5oCh2yNQhB2MZJrLLiaNfP9zDhqGFQjv0xzIMEsuWE1+4iPjs2Th69iTnJ2cSfustXFWVeEaORA0Gv90Y/QjJmoanupr0pk10vfEmOWfPJPzGmySfexbP2LFo0w/G538ZR9v7pLz9kGQJCWi5667uax3Z6STV1IS7eigYJpKmoTidUL8c2jfaK2rfCG/9yj4eb5kN6TjEOyG3L7iDO3Yq3gGta+CzB+zgtH6HwvhLoffUbx6cIAi7oBUVoubno+bmENvaSOyTj1FLStDq6tA7O1GDQXxTppBYtgwzFiM+7zOcgwaS2rQJ98CBqAdMtUNNo1EswyBdX4+juLi7fdegQXS99DKeESNovf9+gscdh5KVhVZUSP3lV2Al7aDG2CefYEQi+KdOJfLOOxht7dC1GnfFQOK7CMRLLFiMMS+J6s+H3D5fvZHZvWHa9YAEc++Hjs0QqoChJ3+zQZNk0Lw7T1dd36w9YfcyaRSfF6OzC++E8SDLGO3tOEIhJGWfZSULwjcmyTJY9jW6f/JkJE0jXVuHVl5O1iGHYqVS33cXhR8pY1sYryrbx1KHIpMRISSCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjCN7Cvf7v/98DhwPXAX77wR9hGkhUswyDnggu6i81d1UMJnnjSbovPJU0l09AA2E891NvakDUNK522Z5BlyCTsQrnOWvAVwMizsVIpsg4+iLYHH6Tt/vvpfPZZPKNGYeo6sqpiJhIYnZ3dRXmfS69fjxnpgkEnQM4XCoVqPoWNH2BZFt4JE3bqZ2D6oTsHkAA0rwB3CEadA5OugpJRsOJ5iLXu+QAK3ylJkvCOGUP5E4/jqqoEWcZTXU1slv0ka9eggWjFxbTccSdbb7iZ9g/WEgmeTKzFiT7gFMgqg4NvhI5N8NSJsPhJiLXhqqwk57TTyL/6KpTsbJAkUuvWEV+wkLa//Z30hg24h1aTf/VVuIcOxT2sGsnlAkNHcrtRcnLQtzYCEDjsMNof/idYFqFTTiaxaBHNN95E2/0PYAFKwE/uZZcROOIIkqtWAhCaMYPmO+4gU1uLa/Bg/FOnsvX631N78SVEP/0U8wvhFEpWFr5JkwhMO5DSv/8NM5XCNbAKACuZ6g4S+Fzk3ffwjh9P6LRT0Yp7fPdv0o9RwwLw5sGY82HiFcjB4E4BHpKmIXvc5J3/E7wTJ2LpOs6KnlgZHdnlQs3LtYObvoJlWpBJY5n2e2x0dqLmZGP913u+K2YqiRIM0nr3PbT+9V6Sy1eg5uQie70o2dkETz0Fth3j3SNG4Js6FSWQ9YUNkMi7/HKUQIDUli2E33mHyPsfkG5o+JqDBe7qYRTfdRfOfv3QysspuuEG3EOGEJszZ6d5dzVN2LvS6zfsdNwIv/463vHjd5imhEJoyTX2C1mB4WfsvU7kV+IccwAF11yOtC2oyTlgAL6JE4jPnYuVydD6wN+IvvsObQ88QKaxAUyT+Icf0nrX3eitrWDoBE88kR433UT0gw9p+sMfab33XtoeeICcM88kvWWLvS4LkOXu0B8zHLE/R6aJkUhgxGLQVQOe3C+cD4yEVS9DrGnvbbOw71gW1C+C6lN2nF4wEPKqvl3b4QbI7Qf7XQWVR9iheKYOiU7cFTuH+nknTcTZuxeBgw+yX48ZTeT9D3aaL7l8BR3PPE3b/ffT9dzz5F10EfFFiwgccjB6aytGLIaVySBJEkZnJ5LThaQqpDZtovA3v6HzmWeQ/QGUYBDPuHEEpk7dKYAEILF0KZE3XiezZQuBww/HNbCK8JtvoeTl4Z0wEdeAAd9ufH7EnL0q8I4ZjaO4B10vvIhn5Ej8B04jvWkz0Xc/JDn6L6h6K87yMsxEAjMW777WkRwOME2Mri57/5UgXVeHkhWw96/i4TDpSvsYJUl26Ej/6XYQQqIdmlbs3KG6z+Dd39vXYpYFa16zAyJ77rdvB0b4f89ZXo5aWEhi2TJ8U6ai5uVjNDVjdHSQ3rQJADUrC9+ECbhHjMA9bBiYFt7Ro4nMnmXfM9ANrHQGK56g66WXiS1Y0H1PQQ0EyD71FOKLFpF/xRVE3n+f1vvuI71mbXcAyeeSS5bg7NcPJAmlsAhr8Ik4mj7AUz1op377JoxGyS6w71PsjqJC7wPhw5vtzx3Y15fPnLFny+80aH6Y/POdp/Wc+PXbEvaIJCtIDgeZrVu7p2WamuxjqrT7gEdB+L5ZWKjZ2XbAEmCZhh3Ua5pYegZNBNAK35OMYd/X0hT7WOpQZdK6CCERBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQvr5dJEF8dyzL+nBfru9/kiJjdHQi+bwU/elPYJmYiSQY+u6XlWVkjxe9qQklFCLd2ICrqgpL37as7ATLhM2zoN/BkOiAT/+KPOo+kmvWknvhhZipFLLTSXzhQrwTJyApCpm6ehw9e+60OknTUPOKYNzvwemzJ6YisOwZkB0oPj9KHy/Bk0+m85lnwDQJHHEE3v2+pNgud4D9RPC599vBI/2nw+AZ29sWfhAkhwPP8OFY6TRFN91Ix7//g+KwnxLtmzyZ1r/eC0Do1FNIrd9A4y9+BZJE8MTjCR75ANraR1FrPgU9BQsftcMjiqrBl4d74EB6PvMMma2NOF5/A0nTMKNRksuXk6mtofWv9+IcWIV30kTyLr8cM5Mh/8oraLr5z2ilpQAoPh9GZ6f97+wcUmvWIDkc5Jx9Ns233oqVyeCqqiLruGPJvfRSUmvXgkPDSiRAUQgcfDDNt94KgHfiRJKLl5DetAlHeU9cA6tQg0F7HDTNLt6rria17mDqL78cdhGuo+bl4ejTGyuVxtW//3f75vxY5faDkTNh3t8hGYZjjqbwN7+h6cYbydTWomRnU/CrX6IbBt5+Q5ELw6TWrqXljjtxDx+OZ/gwjM4ulOwc9LY21JycXa7GskyUYIhMfQNqfj6pzVvQSkrB3LOCDq24hNxLLwHDRNI0kjU1ZCkKqbVrsNJpevz5ZjAM9PZ22v7xIIW//x2Ff/wDRmcXnmHVuIYMIblqFTVnzezex7Xyckrvvw9nRcUeD5ficRM4aBrecWOxTBM1yw47cfSqIPbJJzvM6+jda4/bFb4ZOeDfaZqam4tryGAktxsrkUDOyqLHn/6Ig/kw8Qr7+7F4+F7th1LUl8AJhbiGjcLo6CCztYmmP/+5++eZLVvwjBwJQOj0M2i87hcAeMaMIfzaa4Rfeql73txLLia1Zg16YyNWOk3X888ROPZYYh9+CLKEmptDuq4OraiI1OZNeEaPxkokqJl5NkooROi4Y9CLfoncEcftbcPh7wFVx4Bj57ES/gck2uGz+6DHMJjyC2heDcFSKBwKgZ2DQr6WZAe0rIblz9rnEgf9Ad69HjJRPOXFBE88kc6nnwbAOXAggUMPJfLOu2hlZfgPOwy9rR2tIB/9vwKdlFCQ0Cl2aIoZjdL2yCPk/vRSrEyGdF2dHU6RToNpEJszl+CxxyB7fQQOPhgjHAZZxlFRgWvoEFy7OIcGSG7YQHz+AtSCQhw9exJ5+23UvDxktwvv6NG4qqqQ5H2dF/n/i6N/f9xDhmIlkiSWLMFKJvEfeABmMkVs7Vbk/c/CbO1EVjXQvnAOp2lYFpjxOEZXGMnhQG9vs7/vCyuhayHMugPc2TD2InB4Yd4/QJIhq9gOZPQXQk7v7W3WfLpzB1e+AON/Cp6dA2oE4dvQ8vNwDx5M+M038U/eHzORRJLkHQIfJEnCO2I4kiJjJhJkmppwlvcksWgxrkGDMDo7MFMp9OZm4rNnY3Z24hk1CiUQwNm/P95JEwm/9hqeESNw9uqFWrjz8Vz2erBMk4Jf/5pMw1bCjgBKwRm43BmCxx9D57MvgGXhrOxPaFQu0opHoGS4/Vnb3fEv2mTfz/iieBt0brG/Y76uXpPhzFdg9WvgzYG+B0HhzmEpwl4iyyBJ6C3NKIGAHU6Xse9byW7399w5QdgDloXkcpLavBnXgAHozc0oPh9IEo7SYhyl3+A4JAh7gb7t/pSq2N+jIoREEARBEARBEARBEARBEARBEARBEARBEARBEARBEARB+Kb2aQiJsHuSLCM7HUQ/nYPVL4bR1YniD6CVFO/JwjgqKoi8+y6SpqHl5WGmUpjpNAqAZIEn1y7KmXUHePNhzPlgGMhOBx1PP42zT29SGzbinzoFS9exANnhwGhrI3D4YYRfebV7dXk/vRTHgGHgydreB9VlF4E2rwRFRm9uxTVkCD3GjEZSVaRAAEv6koIizW0Xjn5u9SvgyQbF8fUHUvjOSQ4HWUccgXvIENIb1hCbNQsM0y7G8HiQPR7ic+eCqpJ/xeVE3/+AzaecjaN3L4quug9PrgnLn4MnZ0BWKUy/FXpPxVFSjKOkmJK/3kPs0zlgGCSXLet+IrKkqISff4HY7Nnk/uwyOp5/jfwrrkDrWU7wpJNIrluHa+hQksuWYaVTAPinHUjHv/+NlcngGTUKZ7++NN1wI5gm7tGjybvwArLPOov0li1EZ80CwDVoEFqPIlruvBMAz5jRBI87Hq1nOc6KChS/XQyveL14qofS8+l/kWloIPrRRySXLNk2SBL511yNa+BAHKWlopj4u6K67SehA5nKs2i++VaCJ5xA3jVXQzqNpDlAllBcLox4HEmSiLzxBsGTT8Y9dAiZ2lric+fSdv/9BI48ksJf/gIlK2sXK5JQ8/NILFtGurYWJSsApvGVXbNME8swkLxewm+80V3oLjkc5F97LZauo2QFsXSDxKLFyG4XyXXr8R94IEpWFqHjj9+hrfan/tUdQAJ2MET0o4++VgjJ5z7fhz8XPPpoul54ETMcBkAOBAgeeeTXblf4elyVVbiGDSO5aJE9QZIInXEG4bffJu+yyzDjMbxjx+IZPhyig8BIg7/H7otzvya9pYXm2+8g8vbbBKZPRysppuj3v6fx5z8HwDdlCvG5c3H27Wsf27fxjhlDy1137dBW+8MPEzzpZNoffBCATH0Drn79CBxxBJKioAaDxOcvIFNfj+zzIckyRns7qZUryfvZZdRdell3uI9WVkLZBRNwtK61C/2F/z0OHxQMhkwCghXgL4ZPboP8qm/XbscWeOZ0iNtPoKdxMcRa4KAbIdyAWtKbgmt/TnDGDPS2VoxojMTCRSQ3rMfVtx+uQYNQg0Fkv98OEstkANDKyrCA+Lz5JBYtQi0uJv+qK5FcLtofepjAUUdCOk1yzRr0hgZ8UyaT2rgRX1kZsU8+wT91KkW/+y2uoUNRPJ5ddl1vayP63vtY276Tul5+hcAhB9P5zDMETz4Fd/VQtPy8bzc+Ao68PNzV1RiRCImnnyZ43HFE3nkX3/77YcViRN79ANegQcjBLMxkEv9hhxF59VUUvx8zmcRRVkp8wULAwtmrtx0ykonCyhftFcRa4L0/wiF/AkWDA38Hq16G9/4Ay5+Hw/8CZWPteYtH2oElHZth3Zv2+UPhEHFcE74TWkkJscWL8Y4ZQ2LJUlyVA7BMg+TSZbiGDMFZvP3egqe6muT69aj5eah5uaS3bCH20UdoZWUY7W0ouTm03nc//gOmore24hk7Fmd5OZ7qahLz52N0tNNy1ytkn3UW3v0mEfvo4+62cy+5BDknh/SmTaheH7GFC/EMGUxClgkccwzBIw7CalmHI7IAdeEf7MDUf58J530IBQO/eiM92SAr9mepxzAoGwfhRjsc6BsNmhsqJtmf1XgruHZ1LSDsLaZpYqXSaIWFJBYvBknCM6waLKs7IFEQftAkGSuj2yHOCxYAFv6pU+19uWzX53+CsC9kDAsAVbbvoToUBd20ME0Leds0QRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQdgTIoTkB0bSHFimCYZBfN48APxTptjTdresqoJl4qzoSaa+Hq20FDMeA91+oiyqE7rq7GLQSVfahXRrXoN+w9FKSvH5/CRXrMC33yTkQBZWJoOkqmjlZXS99DL+g6aRP3Qo6AaOshI8m+9Gevp5OPgGu/AH7AK8MRfAihdA1zHb2zE6Oki1d6AE/Ci5uchOJ1C+8wZ0brYLUiuPsAuQEh12Ad/+P4eskr0yvsLeJckyzooKHO4E5TdfTjrlQ83PR8nKIrVxEwCBQw6m64UXSa1di5KTQ/D442l/8RNaY1FCUybgKU6g6K3QsMDeJwsHg78AZ8+eOHv2xIhGUQvySa5ciXvECDI1NbinTSM2ezZkMqRXrKS9vQPvhPE4B1TiO/AASKdpe/hhZK9d2KkWFpF5620AvBPG03KHHSwSOv00jLZ2as45F9nrJffii0BRSK1ahW/y/rTeex8AWcceixmJ0HDNNQD4jzmavHPPRSspQXbYITlafj5afj4ld95BcsVKjHAXjl69cFdWIjlEkM53qnmFXeTbazKpSAXu6nqsTAZ9fQNGRwdyVgAlKws5ELBrFSWNwFFHEf3gQ4yODqxUEq20BK24mPBLLxE6cQaeESN2Wo0kSVjpNI7SUjJ1tTj79LEDQaRdF3LEFy+m44knydTVEThsOq7KSqLbQkisdJrIW2+RdeQRyAE/WkEBsblzkWQZtagISVVQ/yskxEqnSa1YvtN6UmvXffsxBFxVVZQ/9SSpVatBAteAATh7994rbQtfTissoOT220iuWIHe2orkdmPGYhh19cRa2/BOmEBy/Xo8zhp48zpIhmHshTByJgR67PmKLAvaN9hF8/4eECr/wo8sEsuXk1i4kNzzz6f90UfRm5vxTJhAwW9/Q+yTTwgcNp26Cy/CUdETSdO6lzU/P8f4AjMW32Ee/yEHk9q8BX3rVlAUjHgcZ0VP0hs34ezdG729HbWoB76pU+l89rnuABKATG09iUQujpbnSKxcRmLJCizLwjtiBM7KSqQv+fwJPyCqEyZcBqtehNeuBM0FE6+EkjHfrt1wI4w+zz5njDbD4ichXG+/VlTw5mMZBunaGhKfzUMJBjGiUchkiC9YgGdYNZmGeiSXm+Jb/kxmaxNKVgAlGKLzP//GSqcp+PnPUUuKab3/AZILF+KqroZ0GrAgncYzZgyy243iDyA5XYROPw33sGE48vO/suup9etJbdgAmTSxBQsJTJtGYvkKAtMPwzWwCnfVtwxoEbq5Bw9Cb24mcNh0Ulu24OzXl/Abb+KqqsIyTVyDBpGpr8c9ZAie0aNwDx2C7PeDBEoohJKVhZVK4Sgrw9IzyM0r7IbLJ0DFfmAZdljBkFNg5ctQPg56TrSDEV64CE79N5g6rHgO1rwOeQPgoBtgzn0w8WeginNEYe9z9OiBq1cvksuWYcaixOfPR83LR3K50BsbdwghAXD16UNq/Qa6nn8e/wFTiX82DyuRwIxGcVdVUXD11XS+8AKRN3+Hd//9ybvichwVFbiHj6Dj0UfIv/ZaYrNn46oaSODggzETSeSAHzMSRa+tRXE4Cb/+Gu7qalIbNuKsqCDyyus4Bw3EJ29Bja6B/a+xj9+RrdC2YfchJDl97eCfdAyaV8GCf0JOHzuYyrK+9Pz8KzWtskNRN7wNBUPg0Jug9Ft+Vwm7JAGWnrGvqxoakVQVJScHywITyQ7RFYQfMgnMaARn794YXZ1oRUX2fVtZRiss/L57J/yI6dtCSDTFDi3VVPv7MG2YuGRxdBUEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRD23D4NIZEk6XDgNcuydp+o8SMlOTSM9g7cgwYSnzcPNb/ALqDZgxAS2eEgtXETzrIy1IJCZJcTM5HEymwrELYsO+Bh8eOw+Am73cEnIGkq8Xmf4ehZgXfCBMxIhPhnn+E/YCqSrGAmEjh7VdD1/As4ysvxjB6F7ASl9gO73SdOgHPfh2Cp/bplNcy6HaZPx4hEQJKQFBkkCbOzCyuZ2vUGBErspw9/fKtduBcohgN+A5p4QvgPnVRYhWeCB0+8FWfwQpofexVn715E330XR2kZ4VdeBSD7zDNoueNOrFQKrbycVNcI9MDxaFkKroYPUT/4E2SVwklPQNFQABSfj8C0afgmT0Y/diux2Z8iaSpaz56k1qzFPXw4iSVL0Ip60PzHP5L9kzPRyspQcnJx9u1Lwa9/RfSjj/FOmkT0vfcwt+1/ao8eWBmd8GuvgaKQfcbppDduIrV+PVnHHoNWXg6mieR0oubn0/bccwDknH8eeksrteeeh2voEHLPOQfXF4qFtcJCUXSyr+VXQusa+PBmjJ6/wTNiBKmNG+2fyRIgYXR1gW4g1c9H7jkRSZJxDx5M5K23kD0eAqNGgdtN+I03MCLRXa9HgnRNLY6SEtT8fGSnEyORwNrFrImVK6k58ydYKXt/SyxaRN7VV+GqqgTdIL54MZmGBizTxEynUYJZaPn5GOEwrj69MeNx9GgULRTqblN2uQgcdTTJFSt3WJdvv/32wiDaXL174xLBI/vc58eNTEcn4ReeJ/Lue3hHjcIyDbqee5bsmTPhnd9B2A6x4aNbwOm3gx32hKHDyhfhpUsgEwdXEE74J0mjmK6XXiaxZDH+yZPJvfhiGn7+8+5zjvisWUiaRtHvf4E06xZKb7ycyPy1+KZOoevll/GMGI6rXz8khwMrne5enaOiJ0Y4DKpK4JCDwQLJMonPm0f2zJlkautwFBejhLKRXU70jg7AQg0Gibe2dreTddRROMrLSXcahPteT9ffHiL69jsASA4H5Y8+gru6+tu/AcJ3Kx2DDe/C7Lvt16kwvPFzyOoBORXfrM14B6x5FT69xy5az+5lnzO+81vw5UHdfHStB5E33yS9dh1qXh6J5cvJ1NXhrh6Kq19/zGQKSZaRJEg3NCK7XahlZcQ++ABX/wGoBfmka2roevVVPEMG4x40yN6cujpkrxfPyJFImkZk1iwCB01DKyvFVzx2t13Xo1HStbXozc1kGhrIOvhgjEgYT/VQ1MJC3LsIwRK+HfeokehtbSj+AJahY2V0ksuW4ejbF72tndi8+ZjpNK5evYm8+SZ6uIvAIYeQadxqf28bJmY8jurLBy0AQ2aAnoYPbrJXUDjEDk3M7Q0f/hnSUTv0cf9rINYOb/8aaj+1521YCK1r4cxXtoc4CsJ3QM3Lw7Is/FMPIPL++yTXrME7ahRGZ9cu55c9brxjxxCb/Sme4cMxUymsZBILibZHH8Xs6iL/6qtIrl5D8w03kHX0MTjKywidfArNt9yCo6ICV+UAMnX1OKoqSW/ahGSa9ucno+ObOpXEwkVILhfht98mcOghpDdsIJzfB9+g0Tjf/ol9DyCnNww5cQ820AFDT4cXL4C1b9jTti6FJ46Fcz+Agq8Z5hRvh+fPs9sAOyDz8ePhvA/sPgl7neRyozc24h48CCQJvaUVZ3Y2pFMgQjyFHzpJxjIMZIcDz/ARoKpkampw9ukDGz6AHtXfdw+FHyl927W8qtjhI45tYSQp3cSliRASQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEYc/t0xAS4CTgTkmSngUetixr1T5e/w+e5HCApiIpKp6Ro0BR0FtbcPTutdtlLcvESibJNDWhhEKk1q3D2X8AVmZbYbDqsgNCBh1nP5FbUiAdxdJ1AoccQvvjT5CpqUHNzyN75kzMVApUFb25BUdFBUpWEDkQQG9uht5l21cca4H2jXYIiWHYTyEGUBQs3YBMGsnpAiQyra249MyXbLwEcx8A1QnubPtp9oseg0HHfqsxFfaR7J4QacA9+yJKjj6LTI/BpFZPwMpkkNxuAIzOTqxUCtnvJzTjBJr/chuYJkpuLjnnnIVnyjQc3jTK+nehsxaKqiFYAoCsaThKS3GcaIfdeCdMILVuPZJDIzb7U4zODkKnnEznSy+Rd9FFOEtLsTI6LXffQ8E11yD7vJjJBLLLCYBnxAhiH38MQGjGDLpefoXMli3IXi/eMWMwWlrJv/bnWLpOcpV9qPJOmkRiyVLic+YgaRqOsnIi775H7LPPcA8bhruqCknT9u24C5COw7L/gMOLM8eB7nYjOxwYyQSy24OVyZDetBH30KFQOwczbxhGOEzb/fcDEDzhBMxEEpJJ8q+6Eq20ZJersUwTfWsjXf/5jz1Bksi74nIka+cYkuSKFXjHj8c1YABGNILR3oG+tYnOp5/GSqfxTppE/lVXogYCSIDe1IyzX18wTDAtUDX7ePpfAgdNI7NlMx1PP4OkKORccAGeUSP32lAK3y/F7cLK6CiBAK3334+kqmQdewxmPA7mf313LvgnDDsDPKFdtrWD1rXwwvngyiI56GpSHcDiRtL1y2h/+GEwDBLz5hOcMQNn//6kVm0/PYt98AFmy08w1b5kGjpx9S5BM+sou/cvpOrbaLrxBvKuuIL2Rx5Bb2zEWVVF8IQTSG/aSM7Ms4h+9DGp1a+Sd/nlAFiGgaXr289V1q7D0bsXliQRX7iQ/J9fQ6amFiUnB6Ork5a77uruS/DEE+3+rVmDlU7T8Z9nRQjJ/4LIVlj96s7T170DlUd+szYbFtnniA4vpCL2eej6d+DQW+DTe8mM/w3Rj2eTqatDDgTofP55PMOHobtcqPn5WJkMsteL7PWg5OQge7w4ykqJvvMu6U2b8O23P3pLM4rfj6TItP/zEbzjxxM643TaH30UZ+/eKD4/iZUryTr4YCSfF2dx8R51Pb1xI0ZHJ67+/fAMq8aMxpBdbvD68IwZi+oV4Xt7mxoI4B1eRXTBCiLvvot35Ai0wgKQFfSWZrJPOZn6Sy4l56ILcVYOQI1GwYLEyhV4hgxF0jQSSxYTOOxwqDoM1r8C79+4rXEXRBpg4/sQLIfxl4JlgKzCZ3+3z2U/DyD5XDoK0aZ9Pg7Cj4ujogItL4/op3PwjhuL0d5hX8tvbbSvz/7rmsVRVkZi0SK04mL0tlYUf4DE0mVYpomzqgp3v7603v8AkqIQOv00MnV1WOkUalER/gMPRAmFaH/oYSxdxzdlCjnnnE2mrh69vR1Lz2DF4riHD6P9kUfJOXsmrffeR+iUUzDDESIrdKxJ9+D67BcQbbbvCRSPBM311RuZaN0eQPI5PWWf93zdEJLOmu0BJJ9LhaFtgwgh+Q5IgCTLxBYswDt8OFgmiaVL7esh6fvunSDsnmSaKP4A8c/m4hpQidHZid7WhmWasOltGHYK+PK/724KP0IZw74/pcl2+IhTtf9O6yIPXhAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQfh69mkIiWVZp0mSFABOBh6WJMkCHgaesiwrsi/78kMlSTJqMEh83jycvfuArpOub8Bj7lzkvoulcVcPpfPf/0FyOJAcGu7hw7Ey2wqXTcN+4va7f4AeQ8FIQ6QJZdIRRD76iOBxx2ImkshOJ4klS3APHoylqijBLIxwBNnjAUDv6MD676J7Z8D+W5btIp2aT5HIoHg9hN+ahWf4cIzWVsxYDEvXd939yFYYdzFoboi1QlYpbJkF8TZwBb7ZgAr7lsMHlomy/kUUB/SYECOd50P96YU033Ufn1cUBaYfSvujj4Fp4hwwAP+0abTe9zfMri48Y0ZSNPNgHK9fA0XD4NA/2QE3/0UrKEArKADAO3o0RjyOlOokdOSBWA1LsAry0WP1FP7yOlLrN9D57//g7N8fZ1UVORdeQGLRYrSSEjL19SjZITJbtgCQe9GFtD7wN8xwGDkQIHjSSQQOPRQ1Jxc1N5eW224DIPvss+l67jn05mZkn4/giSeib92KVlqKs6ICeVvwirAPtG+EyddBJo5TasPQFKIffoCkOXD274/e0oxn9GjMeAxyi8CyiLzzDsET7cAFdIPYnDlE33sPgHzdwNmzJ5Ky41NirXiC2CezUHJyUPPySG/cSPs/H8E3efJOXdIKi9Db22m97z6UUIi8y3+G3tZGzvnnoRUVITmcWIaBkU4jyTLR998nvXkzYIdR5f/iOiRp5wo8rbCQgmuvJXT6GUiKjFZcjLStuET432NmMqRWrSK9eTNKdjaOvn3R29u790Urk6Hz6Wco/M2v4b+/d4M97eL3PRFuAFMnXnkdNb/6K1pxMf5p05A9bor+cD2Nv/4NGAadzz1Hztln7xBCohYUYOoqW371D6y0HWom+3yU3XQZkXc/RG9uofXuuwmdeirOygEgK1jxGJE33rRDy7pZOAcMAEXGNaA/nc89j6QoSJqGe/gw1Oxsci+9hMbrfoEZsU8J3SNHEjz+eDq3Bf90PvssueeeS2rNGgAyTVuxLGuXnxXhB0R1gb9w5+LuYNmu598d07BDHAafAIoD3EGYfQ/Ufgb9p2MMOo3oikYydfWkN28m8sH75F10Ee2PPU7oxBlgmqhFhWjl5chOJ2pZGY7sbGKLFqHk5eE/4EDSW7ZgdHTQ9dJLBI8/DqOzk9js2WTNOAHvmDF0vfAiktOJEgiApuEo2XV41a6ka2qw0nZAn5VKE1+yBPfwYbj798NRVPjNxkTYLYfPQs0O4p8wgcjHH+MZNgwMA6O9HSuVJmfmTKxoDNfoUaQ2bABDxzNwINH330dSVbSyMsxEArJ7QbLLDhkZ/1O78UQHhCrAkwPvXW8fczUPHPBre778KvvvpuVgbSsAdfq+v8EQfhTUQADJH8BR3AN9qx38BRaZxq2k6+pwVlTsML+jrAxHnz7o9Q2EX7WDoySPB9ntxjtyJGa4CzMSIf/aa2m56y48I0bgHjwYKRIhePzx1F5wAdmnnYYc8INhoje34B43lkxNLegZUhs2YnR2Ejj0EDqeeJK8n15K24MPoebn49tvEtF1JvLkO3F0fgbePPtzpRXtZiNd4ApCshNkBQoG2cFUzm9w78DhtcNQ9dSO08V9iO+EpSqYqSSOwkIi778Hholn5AjMVBr25NaXIHzfJECRMSJRIu+/j5XJkHXE4ViGAd58+76mIHwPdMM+11QV+xrZ8XkIiSFCSARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEISvZ5+GkABYlhWWJOlZwA38DDgGuFqSpLssy7p7X/fnh0ZyOuzC9EiU1Pp1WOkMSl4elp7Z/bKaht7aim///Uhv2IizX1/0pmb7SZwADg+0rodJl8O6t0B1w+hzsDIpPEOG0v7Y47iHDCHT2IijuBgzk0F2uVBzcuh85RU8Q4ZitLYih4JYxhcqQ8ZcALl9t3VCgtHnQf5AJNnETCZR/H7Cr70GDgfBI4+wfyl/V7IrYPZd0Lxy+7SJl4M7+xuOprDP5faFcZfA7LshVIGy8gXcm17DUTAS12/PxghU0vHUUyihbPQm+wnwgUMPoeX2OwDwjBmNd8IEmp79DGfpGQQK++B6/gIYMB0qj9xlGMnnFI8HPB6cmgofXAidm2HSlbDqHvS+h6Oe9xOa/nQb7qFDib73Lv4Dp+EaPIjE0qX2fgs4evUiuXIlZjiMEgqRc/55tP3t77T/7W+oeXkU3vBHAscdR+ydd8Cy0JubkTSNvJ9eSuu999H+4INITieFv/0N7pEjcfTogaTu88Psj0/pKHjuPIi3IQH6qGq8EycSfuttUq+/juzx4B46FDnghvJxSA4HoRNPpOvll5FUFTORBEkiOOMEOp/5Ny13343vwANw9uy543oMndxLL8Ho6CBTX4//wANIrd9ghy99cbZYjLYH/0FyyRL7dUcHW3/7O/KvvRZJkel87nkS8+eDopB9xhm4Bg8iXVuLd+IEZK+P2Jw5hF95lcBBB+1ycyVNw9mz/DsYSGFfi8+bR+vdd5NYtBgA30HT0Ap3DiFIrl4NPXtCuN6eoDhg/6vBsYfFbf5CzNJJtD7/CaGTT0YrL6P13vswmptxDqwi74rLabnlViRJQt0W7gSALFP461/Q8ewL3QEkAGY0SmRZI74pU3D17k2mtRUkiYarrwFdR3K5yLvsMtr+/neM9nYCRxyOkpuHs38/JNPECEfwjhtHpqYG54ABZBoacA8bTutT/+oOIAFIzJ+Pd9w4+xhtWaDr3cdrgOwZM0QAyf+CrGL73HDzx5BJ2NN8+dDnwG/WXs2n8J+f2GEkAE4/TLwCNrwHDh/JWDGRd58hPncujtJSCq64graH/0nW4YeBJOGuHobWtw/OL+zremsr0Y8/xtW7D9FPZ5NcshQzkSD/8p/RfOdd5Jx5pn28R8JKpvBOnEimthZXZSV6SwuesWP2qOup+nqSS5bQ8fgTYFnIfj95l1yCaRhoxTsGmehdXVjJJGpengib+oYyzc1IqoqanQ25fdDyYqTWrkPxemi9277sVAsKcA8dSmzhQmRNIzJrFrnnnI0Zi6Hm5eGursaMRHANGYKZSGBEIig9RtjXPyueg47N21c47Q92OI6kwCe3Qc1cKBgCJaMg1gyDjoM1r4KvAPIqv59BEf7fszIZMm1tKF4vWm4uRmEhnU/9i0xdHQDe/fZDb2ndKYREkmUUfwCdetzVQ9FbWnEPGUymvgHf/vvZIaXV1cRmzyZ00okkV62m48kn8YwdC0gU3XgDrff81T42DhlCcsMG1KJCvCOGA6Dle8m0xIkvWIhaUEBi0WIC0w/F6Oig7cGHcPbti+I/HiW+AqV5PlTsD4HdhJAES+HgG2HxE/Z3Su1cKBxi3/f4urJ7wdTfwFu/3D5tyEnis/odkS2wdB1XVSWWriMpCo6ePcE0RQaJ8L9BUdBbWvGOG0dy+TK0wiKwsK9Nxpxvn58KwvdA3xZivlMIiS5CSARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEISvZ59Wx0uSdCRwFtAbeAwYbVlWsyRJHmAV8KMPIcHhwEom8QyrJvrhh0gOJ55RI7H0Lwnu+AJJkcnUN+Dq2xdnZSWy00m6uQlL1+0ZTMMu1Nm6BHpOsqc1LEaqGIkRixKYPp34nDk4ystxDx+GpRvImoYRieIsK6fr2WdRCwsJnnACSCoceQ8EiqFH9Y5P805H4a1fIk17CKOzC/ewYcg+H0rAD6ZlFxLvSqITjBTsfw2YJhhpWPoMjPgJuLO+zagK+4rmtguBe021C41HnAWyimKZeOQYlLsof/RR4osW4ZkwgfisWXYABCAHAnhGjaLlttsBMMeMRg5kk8o9BWfaiXPzLKTsnpA3ANzBL++DLx+O/is8dTIoGrSuQ229nayeh6Dd/Cv0pGIXoIaySa5dQ845Z+MoL8fRrx9qKESmoRGArGOOofWv99qFp1VVBA6bTutdd2Ol04TOPMN+mrgs4582jc7//AejsxOtuAeh08+g7aGH0W+4kayjjyb7J2fiKP3y8BRhL2jbaL/vo84By0AyXWQaGvGNG0t602a04h6kt2zB2a8f5PRBMgwsPYOrcgDhl19B8ngInXACciCApGlYqRRWKrXTahw9exL54ANcvfsg++yiIq1XxY6hDYDe0kJ8ztwdF7YsJIeD1MqVdgAJgGHQ/vDDFN3yZ/KvvprIO++gt7UTOnEGekcn9uOVhf+P9LY2wm+9Teczz6Dm5FBw3XW0/eMfRN96m8Lf/36n+Z1VVXDQTGhcDHoSCqqgYPCerzC3H8bYK/G5Gwm//gbRDz4g6/DD0FtaCb/8Mon8fNzV1XhGjcRfAY7fX4AR13GEVJzMp72ufofm5EAAZ98BtD/1L1KrVuGdMAHZ57W/uwErmaT1/vspuecerHSaTFMTyaVLcfXthyXLpGu24OrTF9njQdI0jGgETJPUihU7dd0Mh5FcLqxEAufAgRjxOI6KCnIvvGBb4bPwgxdrhc/+ZgeDGRmQZPuYnd3767elp2HrcjukTlJg5QvQsgYiW2H0BSTbU8SXrkRxu8k+9RSsdIbWB/5G1uGHo2Rn4xkzBs+QITs1m1y3Ds/QoYRffc0utB8yBEfPctofe5zAIYeAZeGbNg3Z5SS5eTPuvn2RvV5QVcxIZI9DQjI1NXQ89nj3azMSof3xxym47locxT0AsNJpop9+SvOfbkZvaSF44omETjkZR3Hx1x+vHym9pYXO55+n/aGHkTwe8q+4HN/UqTh69kZetIrIm29tn7epic5//wfv6NGEX30V74QJoKhIqkZizRqc5eVgmJjRKEpuLkY4DOVjIdpkB5Dk9oWBx4BlQioMxaNg3RtQdRSUjYMnjrOP2wBrXoej74feU8Gb8/0MjvD/WmrzZtr+9nfCb7yBo3dv8n56KemNG7sDSABiH31E4JCDYfSonRuwTNI1tWhFhag5uZiJBEoohJKfj2fsWNI1NXbIaSqFEgwSPOF4Iu++hxGJEDzheJTsbEKnnkp8zhwyW7YQ++gj1Px8HEVFOApLcCy/Hnn0UcgeN9H33kfJzia9aRP5P/sZqXXrSK3fQHLyz/D2nQ+186B42O43euAx9t8vXrR92vJnYeYbUDR0zwdPVmDEmfa9jfZN4C+0lxf3Ib4bkoTs8ZDZsBFnrwqQJDJ1dbgGDkQyd3/vSxB+EBQFDAPXgEpQVVIbN+Ls3x9WvAgFA8ElgkiEfS9j2Nfk2rbrE4eiACKERBAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQfj69mkICXA8cLtlWR99caJlWXFJkmbu4778IMmyjOR2A+AdOw4ksNKZPatFlyQcPXuSWr8erayMdF0dWn7+9hASh8cu/GzfBLE2u9AmGUGSLcyuMLLfj/+AA0CRCb/3PnkDBoCqkl6/jvCrrwGQ3rCB5ltuoeSuO+3gEYcb5P/ajRY/CZaJlA6jZIfQtzbhKCkBRSZVU4Ozf78v6b9iFxF9cocdQOIObQskEUUo/1M82dBnKrz5S/j0Hnta3gD7yfB1n+EuGYX7xKPxTZxA859vQXY4APBPnULX8y/YTYwdi1ZYQMvtdiCJa+hQ8i88C632AzTPe0jDT4VgGUhf8sHoORHO/wjWvW2/liSU/vvhn/tn9NIDSRx+IJbkJrFoEdF33iV0xhkEpk/HjESQ/T4SixYhORyYkQgAgenTab7lVgC8EyciKQpGaxu5l1yMVlxM+DX78xE84QSab70Vtn3mOp54AjOdpug3v0bStL080EI3WbULej/+C5g6yrQXkVQVZAWtrAxJVUiuWIlvW7CIGY+jN7dsLwTv7KTlzjsp/MMfUHJzcfTsibaLYm8jEkF2u9FbW5EDAZKr1+AZNQqzowN824OYZK8XtbAQfevWHZaXnE5ic+bs1K4kyZjhLjwjR2KlU8g+P67CQtTs0F4cJOGHpOull2i++c8ApID4Z5+Re/FFtNx+B5LLiZKbi3fcOBxlpch+P94xYyG7p/3nc+EGaFoBegryKiH3KwIdVAe6kU3TDZd2H5/aN20i+8wzUQsKiH/2GcV33YWrKID66EQ0AG8uiZzL6Fyn4Zs0Ee+Y0bT/8xGMzk5CJ5/E1uv/0H2MjL7zDnpjA/6DDiLyxhsAmF1dWOk09T/9KWYsZncjP5/C669Hzc4m09iAkp1Dav06XFUDsWQJ30HT6Hrm3zt0XSsrQXY68R04hZyzZ6L2KCX3wgtQs0RR8P+M1nV2UEjpaDuoQVZh/btQNGzPCsy/qH4BfHyLHWwiqzD2InB4wUhj1i4gGR8KGR1nnz7EF8zH6OzEt98klFAI38RJuCoH7NRkcsMGjHAEvb4OJSuLrCOPQG9qIvLOu7iHDkUrK8XVvz9ZJxxPcu1atNwcMk1NKKEQqbVrcQ0aiJncObhqV4yOjp2mZWprkb+wPydXraLuggu7Q/vaH3wQgPwrr9jjsJMfu/A773SH2tHZScNVV1P60IP4xo/HSiV3mj+xdClZxxyN/4AD7CCxBfNxVVZiJRIYnV0oPh+pDevxFRdDKg2BPvZ1StFQ+/zjk9vtgB1PNhx6C6guKJsAnZu3B5B8bs69UHn4dz8Iwo+OkUjQ/KebiX7wAQCpZcuov+RScn966U7zprds2WUblm5gdHWhBPxIDieJFSvxT52K7HTi7t8fJStIcvlyul58EVdVFYklS8g66kj01jbS69eTd9lPqb/iSrKmT8dVWQmyTOT998k++WQkdxD6Hoi74SPk6v2QvUfQ9uCDhI47jta//53QiTOQFJXEqrXI/cpx6/WQSYLm2s2Gp2HWHTtOy8Shdu7XCyEBiDZDossOICmosgOzhO+EJctIkkRi/Xo8gwbaYXQ1tbgGDbav4wThh84w0HKyic6ajbuyEqOzw74/ZZqw7nWoPtEONRKEfUw3LGQJZNm+X+pQ7euHz8NJBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBGFP7evf7m/87wASSZJutizr55ZlvbuP+/KDJDmdyC4XifUb0PLzwLLItLejlZbufmFVRQ2FSCxbCrU1GIkkrv79uosYYdsvxFcdBeveAjMDfQ8Ey8JVVUXbww+hBEMY4TD+/fbDSqWRZJnwW2/vuB7DQN9aDx9vK2ia8DPY72o7lAS6Q0MkK4ns8pNYtQrP0KGg65ixKJb+JaEiTq8dQNJ3GuT2h4ZFMOsuGHDE1x1G4Yfg8/AYfxEMOg7e/+P2fXHsRTgnX0eP228jU1dHYulSUFQsw17GO2YMLXfeCUDolFMwOjupufhKJIeD/Kt+im/JO2hqDKnycMjptev1Z1fYBc+KBhX/x959h9dN3X8cf2vcva/3tuOROHvvHQKEDaWMFiizpYxCW+hugbbQ/aNAW6BAd9l7r4SEBLL3coZjO9573T2k3x9K7BgnIawA5byehye+50pHR7KupCv8/WgO7HwBuqpRux4kY/RpRPPOxZSdRfv9f0VxOWn74x9RfD4ybvkZvq9dAoqCZDKhZmcTqdgJgLmoCHNREa13/tFYtYULkZ0uMm+7lVhNDbqm9xX4H9T9zDOkXv0NzIcJtRA+Jq5MWHwrjP0KWNxIJhVJUYhs3YJlWDmx/bXYJ05AD3ZBZw2ofgJLlgzqJlJRgfvMM/CeeSbKIaEiB+mxOJbCIoLr16N0dwE6kiIPKj43paWReeut1F17LRzYp10nnYji8eC//DJafvd79FAIANnpBFWh8/EnSLa1GR3IMhk/+hGJ7m4RtPA/KN7aSvuDDw1o02MxtHAELBasQ4eSd/99tP/tb3S/+BLxmhpCGzaQ+cMfYsrKMmboqILHL4amrcZrqxcuee6ohW6RHRWDj08vvYRrwXxi1TXYxo1D6d7b915o2M3s/+n96BGjgN5UWkrWL39JZM8erEOHEnhrKdEDISSAEfQzZy4HWxSfDy0S7gsgkT0e/Jd+DS2ZwJSTQ8c//gmA+9RTMWVnIUkSKZddRnx/LaFVq5DMZlK/fgnOMjuun52JkmxFHlJoBE4Iny+SBJOvgjdv6b82KDv5g/cTbIPnrjX+lSTj3C7JMOnroOuEE0XEdq5Gj0bpff45rCNHItsdmHJzsY0be9gAEoBkTw8k4miBIIrXQ7RiF4n2dizFxZiLh+CYPgP7qJEARPftw5SdTXDderRAL1o0gmy1QTJx2L7f63ABV+YhQ7AUFfW9juzafci1u6Hr8cfxX3IxpoyMY91aX1jJYJCuRx4Z1B5csQLn9OlYSgZfN9onTqR3yRKi27cD4L3gQiRZxlY+nNDGjUYgg8kMySS6loSuOsgeB+VnwJJf9HcU6oDFP4chc8HiApPNOD5Huvqn0UUBqPDJSDQ29gWQHKRHo4cNQrSOHHXYPvR4HEtRIZG9lchmM6rPhx6PIVmNIBBraQmyx40ejdD57HM4J02k6+ln8JxxOno8QbRyH9m//S1Nt9wCmoaamoJlxEhi+/djKSiAwhmgWrHseh4pczKxE06g+5VXSLv2Glr//BcckyZhLikhtN+KKd+M2rYbska//8of+rlSLaBYjPseH0TDJvj3WRA+EBaVPhwu+C/4j/BdU/hIJA7sb7k5xKqr0XUdc34+ejIx6BwoCJ9Zuo5sNhGrqSEZCWMfNRodHaI9gNiPhU9HXNNQDwkuPBhCEk2Ia1BBEARBEARBEARBEARBEARBEARBEARBEARBEARBEAThgznej9NeeJi2Rcd5DJ8ZkWCcRHxgIIdkNgOQaGlBj0bRkxqJ5mb0YyiikVQTyVAQc0EhuqxgSktF1w8JRVBUsHsgHoaciZA1FmQVyaQS3VeJ77zzMWVk4Jw2DTUrC11LorhcqKmpg5almA4pun/nj9Ba0f96wtdg/k+Q/IVokQjWkmIiO3YQqazEnF+AnogffgWivXDiL6G3GVb9GdBh2rUQ7nrfdRc+WZFgnETsCOExRzLiTKM4eNS58O49A4uJVv0FWitQbDaspaVk3X477jPPwH/p1wDQosb+paSmgqLQ8/LLIMukXnMNoTUb2X/7YzQvaSNaX4e+6XEjsOZwxUqZo+HiZ6FkIdStNdrcOShlM7Dv+BXevFZ8550NigKAZFIJr1lL4K2lSCYT6d//Hnokguwwit5dJy6k67HHAPCccw5aMEjLHXfQdNvPibe2Yhs9Ct/FF6OkpPQNQfX7kSyWD7bthA9GS8Lsm4niJt7ZhJ5IoPj9gERk2zbibW3IFitYzLDfCDdQs7MHdWPOzSX92msHFIMPIMtINivmoiISwSBqahpaJAIHnjB7KOesmRQ9+STZv/8dGT/7Geg69ddeS9s9fyL9pu/2TWefOpVEQwOyxYL/iitIufpq7BMm0P3cc8d03Bc+fyRFRT5QzDug3WIm70/3YMrPJ7q3EmJxbKNHk/69m4ls2kxw1ar+iauW9weQgFHg/s5dkIgN6LPvOiMWRFIG70+K04lktZJ203eJNzSQtGbDiLMhtYzuFbv6AkjUzEy8p55C3Y030nbXXdRddx3OObOxlJb2j99sNq45ANntJvv3vydeW9v3vuf002h/8CEkoPX/7sS18ARc8+bR+8YbdD/9DFpPD5aiInL/dA9Fjz5A0YO/IsW5DPNr38QUqUS2OvoLg4XPF0e6cd7XDrmO2P3qB/99Bluho9IoMD/xdkiEYeWfYMM/iVlLCSxeRse//k3X44/jXHACaDrW8nLU9HRsY8YctkstFkNPJIlVVaFrGsF33iUZCGAuLMRcVIRt5Mi+ABIASVGQU9KwzFmInJWDOS+fZG+PEYpyDKxlZWT8+EdwIBRASU0l647bUf3+vmkUl2vQfGp6el8IgHB0ktmMKXdweKOakQmAzdaI/4Kz+n5n5qIivF/6EsTjWIcPx3veeUSrq8BkQrLbUbxeZH8KlnkngjcFPZGAB+bAst8YQSTv/d131UDJAlDNxr4/6ssw/6dQvMB4f9Z3jYASQfiYyRYL8mGOH+acXCzl5X2vveefj33c2CP0oqOkpGIZNwHTxGmYyw6c59X+IBNzejq2iZNwz5tLz5IleM88k86HH0EL9JLsaEcL9JLyja/jnDMbNTUNU0YG8aYmY2arBwpnQtMWzM1v4po7A9u4cXQ99zxp11xDvKmJ9vvuo/vpZ4jopdBZBckj3Ds4yOaF2d8DxQyzb4Jp18HYr0JKMcSjR5/3oGQCVv554HmpZQdUvX3keYSPRtNAVjDnF6COn4J5/BRMOTnosowmshuEzwNFRovGsAwfgTJhGtax45EdduPe0rxbIKXk0x6h8AWVTOqoSv/1qVkx/tdPTISQCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCB+QejwWIknSN4FrgGJJkrYc8pYLeOd4jOFTEQtDdw3IKviKQDaCDno7wlSsbKJiZRO+LDsTFhWSNcQDgKSq6IC5IJ9YbR2SxYzidh85uOMQkiIjSRKq12v8bLOBpqMnDyn6VCxGsZwjzfhXS4Isofj8xBsbsJaXgyITXL8OS1kZakoKmT/6EXXf+lZfyIN1aClWuXLgwkPt/T+rdnjnbqThFyGp5Zjz8ohVVYGuo6b4j/xUYnsKPPMN44nhJQuhZSes/wcMO/UYN7jwcQt0Rti9upEd7zThTrUy8dQisoo9SMdSbJszCS59CTqrDzwJ9j16m/p+VFP8OFMmYy0tQ01LR+vpAVnGNmYModVG4b3vy1+m69FHidfXYykrxZRbSPPd/wZZIuUrLqytLyDbrEjZY8BmhJcgy0ahW1o5VLwA1StgwqXwxs8gGcfcuouMvMnEJp1CcMU4JLOF8PbtxPfvJ7JlC7LLife8L2MeUkz3Cy8iKSp6LAayjCknm+6nnwZJIu3b3yawZAm1V16FkpJC+s03EauspOfll0m76SZMhwnyET4+YXM2+9pT2bRewmqTmFCcicPchCk3h/CmTVjKytBCQSQk6KhEtlhI/fpVhN59Fz1uHFvVtDScs2cd9mnxAHTWIJlNJLt7SLS3Yx85CiXFT2T7jsMG4EiKgrV8GLHaWpp/fnNfe7Kjg8BbS8n5y5+JVe7DfeopBN99F9cppxCrq0Mxm1GzMjEXFh3b50z43FH9PtK+fSMNN/XvF4rPh3PuXGzl5fQuXUro3XeNgA9ZIrhmLb6LLya0bh3es882ZmjfM7jjps3QXQeJMD16DhXrOti1qgl/tpOJc5x47U0oqakk29r6Zkn7zncgmaDuG1djLirCMX06rhO+jzVRQfzOF/um85xxOm1/faA/1EzTaP/b30m95ptE9xhj8V10EbLLSe69f8FSWoo5N9cIcFIUSCaRnS6SHR0kW1qxlJWRbO8wAqaA+P79WEeOILRxE8Fly3BMmYh7rB89bQSS3Qe7X4PyM4zrA+HzJ9IFPQ2D28MdH6wfeyr4Co0wh9X3Qdd+sPnAkU5ocwUd//gnYDx3vvNf/yL1umvRdR375MmDgn+SoTChNatBUQmvWUPHf/6DHo3imj8fU04OstuN4vVgmzBhwHwhdy5bd9ip3xckr7iAYUMV2PYu6DqJ9nZkpxP5KMFjss2G78ILsU+bhtbdgyknG1Nm5oBprKNGYi4uJlZ54Fpblkn//vdQPZ4Ptr2+oGSTiZQrryC4YoVxzYYR9uKcMR0Ade+TpGWG8dxxOZpmQsuaRHDFCsxFQzBlZxGvrcM2egyyoqCFQignnE5Nt5fdawL40s2My83EkTYctj9tfP8pOQH2vNE/AE+uEbSw9Newf2V/+4xvw/TrIXfiR1/J3kYItoMz3fhPEABTTg7p3/8eTT/5aV+bY/p0bGNGk//Qg8T270e2WDAVFKLYbYftQ7I7CAydwcZ3OgnUxikfP5J8fwCJgde61pJiEs1NuENhul99Bf+ll9Lx97+T7OjAOmoUqd/8JvH9+7H4U0BLEt1biXPKFGNm1QylJ4MnF7teRXLGdCRNo/1vfyPR3AxAtKKChh/dQtHNJ2Cy+cCdaxz/5SNk6JYtgq88Ac99s/98s/peuPBRGHoMmbfxkHEd9V6tu95/XuHDSSYhM4/meCqb3+1EUSXGz/KTkmwE/QOGnwrCp0GS0YaNo6rJyu7N3fhSixg7xoMn0QyBZuN+ayxoXK8q5gP3hI93DrjwRZTQdNRDgnLN6oEQkqQIIREEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRA+mOMSQgI8DLwC/Ar4wSHtvbquf8AKxM+JzhpYfJtRoCabYOZ3YPJVJK1+Nry2n23L6gHoaQtTv6uTc38wkZRsJ5IkISkqpsxMQmvWovX04Dr5ZPR4Aj2ZRFKUIy5SUhTjKemKgqIqSKqKZDEPnMhkB1e2ET6iJcDmQJIlVJ+P3rfewpyTgx4Oo/h8fcEnjrlzKHj4v0T37EFxObEmNmPe8Jv+PlWrURR00O5XIdqDlAwiqQrNv/pV31uhVavIuv32w69AuAumfAO6aqFhIxTOAHcOhDrAP+SDbH3hY6BrOlvfqmHD68a+2t0apmFPN+d+byyp+d7370BRoWA62PzGk6jbDwmuUcxG4fB7qD4vnlNPQYtGUbxe2h9+GHN+AdHde1C8XuL19SBJeM46i5bf/g6AlCuvpPftdbRs3oRj+mQ8cxTk7gpMpRNAUsCRYhR8nPhLeOQCSMb6n6hddhJS9jgsa35C9hVfJdTjI7qnisjmzVjLh9H2578A4LvsMjJ+8H30RBLbpElEKypIdhpPynZMm0ZwxQrCGzeipqeRcuWVdD32OPHGRtynLDKChXRdBEp8gvbVOFj6Unff65f+VsmpZ5YQ/sVtAES2bceUm4vtZz+AvMkA2MaPp+DRR4lWVCBZLFhHDMdSVDS483gEtj8DS+8gmvZ9ZKsFy5AhoGtooTCOadP6gkwOJ1ZTPagtsnUrkbFjMefnYUpPR01LQ+vtJfTOO0S7u3GddCKyz4siCs7/ZznnzyfvoQcJLF2GKTMTx6xZWMtK0aJR4g2NRCoq6H7uOQAcs2ahOB04ps8AQIvF0IcsQtn034EBYOVnwKMXkjB5WKfcwc61RvhTT1vEuM44I5X8m08nVK+R7Apinz4TOSWTmssuJ+266wiuWE7XY48R27+flCuvwHueg+CKd42+ZQU9FBq4EskkanoGKd/4OrLNRnDlSkKr15D589twzZsHgG3kCPL/9jfaH3oIFBlTbi6YzThmzKD9oYewT5uGpXgIks1GeMNGQmvXovh8uE4+mZan1pJobsF3yiycsxaitr4LkW6wOD/ZX47w8WreAR37jGNv7Zr+dkkCT94H68uZBmfdC1VvGwWd4y4Cmx892Eb3s4sHTR7etJmUa76JpaBg0HuhNaup++Y1ZP/ud7Q/8EBfe++bb+I9/3wkhwP75Mkodnvfe8HuKK8/Wkd3axiAHe0RmuvsLJw3Fq2nl6qrv9lXfG8bOeKIqyGpKtaSkiO+b87NJe/++4hs247W24ulrBTr8OHHtIkEg23cOAoefcQ4x5vNWEeM6D/HjzwX+YVvYW1ai14wh245n/COnXjPPpvu554jtncvzrlzcM5fgJKZxfYmL9tXtgIHvrft7eXcK39Oivte2PIYnPt3qFxifLeyemD+z4zvLocGkIARiDD+YrC4PtrK7VsKzx4IWvAVwtn3Q/7Uj9an8D/DfcqpWAoKiO7bh5qainXECNQDYYiq33/UeaNVVQRdebz8YBVa0ggdWfVqmORJOYw9TOCebfRoovuq8Cw6hdY77+wLGo1s3UrbAw9gzs0htm8fpvwCLBYLkd27sZaVGTOPPMf4bvjOXVin/IhoXm5fAMlByc5OYnI+pqq3YeWfYO6PYfwlYDvM9bHNbRT9vzfw6s3bIH8a2LxH33BWN4w6D5b8YmB70eyjzyd8aLqq0ipl8tZT1X1trz0c4NTLh+DWRaG88DmgWtheaWbHKuPY1dMWoaEqwFlXFOBa/zYMmQkr/2KE4apWmPsDmHDZ+x+PBOEjiic1TEp/4I3lYAhJQhxbBUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQhA/meIWQ6LquV0uSdO1735Akyf8/GUSy5THY9pTxczIGy34NWaMJpMxnx/KBxTGJmEZHQ5CUbKOwVpIwAhYOFPt0PPQQKVdfjRaJoDgcR16mqiLb7WjhMHo8fiCUxNbXjzGNFdQoaHHQTSCroKokOjtxz59P75tvIjscOGbPQk8YT6CVTSbs48ZhHzfO6KMhC2pfgJbtRkjIGfdAaln/MoJGoZ5ssRJ4bfmgYQZWLMf7pXMGj9+RAjtfgM5q8BfDzhfBmwcjzj7KhhY+KYGOIFuWNgJgsaskYhrJhEbb/s5jCyE5SEvCpCth43+geTu4smDGDUYQyRHIFgvuRSdjGz2KWEMDwTVr4MCTPC1lZYTWbwCMQv7w5s2E1q7FOmoUstNL7Y9+ix6Pk/7tTEymCKrPhUlZhhTuhPMfNvYvMApFs8YYT6oHzA0bMXvziZ75MKH16/vGItlsyGYTTT+7BdnlIuuXv6R36Vuo6RlGN6NH037ffQD4LvwKLb//A3osBkDH3/6O56wObKNGYcrMPPZtJhyzaDjO5hVdAxt1aGjSSPX7SXYYp5d4XR3JYBhyZwEgSRK2EcOxjXifwu6mrfDs1SSGfQViGrLPj97ZgR4KI9tV9GQCxe0+4uzmwxS/2ydPRvV6SbYZARJaKDTwmP+3v+O/6iqSXV2ovsFhPcLnn2K345wxA+eMGQPadV0nunMn0V27+tqCy5djnzYVx4IFhDZupP2vDxDbvx/vmd/FXbgX085/wMhzIRGF1goC035PxbM9A/qNR5N0xrLxb7wWq9kBZgf4R9FTq+E77zza77+/L1ip57nn0Lq6yPzFz8n69a9o+8u9YDKheL0ku7r6+pTMZrRggPYHHuwrPAZQvP37rKSqOKZMxjZ+HCSTOGfOJLqvClN+Phnf/x69b75J97PPYZ88GefcOYQ2bsR/ySVEKiqwlpahF5fQ/vgrJE+aQcq4ImPcwudHtBde/SHkT4FhpxvXA/XrwZ4C064zrgc+KIsH8qZC6lAjaOzdu5FSSrAUnkho9XsmLS7GOnTooC70ZJLOf/8HNS2NyLZtg94PrlpF5i0/w1paOqC9s667L4DkoPaGEIGYH1OkjURLC4HFiwmvX0/h449jzv+AISuHMOfmYs7N/dDzf9FJkoRt+HBshwtvKT3R2P9W30ciZy7Jhm48p55K8+23o/UYx86ux5/AecqpJItGsPOBur5ZzTaVZEKjozFMisUF6SPB7IQ5PzACQRQTxIIQ7QF3Nphs/SF8iYjxnfCjaN8Lj34VYgHjdWc1PHYRfH0peMT+IoBit2GfNAn7pEkfaL5EVzcNt95G6OKf9gWQHLRlRTOlZSqK2TwgIE9xubBPnEBg8eIB1wEAkY0bSbnsUrqfe55oxU4Ut4tkd39gIP4i2P0aTLoc055HsJWfD4oCyWT/NJKEkpIGUi/Ew/DGTyBjBJTMP/xKRHv7f3akgd0PkU5IRo9tI4w+zwjN2vwIqBaY/T3Im3Js8wofmKbD9tXG9zTFJKOaZKKhBNUVAXKHeT/dwQnCMQglzOxc0waAyaqga7rxnatLJz3SZZz/K14wJk5E4M1bIWMklC781MYsfDEkkjqq0h+CbBYhJIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCMKHdLxCSB4GTgPWAzogHfKeDgw5TuM4PiK9sPWJwe3VK1AyFpCS66B4XDqJuIasSIR7Y6im/qcUxmr2DwwOAXpeeQX/1y6Bo4WQmEzIdgdaOILicgO6UfB5aDGPYjYCSHqbINYLtlQkayGyw4EejeJaeAJIEsmubtSMI4QmZI+DCx6Grhoi1jya2t20vFyNL91OZrEH1/CzwJuHFI8i2+ODZpePtA6xIOROhtKTjICTotlGIkuk+/DTC58oWQuRVeohp9RPsCuC02fBlWoj3BOhsbKLtHwXqkl5/458+fDGYkgbBuVnQs4ECLdD2y7j05818oiF5aacHEw5ORQ+9ijxpiYc8+aRqKtDUo3lWsuH0fbnvwDgOvFEWv/wBySTifSbbqL1nvuJ19biOf987GNGIulmLIkKLOY2pKGng2qC7c8aC5IkmHEjJKJYNv6GvJ9cSUxLIbhmLcmODmL7qgCwjR5F1+OPkWhtxVpahv/SS0m0tWHKySFeX48ej/cFkBzU89LL+K+4QoSQfEJkRcaTaqFsUibJhIYsS8RjSSyRDvTowKJD2e0zwmc+iI59xr96EnN+AYmOdojF0RMJdE1DdrlAlo84u230aHyXXELnww9DIoFl+HA8Z5xOZH8d0ZGz2PjmfuREAd7v3070ntvRgkEAeg8e84UvlmSS8KZNg5rjVdVoHR2E1qzFMnQottGjCW3cgjasjNTrNiB174d9S2H2zciuXEYvyMNiU9GSOopJZuc7jSjygaL1WBC0BKSXo4SCyFZrXwCJacQouOCb1Icc9GwPkT11IYVz54Is45gwnvrv3kSyvR3Z7Sb7V79COrj/Hyg+to4ejXXkyEHjl00mOBBohq4T3bOX9gcfwrXwBOyTJxvzSxLe887DXFJMzyuv0PPss0gmE76vfoVgRQ3usRMwJSKf0IYXPhGBFqhaCkNPBsVinGd1HWw+aNlhhDR8EA2bYP+7IKkw+7vw/PUwZC4UzsKbzKPntaV9QTmK34/n7LOI6Waat7bRsr+37zrV6TUj2aygKJiyB4/BUlyM4vf3vdaiUSIVFehdh7/m0bva0ZRA3+tkVxfRqn2HDSGJhhO01fbS3RrG6bGQWuDE7rJ8sO0gfDTuLDjhVph0JYmGbhLblmC22voCSA6KO1PpDpgZf3IBdrcRnNfVHEI1K1iz7bB0OZx8hxGIU78BqlaAKwPq18HUa4ywk2gvjP+aEUzpygZv/kcbe1dtfwDJQcFW6K4TISQCYISZtdUGaG8IoJoU0vKdeNLs7ztfvLaW6M6dyPrg7+4Wm0qyoY6OFW9iGzcO2/DhKE4jPNVWXk68rm7QPGpaGuFt27EOG4p11EgSw6dS2WYn/mo12aVe0gvcKI5UaNkJZSdhi7WSevXXafvzvX19+C86F3P98zBkWn/HTVuOHEKSPc4IBZp5IwSajXNQyUIjhPVYePKM89SYC8CRAamlR73GFz4aWQdPupXRC/LpaQsTCyfwZTmI9oRBPXJYqSB8VsiKTv5IP8OmZNLeEERRZZw+CyYpBqnDoG334JnqN4gQEuETF9c01EPOXyblQAjJof9vQBAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCOwXEJIdF1/bQD/xYdj+V96kw2I0yjeB5YvUbAQU8jpA3D6bMyZkE+i/+xoy9nJCXHwYhZOX2zqykpg7pU/H4ky9GLFCVFAbMJ2WJBT8RBVkBW0KVDimd0zQggCbYY4+ypR/KHUJwOghU7sRQUABBvasJSVnr4BdWtgycuRTO52J59N8FYArNNpaMxSM22NmackI1t6ZchHsI3///ofe31/qcjKwrec845fL8WN/Q2woZ/gD0VKpdAxigYee5R11v4ZDhcVopHp7D0kb0A+LLsDJ2aic1pZveaZoJdUUomZLx/RxYXLPoNrPkr+IqMp8Fu/LcRkJMzHhbcYhQRH62LoqK+/0Lr14MOvW+82fe+mp5OrLoaANdJJ9L5xBPEa2vxnn8+kZ076X7sMRyzZ2MfN462nZWYi8fhnjsLxbcRpfIx5KyRxtO4W3YY/VW8gDr5arJ/cRs9S5ehB4P0vvEGtjFjafuLEXqS7OggWl2NfcwY7Dd8i8Yf/RiUwYVqstuN4vW+/3YSPhSTWWHk7BxeuX8HyQNPdnX6LJxwfh49BwI9AKwjh2NV9kNwKDhSj30BjjQoXoCaVw6SSmDxEmSrBVNODpLdTmTDRrqffgb3opNxTJky6PhtyszE/7VLsI0ejRYJo6alEdm6jcDIeezZFsGTakOSbWwLFDHyhp8RvuP7gHHMl222j76BhM8V2W7HPnMmlmHDjAADSSK8aROOObPpfvFFOh58qG9a9+mnE9q6jUTkS5iatsKqvxhP2Z5/P7U72uloCAEgyRKzzislxbfXCH3wD4F5P4KMkVis3cRqaoxlO+zEL76Zxl4nDp+FYEBjxZN7mHVeGd50O+rUqRQ++QTJ1lYUvx9zbi66plH4+GPEKiuRnU6s5eWDApdiDQ0E336bwIp3cEydimP6dPRkgtTrriPZ0UFwxQpCa9ciOxxk//53dD32ONHdRtGeHo/T8Y9/kva9m8HhBfl4ZecJHwuLG7yFYHJApBMW3waRLnCmw2l3QW/DsYcytO6F/Ssh1GFcKxbOBGcm5E2GJb/AanZQ8OPvEu2xgjcPy4jRqHn5rH+5mrUvVfd1k1vu48QrRuC/6GIiO7Yju5yYy0pJNDZhvfByYqn5WEYPRUnxkOjoIFZfT3DFO0R37MB+8ukMGeFl3/bevv6GjnFhD+4hvG/PgOHK1sFF75qms/OdBt55cm9f27Bpmcz8cikWu+mDbFnho0rGYe9itFgBWjiCZBq4/a1f+gorl/ZQva0TxSQz5cwhdNQHcPqsSBJsWtqMc979+NJSwZsH4y82gqBe/i6cfT88fgnEjWMw256C0++B9OEgf4Tfs5Y0whRm32SE+ux9A2rXgGoBm//95xe+EBr2dPH83ZvQEsaNBneqldOvH4M34yhBpoBktUAigd8SxuE1E+zqD1WcPC+VztuvxnfhV+h59jm6n34G7znnoGZmYM7NhVGTsP7fP6C+hsSz/yZRXUXajTeSDAbpfvFFbNf+gFde6CXc23FgYXDatWMoyB4PndUQCyGbbPidy7HfcT3xrggmt4o1LwV55waoert/oEc7Z2SNgQsfhScvhWCb0bbjOSOAdeLlR99w8QhsexJevtn47HoL4bx/GMEmwidCUiSGTstm/avVZA3xIskSDXu6yBvmRdJEobzw2We3apRPy2LP2mb8WQ60pM6+ja2MmZsNEy+DtQ/B9G+ByQqSAu17jO9hgvAJSyR1TEp/9rv1QOh5JK7x+vYmcnw2RmR/wHBeQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAE4QvpuFRzSpI0/mjv67q+4XiM47hRVBh/CTx9FXQZxb2kD4dJVxAOxFj7UlVfAAlAe32Q7tYQKTnGE4Ud06eh+HwkOzuNCWSZtOuuRbEf/SnGksmEpOtoiTh6bwBd11H8MiQT/RMlE0YR765XjUL8aDcUzkfXNPR4HC0UQlJUSCbRk9rghQTb4JlvQHct3TP+iKpk0ry2iZRsJ6HeGCnZTjo7NGwHCu/se++h4FfX07OhBmwe3CedhG306MOvgBYHSYa5PzKe6O1Mh3Cn8RRx4bgLxa2sfWU/AJlD3GQWe1jzfBVaUic1z0leuY/utjCe1GMISkgtgZN/BVsehfX/6G+v3wAb/wNZ48D2/n8Eb8rMxHPqqeiJBKasTMIbN+E6ZRHBZW+jeNzGNLl59Lz4EmCEk0Qeeww1LQ3rsKG03nUXamYmtvETqP/hbcTq60m//ptYvMWoyTzMigs5WAeTr4LeJsx1z5K66CRiCQ+R7TvQNQ1UFSQJyWQiuHQpwRUryLz1FtJuvAFTZibmoiJiVVV9Y06/+SZMaWnHvuGFDyQRT7JlSW1fAAlAoDNKZ0uYvNu+T3DlWqyluTizoqivXg2+R2HoomNfQMoQsKfAm7eiTX8c5/z5JLs60QJB9HAEyekk8MYbBN54g9RvfYvUq7+B9J6npptzcghv3kJk+3Ykkxm1pJQALsI9bSiKRCKmYbapRHLLkd1utECAtG9dj+I4esGo8L9HkiTcpyyi/cEH0WMx9HgcNSMTNS2Njr/9fcC0PS+8QNp3vwuRdnj9x33tLeHsvgASAF3T2ba8ntLvTIeS5UYAmdnYt1SvF8esWdhnzoCMfHq8mXRVtoAOkWAcl99KV0sIb7px/WHOyoKsrP7xyjK24cOxDR9+2PVJBgI03347gcVLAAi8+SaOExbgv/gSotu3o0UiOBfMx7lgAS2/+x1dTz9NMjD4nC/JEqbq56B85ofcssKnwpkG59wHbXvhrdshETXaAy3wwvUw8ztQOAuyjnBdeFA8AlVvwa5XoLPKuI7Nm2yEmL3xE7B6YPr1WMIdWHwxGFIIebl0toRZ/0rNgK7qdnbS0RAke/w4dF2j5/nncUydRnTul1j+ShsZXg/S5ig53QHSmtfT9Y+/YS0rw1xUSMt3v8WEex4kP8NKd9JNSqqCfdc7WLIKaLr7zr5lOKZPx1IyOMivuyXEymcrB7RVrGxi+Mxssoq9H2YLCx9W8zZ4+bvo854hXrUPxWrFOm4ckY0bAdBnnUb1s8b3sMJRKbi8FiKBGFWbW2lvCDJ0SiadYTs+b57RnyfHCEewpUDTtv4AkoM2/APypxn78NSrjc/GB7X7NXjiEiNABYxQBUmGMRdCSvGH3BDC/5J4NMnaF6v6AkgAetoiNFZ2v28IibmgAN9XLkTZvYGTTp1AU12MqGQjM0sh+eCv8Hz5PNrvuxdLaRnO2bNpuPlm7DNmoJz/DXZXRAj3Oti/M5OJ1/yBsjIVra2Z2Nq1OKZOpbEhRrg33r8wHVY/v4+sG0ZjzpsBHbuhuw6ldAaO3mpwmMDmg61/g2nXQ6AJKp6H/BmQO+nIK6GoRtDqwQCSg5b8EoaeAq7Mw88HRhDlc9f2v+6qhmevhUtfBLsI+fkk6JpOoCOCJ81O074eXClWetvDREIuouE4duenPUJBOLqYZCXQGSQeSdLdGgZAViR6u+PQuxMmXw5PfK3/mJQ/HTJGfIojFr4oEpqGekhAstNi/K+fpu4IP3x6K1aTTMUvPsA9MUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBOEL63g9Uv4PR3lPB+Yfp3EcPxUv9QeQgFHYUvMuibKhREMJxp2Yj6LKyIpEW12AaKg/KMRSUkLBf/5NaP0GtEAvtvHjsY14/z9Wl+0OSCTRgyH0ZAI0DT0YgvghRT8SEGg2ivCbtkD2HORwJ0gy5uxsuh5/HD2RxHPWmQPDSw4KNEO78QT3pH8YscYEqTlOetoiqGYZu9tMQlYgYyQMXYQkSdjb12AvC8C5fwPLUapJVAs4M4xCVUkGXTMKjUaf/77rLnz8kgmNaNDYBwpGpbL6uX0AlE3OwOYys/yxPZjtKtPOLiZvqB/FJB+tO5AVoxD5vaqXQ3ctWFwgv08fB0iqin3CBOwTJhBvbiY0Zw66pqGkppLs7ETNyCDR1oYejQDgOnEhXU88CYDvvPNo+cMfIB4n9frr6X7pNaK/34l11Ch8F34dOUfG3FGDpfJVpO5qWPZbzJe+RPavf0Wsrg49HqPr0cdIBoNG3wvm0/XwI0R27MBcUkLat28kunsPya5OLGVlWMeMOaZ1Ej6cRFwj0J1g7Al5mCwKkiTR1RIi0JvE0/sIntIu6HkeWnuMGRo2frAQks5q2Po4AIrdSbK1DWJxJECPRTHl5WKfNYvQ8uW0//WveM443Xgq/HvIVguKx0vnI49gP+McEv5JpOW7aKsNIEmQM9QHNhNpN30Xa2kptpEjP/K2ET5/dF0nuncvisNJxz/+iWSx4LvgAuLNzUiqih6LDZjelJmBKVLd3yDJRGMmYOD5O9ARJR7XCIaMAk+bK4k/y47JomLOzib7l78k0BGmbX+SlBwn3c0hJFnCm2EnGT9MINkxilVV9QWQWMrLcS9ciJKWSrKzg65nnyG2ew9KSgppN3wL96mnEtmyBdfCEwivWz+gH7MbSB9nXDEKny+5U6Bxc38AyUHBNuM6b8X/wdn3G9eAR9Ky3SgslyQommM8RX7Dv40QEy0BM26AFXeB1Q2Rblj3EFz8HEl1LJo2eKdJxDQks5lkZyfdzzyL/eKrqNgepWxKFtVb2nD6LARD4PTm4V54AsEVK0DXsI4cQfCBu3H5/WRMmAA9SSzDS7GOHEnO//2ByPYdmAsLsY0dg5oyuGg9Hk0ycnYOFpuKJEn0dkTYubKRWDj5Ubey8EF1VIHZQayqBnNBAfGmJmzDh+OcNRMdiOTmMvEUD6pFJiXbyebFtXQ0BBg6NYvy6dmseHIPOaVlA/vMGgPF88GTC3O+b7RVLYP9q4z9X7EY38Oat4LzA3717aqF56/rDyABWPc3uPhZI9xEVj7CxhD+VyRiSXraje8+7lQrw6ZmkUzqqGaFRCyJaj7yfiKbzaRcfjnhHTuw6L1kVS5Dzc5B29hDx6b1aONGoQVDOOfPp/X//o/Ua68hkTeUmpo4nY1BZEVi/In5VG5uI3NYKWnDfCRqamj53e+J3Thj0PIiwThJXQJVgT1vwMhz4OkrQTtwPLS4YP5PoH698Zm57DXwFxkhpUdzcP60oVB+OiBBbzPo73Oc7aoZ3Nay3QjNEiEknwhN05EVGV+mg2BXlN72MA6fFS2pkThutykF4cPTkdF1nYwhHpoqu5BkiYwiD3pSB2+uca16aCjS/nehtQIyDh/eKAgfl3hSR5WlvtcOs3FM3VbfDUAkrhFPapiUY7v/KgiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIHxxHZe/7td1fd7xWM5nRjIGNSsGt9etwz7pG8y+oJRlj+zuC3jIHebDlzXw6cSW4mIsxYd/qnVjoJHt7dvpinZR4i2h3F+ORbWgupzogBYKIZlU0EGLRtGShxTdKGYjfEQxQ+5k6N6P3LgOPTKUjr//o2+yrsceR83IGLxwm88o/gm0gCyjo+Py29CSOmabSjKuoUs2yJsCK+40ikOzx8H8nx09gARAl6BlJyz4GcSCoFqNcIpo79HnEz4RDq+FUfNy2fj6fhIxowjd5jJh95jZ9EYtSDBhSia1OzrY/nY96cPt5I/wk5GWcuRO08oGt2WMgFd/CMPPhLFfAbP9A43TlJGB58wzjfGNHUt4507skyfT+KMfITsOfK5kGV0z1kGPxyEex1JaSqxqH9GdO7GOHIlt7Fgaf/RTJJMJ38UXYyn5CaoSwpLtw7RvGUpmB7aCsZiuuALbuHFogQAA5qIh9L72OgCeM06n/oYbQVWRrVa03l7cp51G1u2/RLYcpcBa+NCsdhNTzihk2cN7CPUYAQ2ZQ9yUTs0h0DQT59u/HzhD2tAPtoDe5r4fdTTj967r6LoOiQR6LIbnzDMILV8Omgb64VMSEl1dtN93n9FPMomsyNicJlx+C7IiI8kSsiLhP++8DzY+4X+KFgoR31dF99NPA8bxqv3BB8m89RZs48YRWr26b1olJQXr6NGgHVI4q2v47R0g2QYEdoyam01rTS+v/XUbWtJ4Y9SiTFwT4xSm5OPLzES1RpBqm3B4LCRjGqpZJpnQkaT+AqYPSj/weXDMnYv71FOIVe6Dtna6n38ez8mL6OwNkGhspO0v95J+03expNlwjM2m0+VC6zXO/e5TTsSW44Sdz8C4iz70WIRPiSxDWnl/uNxBZieEOqBurXGdd6QQEk2DysXGtDkToHIJ7H4Fxn/NuMac80N6khE2n/lbVnXtJt+ewRRHLoU7X8A1ZyI5Q73U7+rq687qNOHLtBOrqUELBEm9/joixRNJ0V3EwgmKRqfS0Wg81T5ZXEj7d64g7bpraf3Tn0m5/DKC77yDZDaheD1Yhg3DNnw4kqJgSk/HOXv20beFJFG1uY3eAyEBqblOJpxUgCfd9hE3svCBubPAm0+sZj/IMj0vvGC0WyxYf3kvbzy4g3gkyfiTCnjz7zvIKvEyflEh9RWdRENx5l88jHBgYCgUVjdMvhKW/JL6jKFs96TTM+5sSqdewfBoHFN3LdSugUgPFM4GRSWaiLK1bSvv1L+D2+JmRvYMyvyHuVYOd0GofXB7LAgm68e+eYTPJ5vLzPCZ2Wx9q47hM7NZ83xVXxDTtLOLGT0v96hBJKrfj2vmTKOvESOI7N1LZMtWTPn5oOsoKSnE6+swFxWSaG2lZ9wZWMISdrcZq92EosoMnZpJR00zvZVbyExPRwsGSTH1IEnygEvkMfPzsDnN0NgLJQtg08NGgIhigmnXGeeERAxcWcZx31fw/gEkYAShliyE1BJ4527jvkzGSAj3gDvnyPO5Mge3uXOMex/CJ0KRQTUrxMJh3Kk2YuEkLr+VSDCBnhCpc8Jnn9luwmSWQQNXig1JwghFlSGZMgzlcPeEW3cf/4EKXziJ9wSMyLKEw6xQ0dR/b722I8SQtPe5Ry8IgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiB84R2XEBJJkubrur5EkqRzDve+rutPH2M/CrAOqNd1/bSPc4wfK8UMI84xiiUPFq5EuiF7PFpSZ+e7TX0BJAB1FZ2MnBM9QmcDNQWb+M7S77CtfVtf2x/m/IETC09EUlVAR4/HDoSQ6JBMQiJBrDdER1ucnsZc7OW/IrXtWazLfwOppTDvJ4RfXzZoWYFlb5N61VVIJlN/ozsbzvwLPPZVFEnDYlNJJnTsbjOyKh8oONaNp9Af1LARtj0JhbNANQ1aTh9ZgaKZsPjn/W35U49cmCp8omRZYtScHFSzYhRXAHnlfvZtNJ7mOmJmNvs2t9LZGMKdaiWrxMuuJW3UOLopGpVKar5rcPF6wSwoPRH2GKEdONKMp8a/9mOofhuyRhkBNh+SpagIS1ERuq5jLR9GvLmFzNtupeM//8V7ztl0/P0fffuzpbyc8Pp1ALjmz6f17rsBSL32GjoffoRESwuWoUNxn3UmJlca5vZtWOq2oE7/Bu4TTkALh5EtVgKrVmEpKyO6ezd6OGJ87uJxtLjxtPqel18m7cYbMOfmfuj1Eo6su7eNirVNfQEkAE37emhosbE8NYuv5k1Brj0Q3FCyEPKmGj9He0GxHvWYlOjoINqdgjbyD5hzMyEpE9mxg0hnAE4+n7DkwJOaisdh/K79X/sapuzsw/YV21vZ97NstmC2KITjGjaX+UBtvvE0cOELTtcJvP32oObw9u1Ev30pyh+TJNduRB0zkuS1F5HISsWScMPEy2Hd3wBIr7yTRZf9lRXPGZ+L4TOyKJmQznN3bkZL6pgsCqPn5yLLElRb2Ny1gylDx0MSTBaVWCQBMjh9ViTZyI74sCwFBTgXLcIxYTyN3/t+X0iP9/zzCaxahee0U2l/4EESTU1IFgvu4iQEq8j6+c8gFkH1mLBUP4ry6lVw4u2QjH/4wQjHXzQAkgRVy2D69fDu3cY+IKtw0h3Q0wDDzzl6kXdXDSDBjmch3AnDTofCmWCyg78Yimbz0p4nuWPt7X2z5Dty+Ouo6/DIYQpOUnGnpVK7vZeUfBsFc5yozZVUXX4lWrfxNPCU392FK6MALalTv6sTu8eMapbZvaaZ0d+6ES0UQE1LI9nRifu884mWTaY1asaqWkgNJrG7j1zUf5Cu6+xe3dQXQALQVhdg6NRMvOkfLHxN+BhkjILihTgKR9F85319zbb5J7J6I8QjRoCj2aYwYVEBkWCCaCBOzlAfW5bUsmdtCyd/fWTffJFAnLa6XkJNKpZhP+Sejv/w2o4HAJCQuGfSj5jzzl1GMKMv39ivU4pZ3bSaaxdf29fPA1se4B+L/kGZ7z1BJK5MyJ8GQ+YY4TuK2Qg08RV+tO2QiEEyChbXR+tH+MwYOiUTd4qFZY/s6QsgAVj5TCW5w3ykF7j72qKhOG11AYJdUdwpNlJyHZgsxu0h1e/HOXky1rKh2EaOJFpZiZ5IoLjdWIaVk9Rl4kmFUE8Ep89KoDOCR7HhSbeh7a4i/IOfEfnW9WTeeivJWCMnXziZDcu7CIc1Rs/NpmTigUARdyZ0VkO4w3g9/QbY8ih01xmvZRXOuhdsRwm5PFTGcJh0BTxyQX9b8zZ463b40gNgOkLoU/oImPkdWPF/xmvVCmf+GVyHCWUVPh66jqRAOBDrC7FRVAmLXSUeS77//ILwKZNkBcWkEA4ksLmMewo6unFftLsZplwDoVYjXElSjGNdzrhPd9DCF0JC01GVgfdinVaV+q5w3+umnogIIREEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRDe13EJIQHmAEuA0w/zng4cUwgJcAOwE3C/34SfuoIZsPYB4w/NAbwFMOZCoqE43c0hJp5SCBhBD8Ge2ICixKPZ2b6T3Z27ObnwZLIcWaxvXs+v1/yacRnjSLOlIckKeiwGdgcAejIBqkrllk5WPVdFRpGb3o4I6VknMu3UEqwtq+Ht32EquXDQsuSSQtrj3aSaUge+UTQXLnkBScvCFFCAJBa7iqRIyJJkFJw6UmHkl4yitv2rYPerRnHR0Qp5ZBX2LoYFt0A8ZBTYde2HWOCYto3w8ets7KFiZSPl07IYNTeHnvYIVqdKTxvYPRY6G0Mgwai5ubz7dCW6pjNyTg57N7Sy4fX95A33kTvUh9VhxmxTwZsLZ99vBNM0bzdCbdp2G79zsx0ivRALGT9/BJIkYRkyBMuQITBtKo7Zs9ECASxDhxKrr8c2aSKxfZVYy4cTr29AixkBFqa8PGI1NSRaWjAXFuKcO4fW3/wWAPdpp+GcPRNlxVIsxUWYCofjXnQy9smTiO7bR/23bhgY2HOAKTsb2S4KjD8pLV0t9DRHmbCoEEkyjqmh3hjR1jj/aXuCWSfdRmFbtVGwnjXamOnde2DTfyGtHKZfZwRGvUe8pYXGW24l+NZbAMgOO9m//wO61U7X2Vezc30XKTkONu/soHScn7K7/4Rz7Ggk5fDF6OaC/L6fJYsZWZFQVAmzTTXGrUjI7ykUEb54ZKsVc3Ex0V27BrSbhhRx2Y4fM/G8kUy85HK2RCp5vfLHPDz0YUakjoAZ3zZCntBQQu0M0V4n66o5JBLg2PR7Wju+w6h5uaDr+LOdLH9sF6m5LrRKnc5WncKvd+F3eoiFE/R2RLA7zTTt68aX6UDNO/w+HQ/H0YPtmOUIeHJBGXxJqXg8pF51JfsvvrgvgASgd/FiMn/yE5Ld3aR961sE163DIlejmdOp/e0TJFr+DYBlaCm5P7gcZcgE2PUajLlg0DKEz6BgK+x6BVbfD6PPh/X/hLk/hHMeMgKg7H7oqjYC6s66zwihO5KOKuM6csJlxrzbngLzQsidDJkjaAw0cve2hwbMsj9Yz65AHUWtNdgefQTX+p0MGT8FbWUlidyFdLyzpS+ARMnKJubLJhZO0N0aIiXHyY4VDUSCcUbOziHhGIt191pSr74aNT+PZi2T1/+6r293HjIujTkXDsXuNg8aejKeRNdBNSskExoWu8rk04r6ggG2LKmlrU5c434q7D6YczOWuv2YcnOJ19YCYDrxTIZomRSO1ZFlCU+6neotbaTkOtASOlpSY8a5Jbxy/za624xCzmgozsrnKtmxvKGv+9PPvYSSsWU0B5t5veZ1flvxL8bMugHvkl8Z33HKFhGMB7l3070DhtUb72V90/rBISTONJh2DTx5eX8Y07iLjXCSD6t2Day4Czr3wcTLoPyMj9af8Jng8lsJpTuIhY3A06wSD/nDU0jENeLRBJFADKvTTDyaYP2rNWx8fX/fvLPOK2XknJwBoXiq14M6dQrWEcOxlBQTq68ntGMnzslTiUWT1GxvR0vqDJ+ZTVeL8b0wtSAfxw030vPiC1jLyzEX5OOqWMbChZPA5cJTXmB03l0He96A7c/AqHONsBB0oz1zFIw42/i8BFsh0AjefI5JoMX4V5JgyHyj6D/SY9yLMOUcfh7VCsNOg7QysHjAPwTShn7QzS98AHEkdA3MVhXFLIMEJotiBDggghmFz75oNM7B258ms4IkgarKSJKEJqtG8NhjXzFC9MC4dvUVfbqDFr4Q4kkNkzzwOOq0qDTTH37e2ntsQeiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjCF9txCSHRdf2WAz/+XNf1qkPfkyTpmP4KW5KkXOBU4HbgOx/vCD8BlYv7A0jAeOL1rpexzRrNlDOHsOzhXcQOPGXbn+2gdGLaMXWbSCa4ccKNPLXnKRbvX8zMnJnMzp1NNGH8AbEuy6CaIJFA13X0WIyoO5u2miAlE9Op39WJJ82OL89DV28zmTtfgGnXEbGPQnksjWRLKwCy203NgnJKtNjgQVQthUcuQDnzaRzeIrSkRE+7gq7rmB2qESsz5Zuw6s/GH9wXz4cFPwPr+2XH6DD8TFh8G0gyaEnInWSEmQjHXbg3yoon9yErEA11kT88jXCvE8Wk8Obfd/QVlKcXuKir6ETXdApHp9LVHKKuopO0fBdpQRev/nU7SDD1zCGYbSp2tw131jjo2AfPXt1fTFl2MtS8C2sfhBN/AamlH9u6mLOyALCWlqInkyTOPZfIjh1IikJ4xw4kkwomE6acHGJV1QC4Fy2i7cEHAfBdcgnhzZtp+N4PkN1u/Jd+DduIZkx5BZgLCnBMmkTRk08Qb27GvmYKoVWrjQUrChk//Smq3/+xrYswUFBvZ8xJQ1jx3739x9QsB5O/lM8F+gVEalfDawdOQTmTjELb138CNh+0V8Ke1+HKxZA+bEC/kW3b+gJIALRgiN63lyGfcgFar4WcMi/N1T1kFXuQzWaS5ZMxpbuOOE7H1KmYigqJV1WD2YKsyqhmFfQkdq+OyWw8NVn4YpNUlZTLLiW4bBlaMAiAmptDYsoYetb2sKT1XZbwbt/0sYPn6J56aN4CK+7se8827XpQLXT6ZvHGE510t0bwZznQdJ3R8/Ko2tKGrEiMmZtPd3UU33DoaAwiyRKappM33AN6mOqtjaTlOrE6jZCFRCxJ7Y5W1r2yn0Q8yfjJOoWeFVhGLAB3Vt/y9USCaHU1idZWPGefTfczz6AFQ8hOJylXXkHDD3+IHg6DJJFy9TdQurbStk4lceA6BCC6aw+BjRX4e++FOT+AuCiW+lzY/iy8fJPx894UOONPsPIeqHnHaDPZjUCyrhrY8QzkTjQqOA8nGYd4GN7+nXHcnnYt7F0CKcY1QlJPEkkODPJzmpz43MOwd+kklq4m3tJCvHIvAGpVJdYbvk28dCISOuYpM9m0opX2ulqyij2YzAlGzMrmnaeM6TGbUfNyAQh1hFm+rP7QPB32bWxl5Oxs7O6UvrZEPEn97i42vl5DMq4zdmEe/mwHe9c309EQMrq1qUw6vQibc3B4iXCcWN2Y7XHSLjqd/Rs3Yjn7QtZtU6mrNL6qetPtlM/IIqfUS8PebiLBGOXTPJgsCU66agSRgBHy0NEQHBBAArDrxW7M5zl5u/4JLh5+MWsb1xJxZsC062DlnyAeIaklCcQHh9CEE+FBbfQ2wSvf779mBtj4byOYyTHzg69701b45+mQOPDZeflmCHXCnO8d+bMofG44vGYcXgt2t5nUXCdb3qplwsmFbHhtP6HuGMNnZZNe4BoQQALwztN7yRvux5fpGNSn4nLhmDIFB2CfMIFkVxdKm0RHY5AZXyoh2BVFS+hsWVxHeqGLEfO+hKVsIuzeQnTbeizDhhLfsgHXvLlGh4k4se0vEe1sx6bYUKuWGWFVgRZwpBmhOIt/bkwrSVD1tnEucR7DPRN3ljHPgluMkJO3fw/ODCicBUPTQRkcGsnO5+Dpq/oD02bcCLNvMgJVhU+EpGgoqoTLZ8XmSZCMy4R6jEDJWFx//w4E4VOmmJIoJhmn14xqjpKIyyTjMooqEbMXYG58vT+ABKBuDTRtMcKOBOETlNR01MOEkADk+mzUdYZFCIkgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIJwTI5LCMkhngLGv6ftSWDCMcz7R+B7wOejEmT/qsFtNe+iz9TZu76FWCSJP8tBMqHR0RCkuzVCzjH8Lbrf5uf7K76PRbGQ68xlef1yYskYXx32VQAkWUZxu9HDIePJ8hLoVjuxSBib00zRmDRkRaK2ooOUOWONJ/6+80fiX3mEzbd+mdHdblRdpipNY7czyEzHe56I3dsML9xAYNiFbN9Vw4bX78dstzPlrIuwukuIxzCKftY+AKEOY569i8GRDmO+evSVU61GodCCW4yn3StmiHQbPwvHXSLQS95wE8H2dWx69S32rkll9IILqN1lZ8Gl5SiKjDvNhmpSiMeM8Ie0fBdrXzSKN0smprPy6UpUk8zUs4tZ9vAuetsjjDkhn5RsO3JgLCmz/oq3+QXklm1Iu1+FeT+Gt24Hkw3Ovg9Uy8e+XpKiYMrIwJSRAUDhIw8Tq6sjOy+PlrvvwX3iQsKbNqHrOsTjyA47stlMZPNmZIedtOuupe3e+2jr7MQyYgRp112LmpqKOT8fe04O2b/9LdGKCpLdPZiLCrEOG/Y+IxI+ilxHAavWtRKLJvFnO0jEknQ0Bmlv6WVxeDGn+Kf0T1y/FoYtghNug+5asHkhEYWWHQNCSOLNzejRKP7LL6f7+edJtrUBYB0+nLDTTcu2btLz3dg9xvG0cV8XeeVHD5oxFxSQ/+CDRCsq0LILiUZlVHMcSd/P8n//m3BvN6PmL8Kbtgh36rGFUgn/m2yjRlHw2KNE9+xBVlUs5eUEUuwM3TMUr9VLqa+UPZ176Ip1UeAqMGaK9hqF7Yda9We46Gn2V6TS3WoEe7hSrFgdJjobQ+QP94Mk0VbbS84wH6pJIavES7g3it0dYPe7L1C3cxMZxUNpHX4ReSOMz0hjZTcv37cDgLIpmfQkbeyJZJOyL4zHU40tNwc9kaDrqado/s1vIR5HTUsj7YYbaPn9H3Cfdhodf/u7EUACoOu033sfjr/8mlDFPwdtj0hNG3z5Vtj1Cow+/xPZ5sLHKNgO795t/OwfAuMugo7K/gASgHgI3rkLSk+E/SuNYAX1MGEc8TBUvAQ5EyB/Kux+DZb8Ehb9DrxGMEiGI4MLSs6msreOYm8xeizBxEAh6+76N/FohHGXXEDGniqiz72A7ZKvE51yCi1WJ1K2xIbXasg0daKoMj1tYXrawmQUuckfkUJ6gQtZkfDl+dHkHhKNTWjeNEI9wUHDPBhGcVBLdS8Nu7vIKvYiKxKb3qxl+IxsknGdlBwHHQ1BYuEE9bu7mHuhKET91CSisPxO7DUrKLztGuq8pxBZHsDhNRPsiiHJ4M20s/nNWtILJEzqVl750yvYXB6mnHMxKTnGMTESSgzqOh5J4tGdOFQH/9z+T34w8WbSOhohbRjkzwCrC7fFzaUjLuVfO/7FtOxptIXbWFa7jAmZB74WN26GmpWADtnjoKdh0HIItHy4dW/e3h9ActDKe2D8ReDO+XB9Cp8ZTq+Vk64cQWNlNyufrWTK6UPY+nYdpRMy0HWI9MZp3NeDy2+lt6N/P9ASOtHD7M/vZSkogIICLCsbSC9wEeiK0t0SpnpLG540Gyk5Tl65fztDp2aSPv5k5HEL0bsbsNXvILhyFZLVSkegk+Wv76Gpuoai8olMnZRP6oo74Mw/ATqs+guYnTD9OtA1IxykeTvYZkD9BqhdZQSE5E8fFCRI1liY/zPjHsTBc0+gGZ74Gly1xPg8HaqjGl78NgMSpt75I5SfZgSiCp8Is2LBZO0l3L2e5Q8/iSzLTDj1PBLxEsw2FXqawJ35/h0JwqdEjmlYbGH2bl3M9mWv4/KnMuWcS9D0fCIJM86eOuO+VvyQcLG2vZ/egIUvjHhSx2oaGCrnthkBXCOy3bT0RGkNiBASQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAE4f0dlxASSZKGASMAjyRJ5xzylhuwHsP8pwEtuq6vlyRp7lGm+zrwdYD8/PyPMuSPbviZ4MkFV4ZR0BILQOpQEtEkyYTG9C+V0FzVjWJSGDE7h3AgdkzdtoXbuHj4xWi6Rn2gnpOKTqK6u5rWSCtOixMUGdlmJREMIskYQSSKQmqui82La0nJcRIJxHCn2UnGE7DrZZhxA8WxKI1lY/npxnvY37ufBdICLim4BFka+PREor2QiLBHH8E7zzxBeuEQkokEr9//e0678VYkKduYbvxlIEtGYalihs2PQrDZ2CZHoiWg7CRYfJvxhPtoD5gdUHLCh/sdfI58pvbdA6x2iVDHarYvfQWAaDDI+pf+zsnX/Ihlj1YT7okxdmE+0XAcp89Kw+6uvuItb4ad1v1GeEzp5Aw2L66ltz3C2IV5VG9pY9MbIYZOyaTdN4aGmnyyciVKZlswSxEcp+dj6t5rFI7lTDA+Q5+gvkCSCROwjR9PoqWRZEcbeiyG4vWipKQQ218DgPv0M2j/299JdnbinDcXNT2duuu/BZJEytevwjFlCmp6Oo6ZM5He8+TR/2Wf5v7rtGah0MW0s4tp3teD2aYwco6TQCBAeXo5HYkIA/agUDvseB48OdDbZByfShb2vR1ct46G73yXREsLsstFylVX0fXYY8Tr61EcDpKxOJlFXipWNeLPdtDZGKR0UiaJRPJ9x2rOycGUnU1ncxBzd4xkvI0X//xrvBlZeDIyWf/SU5gsJqade+HHv6GEw/osHnsBrCUlWEtK+l77gR9P/TH3brmXp/Y8xdj0sfxkyk/w2w6E3+iacb49lK5BTxPtTYdkt0mgqDJNVd04vRa0pE48liRb8+LwWnClWjBbYrz7xP0E2ttIyS2gbX8VL/zxF3z1jjvxpKWzd4NR9F42OYNgZ4Tdq5v6+l5wkovU117DPn0azb+8vW+xidZWOh99DNfJJ6NmGNdF/ssvQzJbSHZ2GmE/URPuk06gdefOAavhHDMEXv0BzP/J4KL5L7DP6r6LooLJYVx/TroSOvYZ++J7tVbAhK9BNHD4ABKArlqwOGDdQ5CMwchzjesCRTWKzwGTbGJR3gLu2f53ntrzFL8v/DFr//5AXxfvvvEScxadSUbh9+ksm0dKnovmql5qtrYxam4u9Xs6Sc02Ake0pE5zVQ9lkzNQTDI5ZT4chV6iUoJEYxNqex15ZQXU7u7p61+SwJthGzDs3o4IDXs6sbnMNFf3UDAihba6XspnZNFc1UPppExqtrXT2RhEMSsffZt/Dn0m9t9YEJo2G9cFPTuI+c7F6YuTUeTG5bey7pVqZFmipz1MWk412956FV9WDh0Ntbz6l19z6vW34Upx4PGrKKpMMtG/n/tybPhTdUriJczOm42qmFDeucfYj0+4FZzpAIxNH8vO9p08t/c5MhwZ/GrWrxjmGwb16+Efp4KsQsYIaN8LBTMhd4IR0Ccp0FUD/qIPt+7KYT5zZgfIpg/X3xfIZ2LfPQZZJV6i4QToxuG4fFoWG16vIWuIl0Q8SVaJl/JZ2ax5bl/fPK4UK66U97010ydjiIc961rwpFrZvLgWgKFTM1n9fBWzzisl1BNj7/pmCkelYisoJpaZibriBbrq63j6r3cR7jWOpbs2bKKjtYMvTzkFmyRD4SxYdS8s+Bm8ew+EO8FbALtfNT4/T38DUkuN84uWgEtfgvTy/oE506FsEehJyJtihADsfAEaNxmfpfeGkES6Dh96Gmw75m3xefFZ2n+1RJzO+gqW/eevpBUUoes6S//1F0765vep3a6Rlp31OUkfFo6Hz9K+e1AsobNn1etsefNl0gqHEO7p5uV7bues791BLJoLtWvghJ9DqNU4b3fsM87jgvAJS2gaJmXg/chMt3F+z/PZSXNZaO0VISSCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjC+zsuISTAUOA0wAucfkh7L3DVMcw/AzhDkqRTMEJL3JIk/UfX9YsOnUjX9b8CfwWYOHGiPrib4yh7nFE807LdeO3OhvP/i8VhonRiBov/2V9ku3edxCnXjD6mblNtqSyrW0Z1dzUus4s3a97krJKzsCoHCoZUFclkRlJV0HVkswlJhkBHhNKJGdRsa8fhMZOW70SXVRhzIaz7O3vOmcSNb11PTDPCUJ6vfJ5wPMwds+7Aqh5SjOTMIDruKvZvbmf2Vy6lvmI7isnEyLkn0F6/D3dGJsmEhm5PQXrlJmMeWYWTf2M8zfhoVJvxZOP5PzX+tfvBVwiRnqPP9z/gM7XvHhCOa+xc8SYAqsnMlHPOp6e1hdXP/oshoyfT252OrMgkEzqxSII5F5bR2xEhrcBFqDuG2WocXuxuC73tEWRFQjUpdDWH8GXaUUwyG16tISXHgcWXwYv/qSUaTDD1jImk+kdjq92Mt+K3qPO+ffTwmo+ROTcXc24u1jw/8aY2HOPKafrdPZiHFANvoHi9JJqMgnvb6DG03nUXyDLp37uZrieepP3Pf8E8Yjjp11yL4vViLshHTU09LmP/NH2a+283HeSO9PL2f/ufKqyYZBZcVUaoO4TD9p4Qm5QSGHMB7F8FBTOMIsaIUXgYb26m4TvfIdHSCoDW20vrPfeQ+o2vE1r1DugaitOJ3hElq9hLS3UPGUUeJBlkaeCTZg8nEoxTsbKRNS9UIUkwbmEvs79yKa37qwn39jDt3K/QUl1JsKsTh9f38W0k4Yg+i8few2kONfO95d+jKWgcf1Y2rKSmu4Z/L/o36Y5041xpTzGK6Q+yuMHqpWCIxM61RpOsSMQjSUonZlC9uQ2zVWHcwnyScY3mmm6CnVEUpZu84aNQzWZaqisZNn02JquVrsZ6PGnpWB1Gkbo3w87uNc39y9Nh5btR5vnCmGr2D1qH2L59ZN38DRSrhuK/lkRLCyQ1FL+PjO/djCm/AOuo0UT3VtHz0kugKPgvPBd7ahRyb4amrcY1iwB8hvddq8cIjFn3kBEiUjQHot2Dpxu6CPYtgxk3Hrmvtt1G8MzUb0IsBFseM/YB75C+SZo79/GdlbfQHGomy5FFx449g7rZsXMrmZdeT9eG5exetZuCMVMYNbecN/6+lwknFxDojuLyW+luDSNJ4PRZmXpGMVnFHgB0TQNZQq+rZfKUMpA81O7qxuG1MOfCMlJy+q9vw4EYsUgCu8dCV3OI4nFpSJKEN93GrtXNNFf1ULW5jSlnDEGSwWr/YoY+fCb2X5sPRpwNmx9lt+vrLH+4P4zB6jQxZkE+WlJjxEwv8ZDEsJlzaa+rYeS8E9EScbpba/B0DCEzz8apF/l464VeettjpA9xkn+KmavXXINZNqOjk+fKY/IpvyZtzd/g+eshtYx47gQeqXiEx3Y/BkCgK8BNy27iv6f8lxGbH4cR54ArE2pXg5aEOd+Dpy7vD0bInQxW74db96wx4MqC3sb+tgW39IWjCEf2mdh3j5Ev047ZpuJNt1Oxqomx8/Op2tKGyapgsalkFLnZnWGjqzlMeoGLOV8ZisNjOWxfejxOtLoaLRTCnJuLmpKCL8PB1LOKCXZGUVWZRFwjmdDwZ9lprw/Q0x6heLybZHQva599G2dKGmUTJxGp3NUXQHJQa+1+uk86C1s8DD31MPws6K4H1Qon/hKat0MiCj2NMP9nsPUxGH4GWDxQ8+7AEJJYCLY/BW//rr9t1neNgFNbyuCVc+eAf4gREHCQYgbfhwz5+Qz7LO2/ssVK7baNzLnocuordiDJMiPmLKBp71aGTPoykVBShJAIfT5L++5Buhait7ONKWefR/3uClJy8vDn5NHT1ojdkwvlZ8Kae43jF0D+NOM7myB8whJJHVUeeH/KYTHuzxak2EkVISSCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjCMTouISS6rj8nSdKLwPd1Xb/jQ8z/Q+CHAJIkzQVuem8AyWfO/pX9ASQAPQ2w5XG09DFUrGpiyLg0UrIdIEm07u+lYU8nBSMOUxTzHsFEkNGpozmx4ETaI+2k29LZ1bGLSDICgKSo6JEwnf/5D1oggOx2kzLnZGRVQpYlisakIssSXU0hPH4PrL4XplxNVaihL4DkoDf3v8m3gt+i0FPY32h1oYw9nwJ9J4sfurevec+alZz6re/T1R7FaleJyW4s064DkxW662D1fTD8zKOvnJ6EzJGw/p9GcWosCGsfglP/8L7bRfj4qSYTdo+X3rZWxp9yBhtffYFQdxcANZs3MOG086jeqtBU2Y1qkhl7Qj4puU5yhvqo39WJy29FNcskYkksdhVdh2goAUDh6FS2LKkDoHRiBqsOPIV72jnF7Hinka7mEO7UQiafNgvnfg1PeifOjOMXyiCnFWPx5WMJ/o2CG+aQSB1NouE0tEAvSmoqejRKot0oAnXOmU3va68Rq6zEPmkSlvJy6m64ARIJTHl55N59F9by8vdZovBhtXe3snNFiKFTMnGlWJFlifrdnbTXBUk4EsRSiozCXdkMnjzjaeqxAORPNR4Nv+dNmGmEQCWaW/oCSPrE41hy/PhPlAh7XaBrdDYF8Wc7cXgtqCaZ9oZecsref/9srOzmnSeNsJRJpxfhSWvhlT/9jnjUOH5Xb1rPjAsuRjWbP96NJHzu1fbW9gWQHNQQbKC2ZQvp9iyIBmH2zbD6fuisAm8+zLgBQs1ku2NMPqmY9Uva0JI6ilkm2hWjYFQKviwHW5fWkV3ipbMphD/bgaZZ6GpupHrTegD2b91Man4hJZOmAVA8Lo0tS2pJJgbX/4V6Ykgl6cT21yA7nWiBQN97prxczFIjCbWYZFs9HQ/9DT0SQUlJIe2661BT0zBlpJP1i5+T8pUzkBo3YDZ1I+ltsG0xlJ8BycQnuJWFj03JAiM8bK8RZEY0AOf+A9p3g66BpkPuRHj4y1A0G7JGHb6feBj2vAHdtUYw3YxvQ9UymHZ93yT7u6toDhlhOMF4ENXjGNTNiDkLeO3e39LVbAQu1GzZwKj5ixh34kK2L29g+jkl7DkQqDNidg6ZJW7szv5CfOnAfz0vvkj8/vsZNWc+k869GGu6D9/otAHLCnRFiIYS+LMcpOY4CXRFCQeiZBa7yR+RgsmqULezk12rmzj12mMLIBQ+IZIEpScR9Iwn3JTOhEWgazq7VjcS7IphsSk4go2Yi/289bfVtFRXAsYxsWD0OHLKx6IldCSThbx8nXNnryaWOommdBvXr/oO1469ls5IJ5Ik4TF7qA42kWaywpSroW03rf58nt7z9IAhJfUkld2VjFBN4CgwPi8F00FSjeP7wQASgLo1ULsK/B8iKCGlGC55DvYtha79ULwA8iZ9hI0pfBZ50uyc8s1RREMJUvNcdDQGKRqTipbUiUeTxCIJzvrueGLhJHaXCcsRQpGSvb10/Ps/tP35z5BMYi4qIuePd2IdOpS0PBeeFAvjTypgzYtVqCaFwlGpbHxzP3MuLCPcs5Wl//hTX187336TM66+cdAyZEXBlFIAyVZ482dw6p1Q/TZMvQYW/xycGTDsVGjYAIWzoWiWMWOoA6zDB3bWtrs/gCRtGAw7DRQVFv4cMg9zvrGnwFn3QfUKSEah8i2YczOkln2YzS4co1giTu7wUbx27x/72vauXcXJ134Hq9OEzZr89AYnCMcgiUpKVi7vPvFwX5vF7mDRdd8jFtFANfUHkIBxj7hxixGCKgifoHhSw6TIA9qumFlEJJ7ktNHZLNvdRl1n6FManSAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgvB5clxCSAB0XU9KkrQQ+MAhJJ9LDRsHt+1fCYkYBSNTqNrcytqXjGL33KE+fBmDiyYPx6baaAg08NK+l0izp9ESauHUIaei60YxsKQlaf3Tn9HDYQC0nh5ie/fh9A9nz7oWsku9xKJJejvC6JIPZn4beptxKFZjLM5csp3ZVHRUAGBRBz8NWUpG2Ld+LVPPuQBJNv6wWdOSNO7dhWJOJR7TiWWMxbL4Zoh0GwU8Ey8zCv9JG9RfH9UCkR4ongcb/w0WF0y4zOhDOO7sHi/zvnYVz//hDhSTuS+AxO7xMvak09CTSYrHdpOe78HuTWXzm/sJ98ZRVJlxJ+bjy7Iz7+Jh9HaGmXJ2ISseqcThNcIVdF1HViSSCUjENQAyiz007O6iqzlESo6TwtGpvPWfvSQTGtllHqaeUYjT58CVYj0+G0A1wfiLMLXuwrT3TcyjG4kPPwvHxLHU/+Q2FJ8ROmEZOoz2++4DwDFjOq1/vKuvi3htLc2//g25f/4TitN5fMb9BWPRdIZOyWTnO43sWm2ENBSPT8PhM5NmSkPubYFlvzUmHn42jDgb9r5uFCF2NkLWaKPgEFB83kHBCUgSJqkZZd9LaOlfJalEcPmtdDYGcaXY6GwO4fRZ0ZPa+461rqIDAE+6DbffQndLMyPnnYDN5UHTkiiqyr71axhzwqKPdyMJn3t21Q5Ami2NIZ4h7OveR1u4DXvjZnjzPFhwC2x/DgpnwsgvQagN3vgZLPwFtrZ3meDdSdlFownnDaN9X5LGfV0Mn55N9ZY2fJkONi2po2RcGiaLQkoOxCMRZl5wCfFYFNVkpnFPBbED1xXpBW7OuWkCPW0hJFlC1/rDSPJLHSRXLcVy3jn4r7yCtrvuBl1HdjrJ+vG3Uff8gUjhjbT9+S+gGZ+ZZHs7bfffj3X0KEwZ6cg2G1ZTM3glWPc0BJqMABJdN0IDhM8+kw3cOZBSYoQkqBZY9xC07DDez5vaX4DZtOXwffQ0QPseGPdV43cP8M5dRhG588C1pKZhP/BeviufTEcmEZcdm9tDuMe4dlRUFYfHT2pBISPmLiARj2MyW9i7dhWlUxaya5WEK9XCqLk5pBe6ySr2YncODILSdZ2uJ54gXmeEp0WWLiaydDHZ//cHGD2wSD3cEyPYGcXiMNFWH6C3PUxWiRdZUVj7YhWFo1Mpn55Fw54urI7j9jVMOAK9s4YmaSab36wkHk1isihMWFTItmV12MwaHVdcgOk/D5NVWkbJpKloySSKycTWJa9hstjoO/plj8Vu82Hf8zqtnQ4uHn4xd224C0VW8Fl8dEe7+cPMO2DlA+ArAlcWVsVKrjOXVHsqbeE2qrqrAHCanDD0VFj1Z6hfD+WnQ/Z42PLo4BVoqfjwK5821PhP+J+WU+ajuaaHxr1dxCJJOhqCeNLt9LZHcKfa2L+9nZLx6ZgsRz4eRbZvp+3uu/tex6qqaP2/O8n+450oNhtmu5lR83Jx5ZjobA3iS3Hh9FlxenXWPPMC08698MCcEslEnO6eLoaNn0zFhjV9fU458xy8iVoI1BvH/Je+A6ffBXVrYfgZRlDI5kdAtRohVyllsP0pI7R02GkDBxxqN/4tO9mY9t27IRmDkoWQPqL/HALGsipehKevhEQUFJMRgDJkHsgDC7iFj5ciyVRv3sjUL12AJBnbOpmIU7tzK6kF43Hao5/yCAXh6PRkkn0b1zLj/ItJxuPIikKgs4NAZwdeWwEk44Nnattz/AcqfOEkNB1VGfi9Oc9v59dfMgIQU51mNtV2fQojEwRBEARBEARBEARBEARBEARBEARBEARBEARBEARBED5vjnf127uSJP0JeAwIHmzUdX3DsXag6/pSYOnHPrKPW/ECI0TD5jOeYg1gT0Gx2UhEE3Q0BBk6NRNZlqja3EZB6DB/oH4YCS1BriuXseljqeutI8+VR3OombhmzK9FIn0BJAfpwV5idg2Hz0Ldrk5kSSJjiJtkQjNCPzoqGWo+l59O/Slb27YSjAX50ZQfkWHPIMuRNXAAkR5Y+xAlk05g+SP/IhLoBcDmcjP3kq9TX5kgmdBIBjpg7FfA7ICeRtj6FIw69+grJ0kgq8Y8U68xXvc0gL/4mLaN8PErGjeJC269g/aGBgAkSWbKWeex/OF/kIjHAPBm5jDlnBsI98YxWRWmnDGE7W/Xs+7larwZdmadV4rmizDl+nQIx5h5YQkVy5son5HFliV1yAf+OD4118metc0AlExIZ/Xz+wAYMSubRFzj2Tu3oJhkpp89hNLJmVhsh39a98fK7ICc8WB2omz8D0rXEizuFIp+fwNxOQPn7JnEGxowFxUSq6pGiwwulgqtXk2ys1OEkHxCMmWN1Q1BIsE4w2dmE48mqd7SRt4oL5F4hJg/jYbZ3yFzxwvIO56BIbONAuDeFpAwihMl41Rozssj6/ZfUv/dmyCRAEki/bs3YrE2wLwfozX3Ivkk7B4zjZXdBLqiaEmd9AJXXyDT0XgzjCCJvHIfybiGKyWNrUteoaO+FjCK5ed+7apPbFsJnzOJKHTsA02jyJPNr2b+is2tm9nZsZMF+QsY4xtG0Yr7QUvCm7fAef+GeBgk2TjvlpwIkgLhLuRwJx50OtR80H3klPmIR5N40+1se7uOyacVsmVJHTvfbWTeRalkFpey4tF/9Q2lfOZcVEt/MENavouUHAenfHMUyx7eRaArSkGpg9GpDUg2E7Zx43DOnYtr9hziLXXEPRLx8HYIdZAMxvoCSPpWtakJLRLpb1CtRvH9mAtASwA6hDs/6S0ufJySMYiHoKMSvPn9ASRgBJOUnw5n3gvurMMHzHTWQqgDbB7orIbKpTDzRjDbQVaMaZq3U/TOvfxm+i9Z0bSa2t5aGq09zPjON4nUt6LHk8RSzVhMbtDhncf+09f9lLPPw2xTmXRaEdklPrJLfH3vRRNRWsOt2E12/FY/AOGNmwavYlfXgNexaILejhjB7ijN1T3ous7QqZnEIgmiYeM6vXpLGxNPKWTiKQVYHeZBfQqfsHjYOK4iga+QbutIdr3VwdCpmTTt66atNsC6l6oYd1IBbqmLbsBsUWjYXUFrjRESIisKs796GVpSwWJX+rrusXvp8WSSak1he/VzXDDsAkyyieZQMwXuAmqDjTD9W7D1CZhxIxoaFw67kFeqX2GYfxhnl5zNW/vfotxfDlXvGJ+LkV+CfctAdRhhTKrZGLskQ7QH8qd+KptR+Hxxei04/Vacfiu7VjUS7IpisqrEIglUs0xXU5i0AtcR54/U7h/UFnj3XbTOThSbDQCrw0T2CDcvb3mc8b6JlE7ORlJkxpywiHce+zfRkHHrx+H1ccKV11HeHWbo9TfTGwrgz8wmIzcLpeJJI8Cq7CTInQSKGSwesKfCiv87sORuWPxzOPNAmFnZydBeCUPm9A/OV2gE+Yw61/i8z/+J8XnpaTCCTuf/tD9gpGMfPHu1cc0FRmjAS9+GnAmQMfyjbnrhKFRVpXDMOJb960EiQSMA0u7xMuurlwG6cX0rCJ9hJhVGzlvIsn8/hJY09tf0wiEUjpmEhAwmO8z9gXE/WFKMkMjc8Z/yqIUvgkRSRz3K/Sm/w0xnKIau60gi5FMQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEE4iuMdQjL9wL8/P6RNB+Yf53F88tKGwdI7oH2v8drshK8+ga7rhHpjjJ6fR8XKRnRNZ8SsbKLBYwshsSgWGoONLK1dyhDvENY2r2V4ynD0A0+CN+flgckE8UP6CwXQdZ30PBfRYAJFlXF4LMaD5RNRyJ9KHJ0HtjzAxMyJ+Gw+bn33VsyKmWvHXsupQ07FY/EYffU0otQso80+hIwhJWSXlQM6rTXVtFRXkppbRLAzCrnZsOUxo4DUPwQmfx1CneDMOPLKSQq4Mo0io94moy29HApnfKBNL3x81M695Cy7Fnv517B7vKTlF7Jnzbt9ASSu1DTKpk5HkloZPd+Davay/pVqwr1xPGk2RszKZuuyevSkTtFEP29pL/FE0yN878wfM9RfRlaJl2B7iNyhXjobg6Tlu6ir6CQRNwrUbS4Tqllh+3IjBGX0vGy62yIs+WcF+SNTyB/ux+W3fvIbIq0MvvYCdNUgPX4JlpRSLLNvwjpHI5Y5heSCWdT/4GfIFsugWS0jhqN4PJ/8GL+gIqZ0VFMPJRMy2LO2GbNNZeIphQR6IhTlFXHlmluJJCJcPGYRF3RPJ6OzxjgO9dQbxxxJAaW/QMO1YAFDnnmaWEMDamoalkwX8is3wt43MU28BySJSDBOWp6L1v29pOU5iYbi6OjvO9a8YX68GXYsdhPRSIJkPIDZamPauRei6zq9ba3sWL6U8lnzPrkNJnw+9DbD8j/A2gdA14ie+Ase7VzD5rbNAGxu3UxFykhm+fKx1q42itWbt8PSX4EzHWbcCCPPgc4aUA8cl0x2TGEfoe4Y7fUBLCVenH4Lw2fmsPKZSrSEsQ/Hoxo731nGhNPORjWb0TWNHW8vYfTCRQOGKCsyhaNSSf+hi0hHL6aOBhQtH/MZd6B6jWNeY46Vu5tfZvGWxeQ5c/nJKbcxpirIeyl+P6bs7P4GXYNIl7E+YFxDLbjlQCCJ8LmQiAE6pA+HvW8Ofr/mHejabwSMnPJ7GHEOmA45h4ZaoHo5tFYY19Szb4K69VB8yFeG1p20547ltxvupD3SDsCm1k10lJ7LttA29nTt4ReFt5MMJAgHeph27lfQdY3OxgY2vPICxRNnUzI+feCwemr488Y/81rNa2TZs/jR1B8x1T8Ky/DhRHfsGDCtOS9/wOvuljDxiBHGZ/eYyS3zsX97OyWTMuhsCDD1rCGsfr6KUE+UsQvzPsrWFT6M7npY8kvY8ojxesLl9BZ+n3CgjoY9XeQO8zFkTBprXqoiLTWOo3Uf3UCwsx1Jlo1ztaYT6Gxn+9tLmHPROGwuY5/d1LKJX62+gx0dO5mcNpaLRl7G/Vv+SjAeJNWWyuL9izmz+EziKZMxjb8E3ZnBM9se4u6Nd/cNb0XdCv564l/JcmYZ3x8zRxqhKTkToKcWRp8PT10BkW5jBm8hjPnK8d2GwueSw2PBnWplz9oWhoxNp35XJyaLjJbQsXhNvHzfFiaeUkjx+HSsjv6Qx3gyzvL65Xgsvdjf06dtzBhkt3tAm9vs5sSCE/nGm99gTup8vindSNO+PbhSUhm36HTQIdTTRev+KkpHDMVXkIqSNwcCLaDaIGscBJuN0JElvzQ6Pfef8M6dg1eqejnkTTLuGfgKB74nK8Z1xJOXGYE9o75svG7YANOuM0LNHCnGtIEWiL3nuiQZN44XIoTkE5WMxWjcs4vc8pGkFRYBEg17KuioqyM1bwIcw3crQfh0Wdi29E1GzFmAw+tHkiUq160mFg5hsmtG4OmO5/uD+NLKYeKVn+qIhS+GeFLDpBw5XMTvMJPUdHrCCTz24xDuLAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIHxuHfnReJ8AXdfnHea//70AEoD6df0BJACxAKy6FykZIy3fzbqXqnD6rPgyHWx6sxaHd3B4weFoukaJt4STi04moSWYmzeXob6hRJPG03stxcVk3XYbqAfyZUwmHJMmYrGpxCJJMovcB4rmE0ZdR+tOUMxURtsJxoNk2DN4fNfjRJIR4lqchyseZlPzpv4BhFqgYCZOfzqyorDyyYdZ+eQjhHt78KRnYLaqxKIJtFA3TPmmUTQ6+nzY+ZLxNOOjUcxGcdDBABKAlp3QXXdM20b4mMVC8OZt0LwVn0Pl3AvmMWzyJHraWgDwZ+cyat6JbHj5eV798+/Y+vof8KV3kjwQIDJidg7vPrWX6i1tRMNxIl1JZkVP4Z6Sv9MRa+fH22+mOmULxaN6OaH4BcaPbGXc3BQcXguqyTg0ZQ7xULuzA4CyyRk07O1i0xu17NvcSvO+bqq3tFFb0UGoJ/rJbw9fARTNhjPugfY9ULsWdch47M5WXJW/pOiWL2MtL8R7wZf7ZpE9HjK//iUUVRTOf1Ja9C4sDpXtb9eTmuvE5jSx8tlKfKlOfr/+93RHu4kmozy471neTM2F9JEQjxoFh8kooA+ocZNUFUtpKa45c7CNGI7cUdFXQG9tfBLZZiUajGFzm8ku82L3WAj3xo46xng0QVt9L4GuCCdeMYKiMWmoJhVdk0nJy2fVU4+x6qlHady7m7KpM4hHIp/gFhM+F2pWwJr7jaJZoJp4XwDJQZvat1GdWW68kGQ4+BTj1DLjvBlsA0+Osa/HQ/Skzad1TxJN0/Gk2VjzQhX1u7uwuUx9ASQAsXCUyWd8ie1vvcHqpx9j4ysvMPbEU9GP8ER6u9uCvzAV1/jR2CdO6AsgCcWD/GbNr3lz/5vo6OwP1HLNqlto8cdJv+5yOPB0ZslqJetXd2DOyurvNB6CfUv7X8cCsPkRYz2Fz4dYEAJtoFigeAHIKgxdBBMvh9RSSBtqBJDEAvDcN6H1kICPnnpYcSe07TZet1YYoTwZ5eDOMdpqV0Owlb12V18AyUHPVj7L18d8HVVWKdKGoQMOj4+VTz7MqqcepbOxnomnnQXEMdv68xijiSj3bLyHV6pfQdM16oP1fGvJt9ibbCTzZz9FPiRQzHfxRVhHjRyw3GBXhGRCI73ATUahm87mEHnlKVisKrU7Otm7roXSielklXix2ETB33G39w3Y/LAR2qTrdJlH8OqDFXQ1h8gs9tDVHKK2ooOi0al4qMFelIbrxAVIWpLM4lLjXP30o9RXbGfE7PkkkxqqArW9tVy7+Fp2dOwEYE3rJn6/7g9cPvJyRqaOREfn4uEX0xvrpa5lK/iG0Bpp4/nK5wcMrzfeS21vrfGiYCaEuyEaMMIcFQts+Gd/AAlAVzXUvHucNp7weaeoMr5MO7FIguxSL75MB/FoEkWRUEwyS/+7i7oD37kOquio4Ma3buRZdSvSOf1BZIrXS8b3v4fidA5aTn2wnp5YDy80PMv+5B5Uk5m8EaNY/czjrHr6UWq2bMSbkYk8bR7K5odg9QNw30z452mQCINsgk3/BZMdUoph53PgzR+0HOypRliIzQeOgWFSbH0Kdr9i/KxrRjBq3mQY81Xj3HFooJk7B6ZcDUNPMc5TYIS3qeIY/UlLSBq+rGzCwV5WPvkIK598GEmSsHs8yIrcdw0sCJ9VCS3CiDkLqN2xjVVPP8qqpx8jq3QoOjK6phthkMPP6p+hdSc0bvq0hit8gSQ0HfUoISQpTuPefHvwONxHFQRBEARBEARBEARBEARBEARBEARBEARBEARBEARBED7X1Pef5OMjSVIGcAeQrev6IkmShgPTdF1/6HiO47jo2Gf868oCi8sILWjZAYko7fW9TP9SCTVb24lFEkw8pZCOxiCapiPLR/5DYQBZktnRvgOH6uCbo7+Jjk5ruBVVMn6VkqriOf00rCNHkGhpQc3IQM4tQF7XSkdjkJQcJ+g6gc4I3gybUaS8+OdIX/0XkzInsapxFbNzZ3N+5pnItd2076tC3tVKwNmO058CTTug5ESUyjANu3YyYu4JqGYzletWE+zqRLXrZJd4SZrCRgGpJwd6mmDmjRALD1qfYHcnnY0NKKqKLz0da/P2wSvdtndwm/DJC7UT1WRaTnmGjs4QVpuFXF+SkVMm8u6LLzNsxhxWPvkI+oECIVlRaN63hWlfmsr2t4O01QXQdXCnWsku9bHqOeMzkVXsYdy0WeR5i9nVtAVbjpVRxaPIf/ZrMOarnPvlhfREG5l8Sg7V23twp9roaAjiSbOxe00zAONPLKBmWzs7320EoHh8GpNPK8KdbkNVlY+86tFElLpAHRISPouP3ngvXosXt8VtFKldWmyEDD1+MZx+N4y7BMtrP8Qy+yZsOevx3nEVyZiOVjiSrt4IwcYY3twEJstxPeR+ISiamUB7jPEnF1CztR2TTWHGuSV0tgSZnTObof6h6OhUdVXxdMtqzhoxCUc0E1q2gCUXEhGjIPlIwp3Gv45UlFgzSAlUk0I8kkSSJeKR5IFCucP3Ee4NUb1lB9Wb12C2OvBmjyBzSDGVG1sYOkll18rlDJsxG4vDSdWmdTTs2snYhacAkIjH6WyoIxLoxZ2WgSc94+PefMJnVeVbA17KkoRNtXFS4Umk2dJoDbfyatWrRu6IyQYzboSdz8NJt8OeN43AjsJZMOkK0BIEs+az8m2dveuN86ndbWLuRcOo3dmBJ83atxxFlUnJ9fHKPX8kJa+AjCHFtNfX8e6Tj3D+rb8+4nB72tvobmpE0zQ66vfT29FO7qjh1HTtHzBdQkuwO9nGCTyH4/bLSYSSmMafhHnY6IEdRgODF9JaAfrhg1CEzyA9CXYfLP4FnHYXnHonrP871K5BG/c1OtLnERrlx6WE8W5/CKmzGrLH0V5fS/W6lXRGT6Bo0kXktLyCtfp1CLYaxeI2L/Wt22moX4knawyxeDfl/nJm5sxER+fturep7q5mb+devjH6G5gjdqKBauKRCONPOYOmyj007NpJelEx3szMAUNuCbewsWUjV4y8AkVWiCQiPFf5HPu69zF87GkUPfkEsf21KG4X5uJiFLu9f3V1nUgoQSJmXBOFe+N0NgXxZdnRdfBm2MkodGO2qeSU+Y7nb0I4aMfA0I/OZB6lkzNRTTL1uzuN31GRB3/G/7N331FyVHf+999V1dU5THdPzlkzGqUZ5SxEkgCRswkOgL02Bq8D67jZ9u9x2uCEEzbYGIwxIBBREkEoopzT5JxnOqcKzx8tC2sFeIMRtrmvc3Q8PdNddevecoWmvp+rknPgq8gDuym65m9pt9o4sOEFShqbKKjKHhM79+/DVziHdEqnJ9lDOB1mZdlK6gP1SEjsG9lHPBFhgdxEIGcRYTmD1+8lnTuD1kmTvi3PcUfsPAJN9XS6xhlMDvF8x/PIvw9aymvIFi8nx+GlL0PDZTDRkf3bH95b/j6oRxD+CFeODUWV6TwwSlljACSJTCobTFZSn0PDgiIOb+2nZnY+0qlQs/ZQO/X+egqL6mi9KZ+yixcTHh9g2qyLcFTVv+V6ZGQskoWV5StpN08yp3Eaz333m+QUFFHVPJtkNMrhTRuxXHwXo+VLKB3ahzM6BNEh+PV1cNWPCM+6m26jkoH+MUrUIGVVJXha10Pm1HcJrjwoXwAvfhGmrEarWsl4ZzupWAxfbhDvyRfObljX1myIjz0nG3jqKYCJbtj5Uzj0OPjK4KJ/gTd+Bs0fgIEDUL3i3RkMAQAznSYVj+EvLGbq0mwu8WDrCZAgGc+QcFhxvMdtFIR3Yioq7Xt2UlhTx4KrbyCTSjLe34vVZkGWzWyg0vLPQ9l8CPVAuB/+y72RILwbMrqBRX778E6/MxtCMhF/5zBdQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQTjXFfG/AH4OfOnU6xPAb4C/vhCS6hXgyMkWkCUmYNbN4AiA3UtBZZz1vzhCQYUXq93C7uc7WXh1zR8NINk1uIveSC+XVV9GibuErkgXsilnCyX15On3SaqKvb4e6rPFQbpunK6Pj44nsVhlkjEtO7msI1sIWZ/WWVW5ionUBIlIhK7fvkRsYIhASRmH968l1trL+R/+GGpOBQwdIjbpY86aqzj48ktkkkkal67AMHTa9wwx3p/gwg/Ww8K7YfQ4NF6RLSzizOLhsb4env3Pb2J1ODB1HZc/yHnn/R2etbecueF1F/wfBkL4X7P56JtyNy/99IfEJidAkph/1Q00Lr2ATFpD17TTASS18xbiLyzm8GsbUSybmHHhdchqTfZvsws48HLPqZ/zkRWJ1x4+ASZUzpqDy2vjF+H9LLjtt8zt3I37mRtwA3mFc6ic+1EShcvoOzFxulkOj4qW1hnriyJJMG9NNWN9UZ7+z30U1viYs7qS3FLP/2hTxxJjjCfHeaXnFSyyhRPjJ3il5xXuab6Hp9uepiPcwT3N92BX7MiyzPTgdCqrV6Je92C2qG30BJz/9+CvQtn0LRx9mxk970GeX+cmPKoCR5i+ooQ5l1bi9Nj+JMMjZBV48ogWTrBzXQdFtTlk0jpbn2jjwg834ow7+eH+HwIwPXc6N9dfj80wskWMRbNAsWYLEfOnvv0KgnWw4gsQH4PIACgWHB6TvpMT5OS7mByOU1STA7z18bvnyEFe+P7XKaqtJ5NOcfjVZ1h6yxdoubiCkc43WHTtzRx8+UUS4TD1C5dgsdpIxqKoup19Lz7LkU0v4wnmMjk4wOq7P03JlHdoq/DXo6QF9v7y9MvKoRN8cd4X+NHBH9Mb6aXUU8qXFnyJSnc13L4Mfncn1F8MW7+b3achG0oyegJKWhhOlNG6exTIHkNbVlVyeFMv4dEkrhwryz9Qz2sPn2DBFdWM9/Uz74pr6T58gIMvrye/spplH/ggmWQC0zCJhdMoFgnHqRmUh9pbefY/voknL5d4KERhTR1Wh4O1X/8an/nER/jb1n86Y9O8WJBHD2IfPQg5lVD2QZD+y/9/8qac3SdTLs0WHgt/GUw9G8g350MgmfDcZ0BPowfrORorZf3/+w6GrqHa7Fzxoa9TGpxCbGSYx7/6FaJj2X11/+uw8urLaPYcBEnGLJzFodGD/N3rf8fn532eoVSYoLOAOn8dPz/0c5BgTfUabmq4iUePPco/Nn0NNWZBt6qkEjEOvrye0sYmlt78QQ6/tuF0of3v2RU7H2j8AD/a/yPiWhyfzcdHZ3yUoD0IgLWsDGtZ2VtubmQ8SSKSIZPSCY8mGOmJMOuCcmQZhjomCY0k6D0+wWWfmIknYH/LZQjvssol0Lbx9Eu7z0m6T+PI7iEKa3yk4hr7NnRx6c1+5NZsiIHy6peIzXuAZR/4EN2H9mePiVXV1M9fjN1loGUMPDYPtzfdzuHRw9y//34A7m38BPYt/Rxe/yIAnmAuCz5xF8mRGK9849v4C4vJr6pl+NVdFC+bwf2dP+KWpluYknPq2OfOheIZsOmb2dcFUyG/EaZfd+a9ZUHTOes+4S+by2sFw6RqVh4n3higrCGIljFIRDOkUxrHtg3QfFH5GcfFfGc+M/Nn8pODP0E3dSq8FdzSeAvTit7+XDwlMIX75t7HE61P8FLXSzxY9P+YccFqwOTo66/i8HqZvvJiHG6D/nQta+17uXPFF8nZ9yuY7CbhKGP9wb10HloLwAFg2tLlrPjQq1hHDyIlJ8BVkA0PmXcnyY6d7N60m+1rnwDTxOnL4cq77qOo70YwtDcbVjg9G2warIbEJOgZ2PJvsOuB7N9jozB0CK68H9Z/BS781z/5GAhnUlU7OQVF9B45xHh/L5IkYxgGZdNmEBntZ9sTcS76sBt/keu9bqogvDUtQ+2cBfSfPMa+l55FtdmIjI9TMb0ZLdUHdaugcxMsugd6doAzCCWz3+tWC+8Dmm6iKm//3xeCrux3kmNREUIiCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgvLO3nxrv3ZFrmuZjgAFgmqbGf02m+GvhCMDr38nO+n7kKdjwj6Bm53KdHImz+JpabA4L6aRGy8UVTAzGyKTfvitG46N8efOXcakuanw19EZ7iafjRDIRopkovdFeOiY73vKziiIDJrmlbsb6ogx1hfEG7eiaDjYvADGrnW/v+jaRdARfzEJl/TTq5i9Gz6Spm7cIV46fiYF+CFTC3l+SV1bJlt/8CtXuwF9cwr4Xn8NqdxIPa8TDabY82UlSzc8W8EcHszN+mm9un2kYtO3eQcOiZciyjGp3UFzfwGAqB2belC1Ili3ZB/YrFv9pxkT4HwnHUmxf+wSxyQlUm51lN3+Q0GA/L/zkfuz5JZTNnAmShMVmI7esgp1P/454aJLI2Ch7n3+EggoNd44VWZHQMgZIECh2ceKNITBh9uoKLBYLb9w/TN6GFlyTJRysmMPQwr8B1YE6uIu8zXdSnniWa68coqjCQrDEiTfXwfhgDICa2fm07R2mdfcw6YROsMhN665h9r7UxWBHCEM33nEbRxOj/OLwL3jw8IN8+tVP89CRhxhLjPFsx7NcWXslPzv0M46MH+GO6Xdw/4H7+aft/8Tx8eM82fYkt228k/9IdnFy3ocYOu8+jH2/hvg4LPscWuUF7DpYSHg0dXpdB1/tY7gz8q6O2ftRPJVgcihOy6oK0kkN1aaw6OoawuMJNvdtPv2+g6MH6Qh3owwfhe5tYJoQ6oWRY5xOaXorNg/sfhDe+DEcfQZJSyNbZLS0yeFNfWSSOhb1rU+lmWSSrv27WHTdzcgWC55gLouuv4VUrJeeI+O4cnxs+vUvkGSFQGkZh1/dAJjYnC7GerqRZZn8ympS8Rh18xfRtusNEuHwn7gHhT9L1SvPKFKL5Nbwrd3fpjfSC0BvpJdv7foWUXdu9jy74GNg974ZQPJ7I8fAX0Ek/uZc8tOWl7L9yTYyKYNpy0vJJHTSMZ0r/nYW3UfHcXjdHHp1A0NtJymoqmFyaIAdTz6Ganeyb2M36767n8f/v9207xshEY1xcuc26hctIZNM4S8sJlhaTrC0gqU33YbSPkFDoOH0ulcUL6Y+HoKln82e36/8Abjzz97+4lmw+hugOrOvyxfD8s+BRYQ4/cXIJLNF3ZGB7H6pZ4vcxutv46XfrMXQs8XhMy+6hLbOUR79z5+y5/mnmbHyIqQ/mLl7y4uvMrz022wr/gyPfu+njLy6m3+f9XV2D+7mydYnOTR2iKfbnkYzNTRD48nWJwG4oOwC/HouI519vPLz+zF0HX9RMR17d3Fsy2s0r1qD1e44o8mhVIjv7v0ucS1++vWPD/yYfOdb7KP/RWQsyXhflL7jE0QnUsy6oJz2vcO4cuz0nwjhCdqZs7qShCj2e+80roG8N49Hql1lpDfComtqUSwyWsZgxooykvE/uB8zTQJFpRx6ZT39J46SX1VNZHSEHU8+BqZOOqFT46vBIlnYNbTr9MeKIq7TASQAkbFRjj39PPpwiOW3foQ5a65G19KkolG8Yya3V97ID/b9gIn0m6F75DedDotEkiFYe/a9ZagfBg5AOv4udZrw18LutuLy24iOJymu9dN1eIzwWJJUNEPTkhLyyj04PNYzPqMZGo8dfwz91D18V7iLTb2bsFvePkgpx57D2ra1HBs/BkDYk0GS4MCGF7B7vDh9frb+7hFi46MUWXw0j9bw/MujvO7+IKOLvsp4DDoPHTy9vKrmOXgLS3jy/gd4dsMJ+tL5mM/flw3giQ4xUrSa7U/97vS1fDw0ycbfPUtyxgffbFThdLA4wFecPS+NHIFwP+x56MzG65lsgGpuPZTN+z/0tvDfI5NJJamZMx/VakNWFOrmLiARCZOOD5CIaBx6vQ/TeIf7NEF4D1ntTmxOJ4XVtTjcXgzDoGnZSvpPHCU01Iv++/N4/97s9cemb4DN/V43W3gf0AwDRX77/9wTOBUmOh4T9yWCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjCO7Oc4/XFJEkKAiaAJEkLgNA5bsO50b8HUv+lWHzTN6H2IvwFLl5+8CilDX5yCpwcer2PGeeV8nbPCOuaRjwdYzA+iNfmJWNkkJBIG2k0Q8OmZItyQ6m370pFlZEVmbxyD6m4hivHhq6b6FYfisNPr0Wh2FPMrqFdfKroQ+zb8DhWu4PiKVMZ7+slHp6kfsEScBdA3hTG+ntZfutHGDh5jGQsysJrb2RyaABvbg3j/TDaEyVp5mB//bPgr4L5d4H25gPO6WQCxaIy3tdDaeN0ACYH+3F4fZirvk26/qMYqTRqVR0Wd+D/NhbC/0o6EWew7QRWh5Plt93BkU0v03f0EBarjdKGJiZ6e7nkns8y3tND96H9pz839/Jr0NJptj3+S+rnzia3rIDcUjeR8STxcHYfKKzxMd4fo2P/KIoqU9tSyNjhDPGEj5Gaq4ldfRHVu74HFQvBYiNQW0NA6+HihW20R6ahKT56j06Qk++kddcwAC0XV7BvYzepmIYnYEfLGEwOxfHlOgiUuLA51NNtbJ9sZ+fgTsaSY/xw/w/52MyP0RnuZH7hfPaPZLfFY/UwkhghaA8yEh8hlAoxp2AOA7EBXul5hSn+Kbitbu557dNE0hE+c/EXqHWVEug38LXcTe+DZz/QPzkUh+nv5qi9/0xmJvDlO9i3oYfypiCZlMaOtR2suK2Oa4PXosoqJiaYsLV/K/HKa3B1A5u/Dd4SWHg3Y2Ytnc93EhpJUDUrl+K6nDf3l+EjEOl/c4US7N/YQ2QsCUD/yUlCI3Eu/HDTGe0ydB1N1/GXlLDvxXXUzJ5HJpnijbW/5bwPfpRjO6JYraMsv+XDDLaeIBEJM//qGxnr6SKdiJNOxOk9dpjCmnp8BYVIkkQiGiERi+Dwev9ov5imSaa7Gz0cRi0qwpKb+6fqcuFcCFTCTY/C8FFITtJnxAi1hSj1lFKXU8fJyZP0RnrpHz9O8cZvwdTLs2Ekv9d4ORROA0OD4BRytOTpP5mGiaLK1DTnsf2pNgB8+Q58hTUMtoUorrVTObMZSZIZbD9J7dwFONxewqOTjPW6ySt3Eyh2s/m3J7n4zipMIxv2VDF9FrKi0HfsCFMWL2P9j7/LBXd8gr+f9nccHT+GV3Uw1VdHYOsP0UMDIMsoOZXZ4mD7f9mnbR6YdxfUXghaAnxlZ79H+PMV7gc9BQVTYfv94CuFnApo/gBRs/T0PjN12Uq8efnEJieonj2P8b4eUrEYLZdczu51TwFQN28hG9a9ii+YR3F9I9279jJ85Ch5axrRTZ0t/VsI2oNMz5tOf7SfExMneK3nNT7dcB+RnhSqPcHcy6+h+9B+FIuFxiXnsX/9cwSKy5AV5YxmD8WHaM5vZkHRAlJ6CptiY0vfFqLp6DtubjqpMTkUR1EVcgqc5JZ56Ng3Qkm9/3QAWyKSYeezHay6U1wEvGdy6+C2p2DkOJgmqqOGpiVxtvyulbwyD7mlbtr3j+BakpsNPPKWwMwbMVEoqm/A5cuh/8QxyqfNxJdfyOTIMG6/h1zFhUf18PGZH8cwDRRJQWlPU1hbj83pou/YEbR0isFjx5h7yZWMdnex+ZEHya+qpap5Dul4giW5M5lS92V8PZPongSKwwGynA1m3PfrbAjJeMfZ95bbvweFM7IhJBf+E+RNeU+6VvjLoNoVVJuM3WWlrDFAMprBMEwyKR1Xjg2n98wQkuH4MKWeUq6qvYq0nkaVVXoiPUymJnFYHG+5jqHYEIfHDgMwM28mFq+b1v17WXrzBxnr7SY8OsLsS64gk4pw+NXNdB/cS+3cBaR0nRdePs7KW98MH7W73BRU1TLa003FjGYMQ6e9J4RtzcP4t/0LyvRrCE+4zm5DeyvJj/8H9qkXQ3wMZAX2PwJtL4MzAIs+BYlJWPF5MHQYb4cDv8l+OFAH8z4KLnHd/G7LoCHJChMD/UxZtAwTk9HODvylZeSWFmOxRug+PE56jXbGfbwg/LlIphNIiszEYD9TFi0hnUwyOdiPMyeANz+feOl8PM9/JhvEN+NGmP8x6HkDipvf66YLf+U0w0RVpLf9e8B5KoQkLkJIBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQhHd2rkNIPg08DdRIkrQFyAOuPcdtODcMDYpmQv0qwITIEHRvBdMgEUkx/8pqTr4xxFhfjNrZ+SRjGlraRPmDEcmkknQfPsjudU8hKwrfXf5P2A0rGUVHlVUssoWMkcHERDM0MkbmbZsjyzKR8QSuHBsOjxVdM1AdCnrLXSjLP41f1llZtpJCVyHR0ShTFiyhY/9u9r7wDHkVVTQuXkEmlQSnHxZ9koIBjWd/8J+oNjs2p5Pug/tZ9oEP09+Rnak2t8yN3ZuAZZ8FxQZ9u6G45XR7LKdmux3uaCNYmkG2WBjr7aawdgoTa9cx9NWvQiaDfcYMir/+NWw1Ne/WSAlvx2Nn3hXXYpomBze8gDsQZPmtd6CoKtt/9wjx0CSSJDN7zVXMXXMNO/TH8OUV0Hv0EAMnjwPQd+wwVbOPsvTGj7Lz2QGcp2bYLqnPYc8LXQDMOr+MQ6/1EQ+ncXqtTMsrIWIWsq/h/5Fr7aH0mZvANOCGX+GvKGb23v8gXHE7E9NdSBJIEqh2C8lYhlRMw+a0MP28UrY/1Yahm5RO8VO/oACX14Y9V2JQ6uW+TfcxM38mh0YPnbHJfdE+5hTOYf/IfiRJQpZkPFYPE6nszPRzCubwowM/AmB11Wr+fc+/IyHxt7P/lkdP/JYjY0dYXLSYO6Y2U1Sn0rlv/Izl5xQ439Uhez9yKi5GQgkaFxXRtmcYq93CvDVVJEIZtsa3cmT8CABu1c3fL/gydtOVneG8YBooKhNaMWt/NkAikj1+Ht06wIpbptC0pCS7AkOH/EZoXAOmiZY2ceXYmLqkGC1tYFFleo6Ok0llZ4k3DJ3+40fZ/dxa4qFJGhYtZ+aFl7D9d49idThoXrWGZDRCfkURvvwCXvjBN1FtNqwOB92H9rPo+luwWK1oWgaHx8tQ+0k8wTwGTh6ncekKTF3/o31ipNOEn32OwX/5F8x4HLWsjJLvfAfH9GnvziAI7w53fvbfyfXkDJ/k4zM/Tn+sn8Ojh5lTMIc11WvwDR0HuydboN6xCZquAsUKsRF45WvZ5di85F/yY+YsD7D79TiyLDH30iq0jM7cSytJJ3UcHpVDr/bRtCxIoDhD555ueo8ewpObz1B7K768AnLLp3N8xyAAkiyx6KoaEpEE7kCQQy+vp7i+gXh4MntcPrUJ7Xt38mSmlb0jewG4s/Y2LvJeyIF927CoFmYX2ygdOIRStejs7ZckCFafg44W/uQmu0DPwO4HoeVWCNZnCy/XfwX3/H9EVhRUm53KWbPpOrAX1WYjp7AYRVUZ7mqnumUesy66lH0vPUvFjBZyyyo4sPFFWndup6plLsGSMmQjjydSYW5suJH2UDZYrMJbwWXVlxHPxJEiNiyqic2e4ZUHHsSXX0AiGqZ9zy6W3fIhXH7/Wc3Od+RT5aviu3u/e/p3tzTeQtAefMfNHe+PkYpr2F0q7hwbA62TlDYGUBSJdEKj99gErhwb89ZUkxTFfu8tT1H2H+A1TEKvjbHgihqGO8N0HR6joNKLblrRG65Fya+GV7+G8/I5mIbBnheewe0PMnDyGHaPl9V3fx5kGJ2Y4KnWJ+mMZK9rKz2VfLviS8RP9JCMRph7+TUMtbdiYGAaJrKiUFzfSH5VDfHQJJGxEfY8/zSrbr+L9N2fZfD888n7209hLS6Gsvnwoech1JsNTyluhrqLABPCA9C9DRQVokPZc8BkT/a4GRDHTuFsbr8dh8dGf+skhVU+bE4V04RUQiMWSmJ3nxn0UOwq5oYpN/Dvu/8d3cxef84vnI9pmm+7Do/VQ62vlqvrr2Zr/1Z2hfaz5OLL2PHEo6QScXx5Bex9/hlmXXwpqs1G/YIlHH5tI7KiMP38i8lkNILFxYz191PVPAddyxAPTfLG3p00r1qDv6iYl3/zBLI0ldlTK8gpPTsspKh2Co5AIbhq4OTLEO6GktlQvhDG27IBPw9fDbHR7AdK58C8O6FrG1QuFAEk54giKdhcLoKlZRzZ9DKyxULdvEVYnS5SiTgOj0p+uRer7Vx/ZSkI/z2q1YHN4cLh9rL3hXX4i0sobZwGyEiyTCYDLP00aKnsuTqnAlKR97rZwl853TAxTbC8Xco54LAqOFSF8ai4LxEEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRDe2Tl5ol+SpHLTNLtN09wjSdJyYArZGtXjpmm+fXLGX7LSuTB0GDZ9MxugEKiG1f8fuAK4cjRe/dVxalrysLtV+k5MUlDpRfovzwj3HTvCwImjlDY2ASYMRLF50lBkJ2Nk+PnhnxNKhQC4vOZySjwlb9scWYF4OIXdqSLJEpGxJE6vFS1/BlaPjdzYIK90v0JLYQvzLU2c2LGVsqbpFNU1IMsKx7ZtoqC2LvsA/Z4HCVsuYP5VN5CIhElGwsw4fxVDHa3YnRU4PCqLrqrG/vxt2SCWUA/4q0B78wFnSZYwdJ3qlnkc374ZTJOq5jlo6TShJ58EXUeyWkkeOMDo/fdT9NWvIlutb7t9wp+eqcqYssQbv3sMgKklK9HSKWRFoWX15ZzcuY2pS87jxPbNHNzwAmVTp9O49Dye/953AHD6cphz2VVMDg1ybMvTNCyZjcvtpro5l3RCw+5WSUQyyBaZeDiNLEu0rKpg25Nt6BkDt9/GtPNqyFy1B19kK4EXvgi3PwMX/jPeoUOsdP+YcPXfk15WwMl9E6dDIOrmFrBvQzeGblLTkgfAyw8eQ1FlZpxXQrA6yIeK/4ZOjuO3++mOdDOWGKPKW0VHuIOrPFdR4Czgle5XuGHKDTx67FGuqL2CFztfxMRElmTsFjujiWzh2qLiRWzq3cSRsSMsLVmK3+7nQxs/zNdavo27x0l0LLvfNy0tJr/Cc66H8a+e0+rE5syw69lOShv8FFT5mBiIUdqUQ/9wP4uLF2Oz2Njev52XuzdyYd4S2PcIxIYBGF3+OonI6BnL3LG2ncrpubh8tmwASfV58Pp3wNBwXrqC4roiEpE0qs1CIpomv9KDK8cGwFBbK627dpBfUYVhGCRjURweL3MvvwbT0BlsPcGUhUvIrQgw3HaC+VddTzw0STIaYcYFq+k/fpToeBxJksmvqObY9tcZ7+uhoKoWLf3fKxJJnTjBwBe+AJKEZLOR6emh/0tfpOKhh7Dk5PxJ+184BwJVBPp3s7F7I8cnsgFPJydP0hho5PrKa7PnVtMEhz8b/FCxCJ65583Pp8LYd36bOZf8O7nzi7HrLnY920XvsWy4kq/AQePCYgbaQlROMxjvDeHyB5h35XWM9/VSv2AxmWSSeDh6epGmYTLUGSa31EMqFiO/qpquQ/ux2u00LF4BksTUZStJJGNUeapZmXMxk/oEMzJFjI8nKZ02C5DoHk6j+BKUVp277hTOgXQ8G4bQfAu8/m1Y/nnY9A0wNALHf8HFN95N2HATGRuh7+hhGpas4I2nfks8NAnAaHcnKz/4US762L2kE3FUu4NpKy4gnUwQm5xgpKudvPp85hXOY//Ifp5uexqAExMn2Dm4k39Y8A+oKQtpI8VIdyuLb7iF0e5OXIEgVrudicF+WgqLz2q2Zmo8efLJ7HlDsbF9YDuPHHuEq+uufttNNU2T0EicTEpHlmGgbRK7S2ViMMaU+YUcfLWXKQuKiE4keePpdi78SNO70uXC/5wkSxRUedn9fBdaWqd+bgGSLNG6e5jyq+7D8/BiMHTkyAB2t4eW1ZcTGhqkcclyQsNDxCfHsbrykCehM9KFRbYwv3A+N/kv49lv/z8MXQNg4ORxFl57E5k6P7GJcWRZpvniNcTDk4RHh/Hm5dO07Hx2PPUYSy5fQ/jR3+BsacF6043ZMKbSOdl/PTsg3PfmvWWwBi74J9j3cDac5PnPZc8FNi/c9BuofItwJ+F9zRt0YHVG8ARshEbiuP12Mkmd2ESKhgVFWO3KGe8v85Tx99v+njJPGVMCU2ifbGfH4A7aJ9spdp99DAUodBXymTmf4d5X7iVtpNkh72BF8VcpbZxGfnUtY73deIK5SIpCSWMTO9c+TsvqNbiD+WDoTAwNsfLWDzLYeoxE2sTmdJGMRVl07c1YrFZeuv8/T6+r69BBrvniP7Poug+w7fFHME0Dlz/Ayg9/DJvLlX1TcgzWfQpkFSqXQNUK6N7xZgAJQO8umPkBWPhJ8JX9aTtdeFuyrGAaBslohLKm6YBEdHyUPK8X0zAIlniYsiQfSZb+6LIE4b0gYzLY3orFaqVy1mwUi8pIVwcV02eRiscwTAVe/Xo21NTqglm3wOwPv9fNFv7KZXQDAIvyzsfOgMvKeEyEkAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjv7FxNK/oU0HLq59+YpnnNOVrveycxAbt//ubr8XbY+TOoWEI6oTHrwnIOvNJDIpymYloQu1slk9Kx2rNDYpom4dERTr6xjfJpM1FUle5D+5hdUISjzM8jxx45HUAC8HTb0ywpXvKOTcrJdzHcE8HltRENJSnASyqdZiQ0SEJLEMlEiKVjeNwu6hcsYfsTj+Ly5RCPhJi24kK0VDo7c+dED77ppbz0kx+gWFQcXi/Ht21m8Q23UFBdSHWzApIJTVdC52YI1mVnHzayxXgZI0NKS2Gx2kgnElTNbEFLp3F6fRimie+qK3EvXYIRT6Dk5BB57VX0iQnkgoI/+TAJb8+ehH3PZQt7G5eeR2hkiCObXgYgUFLK8ls+Qu+xw4z19WB1OCmqb6DnyEFmXrQaq92B1eHgtV89gJ7J5gwdenk9l933FSxWheK6HPyFTjY9ejJbMAmUTwty4o1B9IyBv8hJ3ZwCdj/bRSalEyytYNnNL+KdGMDtdkHNSqyKldzx15ktH6b0gmtIez0c2Qw2p0o8lH2YPrfUw46n20GCBVdWs39DD3tfSuHKCbL6iuu5rPJaHrc/zNq2p/hg0weJpqPsH97PVxZ8Bd3U8Vq9nFd2Hkktyb8u+ldGEiPcN/c+Xu5+maZgEwuKFtCU28QDBx8AYFb+LL6797sA/MPxz3PLxR+kSW1memETucU+rLYzi/uE/7vx2Dhte8ZYcGU1vccm2P18J56gneL6HO6bcx+/OfkbRhIj3N50O8PxYVKJSSwzrgfVDoCRTp61TF0z35zlPTEO+37NyAV/T8qRg91UcHhUOg+E8AYdRMaTVDQFgez7I2Oj9Bw5SFFNHardQee+7cy6+FKiE2O07txObnkluq7TeWCMwsogL93/S1SHA7vLzbEtm1h68+0ceKWP8qkQnRijfOp04qEQ3rx8ktEIkvTH96FMby85N96IJS8PIxZFyckhuul1tJEREULyFyjmLWK47gI6X36cpmATdf46Tkyc4MjYEXrrLOS2bYQZN0AqBIo1mzpWez60bnxzIcNHUFx+DDnJ2DFOB5AAhIYSTAzEqJqRy8k3HqNu3iK0dIpd657CX1RM14G9BEpKKaydC6ROf07XDZAktEwaf2ExsqJki4WjEbx5+Yx2d7Lklg8jb0vTvzdKw6wmgnVD7N7xMpUzW9AzGfqOHia3rPwc9qZwLowZOtGyOZR078DScjvo6WyYwnlfRDE0Gtwy7UYJa7/zNQAsVuvpABKAuVdcy+HXX6a4rgFD12jbs5N5l1/LrnVPYnM6mbrsfFSrgxZ/Cz858BMWFi+kwFnArsFd9EZ7yc+U0n10gkChA4tqZe+L63B6fZzYsRVXjp8lN96KrJx9LE1mktzbci/ru9Yzkhjhg00fZP/IfsLp8Ntva3+U6ESKyHiSniPjBEpcOH1WnB4rsgypuM7u5zvx5TuYt6aaVPyvM3vxL5XDpeLLcxAocnHo9T4M3WTK/EKiCQeedDZ4yfSVMHDyWcb7e/DlF9KxbxeVM2ejOlykk2lki5XzSs9jduFsNvVuItU9fDqA5PeOvP4Ks/OuJREKkVdRRXRynPbdOwmUlDJ4+ADHt25i9qVXYk3r2K02whs34r/pxjMbm4rCngfffD3Wln3deAU8ffebv69cAn1vgGqDvEawOt+t7hP+wthdKg63ii/PyfEdg2hpA8UiYVGdyIqE02s74/2mZHJV7VX0Rno5MHqAhkADl1ZfesZ3EG9lKD5E2kjjUT0sKV2CoSq4g7lsfexX+PILGOvpQtc1imunUDKlkdG+HvqOHyOdiFE2dToDrSeoqi2nOqRzNB6nadlKrA4Xbbu3n7WuI6+/zEUfvZfauQtIxeP48gvwBHOzf4wMwotfhLIFUHcR9O0CXyloCSicBpk4bP8B8Zk3MZJXS9AVxP0n623hj9G0DFoqxYkdW6iYPgvThJ4jB/EXleAvzqVpaQDNlwBy3uumCsJbSsXjWKwqJ7ZvoWLaTEJjo0TGRqic0YLV4UTXk9kAkimroXAGnFwPk12w+N7scUmW//hKBOF/SDOy30up/50QkrgIIREEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRDe2bkKIfnDp1+rz9E631uT3dmZqJuuAlcedG2B9lchMYbNZWfXc100LixCkiVioRTj/VHkP3hIOBmPkU7EqZ+/mP3rn0NLp5m6fCWpWBS7pHB84vhZq3ynIklZkXDl2BjpjTDWFyG3zEM6pTMUHeL50WcodhXzlQVfYTgxTGQyykR3B7MvuYLRnk48wTwUVQVMcPhh/l0kByeYfemVhEdHiI6PUTd/MeN9vajOKOmEhcJqD0z2QP2qbIHPvl/B6m9xYuIEvzryKyaSE3xUvZLoxBjuHD8Wm410MonP4yPRcYDwgw+dbnv+Fz6P7PH8KUdH+G+QZAXFogIJ/IXFHH39FQBmXLAaQ9N45jtfx+Zy0XLJFXhzc3npR9/F0HUKauqom7cIRVVZcPWNtO3eQbC0HHcgyP5nn6KwvhFZncHJPVEWXFlNToET1a7g9tsYaJsEoH5uITueaQcTqmblEih0sfGBVlS7zKxVboqqxvDWXoCkpXBIv6Ry89+gtdzJpdfPp29MI1jiZnwgRialA1A+NUD73hGiEymCJS5qWvLZ8Zsu0kmdabWruf3qOwlbR8jx+ij3ljMQHaAt1AZASk8Rz8QxJZNn2p7hyrorKXQX8m97/o0PNX0Im2Ljjul3YLfYUeQ3i5ozRoafd/wEgOenPY/VFjh3g/c+IlkkyqYGSMU1Cqt9FNX46Do8xisPHaPgNpkDIweQJIkTEyf4wrzP47KVwZbvwkQ7WGy4r7ke1ZY4va8AtFxcjjsnG1ISzyTYdN33+fGhBwinw/xg2i8wxwzsLpWR3gjeoAOAxKkCjnQyQe3s+exf/xypeJzGJctJxiI0Ll5BUV0DqVgUTBOP30ZsMsS8K69jvL+XRCTMlEXLGO7qwO6txjCzhU3uQBBnTg6maWJzuTBM44/2iaWwkOSRIyQPHDj9u+DHPoYSEPvgX5JYJsaWvi387NDPwIQvzPsCnaFOnu14lul507m44mIsJlC1LHt9sf/XABi+Uk6s+mfaGy8gKNupGj5O0LDS22Pj5OZRLNazL/1G+6LMWRVES12AoqpUNc/FX1zKcHsbZU0zkGUZLZM64zNT5hUyPjCK3eUhnYjj9OYgKzKxyUkkWWK4s52eQ4eZaK3ANEG1W0hEIpQ1TuPAhhewqCozLlhFOpU6qz3CXybDMNg1tItDqV6i0WNUBiuZUtbMlNF2OO9L8NJXIBNH9lfQ578HAEmW8QSCp5eh2h14gnkUVtdx8OUXkRULMy5YRXhkmIs+eg+R0WHCo6PYXDa+v+9bTMudxqy8Wfz04E9ZWLyQy52X44r7GY9PomUiqHY7tXPmExkbo27eIoY6WrOBKG8hYST41q5vIZ26bTkxcYJPNn+SUnfpW75f1w1Cwwn6jk8wOZSgbm4ByVgGPWPgyrEyMZTA6sheF4SGE2x7so1L/mb6n7LLhf8jh89KQZWXN57poLTBT1GND4Bw2KAodwqMHieeVimub6BkSiNjvT1UTJ9FJpXC0GVCwwl6j0xw3/JPc6BrMx/2XwOZyFnrUSwqKipJXWdiaBDFYiE6Pkbrru0U1zfQuGQFmXSa1LoXME+2Evz4xwHQdI2MkcGhOmC8A1RnNuTRWwy9u6FrO8z4g7CSxZ+C3p2w/h+y+/msW+G8L4K36Bz0pvCXwOZQ6B6MU9OcT9veYVSbgi8vG1STSWmotjevERyKg9d6XuPI+BEkJLrD3RwePcy3ln/rHdehKioLixcyK28WHaEO3P5cdu58mOW3fJiR7k7SyQTFdQ1omsbk0CD1C5cQHh5muLONnc88gS+/kIKqGsy6GsqNDOFwCFmSsNjsZ6/LZseiquRVVJ3dkHQsGwzbuCYbCNt0NTx51+lgVHIbOHDXelpD7fSO78MbbWdG3gxaClrOXpbwJyfLMslYlMoZLRx6ZT1IMH3lxaQSCUxDxzQlNnZvZHb+bBpzG9/r5grCWSQMDE3HG8xj17on8eTmMeuiS9F1HRkwtQxc+K/gyoVn7smG8gG0boAPvwSls9/T9gt/nTJa9vsiyx8JuQm4rIzHRAiJIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiC8M7OVQiJ+TY///XyV8Gyz8GO+8HU0WbehjL7w0h2P1o6Rt2cfPZt7EHPGPjyHEw/r5RMysDhhujEOEPtJ1FUlVhoguZVl2EYBpiQTsRJaklm5c9i3/C+M1ZZ5i172+bIskx0IkVBhZfYZBpPwE4mqSFraRRJYd/IPvqj/SDB+Y4FJFxuohNjBIqzy+xvPUZZ03QwDDi2DkflHWx67AfklVfiCeZy+LUN1M9fgiSZ2Fwqhi5BZACOrcsGlyz4OEOqlS9u+iJzi+ZSk1ODMmHB0E4V/ptkC5xlCUtBPrmf/CRoGqaWYfLx3+G79FJwipm8zyVnTg7zrrqe1x76KYaeHSd3IIisKBzY8DwAxWXZgiAtnWbhtTcx0HqSQHEJmx/JztRev2AxMy+6BFM36D9+lI49u+jav4em84aZteoCXv15DzNWljHv0ioiEwkqp+dyfPsgWia7v6t2hWCxm13PdQIwdUkxoX4YbIvgLW/FWSahV84nUPlbovER9OQw9kqYO7OCbY90o9qzxb/BEjf7N/QAUDungB1r27M/z87HE7Sz+ecdWF0K01f52JW/my9t/SJ5zjzKPeWsa1/Hp1o+xb/t+TdWlK3glZ5X2Du8lzum38GDRx5kMDYIwMKihdzedDs3NdzEs+3Png4FmlMwB7/d/+4P2PuUzapSPsPJrrVdDLaHkSSon1+IN+jAp8l8dOZHswEeio3XujdxVclFONJRqFpOxu7lt2OPsugjVzO6Syc+rBFokcmfaQVgJD7CYZvCt7b/OxdVXoRNsSEhEZtMESh2o9oUPAE70ckUrhKZ1olscXt0YoyZF67GMAxM0ySTSJJOJTA0DU9uHlo6japYkfGy/je/oLi+EZvLxZ7n1tK8ag2JBGAaWKxWOBU6kkkm8OTmgfn2p1Bd18AEIxQGwyD3k5/EzGTANAive4aca685F0Mi/InsGtzFb47/hmWlywBY176OpSVLGUuO8XL3y7ROtLJq/j9B5RJ45WtQNg+mXMKOghoePrmBy9zXM9JrJZ7bTG1NKc/+x0EkCVouqjhrXbNX+0jFOtFSKUzAolrxFxYTHR+j//gRdE2jbPpsShpsKLJM/fJcwrYRrDEFq8NObGIMi9UGgCRLp6/0+o8dwJffQDycRlFNdC2D1eFgzmVXAhLJaPgtLwrTySSTg/0Yuk5OYRF2l/td6WPhT2vfyD6+v+/7XFx+IR5HEY+ffJzLqi+jPB7C0bsD6i6CgqnES+fj3Jk9J08770IUq5VAcRnj/T14gkG0VBKbMxtyJssykbFR/EUlHN+2ialLz8fqcJI6PsLnAh/m+dhmfj78c84rP4+0nmZp7goS7RlGusKU1Dl4/fmnyauowun1cfDlF6lfsATr21xP7h3ay92z7kYzNJBARmZ7/3Zum3rbW75/sC3EYFsIb66D0sYAx7cPUtrgZ2IwRmmDn/UPHKVkip/mC8vZu74bXTNIJbR3rf+F/zl/oZN963uYtryE6HiSnc92AlA5I0jhmifxPXUpks3FUEcbDo8Xm9PJgQ0vUFhTR1WzlfwKLzl5aU5sXk95RSWZ8RSewgqsDifpRPz0elpWX87udU8w2tPFNV/8Z4a72qmY2UzNnHl0H9rPnuefZsXtd+KqrCK0axeGqbN7cDeJTIKuSBclnhLKy+dSvfLLsPOn2UCFyqWw6uvgzsuuxFsMqUg2+BKgcEY2cK1rC0y/9hz3rPDnSrVZ8ObZySR0yqcGSUTSyBaJVFzn9cdOMO+yatz+bNjHQGyA7kg3H5/5cQyy92YW2UIykzxjmUOxIUYSI/htfko8JdT6aplXOI/v7f0e9zTfwxuju2i+eA1bfvMr8quq8ReXcmTTy8y5/BpyyyrBMBloPY6p6yy89mZ0LUM6mSRpV1n3zf+PZCwKwPkf+Tgnt2/BPHVtLCsKU5etfPuN9RTB0vvAnQ+OALpuIK3+NvKhx6BrCyev+wn7x/azrm0di0oWMZIY4Xv7vsfn5nyOxqAIvXi3SaaUvc+RJFouuRyQyCSzASSZVBrFkqJUruTBIw9y14y7qM55f2QZC385JElhcmiQvIpKgqXlyIrCYNtJcgoKMQyDTDpJa+X51D71Qbjwn2HDP4KWzAYhdW8XISTCuyKjZ8+RVss7h5AEXVZah6PnokmCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjCX7BzFUIyU5KkMCABjlM/c+q1aZqm9xy149yx2GDDPzA5/4sMO2cwORnFM+mlYGQc1epgoD3E7FUV6JqJokh0HR6lujkXgNDwAHpGR1FVBltP0JtOY3d7mBjsZ+lNtxFNhPhAwwcYjg3TH+vHIlm4Y8Yd1Ppq37Y5kgzR8QSBYjeuHBuYoOsmVmzopo7P6qPMW4bH6iEWiuPLK+DQaxvw5ReQjEQpqK5Bz2igp6BiEZl4lAVX3cDJndvoPrSfypktSJKEJ2BloDWJx2+DikVQsxIUFbq20lsxh8trL+eXR39JsauY+TkfweHxnK6plxULkiQhlZYxes+92d+53eTdew9mWszQeK6NjA8w2tPJ4htuxe0PIMkyZU0zaN+zE4DSxml4ArlsfexXAEiywiWf/AyHX3sZgOZVaxjubOfFH/w7Do+XOZddzcV/cy9Wu4PDm17m5R9+g+kXXEpOQSkv/riVurn5VM3MIxXLYFGzD8yX1OXQeXAUgKqZuYRHExzZ3I8kZQNJlLQTySvzXOp1MvYEjx5/lFVVq9jYvZE7r/wYPmcp0y8sZrw/Rn6lh8H2MFo6+1C+3aXizXWw58Wu7PKLc4n26aihIJ8o+izpnAj/vOsfKfeWc3T8KAAN/gbuP3A/FtmCLMmnA0g+2fxJdg7u5GMbPkaeI4+PTPsIT7U+Rb4jn0/P/TQpPYVLdZ2jkXt/mUhOMHkgRlFtDqUNAWRZIjSaIFDsYowefnzgx8jIyJLMlxZ8iRFvLeWrvg7jHQzm1RAkxk+6/5Obmj+IO+bEmaMylBlEjcl8Z9d3aMlv4fop1zOWGEOTNGRkwmMJOvaNnm5DeVOAvKl2tva9zny5kv4TxzANA9VuZ3JwgCU33kY6HieTSmJ3u7G73UTGNTBjLLj6Bo5ueY3Rni4al6wgEYkgWSRkScbudmMa2QOkJEkoFhVTOjuyIZNKMtjWymh3B0gSuQXFOFeuZPS73wXTRHY5ybvnXnEc/QvTFe7CpbpY27oWgMZAI4qs8Pm5n2c8Nc5z7c8xKGkUO3OhcDrMuJmQv5SftT7KLZa7sU/4kNwgZyRObB9h+vISDrzcS3QiRdWs3NP7cNlUP4ocJZNMkoiEMU0Th9uLNy+fiYF+HB4vZU0zSGQiONZEKfWUcfOLV6DICo/Of4hYj4z0BzMtW51uJEnColopqptF2/5sIX5+mZVM0sfWxx7Gk5ePoWlkUinyK2vO2O7o+BibH32Iw69tzLavaQYX3nk3/qLic9Htwv9BOpXkyyV3E+rsRVYs/F3RR+lTInTUTWOqKwj7HobObXQ0rqas1sWCa24kWFIOwLTzLiA2OYFqt2N1ONnz/DN4AkHGB/rwBILkVVRTO28xsdAk4eEhAJS4zLXBCzgUP8HCooX8cN8PudUfYCQUo2J6LulEP3Mvv4YT215nrLebmjkLMPTsjPVvpd5fz5e2fAnd0EkbaTyqh8/O+SyydHYR31hflMH2EIqqYLEq7N/Qw6wLyznwSg9zLqnixBvZ64O+4xP4C524cqzEJtO4fNZ3qfeF/w3VaqGg0kNsMk330XFmnl+G1a6AJDE2oeL78HrMzgEqZ7ZwdPOrGFqG5tVrGGprRZI1ug6P4/F3UtrYRGh4iFQ8immaXHrPZ+k7fpTQ8BD5ldX0nzzO/GtuJBGaZHygj9cf/gWeYB6e3FzyK2vQMxlIZxh/4AHy7r2HocQIn37tG1xQfgEz82aytnUtq6tWUxEeRrF5swGPXVtAz8CND8Pqb0D7q9C7MxtGsvBu6NwMyRDEx7P/a/e9190t/Blw5dhQrQrdh8conRLA6bNhGpCIpiid4qfnyDgF1T78hU5UWeWO6Xfws4M/I6bFkJGxKlZmnjfz9PJ2De7ivk33YbPYsMpWPtXyKXJsOewY2MFdM+5i19AuDE2jIrWExTfeSveh/XQd2EtJQxPpeByr04HLH0DPZLLBpq+ux5XjZ7irkxUeDzlFJQy2Hs+ua90TXHLPZ+ncvweLaqWsaToW1YZpmkiSdLpNY4kxMkYGj+qhrX4FclrBNpYhNDCA1V6Af+HXsS0eImxmGE+MU+mr5BeHfoHdYueGKTfQH+0XISTngCEZWB1O9r6wDofHC5JEPBRi0XU34QoESYRjBEYquK70JiZTk+91cwXhLKZkkldRydbHHia3rJx4KIQnNw/TNLGoKnomiTJeDHPugHQEVv9/8Nxns+du1f5eN1/4K5XS/nshJDlOK5Nx8f2QIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiC8M7OSQiJaZrKuVjPn5XJLlLTbmbIt4DQyDC6phEen8DgJDlF9XiDDnY930nNLC++fJmG+UGMTLa4PJPKEI+EwDSYuvQ8hjrbSYQmaVl9OZNDg7inBFl3Yh2fmv0p0noaWZLRdR231f22zTFNcOXYGWgLESx2MdwZpWSKHwyThw4/RJWviqqcKiaTk5Q4qpkI9VI5o5m+40exu1zZh+kxweaGdBybx8/ejY9TObMFvWEqEhLh8VG0jE730XGK6nLgwG8gOpxtwNQrka0Ouga6uKLmCvqifciyBdVuxzRMTEwUi4IkSZimQd4992Ck00gWhfBLL+G95JJzMGjCGdIaRze9gmmYXPTRe1h+60cYajuJJ5hLeGSI8umzTgeQ5FdW07hkBUdffwWbw8HqT3yayPgofS8cxupwMv+q69n2+COk4jEqZ82hcclyShuacObkkAidZOkNATIpG6/9+jiBYheePDszzy9juCuC3akCkFvmYee6DgBaVlXSvneYw6/3A1A7r4WaZTn05PczM3cmLfkt3L//fp61Ps2qKasorq2iVikh/Mvk6YCTssYAbXuHT/+sWGTeeCa7fNVmZcmtc7ih+iZeG3oFry2bk6SZGlbZikW2kNASAMwtnMuuoV1sH9gOQDQTpXWylX9c9I+80PkCd710F26rm8/M/gzLSpdhs9jOxei9b3jxo+Qp7Hy2g0xKx9BNcsvcFNX6iBXAna470U0dp8XJroFdFNhzKX/1a5CKIF3zY8bHe/ibnPvY8ZNeDCMFQMuaUjqmd9I62crqytVEQ1E2dm8knA6zZuYNdOwfpbTBT26pm7H+GN1Hxqlc4qMv2UcmXUTT8gsYbD1OMh5jzpqrGR/oo2TKVJLxGC5/ALvLSzplwcjYadu5lZqWeRiGTiaVQrFYkCwKkqyhWCzZ8CeyIU2yLGPoOqHhITzBXGQle2odamtlpKuDdCIb9jAy2E/u7FnkffKT2eOoIjO5bh3uiy58bwZJ+F9rKWjBZ8sWjtfk1CBLMmtb13J84jgfnflRXHY/VJ8H+U3Qt5N4bIh53iUEUrlsf66dTEqntN5PdUsuLr+dA6/0cmzbACVT/Ky4eQoObwabSyU+0U0inA0gAZNENIwzJ4d5V1zLcEcbbbvfYP41N9MW3k93rJvVVatZ27aWlJlEkhQUy5uXkxZVBUli6c23U1DdxJGtPQBIikR4dJg5a66m5/BBFKtKyZSm0/vt73UfPnA6gASg5/ABDm7aQHJ+IeF0mKZgEw2BhjMKjoU/D3ljKmPd3RiGhiRp2EJJcnUruk9nb34Vded9gWHVyuttL3OpdTGyrDAx0EfXoX0U1zUw1NFKUV0j6WSC2rnzGe7soHHJ8mwAk2Hg9HgYGBpA1zUUi0psfBynx8enGz7B4bHDfK76yxxc34/daWWwLcT8yxU69+2hcuZsDF1H1zJkUikcbs9btr870s0d0+8gko7gVt10hjtpC7VhVc4MDknFM/QdnyARyTDYFmK0L8qCK6tp2zNM5fRcVJtMsMTDUEeE6ESKwfYQuaUeyhpUAsVvf60uvDeqZubxyi+PMe+yKg691kd0IgUSBEtcBO+aTiadxtAy1M5dgCwrHNm0kablF4ApU1DpxeYsIDo+hp7OXkOkE3EmhgYpnzaLjT/7PslIGF9BEdt++wgLr72RDT/5PotvuIXw6AiTg/1YHQ5mXnQpTgMsN9/M+EO/xPXNf+BS81JcqovB2CDVvmrWd62ncs6HqS+aBloSHDlgaBDqg+nXQ82FsPNH4CmEDf+QLXKGbFiJ3Qszb3zvOln4s+H227FYZcoaghzZ0k/l9CCJiIlhqGDCpkdPoqgyl909g8qaSta2reUDjR/AxDx9Pb21fysLihcwEBvg+3u/z21Nt3F8/Dh2xU5nuJPGYCMXll/IG0Nv4LQ42TG6g0DZTWz7zcPUzllAQXXtqeN/L1Ut89DTaapb5mJoGpIsExkdYc5lV9J//CiLb7iF3331KwCEhgbp3L+HiunNhEaGGO3uYmKgj3h4gsqZs0npKV7reZWj48dYWrSUtnAbDtXB1EgREwODaJk0WjqF2QOpAhuaO4Vu6jzX8RyQvYf72aGf8bUlX3sPR+h9xJSIRyO0XHIlvUcPYhomjUtWEA9NYrU5SElpeo9OUqz76fAfoCPUwcWVF7/jd16CcC5Jksx4fy9zLruS3qOHKZ7SSG5ZBbqm4Q7kMjk0QDoVYLDoIgp/2wLeMljxRdj8b1C+4L1uvvBXKq1nQ0hsfySExGO3EEvr6IaJIr95X91z+AB7X1zHgqtvJL+y+l1tqyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgvDn752fShX+91wFhJs+QiaZAsNAT6cxDINkLIqWmUBWJC76UICBYw+w+dd/z+513yc62QeAaRjomobN5WHP889gd7kprJtCz+H9WFQVI6Mxt2gur/S8woudLzIcHyahJ7Bb3n42TVmR8ATsaGmDw5v7MXQTi1UhlU6jmRonJ0/yg30/IMeWw1h6HNVmw+72kFdRSbC0PFskbAK6DjUr0TJpCqprGDhxjEwyQe+xI+SVVaClMzQuKiaTTMPcu2D5fbDiC+AMYBoGSGBVrBS7i5EtFixWW7aQ3jRRVCsmQG4uoz/5CeFnn2XsZw/gOe889FjsnAyb8Can6qBiejOmaRAeHWbLo7+kqK6B5lWXZcMRtGxxoyTLNCxezmu/eoD2PTtp3bmNjn17KKypZ9Xdn2b2pVey8+nfkYrHaFyyAsWi8Pz3vs2uZ58kNjGOoWnEJ9pw5Qyx+Lp8EtE0kZEkQ51hyhr9TF1ajCxLmEY2pMfhUUknMkwMxpFliUVX15CK6Gz8jzaad68hL1bOw0cf5uTkSZaULOFru7/G7sxWPnzwBiKXHcRRYVC3MJd0UjsdcFJU66N1dzaQpHSKn+aLytn/3AC1m1by+bx/ZZpvBh7Vw8aujVw35TriWpygPQhAc34z2/uzASSlnlI+MesTDMWG2NaznRn6fL7i+Q6f9vwjff0jHBk7cq6H8a+exSITHouz7MZ6Zq+qYOFVNcy6oBzDNPnOoW/ROtnKQHSAXx/9Nc2FzUQzcZjohOgw5ngHc5wL2fPYAMap/QtgzzO92MM+ZEkmaST5j73/wVB8iISWoCfWzeJrajENk8Ob+9HSBkuurWMsM8L5FedjczjZ9+IzuANBCqtrObljCy6fDy2TQksm0TIZZFkmHkqDaRAoKSUZi9B//ChHN7+KJ5hLw6J8JFlCsWTDHAAsViuSrBAbG+Pnn/4Ym379c8JjI5imSSIWxdAy6LqGrmsYmTRpi0z0jR2E161j7IGf47v0Eox4/G16Ufhz5FbdjMZHKXIVUeQqYjQ+isvi4ur6q1ldtZoHDz2YDeIwdXjlq/DSl8k9sZ55ufM4snkAXTNYfHUtJrDl8TYObOhhybV1WKwyWiZJJnmYF773RQ5u/B2GppGKv3meTScSGJpOMhZh++9+Q3F9A5IJK40VLNTmsTp3DQ2BBtJGCtmioFh/H9IgoagqkgRaOk0qNs61n5/DBR+aSk5RAFdOgE0P/5yOfbtofWMbmx5+AJv7zGLSvqOHzuqLzt07ee7EM3x9x9f56YGfimPpn6HI+BjxiXFM00BLpTAMnVQijlu3s659HTbZToep0R4f5ALPEjpPHMU0DUzTpGHxcsb6eqmYPgunL4cT215nuKOdkoapmIbBcGc7VqeDWCiEns7O2J1JJrA5XYBJmaMUv9WPJxEEU2KoI0x5UwAwcAdziU6ME50Y4+QbWwkUl2C+RfszeoaxxBhb+7cymZrkuY7nmExN4rGeGVgSj6Tpbw1xbMcghzf14fRZmb2qggMv91JUk0PFtACvPnycvS91Uz+vEIDCKh/Nq8pZdE0tdpf67g6E8D/mzXVQNSuX8YE46YTGvMuqmLO6kqoZeUQnE0wM9DA5NE+FsdcAAQAASURBVEgqGuXo668y7byLGOpoxeYOEptMYbU7MDIZDEMH00TXMqQTcUxDp2RKEw1LVuDNL2DF7XeAJLHitjs5vOllDm58kZ7DB3njqd/Sf/wo+kvrCa1dS+D224hlYuTYcuiN9BJ0BNnQvYESdwkZQwNTA8UGBx+HI0/ByRfh8BPQux3qV4OvDBx+cOXContg2Wch3A/J8Hvd1cKfCafXxuRwnKqZuZzYOUT3kXEkZNwBO4ESFxd8cCpjfTEOrx/kauet7Onfy6s9rzKaGGVz72YqfZWYpslwbJjl5cs5NnaMCm8FuY5ckloS0zQJZ8Js6t3EirIVxDIxUmaaWRddxvFtrzPa1UkiGsE0TGRZ5uCr6wkUl7LzmSc4/OoGBlpPEJsYp2JmC1aHE0l68yur4voGkpEwe55by7GtmzBNGO/ro3vfDjp695PS00wLTmM0Ncp4cpxyqRAtkcAwNLR0GkM3yCSTBAwPn3vtcwQdwbP658TEiXM4Gu9fsizh8uZwYMNzBIpLyS0r5+DLL2L3eJEUGUmWcAesbH+yHWlTMY5QDvtH9r/XzRaE02RZQrXZ6T50gKL6BmxOJwc2vIDFakOxqsTGx7C7oux7PczYqiegsAnsObDq69kwsUzqvd4E4a9Q5lQIiar88RASgGhSe/OzySRPf/trnNyxlbXf+iq6pr3dxwVBEARBEARBEARBEARBEARBEARBEARBEARBEARBEIT3Ccsff4vwv6La0VMympYBSSKdiKOadixWK6auU1IvcfT1F5iyaCnVqRSqzcahjc/jy89FkiRMXUPLpJh54Wp2P7eWRCRM5YxmVJsdh2JnNDFKV7gLVVbpDnezrHQZsvT2Dxkrisxwd5iKpgCF1V6sDgsTAzECBcrp94wnx8noGcqdxaTcOsloGIfHiyzLmKYJmKAoYGooioWc/CJ0TSNYUk5eRTU2lwuby8a+DeM0zC+AXT/LPlyvp8FfiaXlZso95UwmJ5Fl+fQ/VAs2pxMtkwHTQHLYCdx6C5n+fqyVlaS6uvCYb1UyKrybTCTKp80gnYijaxlUu53WnduwOp0suOZGgmUVKKpKYXUdnQf2AGB1OFh8/a0c2PgCj//rlwmWlrPslg9jczrp2Lsbf1EJW3/7MBabjblrrub1Rx5E1zRmXXQpqs2Glu5n1sp8gqUqoVELu57vZPmN9cy/ohqHV8UTtOP0WpkYyoYp1M8v4Oi2ASYG4iiqTGldELNb4uO2LzJY38mI2QPA7qHdzCmYw0/af4Ds1jlR0MoFtZcx3TaVoZ+FMfTs/iVbJEoa/OxY2w5kA0/KxorIdxXwvbk/pt04hiRJfHXxVwmnw/zLon+hK9xFgauAwdggV9dezb/t/jeun3I909LzCe828JWCnlRx7a/FCFqg4D0YzL9i8XSCgkofL/3syBlBNStvb+Sm3Jt4/OTjpPU01065lqHYELUF5eAqhEgfmt2DU/eQikfPWq4Wg4SWoH2y/Yzfp20Jxo7HKG0MkF/pxWq3MNoXoXJZEQW5ftqPbWHKomXse/FZMqkk9fMXo2s6smzJFlzqGiYmshVsLiexUIiSKQ0U1TeiKBYOvvwiUxYuITIqMXDyOL1HD+HyB5gc7GfxDbfhKyxk7pprUFSV1p3bab74MgB8BYWER0cwDQNvXgGYEv6bbiK6fgO26mqSR4/hXrny3R8Q4U9GkiQ0483CH83QkCWZ59qeYyw5xieaP0E0HYWJQejcBIDa/hreef/EWP8QTUuKOfhaH+HRBAB9JyaZGIqz4sN1kBlg6EQfsy6+jJyiEgxDJx2Po9qzYWbJWBTD0ElGYyy79UMMtp6k9+h+dj3zJKah07TiUv5lxT9gwUSTUyiKBZvPhaFrZE/XElt/+zAX3PEJqlrc5JV70DMZNj207YxtNA2D4Y42mpadf/p3hbVTOLDxxTPe562rQJM7uLv5bp5tf5bPbvost0+9nYsrLsbv8L8LvS/8T8VCExi6AaZJKhbFLnnJnLq+3dq+lUurLsXjLYSwSrR/GEPX8OUXEBkfRUtlr3f7TxxFtlgonz6LobZWdjz5GA63h5ZLLsflCzDc2YauaUiSxOTQAKUN01CtNjJOiWZ9HuHOJIVVXnx5NlRrFJvTQXHdlOznMgZTl65kpLuTmrkLz2p/T6SHIncRHeEO9gztYUbeDErdpdT5606/J53QGOoMMdYbpawhQE1zHh37R+k9PkGgyEVBpZe+YxNkkjoAkizhy7MzfUUJgWL3WesU/nxUTAtybNsgs1dXsvuFLtIJDX+hk2BxCkmWsbvduHICLLzuJgxdpz5vKb3HY3gDTpASGJgYuo4kyYRHhgkUl4AkkVtZhZbJECgsZutjDzPYehxffgHNqy/njad+Szw0CcDBl19k6m0fJVBWTvLQYbQFNezs3onP6sNj9TAYG6TUXUpQtkPH61C5BJAgb0r2Xmvb96B6BRRMg9YNsOy+7O83fQuSk2DzQm4dTLkUZJFB+n6nqDIunw0tozNzZRla2kC2yOiawbxLq9j6RCvh0WT2zRJ87iP/xG/TD7B1YCt1/joUSUEzNAL2APFMnEg6QlJLosgKqqyi6Rpj8TE+2fxJHjn6CHfPuhsrXtrf2M3cNdfQffgAsixTUDsF0zBoWnY+iUiYZDSCOxBk9qVXsfeFpzn48kuUNEzl/Dv+hk2/fIBZqy7D7vGx44lHcXi8LL7+Fka6O0jFo4wPK2iDGmq1hZScoifSg8fqwWU6SGbCYEJsYhy7y43FakU2IJ6J47P5zuqfSm/luR2Q9yndMEjGo1RMb2bv889gYjJ12UoSkQiYJlo6gd3hpPnC8mwm42AObpeEWWxmQ/gE4T1mGiZ2pwvVbj/jmlVLp06F9mpY1Dj+Qj9dkz6khk8ReOZSsNgACS79Nsy4/r3eDOGvTFrLhpBY/0gIideeDUaMpDL4ToUzn9ixhWQsyrwrr+ONp37LyR1baFi8/N1tsCAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgvBnTYSQvFvC/ci2OjCzRRKSkg37MA0d0zCQ5TgOn49MKlvcpmsaNo+b0NAIkiIjW1SsNjv7d26j+eJLMXQDLZNmuKuDxjlNFDoLWVG6AlmScatuilxFf7RJFlVmqDNMoNhNaDiOrhlIxpu7gCIp2FU78UwSq6pimiaSJGOa2UJKThX4Y5rIFgsWm5XKmS289sufERoaxOUPcN4H/wa7y4OmGdkCuNFjYLGD1Y0smTgtTiaZREI63S8WiwXTMLHaHZi6gWYaqIoFtbQMM6OBRcXIZN6VYRLengQkohGsTicF1bXkVVTRd+ww+VW1vPzz+1l8w60sufF2eo8cREtnZ3Gddt6F7HjqsdNFlcHSMoY723D5/DQsOw+X10fj0vOwWK3sfXEdeibDrIsupffoIfa9uI6yphlUTJ9F266tuHICrLl7OZlUiOikyaFNcZbdWE/7vhGcXiu9Rydw++0c2zYIwOyLKzi0qY94OA0SVDQVcP4FMxmrDPFk329ZWrqUhJbAqlh5ffg1XhnayEVlF3Prx+/Enrah2hTyyj30HB0HwOm1MuvCcnY920E6qaPaFebePJOJYBudkU7CepLecDcuq4d7m+/lS1u+REpPoZs6JdYyvCMKg+NPsmXzPixWG7NWXY8zMue9GMq/al6rj+2v9J4OIAFIRDKM9UZ5IPIAl1ZfiiqrxDNxmoJNlMh2mHZVtmBXsaLbUrj9NqITb85ELMsSY5ZBrqu/Dokzi9ysmgMwGeuN4st3MD4QAwlchhe31Y3FojLS2cGcy67EMAwS4RDJWAQTEz2jYeg6pmlipOO0791FoKiYVx/8KVo6RcWMWbRcciVaRiOdiJFbXklJ4zRM00CWZUa6OsivqkaSJSKjI+RV15BJJVFUlee/+y2S0QgAFpuNNX/7BQDU0lL0aBRLbhBTzGT7F8UiW7ApNkxMJCTsih1FVriy7kraJtt47PhjfHP5N+EPgkpITGAd20FpYx0Oj/V0AMnvxUNprBaVRCROeHSYYElZ9rrEBMliIZNKIcvyqTA0nVQsyoGNLzD/yut4+ec/Or2cAxvWklMYJFBSDqaZDS+RZHy5eaTiMWRFIb+qls79e5h23oXZD0kS0lsUvysW9YzX5dNmUj59Ft0H9wHgKyomOTVAiy/Ad3Z/5/T7vrrjq6iyyjX11/wfe1r4U5DMbHiNrCgoqoppGiiKAkiE02EUSWEyMUlxRiack4OWTqGlUuTkFdB/4hipWBRfXgGGrjHW00OwrJz86hoURaF153byKqrQMxlM0yQRDmeDc5IJfIVFJOMyneujdO4bo2qmG7vjCHvXPYWu69TPW0T9wiVMDg/izglg93jANM5qfywdI5qOMjU4lZqcGvw2PycmTjC/aP7p90wMxek+MoHNbiEVz3BoUx8zzivl6NZ+Zl5QzsRwHOupIj6n10p5U4Bpy0pw+23nahiE/yVfnpPGxUWMdGWL3xddXcNIdxSrU8fh8eLNzWff+mfpPpC9ppt/1Q1Ewznkl/swdZN0LI6eySArCql4HEW1YhoGgyeOUdU8h9DIEJUzmimub+DgxhfZ/MhDzFlzFdt/9+ipFkhoBfmkn38RT0sLE3qUOn8dw7FhMkaGVZWraAg24Nn/GFidcHID5DcAJqQiEB8HRwC6tmTDSI4+DaozG0ACkArDE3fCR1/PhpEI72s5+U4G20N0HRzFl+/EX+RiYjCGzWlBknkzgATAhF1Pd3PtFR9iYcNSjqYPMhIfYTg2TNARxGv1sqB4AQdGDlDlqyJjZBhLjnFJ1SXcsf4O3FY3oXQI3StTUFVDPByipL4BT24erz70U2KTE/gKCll03QcAmHH+xWx+5BcEyyqYtuICdD0bkrbq7s+QCIewqCrDHW1ceu/f8cIP/g1vXj7TVlzAwImjFNY2MFUvp0+dJCdaSqpnmFheD05vDht+8n30dBoApy+HCz/6SUxMXKoLh8VBQsteL1V5q5hbOPecj8n7kWqxIEsyiUiYuVdcC5iEhoeQAEVViYwNU1Azg40/P0oikiFQ5KJSz2XQHqKwyieCSIT3nGxRGO/vJaewmAVX1SIrMl0H9uEvLkNWVAxDR0sn0fQMZhxOdgeZcu0Gcrp/A6YJ0WGY6AV/6Xu9KcJfkYyevc9RLe8cQuKxZ/9bQCT55ncJPYcPYvd4WXzDLRzZ9DJHN78qQkgEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRDe50QIybvFV4IST6AoFtKGgWq1IisKpmFiGDoWVcWXV8BYbw8uv590IoE7J4BpasiyAqaBoekUVNWw9fFHwDSxuz0svPYmtvdsw5ebR6mnNFsobJg0BBvesTkWm0zl9FxOvDHI7uc78eU5mL6iFJk3HzieUzAHi2TBlCUsFguyoiBZThUjY3K6xF+2oKgG/qJSnv3PbxCbyIY2xCbGeeH73+KSe75KMqrD7h9AoBy0NAwfRZpyfnaGZEXFMA0kiwW7y0MyGiEVj2JzurC7PEiqDS0SQXG7MRIJbOXlmLr+Lg2U8HaiZoLw+BglU6Yy2tNFMhJhuLOdQHEp8664DrvbQyYRx19SSkFVDV0H92G1O08HkDSvWsNwVzsnHv0lALllFdTNX8RIZxvLb70Ti2rl5I6tOH05jHZ3YnO5KG2cxuZHH8Lh9bHgqht45Rf3M9Hfz+IbPsCM5XkkQkeomRUgMj7IxR8pBCnJnNUe4mEbLr+OJ6iSiKRZcFUNnQfGeO7fj1DoX8DXLl3KN07+PTU5NTTnNfOPi/6RHx34ERt615Mw4lxSfQlL7miic0sY08gWNDUsLGLnug4yKR2rXWHWheUk+w0C49VMc4LfG8I4+TRdzdeTwcI3l3ydmH4qyCJj0n/8NXqP7ANAS6fY9fQvWX1PJVBzbgfyr5yeMUhEzgwp8uU78ATtfHfqDzmc3E88E0dVVL6x8xt8Zc7nOD8RBcBUrAwZfTTfXM/eXw8TnUhhc1qYf2M593V8HLfVxSeaP0Gpu5TeaC8A+bZ8jFIbRzb307p7GH+Rk6alJdngJcDQdZBg629/nW1LQSEtqy8nFYui2mwYugGGic0epXTKVJ79z2+cbnfXgX14cvNxBwqxuzx48wtIhCZJJxM4PF7KmmZiaDqmYZJJp7LxKJJM39FDpwNIALRUiqObX6UlbhBbvx7fFZdjmzIlW+wk/MWQkLBb7KfPvnbVjizJ7Bvex2s9r3FL0y3ZollfGeRPheEjAJSu+zjyNa8xPGBBkv5g2CVoXJSHkY5jczmygWHpdDZwDLCoKgc2vIBpGDQtX4lpmlTMaCYyNkr3oQNnta915zaWTWlESyWQFQW728tL9/8HiUgYJIlZF19KUc0U2nqOkJtXgs/uY+5lV9FzaP/pZciKQu3chWcs15dfwGX33sd4Xy+6rhFzm9z9xqdZJi07qw2/OvorLq68GLfV/afpdOH/xOZykYxEUO0OAEzDQAJunXorDouDaCaKaViY6O8jNDKM0+dD1zJUt8yl88BeiqYEkQBd09j221+TjEVBkph54WrSySSJSBhd10nFYhTXNZJJJ7G5PYwcsJBX6sXlsWF39rDl0cdPt+nEji34i0somzaDke4uXL4cLKfaB9A+2c7JyZOUOspZ6F5GyJygNXmcw6OHmZU/63QQVWQ8STqhMdEXpe/EJK4cK3NWV3D8jSHq5xfiy3Pw4o8O0bKqArffxoUfaaK4Nuf0ekZ7I4z1xVAsErllHnLynediSIT/gaoZuZgm5FV40DMZKqen6Dt2kpKGRo69/hrdB/YB2Wu6Lb95iGW33Efb3mFqZqWxWFViIY3IQB92twctk8HEpHLWHHqPHebgxheBbPjB4htv5bWHfob5B+fkxqUrePWRB1l45RWYPf2MWOL4rX6qvdUktSSX11xOKDmB/ehaiI/BwnsgFYLBA1CxJBtMkpwEew5oSZjogvJTATq5dTDjxuzvB/aD1Q3ePx5eKfx1Ge2NMNobw6JKBEvd2JwKjYuLOb5jkPZ9I/jyHRRUenF6rWd9NhnNEO5LUxSspbCghCG5h3g4jRadpD6nni39W7DI2UCJjJEhnA6TMTME7AGurruanx78KV0FndyVeyUALn+Ap/7fP6FlsqEgoaFBouPjODxeDN2gsKae/Kpqtj3+KKZpYHO5uOij9/DKgz9m/tU30rBkBe1730BLp5l23oUc2PA88668nlQ0Qv/RI3iDuUwMjnPi1VdpueQKEqEwC666AdM02b3uSeKhSSKjI5xXdh522c5XF3+V8dQ4DsXBrPxZlHvLz+XQvG/pRjZkMTI2wvFtrwOQV1FFYU0dsqwgyypGxiQRydCwsAhJgn0bujn4Si9zLqmkcXExDrf6R9YiCO8ewzAprK3njbWPn/5OtGHRMkxDJ5NKYrHaCA0NUd4Q5NnvdaOoMqpaxjQ1gLV1LYwcg8JpIoRE+JNKnfpeyqr8sRCS7PHzD0NI+o4fpmTKVGRZYcqiZex9/mlS8Tg2p7hvEQRBEARBEARBEARBEARBEARBEARBEARBEARBEARBeL8SISTvlkwCSyyMrLqQZAmL1YYsy0iyjAkoFgVFtaLasrOjW1QVi90GmCiqBV3TkBWF+OQki677ALqWwaJa6dy/F9uFtXxh8xdOr2pG7gwWlizEqpxdNJRJabTtHaFj/wiKRcaVY+OCD00lPJpgrC9CRVGAu2bchVW24lJdyLKMbFGQFBlZsWQL6k0TxWqF05PNKlhUC4lY/PTD9r+npVOEh4dwBepg8Sdh70Og2GDJp1CQUCUVVVZJ62kUWUGSZWRFwRPIAwk0PYNu6KgXXokjPY4+GULxuM8o1BPOjaRFp2TOLF75j/9k+a0fpv/EceoXLMEdCDLU3komlaT74D6mLjsfq93BhXfeDZKEJMlYbDYsVit9Rw8D2YKi+gVLsFitzL/qBk6+sYX2PTuZfv5FFNTUUdLYRE5+IYdf2wjArIsu4fVHH0RLpVh03Qc4+PJLjPV207B4OQU1dWQSCXRfhGQsgjcvj9DgdrY+sp85l19D3WyFZPQgU+ZVUFIfxOVzk4wo/H95PyITiLK+6xkSepzm/Gauqr2KPGce0cQEPcp2WpYVktHr6D48hiRLZFLZ8JuWVRXsebGbdEJDscjUzSunvjkHv6+f+p69sPsXNCkq3df8hBnB6cQzE3Tt33FWn4ZGeoD552wM3w8ySoq6uQXsXNcBwJT5hdicFjr2j4JpUlo1g0TlAKPGMPe23Mu/H/ghsxvuIscTxPQUYMb62S1tpuqDtTiSQbDrRN0D9B7tgRg4FAdX1F6BaZoYpoHT6qS1bZy6uQVoaQOLVWaoPUReuQfIhpD48gtZdN3NGIaBJEmMdHVQWNeI3eXCNAx0PYPdLTPSOXjW9rTt3I6qWqmZuxAjkyEVj5NOxFFUK05vDrKiAOD0+ZCQkEyT8OjI6c/bnC6qWubizc3DN3cJzpkzkSwKI9/5N5ScHIr+5Z+x1YggnL8EqpI9X6aMFBISFtmCIisUuYq4rOYyhmJDkAfIVlj9DTj6DPRsh4olePwScVNh4dU1aGmDeDiNNximbedvObapjfoFS6lbsJhEKIQ7N4/B1uPseW7t6XXvX/88i2/Ipevgfpw+H+5AgNad285ony+/ENPIXrM4vTls+90j2QASANNk3wvrcN8YIBmPMpY8RGRkhNoFi7nu77/GSGc7sqKQX11LYW3dWdvu8HgpaZgKQDwT574593F47PBZ7wvag1hkcSn750B1OLGo1uy1rtWGaWQL4HRdp8JbQTwTJxWPkI7kMD7QR82c+WQSSXQtw+TQAIW1dUwO9GHoBqHhAVouvRJD01BUC4df3UjZ1BkYuoZpGLj8fhRVxeZwYmSc9J8YoazRSuV0iZPbj5/Vttad27HaHYwP9DJptSFLMlMWLeXQ6CHuWn8XX2/6NpEDKhP9CUJjFuZdtoKYJ0b7ZDv17gYObekhNJDAm+eksCaHkil+JofibHuyjXlrqgkUO9ny21ZUu0LF9CDTlpfg8tlOr3+wPcRT/7YXPZPtE1eOlcvvbSZQ5Do3gyP8t2RSOntf6iI2kWLBFVZ2r3uK6pb5REZGzzr+AejaIJmkB+nUtW8iHCIZi1I+bRax0CR2l4fxvl5ad25n4bU3Y5rZgvt0IkFV82z8RSVUzpxNadN0xnq6GOnqYPsLT3PZVTdTrCTQ7QY5thzsip2jY0dZmEygjBzPhooUToe+nVC5FI6ug4V3w7bvw+J7s0Ejs26CA4+BzQvNt8KGf3gzkapyGVzzE/AUnuMeFt4rA+0h1v7hMchv4/zbGtn6RCvVM/OonJELZMNGAsVOJDkbcvp79fMKkBTY/GgrRXU5FNVUcaw9hN2t4i0MMKd6IUetBxiIDlDgKiCSjjCaGOXeWffy3f3f5W9m/Q0nxk/gyc0jOjHORH/v6QCS39v2u19z1d/9A+HhIRR1NgMnj7PwuptPfwdyYONLNC07n62P/pI1f/t5dj+/lsLaejoP7GX+1TdiUVUkScLqygZV5VdWU/rxT9N35CBI4A3mcWzb68y74lpef+RBDNNgenA6kiThsDiY4Z5BY27juRsUAQnIpFIU1zdSMaMFMDF0nejEOEYmgytYip6BwhovpQ05TA4lmLO6ksh4km1PtuHLc1DTkv9eb4bwPqYoCl2HDjDzgtUYhn4qVGcUQ9eRJAktnUa12eg6uJvzbptPZFyl5/gEuS1LKc/ZCzOuh8NPQfWK93pThL8iGT17/rZa/lgISfYeOpLMBvrGJieYHBxgxgWrAaiZPY/d656k6+Be6ucvfhdbLAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIPw5E5Wb7xZDR/XkIidS2QASi4EkySgWBQwTJBlZllGsViwWK96SAuwuN7IiI8sWJEkCKZv6se23v0a120knEyy54Va69dQZq7q89vK3DCABGOmJkk7oBIrcyIqExSKhpXVSCY14OINsgs/qI9eRS4O/gcH4IFarA6Q0FpuNdDyOpCjIiiXbbgC7BzmtoaTSWKw2tPQftEeSsHt8aBkDxvZDzfnZ3x9/HkvtsjffhgSKhCRlA1nSyTiKquI4NTv9aMyB9R++hDYyAopC3r33YiuvQHG+OXu98O5SFRXZiHHNBz6CVFrOYFsrY73dHNjwPBarjflz5lNQVcuRTS+jNUyl5/BB5l9zHfOvvp6Tb2xjor8PAE8wl7p5C9nym1+eXnbDkhXMvfwaJgb6eeUXP8aXl0/9wiWoDifHt7yGaZhoqRT+omImhwYY6+2mcmYLmVSS1x76KWVN0ymbOoOjm1/F4fXRsnoNJY1NRMfHaNu5nYYlyzm5/Uk69++hbsES6uYtZ2IwiRzN4dbqD5HRBrA5JUglsThdSPIwFj2DD52ksZOr/2Y6kaQDRZWxOSxExlOnA0gWXl3Dodf6OLZ1ELd/LuffXE7gsgtxvvARyru28W2lmP5ADa1lQ/QcOXBGn3pzA+d0DN8P4lqCYEmQuZdV0bp7iMIaH1pax+ZSMU0Th6KSm6zHl+thIjVBqbuM1MBe6NmJWX8BmqGhGzqH4wdQZAUlpdDgaji9/PHEOHMK5tAeaielp5BRsDos7FjbDoAvz0HzxRVIp2o8LDY73Yf2ExoaQFIUJElm2Qc+hGlkcOb4kRUZq83O3peepXrm7LO2J7eikmQshmEYdB3cS255JS5HkEQ4xIRh4M7LBjapVhuyRcE0Tapmzab74D7mXnEtnkCQ3c+uZbi9Fb/Tjf3xpzBOtlL0ta+SbGtj7KFfUvjFLyDbbGetW/jzIpPdqSQk3KqbanUKXsmNO+Dm+MRxSEFKS0HxLOjaBvWrYNrVYGTo6nTx2kPZMAZXjo0l1+Xx4ve/TTIWpXbuQuwuJ3uee5pgSQmKVWVyoP+s9Xfu34Mzx8/e55/mui//K4f8QaITY0A2JKS6eQ6maWKaEhZVZbij7axlKKrKzrWPEx0fw2K1UT6zhZfu/w+0TAbTNHAHcrn801/A6cvB7X/r46NTdXJx1cWUect4ovUJQqlQdtmSwp0z7sRusf8pulv4P3K4Pah2O1anC0PLYAKyoqCn0yxOlWGMxyjc1020XqVhyXL0jEYsNEEqFsWZ48fpzSFYXEYqlaDzwB4ObnwRSVHQ0xmW3nw76WQcu9dHIhwCw0CWFTx5+ex4Zoi5l3qJjHQx0DqOr+DsYIVAcSmKqtK26w3mXXEt2594FG99BeOJcb4w/csURCuJ2dJ4ggZ5FW46Xgtx+eobGB8NY+23kwxpBEs9TA7FCY0kaN83Qkmdn+rmPGRLtljfE7Bx0R1NFFR6z1i3rhnseanrdPE/QGwyTe+xcRFC8mem78Qk0fEURbUe2nY+Q8Pi5Rzd/BoV02cSKCmj79iZQUgOt4fyaU6GO9qYGOzH0DVq5y7k4Ksv0bTsArRUEllWWHDVDex48jES0TCmYZBXUcXiG29D1zS8BQXoWoaBE8cASMai4PUwtTNKxA9uj5vB6CALdYma576YXfHSz0B4AErnQyoCCz8O4x2w8ssQGwF7AGbdAsE66NoCO+4H2QKlcyGTgM5N2aASEULyvqBrBnv/6zFoIsVob4SGhUXsfqGTiqZcXD4rw91hCqq9rLytgQMbe4iF0lTNyqOw2sf6B44gSVA+LYBkSniCdlw+G3aHhWCikgnXMEdGj1DsLkZDoyPUQXN+Mx+Z9hHGk+OUe8pJpRIMnjhG0ZSzwz4U1YosybhyAkiKhZGudrY+9iskWUa12Ziz5hoqGpqondGMZLEwY+XFHNjwAqah4y8oIhqayAYBJeLYHQ4MTSMVjWB3uRjt6QLDZN7l1xCdHMebl4+jrICgzaQz3Mm04DQRQPIeMEwDh9fH/vXPEw9NICkKqtXGkhtvw5Qgk7QgW6BudgEbfn4UAE/QzvTlJZx3SwP9bZMihER4T+mZDBXTZrLp4QeQZQVd08irqKRy1hwwORWOJOMN5qFrk+x+LsaS6+vQ3TKaqwzLpm/Byq+AYYD8zoERgvDflday53ur8t8NIdEA6D0VIF3a0ARAcX0jNqeL9j07RQiJIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjC+5gIIXm32L1Y2jah2Oaj2h3omQySLGNRrUiSjGq3I8kyLl8OgeIykvEoqWg0W5CWl3cqgMTE4fWy8NobSUQiOH05DHe209h0Pn6bn4yR4eaGm2nObX7bZqTiGaxOGV++h9xSD9GJFJGxJHaXhdIGP4YODpuDgD1AQk+gyio5Tj8JJYp8qqpeS6WwqFYMXc/O8OkvRwp1kEklWXLjbbz6y5+enl17wdU3gJSTDRlxF8D2H2QfqC9fhGqQnS0cCYPsbOCKRUVXMlgdCrIsY5oGpq4T7Z8gJ3Uq3ETXGfnOd3DOn4dz5sx3e+SEU+RECl/HJMeHBzj62EPMWXMVTm8ODo8PTzAXRbHQumcbiqqSU1gMkkTnvn0kIiEWXH0D0YlxWndtp2HxcnY+8ySQLUifu+YaTNMglYjjCQSxu9xUtcxl3wvPEhkbYfrKiyie0kheRRW+/EKG2lsBKK5vYOtvf40ky1RMb2bzow/h9OUwfeVFvPCDf8eiqsy9/BrKps9kfKCPKQuXUb9gCal4nBd/+E1kRWHOZVcxMTBKKmEjmOPD8tQT2OfNId7VTrqogGR9DV2T/QTLhgjl6Sy+sZSDG0dJxbOzg9bNzefgq72EhhP4C51MXVzMlrWDJGMw95IXKSqRyTn0I/K3/5TAoo8x1HGSdCIBQFltAwUO71t3tvC/liCKwwzSfWiUBVdWk0kbOH028ircZBIao71RzIxE2khT5Crig1NvJ7/vMLATFBXDMJAkiYyRQZEVOJW1dGf1FcxzV2F3FfCtPd+h1F2K3+4n6ohydEs/kgRTlxYjyRKbHzuBYpGZf2UVejLOlIVLMHQdLZPh/2fvvsPsuOt7j7+nnd7P9l612l2tepesYjVbttxtbFwocRxiSgihJiFcSEJyKbk4ECAJHRfccS+yLUuWZPXed7W9t9P7lPvHkdcIGUgA2Qbm9Tx6pJ0z85vym52m8/2Mw+1hqOM0pQ1NDJ4+wbTFyzEQmRjoY9aay6honkH/yWMAWOx2Flx5HbHQJGomTd3cRei6hq6pODxeJFnGyKkIgoCmadicHsb6unEXFLL+Q39FJplA0zQWXHUDZ3a9xgv3/5CN194CX/oyI//yrziWLMFaU4MWCiGWmMXH7zRVV5lMT+KQHbgsrrccRxAEGixN2DtLOfXKOLIlx5wrK2gqFxhQ+hHEfFgZWhbUNNj9xI1idj/aB0B1W5CiajehwR7SiThWp5OCqmoUqy0fHNLdhcMboHHJMg5vfva8eQcrq+g/nt83wyPDXP7RTxIdGwEMnL4AmWSSXCqFzeUml0lR0tDEcMfp89qwe7zEJ/PBJctvvp2uQ/tZcNX1pOIxRElCVix0HdrPmV07aLlkFc2XrMbh8b7ltmgJtvCjDT/iwOgBUmqK2YWzaS1o/W03v+n3zO52IysKFrudbAokWUYQRRDA0DT0SISxr34V/8MPEkvFSScSKFYbnoJCOvbu4rX7foS/tIwlN7yXssbpFNfWo6sqdreHM7t3svyWOxBFkcF4jFQ8hkdTEUSRBVcUkoqcJTI6jGyxIEgihdV1jPXkg6LsHi+ljU0c3vwc1TPnMHD6BE5/gK5QF2PGOAutq+k5NY6a1WhZXo6BQXVrgHg4S7RTI5UI0bykjIObexk8EyZQ5mTpdQ3se6abmZdW4PRYKKh0c8XdM1FsF95WaapOOp6jbVU5FrtM/6kQI11RouPpt7uLTL9BPJTvE1+RnZGONLlMhqq2Wez5+aOsvP2DjPV0Tl3TVTTPwOJwkIpGiE2Oo2Yz546FGZqXr0bXNLoPH6C8uZXI6AhNy1ZgczpJJxIcfP4pUpEwmq5RMb0VQRC45Nb389T/+1ealq5ATKWZ+PZ3WPi5T7MvF2eJr4Hivv2w+G4obIJ0BGLDsP1rcPn/hforIJsANQlqBtxlIMngLoVgA4x3wII74eyW/L3Zhi+Dmn0nN7XpbaTldCKjKRweCw3zi1AsEr0nJkmEsyg2ibnrqjm6tZ9EKEP93EIyKZWmhSUEqux07Bpj6GyUbCpfoOwpsuMttOPwWKhq9ZOM5pgYiCHLMo2uJoqbi3m191WcFieqoXJo9BBtBW0kc0kCrgBCWiI6PoZu6MxefwWHXnwGh9fH3MuvQrHZSMYiOH1+VDXLmd07mXP5VVO/N7Iio+o6T3/3GyhWG0uuuJbWBUvw1NQx1tuFrmk4A0Ecup/Y5AS5dJqCqhpe/O43WHLDexnp7ECQRFz+AFf81ac5KfShiAqLShfR6G98h3vpT1c8PMmM1evQ1NzUOT80MogoCFjskEnqDHflw+fq5xbi9FnZ+0w3ggBzLqsmFc9hdynv8FqY/lRJVht9J4+y9Ib3koxGkK1WMAxy6RSg4woWERsfwestweKws/iaEg5u7iUylqJ+5o0sXncZPrHPDCAx/V5NhZDIvymEJH/sjKXzzzoHTh9Htlgpqq0H8vdy1bPm0nVwH4auI4gix8aP8dW9X+WKuiu4qemmi7gWJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUymdwszhORiySSQKuYiRSwoGFhsNgwEJFlCVmQUqxUwKKyuJT4xTiISJpfJYPd4kC0WHG43oiQTHhmiffdOvMUlhIYGab5kFRgGn130WRRBoSfag91i/5WLISsSLr8NA51EOEs2peIptOMOWug8NIm7yIJTceK2uDEMA6/iJZ6NIwmg6xqCKGKxO9ANHVGSyGay2Ox2REkkk0yi6xpXfOzTpKIRbC4XNreXXNaKIIjk7LNQZt8CugbeCiy6BoCBgSiICJKU/yOKiIKQDygRBHQBSkokktHoeeuiDg2BGULytvFpNraPDHB013Zmrbuc9t07Ge/tpu3SDaRiEV67/4f4yypZdM0NJCNhkuEwBdU1+IqLad+9g2lLLqF+3kJEWT5XiAELNl3P0VdeIBEOASArFjZ94nO8+tPvExrsp37+IiRFYddjP8NfWkbNrHkkY028/vB96Fr+y/RFNXUMnDoBQMslq9n9+EOo2QyLr3sPux5/CDWT4ZL3vo9Dm59h8MwpFlx1PStv/2A+CEiSiYz2I0oSMVsQadMVGHYP0tAotqzB+Ic+TmFRMb5bbsbefgahupKV164nnrbQsW8Up89GZHQYgKbFpex4rAMMmHd5Nb0no2x7aIKGubfQMK8QLZ7gur/6PJHRQWRBxHrwAMLRYzBv/jvQm3+8nKKLyaEEpY0+xnpi1M0pQJRFoqMpBBEKKl1oqsaZ1ARpNU2xvRDh2U/kJ1YzjKfH+c6R73DnjDsxDIMsWQTg7tO7kPu/Sezunbyv6RY64/2ougqGQcP8YgIlDlLJHFa7Qv2cIk7vHmbvU90svcbF64/cj2KzYbHZmejrY9X770RTcwQramjfvZPZl22iccFysskkravX0bpqLWo2g93tJZNO4ysoAllCj0aIDo+Sy6Rx+vx4ikoQZAkAT2Exw51nyKVT1M5ZQGx8jEwiDoDV6aLlkktp372Djp6ztDY0kDlzBsfddzP6ta/hWrvmHeot0xv6on3ce/Jenu58mlpPLZ9Y8AnmFM5BEATOhs9ydPwo8WwcRVQQe70ceTp/3MkkYfu9naz9UBPuUhfGG6k5mTiMHoeff4jc3C+QTtRidchMX1LCUHsEb0H+OqG6bQ6SLHHohWdYeM2NyIqFdCJGbHyctXd9lJf+65sA2N0eambNxdB1Jof6cfgDTA704Sspo/vwfrb+9AeIksSM1eupnjGLTCLJ0htvYfN/fYvYxDiCKDJr3UbU7JtBCy5/AMVq4+UffBddUwmUV2C1O5m1fiOzL9vEa/f9AIfXR/PyVb9yuzX4G2jwN1ycTjH9zgRJRgAUqxXFZsdid4BhoE6Mo09M4rz9NpK5DJqqIisKdrebo69spnP/bgDGerrQdY1jW16cCrBJxaKsvO2DZJNJwiMjSLKCpChouRy5VJLI2D7CwwO4/AF0TcMXKGD68pUs2HQtmXSK6OgIOx+5n8KqWjLJBEU19TgCAbxqA/X2OWRTKrWzChFFGOoIE5/MUFzroWPfKHVzC7HaJYbORqifU0h1S5BcVmO0O8ql75+OgIDDa8FTYEcQhLfeJqJA44Ji9j7dRTqeo2ZWAXM3VFPe5Hv7Osb0P1I+zU8qlgMD6uevQxBiqNksstXCzkfu45pP/QOjvZ04PD40NR8OFhoeJBkOo6sarkCQdDxKoLwSm8uD1eEgOjrClh/+J96i4vzvAzBzzWVEx8cAg4N7XmfawiV4Cou56Qv/QmoyBCOjpE+cQEim0dEpLpoBkhOSY3D4AejaBoo9HyyiqfniZZsbcJ+/QpIMRc0wfSM8/9k3h3e8BLc++rZtV9M7y2KXmbO+ishoiqNb+8mlNOrmFlI7q4DJoQSvPdhOy7JS7G4LuqZjdykMtoex+xTKZrkpm+5DyxhoqkbdnCJiE2km+hOMdEdoW1lOQaUHNavhTxXhzAa5LdiAYdPZm9xBT6ybw2OHmVcyD4fmwmrYcRcUko5FiYyNcOXHP4skSxx9ZTMFVdWM9fbgKSjEU1DIzDUb6D9xlMjoMP7SciKjIyy4+gYUi4345ASbf/o9Nt3yQcZ6uxGATCpBNpXCW1RCeVML4z1dZBIJmpau4OT2rbSuXks2kSCbTtN79DBV5ZUodjsBr/+d7qI/WYIg4AkUsO2+HyJbLNg9Xib6+1j9/rvQDQNDk4hNpqmfW0TXoXH8JU72PduN3a0wa00lqViO49v6qWoNUljl/pXnYZPpYhEMnfJpzWy7/0cUVFSRSSbQNY1LP/Ah4tEoLr+PyMgAocEBypqmc2RrP9HRFDNWlmNzKhxpd1HZUEdpPIfNDNMx/Z7kzj03VaRff0x0nwtPjKbzQWMDJ09Q2tiEJL/530R1c+Zz5vXXGOk6S2FtHZ/Z9hl6Y70cGTvCpVWXUmAvuEhrYTKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkercwQ0guFpsb6fCDSBW3YOg6hqGDYSDKMqKsIEgSoiSjqyqJaIRcOo2BQSoSweH24isrJz4xQXFdI0W1DUz091I1Yxa5TBZVzfJk75O0FrSS03II/OovF7sLrGRTKqlYjtcebCcymsLps3DJTdMQRANJkFhXvY7R5CihdAif1UdXrIsCUUQQROyucwVtgoBu6ORSCWx2Ow6vHwyDTCJOLp0GDNKJOEFF4dSuIZqXlaPldBSbFwwDJrux1yx/c8EMEGQJUZKQFAVd00AQEGUFEUhpIghCftpzpEDw4vSV6S1lgRP7dgHg9PkZ7+3G5Q8iWywcfP4p/KVlNMxfyHPf+jdymTStq9ZidTiZ6OvG4fUhSiJNS1didTgIllcSHh1G17WpAJLGhUsprK7h8OZnqZk1lxmr15FLp3j9kQcQRJFF19zEeF8PidAkS264BavDhcsfJJNMEiivBECyWMgkE9icrnyQTzpFw/zFnNm9g+GOM8y5/Co6D+xloq+HOZdfRTaV5OzeXTSvXIO3qJjo2CCWKgVl1XKyhoj7p4+RHk0S7zqO5PDiqKhn7O73YW2bzdr3fJRoEqwOGV0zSMWyYEBJnYfJwQRdh8epaQui6/DMt48B+XEXXtnKjqc6mbtgDb5Gs7jk980j+khJGWwOhfLpfrScwYEXeujYP4ooCsxYWU7jwmIskoWEmsBmqYJlHwdJQZOtHBw9yBeXfJH+WD+CIBDLxPjoKx/jO/M+wuLoEO5kiLWvf5+B+e8jKwgEhCQRtw0DUCwyggCKTaKyJcDw2QjJaJz5V17LeF8P2WSS6ctWMtrdRbCsAkmRKZ/eQmxijJqZc0mGx4mODufDl3SdZDSCr7iUTDqJ1e5ktLuTQGk5oiSRSSaJT4zh9PgwdAN3IEjv0UNY7A6yqRSxyXEkKR9QEp8cx+H10bBwKQ5RQovHUSorsTTUo01MYCST72if/anLqBn+4/B/8EznM1zTcA3lznJOjp8kp+YQBZFX+l7BY/EwmZ4knclgO9B8QRt9x8KUVsjYxXMhZIoNjj0Ki/4Cl8tOzQwPFc3F7H6ii6oWBcnqoLJ1DrlMGkmxMnfj1Uz093J487M4fX7mXHYl3sJibvj7fyKbTpGKRjm57RW8xaVc8+l/YLjjNIlIhFw6xf6nH59ajj0/fwh3IEhBVS3x8ATrP/RX5FIprE4XHft24fIXMPuyK/EWFJGMRek5fAB3QQGz11/B8Nl2jHNhZ0Y2y4xVazn68gs0Lb0EUZTeru4w/Z4YhoGu5sim07iDBWiqSjadxOnxYikrR9UMpLZmtHQawzDIZdJYnS46D+yZasPm8qCrKq0r1zLS2UHZtGbsbjdn9++hZcWldB7YS9ul60iEQ0RHR3AHC3EFA2DoZNMpDMPg9UcfYNmNtyGIIgeef4rhjjMIgsi0xcs5vu1lZq69glwmiFWws/vJs/SdCCErInPWV1FS52VyMMn2hztoXFDE5u+fYNFVtTi8FrSszr7nusllNMqbfISHk+x89CzuoI1lNzRQMT2A1X7hbdVYb4xtD5yZ+rnr0DiOSywU1Xjeln4x/c8V13kY6Yqw+8kuyhpcNC1yExkZZvaGK9jz84fJZdLYXR52/OwntK5ci9PnJx2LoVitpGIRJEnCUVR67no1gNVu56Xvf5sVt36A8b4eMskEZY3TsTqdWB1OYhPjLNh0HSNn29n7xKModjsbP/RxJu/5FrbWFnDYcMgy0UwUj90D42fBXwuVi0AQof1FqFz461dKy+WDSxQHzLkNbF4QJZg4C/Wr3pbtanrn2d0WXv7RyamfO/aN4i204yu2Uz+3kMmhBMpEihkrK3j13tPUzy0CDNSszvHtg2g5ndW3T+fwy70ESlyc3j3Mmg9MJxnNsfPRk6RiOQKlTpbf1MhoVxSb08Icz3Lm+5axO7ONaCjJyFaF/mMjbLhzMaGhbtr3vE5osJ/wyDCljdMwdJ2x7rOcef011t55Ny5/kLKmZmpnz2O0p4uK5hlkk0mall7C/md+DkD/6CC+ympSsQijXWepmT2fbCp/vewtLkbNZXEFgsgWK4WVNWhqjlQsRnhkkMjYCEdeep5sKsnVn/o8pQ3T3pnO+RNmYJCMRph35bVERodJhCZpmL+YyMgQuqahqiqJcAZPgZ3CKjdDHWEA5qyv5sirfTQvLkVTDXqPT2IAxdXmedX09pItVsZ6ulh2020MnTmJzV1PsLySdDxOSXk5I+1nKKiqYby3m4n+PlbcWMzkUAETgzmcXisWu8L4iEY4MsSsVRWIkvhOr5Lpj0BWzYeQWORfvz/ZFAmLJBJLq2SSScZ6ulh03XvOG6d29jwQBLoO7uO43EtvrJePzvko3zz4TV7ofoFbm2+9aOthMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWR6dzC/5Xyx5FJQPhdRlpAtVmxOF55gAVanG0GUwNBRc9lzRZsqqXgUgGQsQiadRBLy4Rx9J44yePoknoJCBk6fIDQ8QDwXI5wJ80LXC5S5yn5tCImuG+gabPnpaSKjKQAS4Swv/fAEZQ1+DENAkRRcioucnkMQ88XwkmzB5nYjKwqy1YrFakNWFNRsDgCrw4EgSShWG06vF1ewAMVqRRBEREni8Et9ZO2VoGYBA9Q0dk1FFEQkUaLB34AqGQiCgCCK+b+Fc2siCMQ0J9bpTfmVkGUKP/HXiIHAReww0y+T7Q6c/vw21/X8F9krZ8zk7P58wXDz8tXsfPh+cpk0C666ntBgP09+7Z/Y/fjDuIOFGLrBwKnjxEMTrLzjzyltaELNZgDwFpfgCgTZ+fD9dB7Yy+EXn2G8p4tgRRXe4hJmr9/IqR1bOfDsE/SdOIqhG6iZNEvfcyvzrriGkoZpyFYraiaDzeVGlGW0XBaAgqoahjvyBb92l5uJvh6CldVkEnGOv/oS1TPnMDnQx/Pf/n+EhgboO36U5771VU7vfJlYuIdotgt9bgOZDdcy6q1F+cYDpG/4KJbQANW2YS65oQ5d15Et+cNneZOf7iPjABTXeWnfOzK1DTNJlRM7hqhqCbJrW4yovext6Lk/LYZgYHPKxMMZ7G6ZnuOTdOwbBQN0zeDIK/1M9Mdp9rSiGzoiEpx4ArZ9FfvEWa6suxKrZMVtdVNkL2JWwSyun3Y93+x/kcSNP4B0FCHYSMVDH6DugdtRJ8Zw+Kxoaj4gSdcMXH4rNTOCSLKIw+th58P3c2LbK3Ts28Vr9/+IypZWgpVVODxeDATsHi8Dp04hCCBbbDg8PtwFhSg2G4IoYnMXohsapY3TkS0WBFHE6fXhDhaiayp2j48Dzz7BmV3b8RQUouWyJMMhdE1D1/JBP5qao6CimppgMUYySfHffg5D11GqqpALC9/hXvvTNpIc4bmu51hTtYZENoHL4mJz72a+vu/rDCeHmeafRp2vjip3FZfVXYaz4MLwIntAIpwJkyOHYRjEDBephR+H3f+N8sxfsmxWD6KkUT87xOEXvsLz3/xbyqc307hoBa5AEJvLxaEXnsYVCDJ7w5W8/ujPePqer/Dkv/0LoiRj6BrTl63E5nIz3ttFzcy5zNlwBWd/ITDiDT1HD2KxWVGsNhAErC43oiQy1t3Fz7/yJQZOnUCUZXqPHSYRDjN7/RVsu/eHnN65jTO7dvDcf/wbitWGxeHAX1ZuBpD8gcpl0mi5HAXVNSh2O65gEKfXRyqRIIqGYVEwjPx4kD/2YRjYnK6pNpa/53b6Tx1nqOM07oIC+k8c5cS2V6hsaaP7yEGqWtuwudxT146iLJGOxdBVFXdBId7CYuZtvIbuwwfwlpTScslalt50B5s+8Xe4guXMXPshttyfwuHJn6v7TuRD0dSczt5nusmkVFw+K4lwPtgK4NSuYUY6o6g5nVxGA2DgdJihsxGKatzEJtK8et9pJgfjb7ldJgbiVEz3UzurAMWa37fb942inmvL9O4hAB37RwEIj2r0nXZRULOGmlnz2fixTyPbbOx+/GGW3fw+vMUl6KrK5GA/Dq+PgspqBFHCwMBbWIiazaBrGguuuoHo+CjxyQnO7t/Daw/8GEGUyKRTxMMhwkODZNMplt9yB+lYjOf+8x6kS5ZScPfdjPhEvrzry5yePA2aCmWzoPs12PbV/J+SNsj+hlAxQQTJCms+Dyefyk+39SsQ7oZU9KJvU9O7w3hvDACLTaJ2VgHlTX7O7B7GHbATKHESGUsxe101Wx84DUAqlkWxyRzZ0o+W03F4LIx2R6lqCXJm7whWh4zDbWXLT06RiuWfDUwOJXjtwTMU1/nIZjSioylGzyRYoK+kLNpAz6FJNFXnhe9PYnE0cuVffw5XYSGSohAPhQgNDTIx0Meym27j6KsvUVBVTXR0lD1PPMJ4bzfZVBI1m6Gotn5qvVz+AKqaRcvlqJk9H4vdTjadIjY5QTIaxWp3UDVzDq2r1oIoYPd4sLlc+EpKGe06y8y1l5EIh3jpe98mHX/rY7jp4jF0A3ewgMMvPkMmkcDh9XF487OIooSuqSiWLLIiER1PUtroxeW34S2yExpKMOvSSkKjyfyzK01npDNCOpl7p1fJ9CdG1zX8pWUceuFpHF4f2VSKPU8+itXpRJYUnF4foiQTKKtEkmX6T53A4RqmotnOSFcEURIwDAOX10p0Iv1Or47pj0RW+5+FkAC4bTKxdI7+k8cwDJ3Klhnnfe7w+iipb6Tr4D5+cuInlDnL+OCMD1LuKmf/yP6Lsvwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTKZ3FzOE5GJRHHDoISRZweJwoNjsIAgoioLFbgdDQ8+pGAIoVisT/X2EBgeIjIxgaDqapqFmszQtXo7d7aZ9904sdgc1bbPxih6Wly9ncdliJtOTKOKFBcoA2XQOxSKRjGRIhDPnfabmdFLxHBj5Qnqn4iSrZ4mn4yCAJMsoFiuy1YpitSJbLEiygp7LF3dIsoxiteEKFiBbrUiygtMfQJQkHB6Zsf4YOiJg5OfhLMSq5ZBFmRp3DV/f93WOJU4jihKSpCDJCrLFAoIAhkH59ACBD36Qwk98gpIvfAFbSyuGahaWvJ10w86cy28HQUAUJWTFQioawXUuDEZTcxi6jtMfIJdOM3jmFFaHk0tueR9HXnqOp/7tX+g5eghPYREntr1MUW0d1W1zQBBoXLiUo1teBMAdLGDFbR9kcrCfLT/6L6paZ1I6rZnwyBAAC6+5kd2PPcjWe3/AvqceJ5tM4HB7ueJjn8JTWMTq99+FlsvhKy5FEEQyqSR2twdBEFHPBZPUzprLmV07AAhWVtNz+AAOrw+n18fB554kWFGJrmk8fc9X6DlykL5jB3nxu/+H9t2PkE61k8qewaj1EXb7QYqz6SMzKJvmxR20kUmq2N0WANSsPrX9FJvE7LVeqlviVDSlqJvtYnQo8bb1358Kw9BBEIiH0oiiMBUI84sGTofwyl5kQUbOpcDqhiUfxuIoJK2l+eS2T/KVvV/hC69/gYfPPMyayjWMpCfIFDTmG0iOw8rPwKrPkfNNx2KXpkJoJFnEYpNxBWxYXTLhkaGpsB1JUVh5+59xavs2nvvW1+k+fAB/SQl2l5voxCiSouAuKMAZDGKx2ZEkGVGUyGXzBfrZZILQ0CDxyQnG+3sxdB1BFLHYrPQcPQRAdHIMTc0BAmO93Yz1dgOg5XJMX7QEb06n6HOfBUkCQaDsK19BDgYvdreYfg2LZMFtcTM9MJ1GfyP3HLiHznAnG+s38qXXv8Q/7PwHPrPtM+T0HN89+h2UWVFk5c3LNbtbIdiYP+YoCYPXH/0ZP/mX/8d9P32Z9sXfIrf+K/iEPqy2Sfb8/AcEyipw+YPsejQfjlNQ1UAyGgGgdeUaXn/kftRMfp9ddPUN7Hn8QV763rd58b++idMfxFtUwvHXtpCMhPEVl16wPr6SMiYG+zm1fSvP3vOVfHu5HOO9XQCMdXey86H7KKispmXFaoY7zuR/b99gGJzauRV/aQVtl264WJvddJEpVhv+sgqsdidWmx1DNxBEAZvTSVbNEQ/6yaQS+WOY1Y7N5UKQJJa95/apNgKVVQTLK1GzWdp376SgsprmS1bjDhYQGuzH5nKj6xpndu3EXVCA1e5g5GwHdq8XQRCw2Oz4SkqxOJyEhxXs/tkce62YF3+Q4OUfx9j52Ai5lEY6kaPn2MQF6zDRH0e25oP9jDcGnvvHG8FTb+g/GaK03gdAOp4jOn5h8aim6TjcFjTVIDaRZvbaSqrbgngL7VOBJKZ3D1EWCZQ6aV1RTsP8IiYG4vSfSiNby3j95wIjnTkWXXsTBjq6rqGrOWSLlWQ0QrCiCpc/gNXuAEGk+8hBcpk0g6ePc3rna+i6zqo77sRit3PguSeRFQvHt75E38mjODxebC43NbPmkoyEYclixlvK+eyJf6U33stgYhBkC5x5Eew+uPTzcMknwFcF9t8Q0GhxwNovwNGHIZa/rsbQYee/w/Dh335jqVmI9EPaDDL5Q+AK2KidVcDMSyuJjqcxdIN5G2vxFNiRbRJNC0tIxrIkwlmK67xMDMaJjqfesi0BEESB2GQaXT//uBgaTpJN5lAzGq///CySLDLeHyc8+GZbWk5n6wNDbHswzYviMSpmzqSgsoqB0ydoXraKnmOHqGxuBUGk69A+WleuoXHhUjoP7qPnyEEwDALllTh9fqoap+PyBTF0HZvTRSoaITY+hiTJTA70MdzZTjIUIptM4nC586EXBYX4SyuomT2P4bPtBMoqGO3qIBWLXMwuML0FDQ1BlJhz2SbG+3roPnyA6ctWkoiEULNZRDlHZCyJ4ckiLwgzY0UZTp8VX4mDTFIlm1TRNR3FJmMA4WHzPtv09pIlmXQ8PnWMCg32s+jqG8gkE+SyWdLJBJKscGL7FrKpJO5gAYIk4nTFaV1RjCAKgMDkUIJUNPtOr47pj0RWPRdCIv1PQ0hUeo8dRlYslE1rvmCcmlnzODp+jAOjB3hv83uRRZk5RXM4MHIAwzDeolWTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJtMfE/mdXoA/WoYO1fNRbHbUbCZfJG4Y6KKOkP+uOQYGAgJqLosrECQ02EegvIpcOo2uq9gcTg7s20Vl60wKa+oQRYmzB/bSVncNZ8bPMKNgBoIhIAlvXcgoSiKSImJ1KsgW8byABACHW5kqHpIlGRGRE+ETBOwBBFlGkmQ0NYeazSDLCgKgqerU9JKiYBUcCIKArmpIViuKRcEdlKls8uPMdoPFA4YGNj9oKlarla5IF4OJQY6HT9LqrESQBERDQtc0ZIsFQRRRbBpDn/r01LzkoiLK/u3rv+9eMv0aFqvESLefFbf+LYotwWUf+TSHX3yS6ctWMtzRPhUaU1BRxXBnOwAzLl3Prsd+RioWpaimjublq9j50H1ouRzByiqSkQgrb/sghq7DuX1v5trL2HbvD9E1FVmxIFusZJIJVt5xJ5HhIboP7UfNZQmUV9C4cAnbH/wphq7jL6tk5e0fIDQ0wMJr34OnsJDLP/IJzuzawcJrbmTrvT/AYncAkI7HcXh9RMdG0M/tw1UzZtGxbzcAla2z2PnQvcgWK4XVNbz+yAMU1dZjdTp59ptfo2H+YtxD/Rx95UVqZ8+jasYsQsMDLLyyGYc7S2WTg/BYkGC5k8oWP30nQyzY6GDnz75KOpF/s3ZV23zql976dnfjHz1VV0nFdKpaggiCQEGFi/G+899mHih1MRQZIZaL4Q0PQLAB9v+IyMzr+Oa2fzxv3L0je7mp6SZunn4zAXsAEMBbBbIdUpMgWUhG42g5jcJKnVQ0jMXuxe52sunDszj9+gC1c+ZT2tCE0+cnPDJMz9FD6JrKmV3bGe/r4fKPfJKq1rlYHHasdidndu9g/zM/n1qGBVffSO28+cgWK4rNhqaq2D0eNE0DjHPhUDZymTTZRAI1p+IrKWVysA+A8mnNaGqOyOQkQm0VmX/6MvbWVoJ33YW9+cLCEtPbq8RZwqfmf4r+WD8AaS3NTU038b2j3yOt5YMMNEPjGwe+wd2z7+afT32ej9zySXzxYlw2J0VVHr7W/mWGEkN8NHc1hx55NN9OIs6T3/kuN/3l7VTu+CLiukXMvXwTwx1nqJ+/EKcvwMRAL+g63sJiAAzDQDsXLlbZ2sZg+ylGOjuQZJmFV99I14HdjPV0UzNrLulEnPr5izizawe5dL6g2OZyUz1rLvufeozeY0doW7Meu9vD8Nl2Vt7+Z7zw3XsAyCQTYMDk0ACC+NYFUUU1tfhLyhjv66H32BFS0QhVbbMoaWxCUSwXr0NMvxeCIKBrGrl0GkEQ0TUVTdMQRbC5PKRiUQzdyJ//JTHfp7JIQVU11372i6RjUTAM4qFJKlvaqG6bTffh/SQiYcqmTWfWustJRsJkE0lmrllPdGICxWqjbt5CbA4n2XQKXdOIjo7SuGgJoSEDNaeh2GSyae3N5RQF7G4Ff4mDZOT8gk930EZoNInLbyUdz/9eTFtYzOFX+pm9tvK8cX3FjqkifVHOt/nLRrujPP/fx6aCTMb748zfWEPFeh9Wx1sHCJreORI6cy4t4tCrYxzfMwJAaChJ2+pyKpsDBMpsWGwKqViMdCaDKMv4iksQZZndjz3IxED+HOwrKWPZe27j4PNPM3DqOIHyCipb2sgk4qy766McfOFpBk4eY8n1t9B1YC9nD+xFsVmZsWY9g+0nifhETkePcSZ8BgGBoC0I2QTEhmHaenjlHyFQB44AJMNQvQyszl+9Yu4SGDhw4fBI32+3ocbbYdtX4eSTUNgMG/4Zqpf+dm2Z3hal9V4io0n2PtM9NWzobITrPjmXqpYAnQfHsDlkZEUkNpHC7rbg8Finxk1Gs3gL7ZzZO0LjwmJO7xrG7rrwGGZ1ykiyxMHNvbReUs7kSIJcSqOq9cKwnIoWH89N7GDNrGUEXB6Kaupx+HyUNjaRCE8SnxjH6Q/gKynD0DVmrF6HoetsvfcHrL3zblx2F+Hb7sD9mU+iVVZhdbmITYxjc7kZ6e4kHY9SNWMWPUcO4Ckqwe52Y3W68BYWIcoyBZXVyIrCaNdZCqpqsLk9F2Xbm341AQFBFOg/eYzpSy/BMAyS0QhOnz9/3hQEguUujho7uXfnD3nwigdZ9d4mYpNp+k+FmLaohEwih6xISIpIMpojGc3i8JjXjKa3iSAgiBJoKm2XrkcUJbqPHqJt9TpESSQxOYHT4yNYXkn34QNUzZiNKIpkUykc3jQVTQVMDlk4uX0Ib6GdkjrvuWASk+m3l9XOhZDI599za4kcsVf7cK+oQHQpdOx9HVlNE0vn6Dq8l/Lm1vwz3l9SO3suh7vvwS7auL7xegBmFs7k6c6nGUmOUOIsufgrZTKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkesf85lfjmX47kgLRIURRwNB1cuk0I50d9B8/QiI0iZbNISsKgigSGx8nWF5B2bRm/CVlJKNhDN0gk07RMH8xx7a8xODpE5zcvoXypmZS6QSD8UEOjx2myFmEXba/5SLIikR4OIWkiCy6qi7/6uJz5qyvwu6xIP3CF5MFBAwMxlPjCKKIYejomo6WVcllc6hqjtwvhJAoigXZYkHXDTRVxdB1BEGkpM7B4g0BlMwoaClIjuWDSAQBi2hhLDUGwM7BnYiShIBAKhZhvK+HRGgSXdPQ1RSWurqpeamjo+SGhn7PnWT6dWxOhdnrqtn/YobBs0WcPeTA5rsWf2kbqz/weezeEpbeeBuTQ4MU1eT7SrZYSMXyb0WfvmwlW+/9AUPtp3GcKyYKjw6hZjMU1zcyc91lQD7YRtfy+9Wym++gfc9OXv7et9n6k+8hiAJ1cxdgdTppWrKCPU88gqHrNC25hKoZbTz9b//Kjgd+ei7oQ2DXYw9SO2ceocF+lt/8Ppw+H+vu+iiD7SeZvX4jCAKSIiMIIulEHIfHc24Z8sXGpY3T6D12BID6eYs4+vKLiJJEUW09B59/Cl9JKXaPh5d/8B1iExN07tvNw//0t5zZtZVUeD+v3f+vlNf3cMVf+ji754mpABKA3qP7SEd/y4JP06+k6hrBcg+5jEYsnKFpUcl5xeC+Ygel9V4G44M8cfYJ1NQk5BKw+G4yosxkevKCNuO5OJvqNuV/kC2QnIDNn4ft/w8tl6Ww0oXT3ccz93yOF7/7JTZ/9x+IjbfjLXJQ0TIDDGjfs5MTr22hY+9Olt30ZvjM5EAf6VgEh8fGvqceIxaaYP+zT5w3/31PPoqgC2AYSLKMrqpY7Q4MVUWQZGSLldkbrsivfzaHJEtgGJQ3tVDe1IJhGIiixL5nfs7LP38Q8Qt/T3LvPvRU+iL0gOm3sb5mPUvLlmKR8oU+VslKJBM5bxwDg5yeYzI9yZfa/5ZPDP0ZP8l+i+3xV1hQsoAVBUvo3Lr9vGnKm1qICwGOL/g2yViE0e5Oeo4e4tiWzRRU1eAvLWfLj79DLpOh+ZLViKKEKEkEyitou3QD3qIS5m+6jvUf+iuOvvwCZ3btIDQ0wMHnn+LQC09zZtd2Nn7kb1j9/rtYd9dHueZTnwfDoPvwAZbddCvdh/bTsed1ug/vp+vQflpXrZ1aNlGSOPT805TUN5JPY3vTrLWX5wNI+nt56IufY8uP/pNdj/2Mh774OXoPH7xIvWD6fZoc7CcVjRAeHqT/1DHG+7qRJAlRUjAE0DWVVCxKz5FDnD2wl/DoMKHBAXRVxWKz4SspJZtKcmzLZrqPHKDr0D785ZVIioKsWOnYu4sdD97L7p8/hGSx4i0uRpAkbE4nY33djPd1k0nGcfh9JMMhNC2EzRHn0tsaps4JilVixc2NxMNZ5qyrwmJ7M8SvuMaNr8SBLIssva6e8EiSZTc0MNwZpX5uIdn0m9e/skWkaXEJPUcnAJh3WTWB0gtDIPpPh5m+uJR5l9fQtqocq0Om89AYBeXui9wbpt9Kzw7k8Ck8BXZmramcKmTv2D9K63ILWm6EZDiMJMu4fMH8PZKaIxOPTQWQAISHB9FyOQZOHaewpo65l19N54E9vP7IA7zw3XuYteYyCqpq2H7/j7E4nDQvW0EyEiUZDrPyox8lZElNXZvc1nwbTf4msPth3vvh0P2w7h+hYgFYPWD3wviZX79eziIon3/hcG/F/34bZWLw3GfgyIOQS8HgAbj3Ohg99T+bPpfKh5iEe//38zb91iwOmTN788E6ggB1swuZd1k18XCGYJmL2tkFaJrOgk21DHVEqJjux9B1CipcU22c2jXEok11FFS4WHR1HalYlpblpVOfCwIsva6BZDRDJqGiWCXcfhv+EgdjfTFaV5RNnfoLKl1UNQd5b/mNWJIGajbHrHWXY+g6uqaRjseRrRYWX38zuXSa1x/9Gd2H9jPUfpoFV99IaHgQbDaMTIb4l/4ZfzKLlsmgWC34yyooqq6hsKqGk6+9iiCKyIpC98H9xMZG2XbvD9l27w8Z6+kiOj6Wv5758w9jd5nH5bebiEguk0bXdTr27qL36GF6jx3G4fUDBq5AgPImH99o/ypjqTFSWgp/iZPCSjd2t0ImkQ8d2f5wO6M9Ucb7Yxx9tY+uw2MMdoSYHIr/xmUwmX4XmprDU1BI16H99J04ypndO5AVBS2XQ1dVYqEJLE4nB597mqqZs7E6najZLFa7g0Q4zGRfJ96CGPOvrMHhtRAPm/fqpt9dTs2HkCjS+f/dkzo0Svy1AeK7hjizaztPfv3LpIf7aD9xitDQIM3LV71ley/n9tFXnGJ1ug2XJX9d0OhrBKA91H7xVsRkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyfSuIL/TC/BHS5RBV5FlhUQmw6s//m8m+vMFV6IksfGjn6Sguo5MIl9kY+g6FocTQRTQcjkM3UAQBGKTEzQvW8Fg+ykqps9AtlhxW9ysqlxFKBOiwF6Aw+L4lYtR1uCj8+Aow50R1v9ZK6lYDptTpu/UJKloDtny5heTRUHEKlnRDA3xXGKJYeiIUv4nTdVAy/3CKsrEJsbY+fB9REZH8BQWs/yWOyiq8hBMn4G9/w0jx99cmLVfQqxfzKzCWQB0RDoQRYm+40fY++SjU6PNvfwqWlauwcic/yV8yfGr19N0cZQ1+rjpc/MJj6SwuRW0nI6a1Slt8JGMZCmsaaOsaRaGlmao4wyiKCJKEt6iYka7zgIQKK/EV1zKjgd/CkDT0hUoNgflTS24AwUY5+ZV2dpG96F9xCcnkGSZxdffQmRkmNM7X8sXxxcXY+g6smLBX1bOrkd/BsDCa24kFYuw69EHKK6tx+n1M9rTxZFXXmTxde/hwLNPsvoDd5GOx1n9vrsQBIH1H/oYOx+6j3lXXsNQR/5N87LFSioaJVCeL8o0dB3D0PGXVDDW2w1A/byF7H3yMRAEimpq2fHgvQTKKxAlmV2P/YyWFZfSsW83oaEBRrs7L9ieicnxi9thf4oUDUPXsTpkREnk2OsDLL+xkWxaQxQFEKDryDjJtjgYYFVskI7A7v8k1riCVZWr2NK3Zao5WZCpdFVS7Cw+N8SAAz9+83NFRtPDvPKDe9DOhTJlkgle/M+v4y/9v4z39VDVNpuBU8fJJBO0rFhDNp2mqKYuv08IApKs8MRXvsji628hm0qCYfziGuUDoHSNY69upuvgPgAEQeTSD34ILxAdH8XmdrP85jsIVFSRSSZIRMLYnPmikEQ4lH+LOAaTA308/Z/3cPWdH0RyuTC9O9hlO3OK56BICs90PkMilyBoCzKRnpgaRxIkZOH8y7Tl5cv5+r6vYxgGX136rwwHRvGXllE2bTqKzY4kKzz77Xumxm++ZDUVzTOobpvDiddeoWPP6wD0HT/CoutuprSpmdXvvws1m+XZb30dDIOa2fOw2B3M33Qt8clJDm9+llQsSv+JYyy54RYOvfgMrSvX8tx/fJ0rPvZpLHY7JY3TmRwcYPaGKxk8c4pULEpBZRWuYAEnX9tCy4o1JCIRyqe3otiLuezuv6NjzysYhsbsDVdS3twKwODpk1NBVm/Y8eC9VDTPwOq8MOTB9O4Rn5xkcqCP1x95gEwyAYDd7eGqT/4dFouNaDbL4RefyZ/DS8t58T//fWraK/7q06TjcXqPHWbhVdfTc+wwGAYF5ZUU1zXSe/zw1LEwPjmBxWZHsMoYusbz3/4GsYl8uJ1ssXLZhz/OWHcX+55+HF3VaFq6kkvvuI5kxIqkiLgCVrScjmgR2Xh3G5HxNIpFwuHNB/OVT/ej5zRmr60kNJxk+tJSXD4Fu8tCdWuQTErF7lbIZXRW3DINl99KsNyFy2+7YJsEy5yc3jVEZDSFw2Nh/uU1DLaHEGUzA/JdJxNnfFzg5RetjPd3Y3XIzF5XRfueEeyuLCNdnZzdt5ueIwcprK6jqLae0oZGfMWlnNz+6oXNJeJUtc2macklnNqxFXeggOnLVnHg2Z+z5Uf/xcaPfYolN74Xu8fL9gd+TDISBqBh6XKm3XgF3tJFTA9MZ0bBDAL2QL7RYD0suBO2/l+oWQ4V80HNwNgpKJ/zq9fN5oGNX4EHbob4aD4tYvknoGTm/347Rfrh7MvnD8ulYKIdiqb/+mknu+DlL8GJx8HihrX/B2beBFYz/OFiE0UBiy1/PbH4mno6D47ReWgMxSaRjGSQLSKKVSZY4WTj3W2kYjmC5S5KGnykY1nUnI6vyEF0IoXdreAJWnF4rJQ0+KhqC5KO5fAU2FAsEmpWJ1juQhQFNNVAEMBiU+g/NcG8y2sASMayJCbPMvLE05ydnCBQVsGqO+6keuYchtvP0H34APuf+TnXfPrz7H/qcVbc+kH6jh9BzWVA1yisrEbTVESPB2XNpeze/Rrdp47l11WSWP8XHyOTTlEzaw5D7WeIjo5S1TaLbff9cGqb9B49xMrbPsieJx7hio996m3vExMYggEIVLfNYeDkMTKpJC2XXEoqmg/l01IZRntSWCUrqypXUWgvBMDutlBY5Wa0O8b+53ponF/MaHcUxSYxfXEp0fE0qe4sNpdCz9EJGuYX4w5ceI42mX5XOT1NPDRBy4o19B47jL+kjLKmFgwBtFyO2tnzkRUrDp8Xq82BxeZAUhTGB/rwFhZhdThJhCcQJCvJiER8Mk15kx9vgfnc0/Tby2o6ggCyeH7opxrKAKCF0hza+Qy+klJKy4rpHIlQNq2Z6ctWXNDW4+2P85V9X6ElW0n1/hzGnfn/p2j050NIOsIdXFJxycVfKZPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwm0zvGDCG5aAwobsFqtzA52DcVQAKgaxq7H3+IjR/5JLlUEpcvQHh0BJvLRS6TwWJ3oOs6kqyAoWN1eqlsnYkkK8QmxilsaCBgDTC7cDYtgZbfuCSaZnD2wBhnD4ydN7y6NUhkTKWgwoliVVBEhUg2QigdYo67AUMAUZIRJBBEEUEA/ReK5dVsli0//m/S8RgA0bERXvn+d7j605+H1Mj5ASQA2/8Na8MTlDnL+MKSL+BK2vCk/cyoW0XxnfU8/5N7ULMZDjz/FDWz56GFI1OTWltbsbW1/TYdYfodZFWds5ksXVqaQlUn6LaiGyIun5XySheiICBLQQCKar5EuK8DWbqJQy9tRrHli30aFy5h31OPAdC2ZgOTg/28+uP/QlIU5lx2JQ0LlhKoqCRYXsXJHa8CMOeyTRx+8VnioQmsDieVM2aSjsdZcesHMAyDvhNHAWhYsISRsx2UVDSwaOX1FDU2kJtIsmb9nehumIwPo1ht7Hn8ISpa2jj68gssv/l97Hzkfmat24jT6+OyD/81mWSCy+7+ONvu+yGljdPpPLiPdDyGO1hAMhymZtY8IP97oGvquYKRMAD18xZx4LknAfCVlHFi2ytkU0kqWtpo373jvO1ZUl1/UfvrT5GUsyBbJYRkjlxSJTyc5MzukfPGmbawmNdGtnJHy23Yjr8IjRugYR2yaOWDMz6IVbKyuWczFa4KPj7v41hl65sTp2PntSVIMpNDI1MBJG/IJBKEhgeRFQvj4z0U1dSin3uju93jobC2ntHuTmasXodssZBJJrG5XIx0ncXpD5AITU615fIHSYQmporuIR9M8voj93P1J/8eMPAWFqPmcpQ1NtF1aD+ZZByb04koy7TMWonT8FJ1WQun6nby2mM/JlNciLXB3P/ebWYUzOCe1fdwbPwYs4tm88+7/5lIJoJNsvHZhZ8lZ+RwKS4EBO5su5OZBTORRZmUmuKJ3qf5y+tu4dRzL/L6Iw8AUDd3IbPWXc7hzc8BcPK1LSy98Va8JSXseOin583bYrPx+L98gTmXX0XfudCHhoVLAHjt/h9R2dpGZetM1t75YY5ve4WuA3sBCFZUceSl586FpznY99TjzFyzgUwizo4H70XN5gucdE1l9oYrWfW+DyHJXhRbCZoeY8cjk8g2iav/6iMUVrkRhDcLpHKZC98Ankkl0DT1guGmd5dkNMxwZweeYBFr7/oQdsOBZjEYG+rBgUgmkWCks4OlN97KzofvO2/adCyGw+dj2pLlhAb6KalrRFJkDr34DMtuvoMDz/x8atyCqhrS8RhGSQGR4yenAkgg/0b6+MQkux9/aGrYqR1b8BaXUT1rPU6/hUDxm4EHZ0728upPO87LgZp5aQV2twKCgM2h4ApYCQ0mKFrixV/q4sT2AZ7/z2NT4xfVeLj8L2ZcsD0SkQzbH2rHMAyal5WSSajseaaLjR9qQ7FIv8umNl0EmZTKq69YGO9PUNbow1/ioPPgGI0Li/EWTHBm1yl6jhwEQBAFUtEIx159menLVlDR3Hre+RpAsTlYecMHsCat1FwxA81p8Pz932T2hit57f4fMXD6BLsfexDZYmX5LXew/Wc/Qc1k6Ni5ncY1q5k1Y9GFC1k8A/r2wJIPQ/drsPUr4CqB2bdCqBf8Vb96BcvnwZ2vQKgnH0pS0AiK/X+/oRRHPjQkc/610a8NEsmlYfAAdL2Wn+/iu2HXd+CZT0CwAepW/u+Xw/S/YrHJLNxUy6HNvQx1RvAW2fEWO+g5Os5rD7az+Jo6tvz0NDMvraCoxkMmpRI6NsGhl/rebESAjR9qIzqRJptWsYWzOL1W9vAktxCDoRRnWzdxcPw0S25cSe+RSfwlLlLRLLpuMNIVY6Qrv9/M3eDhtfv+39T1wuRgP5v/+1usu+ujSIrCRH8v1bPmEh4eZta6y9l27w9oXbUGT0ERmWQSwwADHcFmI9PSRPfTj0wtpq5p7Hz4fi557/uY6O+loKqa0Z4uoqNj3PHpbyCnJTJCmv07nmCo4zTJSJjJ/j5c/sDb2icmEAyQZIno+ChFtXW/cN/kRtN11FQCyeJiY91G7mi+47z7M5tTAQFki4ivxEH7vhE23DWD1352hmQ0m29fFFh6fT1Ht/Sx+NqGfDjl/4KhG6iTaYyMhuS3IjmUqc/UcIbcSAIBkIudyD7rr27I9EdLzGaxOlxouQwVza2Iokh0fAR3IIggigQrqomOj+H0+XEGguiqyskdr+IpKCCTSFBUU4usWHA4Ewx2CFjtNqyOxEULIdGiGbRoFtGlIPvyz+rUyRS50SSCJCIXO5A95r78hy6r6Vgk8bx7bAB1Mn+fnQ0nGDpzijmXX0WVpYZ+bYybP3sbgnh+SOJAfIB/2vVPLC5dzF/K17LlpW8z3ttNYXUtXquXInsRHeGOt229TCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplM7wwzhORikawwdhp75wtkkhcWG8Ymxslm0oiygmy1IooSAgIIIlaXCwwDi82Or7iUZCya/8wwsDodSAhcP+368wvlf41guQu7WyEVy00N8xbZGe2N0bF3lLJGL4HSfCHH9499n6H4EFevXocoyWi5GGM9XeiaSlFtA1aHa6qNVCwyFUDyhnQiTjI8CcovFQwLApmay/EmihBkgSusa8n2R9D1NJIhUmqp5dZPfo0ff/mjYBhk02nK//0eYs89j7W5Gecly1GKiv4XHWD6fTjcNYFjNM3KiSxKuYKMRi6loenQmYiS0gxcARvFNgsOtxtvUS3u8Bkqr7uElKuOk9u3IogimqoiiCIuf5CjL7+AIIpc8t73c/C5J9n/9BOs/sBfUFXZSkP9As6e2ou3qBTD0AFYcsMtnHhtC82zV2BzOfCXV1BbO4clV9yMxbCg6BaSzw9iLygg8+I4ucEESokD99wiXLKbG//s/5Bz5pgMDeH0Beg8tI9lN97Kniceoe3SdZw9sBdRkqhsnsHsyzZRUj+Nqz/590z297Hmz+5mzxOPYLHb8RQWMdxxhuq22fQeO4LD4wU4Fxgko+Vy6OeCKUa7ztK4cAmx8TGGz55BkmVmbbgSX3HpO9aXf6xsmpOu4xNUNgfIpFSaFpcw2nP+camyzc/6kvUsdFaD5ygkJ8BTRtAeYCQ5yi3TbuHW6bciCiK6oZPJZRhJjFDsLAa7H9Z+CcrmQCpEOmWQTVkRJQld06bmYbHbyWWzSJLEL5Z7qLkcbpud6hkzKaispqi6lmwmg9XlIjwyzNFXXmD5zXdwasdWhs+2U9owjRW3fpCJgV5+WTISJhkJs+WH/4XN5WbDX36c8NgoGAa+ojIqq1oJFlSQ642j57IIAkwvXkzgL8qxFTkvKCwxvTtUe6up9lYDMLNwJqOJUfw2P1k1i2qofHftdwGwiBYwYFPtJh5qfwhN1xjq7qDr0JvF792H97Py9j+jsKaOse5OgDf3UyF/HQFgdTiJhyaZuWYDxbX1nHn9NQCKa+vZ8eC9tK3ZQDISZudD+bCIhdfeRNvqtcRCk1S0tHHkpecB0LJZBk4dw1NYiK+oZKqgOFhZTUn9NF5/+D6al69C08YJlueobK3CHXRSVO3Er59BiJaCt3xq+csapyOIIoauTw2bv/HqqeOt6d1LtljRVJVr3/u3GJEcRk5HkiQq3NOxqmexOPIFlb943HyDMxDA5vYw0nEaw9ARRJHwyDCzN2zi7P49OHx+wsOD+ErKWHzde9A0lSO5boomI+e14w4WMtp99oL2O/a8xoKrrkbXZELDCUBHz6nYdAeVzX5ioQzRsTSaqnP2wCiz1lRhdcg4/VYmBxIU13qQZYnYZJqdj+XbX7CphtI6H6lElonhOC6/jWxaJTqWQpQENM2gYrof2SLReWgUm9PCwitrUXM64dFk/tcRUCwSTq9Z8PlOi0YlJgbTLLuxgZ5jE3QeGqO03kdBuRNDn5gKIAEY6+6iackl7Hjwpyy69iYyiTjNy1fng/QMg7ZLN1BfNRe1NwGGjpbIkTo6xjW3f4ZIepyr3v9ZvNWlNDUtw8ipDPSfYvrSFRzbshmAVCqOpmtI4i/dP7qL86Ed7S/A+BlY8wWIDUI6BGMnwVMG0q+5tfdV5v/8LvzVsO4f4emPvzmsYT0UtZ4/3ugpGDuVDzqRrHDvNVPnHwoaYeFdsPu7MHzEDCG5yNLJHKlYFneBjUVX19F3MkTvsQmqZgS55KZGEtEswTIHV/3VbKITKaw2GS2rc/Rg/1Qb/lIHbasqsDplAhYHo70xug6P07KsjCXKJiZtp/FkdlLf/jLlJbPoye2kYl4ZTilAImphpCtKoMzJ5GACAEOLTF0vvCE2MU4yEkaU8/t9eVMLiALjvb20rVnPxEA/Npcb2WJhpLODkoYmfP/x74ycbb9gnRWLlfLS6ZQHmkARyM5LYtccJA+PIVW4sefsrFz1PgaTZxnt+vHUPE1vL0MAEDCMN68LdE3FYnciyQrpXJRAUYqr/Vcznhyn2FmMQ3GgJXM4ohnqXTIN729GciuUF7cQTmSnAkggHyLSsW8Uu1shEc7gDtgwDIPceBIjpSN5LcheK1o8ixbPIbpkjLSOnswhuixkzkwSeaYLI6ejlDkJ3NSEUuIkN5pk/EfH0Cbz+7AUtBG8vQU9kUOLZJD9NpQyJ6LVfNT6x060OrG5XISHY0hKvr8FUcrfcwsChq6x8+F7WXLDrQB0H9pP+fRW7C4X0bERJgf7qambjTwkMn+aH8Emg11Bi2WR3JbfOH8tmkHPaBiajiEJiIIAOkg+K3pGQwulEW0yctBOtjvCxM9Oo8eyiE4F/03TkDwWxr9/DD2ef04slzsJ3toMqkFuKA6SiKXUiRz8LULLTO+YrJoPIfllejLfzyNDXWiqSkXzDNwDMvGs9pbPie49cS8GBv+47B9xZmS28G26Dx+gsLoWgAZ/A+2hC8/BJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUymPy5/EN+MFwShEvgJUALowH8ZhnHPO7tUv4EggrcS+ndTVHXrBR9PX7aCdCyKYrMhyTIOrxcQEEQRxZIvRtQNHdnuQE5nsDoc6JqGbLGCwXkBJIOhJOPxDOGUil0RKXFbqSx4MywkUOrk8g+18frjZxntjlE2zUfj/CJeve80oihMvRVWERX6Y/mCoyFtDEcqx8vf/zbZVAoAUZLY+NFPTbXrChRcUIgviCJutwNs1fnCs1x+2tDyb3EyshDpeJqmVh9GSiXbHcVS4QZBIDeUwO7xU9Uyk5Ges0hKIbGiYkr/adnvqUNM/1sT8QzVo1lyT/cgNvhA1YnuHcG5sASxE9yHRymaWYhSC9l4lFiBndhkhlRyGRZvjgpXJ7d85A4iORvTliyn98ghsqkk/pJyVl7zfgAu2XQHXnchtqSD+H392KvcLJixiexAnKo7/hHJIoEsULuslfTRSdyrKoi/OIDcZEcIa0gBSBwZQE9rCIpEbjCB6FKwzywk8mw3AHKhHceSUkRrGe62EurmLcJAZvUHK1AsUFLfTGRsCJc/SHR8lMjoELLFis3tRrYoLLnxFjCgum0OI50d+EvLCFZWk4rHaFy0jDO7tjNzzWXse/pxZMubxSI7HrqPxoVLaVl5Ke5gIdsf+DHl05rxl5S9/Z35Ryw5rlLZFCA+mcbikAmUOVm4qZbDL/chKSILrqildkYh0+1lMNEB7hLw1YCrmCPjR/Hb/OgRicRkDqtdwVWk4LK76In15ENIPJWQjkDPdvDVYgg52vdnWXT9nex+9PvomoqsWFh7519hc9nIptIMd7YTn5ygpGEaJXWN5wrqfCg2O7HJCQoqqwmWVeD0+VEzGbb+9PvUzZnPwqtvQFfVc28DNxAEcSqMB6CsqYWzB/YAkI7HOLHtFRZcfRN2p5cyqR5jSEWXsmT7YyjFTgzdINMdoWzhNMSq377I/dhghM3HR5iIZ7h6Tjkuq8R4LItNEanw2yj1OX/XbjQBhHopz0Qod1eBM8ih0UOcmDjBk2efRBAErq6/mumB6ayuWM1D7Q9xS9MtDP7w2anJq2bMomrGLHoOH6Souo6W5avo2LsLT2ExWkajaclyTu/Mh43omkZhVQ0ntr3C2X17mLZkGQeefRI1l0Ox2rC7PRx9+QUAFl17EwOnTrDn8YcQZZk1H/wQ6/78w8RDk4z39dCwYAkntr7MJbe8b2pZpi1ayvGtLzNj9Xpef+R+bC43rkCQ1pVrqW2uJ9Z/iN7BCQodWTxzNkGgBoDi+gZu+Ow/sOvxh0nGYsxZPItG4TBMtOaL703vWu6CQi7ZeCv6QA49lEF0K+gZDX0yhTJvBoXRZ6mZPR/D0FFsdnLp1NS0FocDLZMhFYvhChaQDIUoqqnH0FRKG6YxY/U60rEohm6QCIcJ1NXQPrmdmmlz4fk3lyEVjRAouzBkobiugehYjpd+dITJwST+EgetK8qJjCYpbfQzLWAlm9aQFYlcRiVY7kKWBbJZlUCpg8JKNwCaqpNNq1z98dkc3TrAged68RbZWXRVHeGRBLuf7MLpsyJbRHwlDorrPAycDtOyvBwMg+h4CqffylP/foi2VRUoNglN1bHYJKpbC7D/DwpOTb9f6XiOgfYQsiIxe10VB1/oJVDqpG1VBbpmEBpJUtHox19aRiqaD70xDJ2OvbtY9xcfQ9c1dE2jtHEarSsuxSX7sYlOkrtGSB+fAAPkIgeOWYUk94/inlOA0+kgtX0C99pq9ESOaQ2LqZzWxrEtm3EHi0g5BboiXTT43+KYV9QMmz8PS/8KXv0y1F8KJW0weAAcQaiYf/E3WttNUDITUiFQbPlrqtETsHsruMuguBV+dkv+c4DC6bD4w/D6t/I/j7dD2435f3vK33IWpt+doRsMng2TS6skIzkOb+ljwcYanF6FeRurGeuNM9QZZeDUJHMuq2bHIycJlDpYuKkOu1fG6pApriukdXk5mqoTn8zw8o9OER1PUVjlYuGmOgbbw9jdChGpFkGspdCbo+Sp62na9G+Q6Iahx0lMdKIv+RolDXUcfXWAgZNhHF7XBcsrW6zYPR6S0QiyYkFTc5zdt5uCymrsLjfBiioyyQSGrqPYbCTCkwiBIAUzZp4XshYoq+KGu/4PYkpAkGUyJ0M4W4PEXx9CkCWiL/aglDmxNQcpL5rGvPVXU1hV+zb3jgnAwEAURRxuP4rNiqQo5FIpJEUBDHY9/jNKpjXxZfkBrqq/Cqtspa2gjcSeYbRQBj2nYS13ocVzuDDwB+1UfriN9q4opw+NomtQXuOhqs6NnsmR6k1jhHNEXuhGm0xjawvinFdC+MmzCJKIY04hsVf6MHI63o21RJ7tmlrW3GCC8LOdBG9rIXlkbCqARLBIOOYXk+2LoY4mSR4cRY/n8F5eg2t5OcJbBAGY/nio6EiKgsVux+pwYBgGkqwgCAKCAIlohN4jBympn0njojUI0h7sLhcv/ue/o2saC9Zdh7w/h7PFjx7OgayCmiI5nEAqdBDVdSZkEO0y6ZyO2yZT67WjjiXJ9cWJvtyLnlbxrK8GVSe2tR9D1fHfOI3o5l60yTRIAoFbphN+rB09mQ/M1VMqme4Ist+Gc34JyaNjaBNp1IEE6nCSyQdPY2Tyz3glr4WCD85AKTbv9/9QZFUdi3zhscdI5/t/YqIPgNLGJjwTw8QzKrpuTP2/AICmazzX9RwrKlZQ4iwBJxRU1dB9eD8LrroegHpfPQ+dfuitg/NMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUx/NP4gQkgAFfgbwzAOCILgBvYLgrDZMIwT7/SC/UqCCDYPdL1GZeFhLrv7r9n+wE9IRiO0rlxDy4rLSUZGMAwDxe7ALUpYnPkvdmvZLIIAsixjsVqRCwsREBBlCcMw4Be+HByKpxmIpDnaHyaj6thkiWSRE0EUqAi8+UXx0nofS66rp/vQOKM9MV75ySkAWleU4ynMv52+2lPNsrJl7BjcwZlUF4WnolMBJJAvWj62ZTP1CxYjyzKlDY0se8/tvHb/j6bGWf6e2yiyJfKFQDfdCzu+gSZaGLGvQknnAAN70EHubBilyIlgERFkCUulG0ERmXfZdeR0ePW+YWrachRUOFEsfyi76R8XOZkj82o+lMZa5yX6Yg+Sx4Igi8R3DmJr8qOnVCIPn8G1tAy1K4p4apKipWWICQuxvipc09xYJhKUzG2CFXegZzRmlVxKcuswnnXVxLb34VjuIrq5B8EmYWvwE3m6C3tbAdrJGOnBOI75xSR3DONeVUH0+R4Q8wU/6VMh3Je60OM5JK8FLZIvBnLMKiS+fSC/3A0+lGIH0We6QDNQat2oc2R6ug5TVFdPKqbj8hdTWFOGbBHRNAeCkCGXSdB5YA9F1bXse/pxVr/vz9n12M9Yecefk4pHqGqbhaQo2BxOwiPDWOwOAhWVjPf1sebP/pKdD99PLp2moWUhZcXTGBvoIlhUQXhk9B3rzz9WFkUhnVCJh7J0vNSPxS4xc3UlG+9uw1fkxOH5haJuyZYPFRFl+uP99GaHqUo1cejHE2SSKpCjcrGLujUiTvu542c2Cs4CKG4DNUXQHQdEOg8FWHLT36FrcSw2H+XT6olO9BAbH2fGyrUo9vwbg3VN49ALTzPUfpp5V1xD4FwR5eUf+RvCI0OUNzUzcPokZ/fvoffYEa79zBfIpgUy6SCXf+STbL33eyRCk1S0tDFj1Vpe+M6b+VvjvV1gOAg6LcQf66bgL2eRm0hibw2ihbOg69jqvRhpDZvL/Vtt39PDMd73/d3kNIO7V9YzHE4xEsvQH0ry1OEh/nJVPSubgjQWeX/LHjSh5eDUM/D0xyEbh3X/CG030x3p5l/2/MvUaMfGj/HPy/+ZeYXz+Picj+OaKKKgYjr9x49isTson97K9p/9BABfSRkFVdUU1TUwOThIsGIG1bOvoLRhOp0H91La0ITd46X/5DEA0vE4czdejc3hwl9axnhv91Q7iVBoaryFV91AaGgIX0kpdrcbq9NF1YzZ5FIpctksFrudbCqFrutMX7aSfU8/xuLrbiaTjOMvLcdTUMTY0AC7n3iO6NgIvqJiri2ZS+BcCIkoSlRpJymrOY1m9WPt/Jd8IbtVhkv/9m3rEtP/XnFNHemuEDn7ueA6S74YTS5yoKdzuNQIq1e3MpDwsOaDH+L1Rx8gMjJMRUsbFocTPZvBV1JKOhbH6nRyePOzzN90Lb7ScvqPHcHmdmPoBt6iYr49/BOe636e/oIebn/fHRx57Ofk0mlmrr2MqrbZFOzcOrUP21xuZq3bxFPfPEIqln8DeGg4yb5nu2laVEI2pbL7qUEa5hYRD2WIjqdw+cNoqo7NqdCyvJREJI3Ta8Plt3L5X7Sx//ke+k+FqJ1VQGGlm9BwAk3VKan1sPe5bjIJFatTZsXN0yhp8JCJqfSenCQRyVI2zU8inGXHox0svroOEMilNXpPTNK0qOSd6Lo/acOdYSYH47j8NjwFdoprPUiKyJ6nukCA1kvKKKoqZOE17+GZe/7vVHiOIIr4SqeRjAjYPf047D7sw1a0gQTR4TEkl4J7Qw1kNBBALnagHp9AUmRCL3bj3VBD5LkufFc3oEYz2EUPszdswlc2H5seYDDR99YhJMEGqF8DE+0w9w4YPQVDhyHYCIcfALsfgvUXd6ONHIfubRAfg5plkInDyDEI9ULHSxAfhXkfgO3/lh9/7BQ0bwLJAlo2P0xXoWIRVCy4uMv6J2xiIE5sIk02pbLjkQ5mr6sknchi6AIvfu84alaneVkpCzbVEptIs+z6Brbef5qXfniCK+5u45L3TCObypEIZUjFc+x5qovcuaL0sd44rz3YzoIraxg4HeLU68MAyIrIZR98iurIFshEYM9/s/+6f+fD2y4HYGn9cubMno9QMZeWTRs58dSbQWoLb7uVTssolUUlrHzfnRiaRt+xw8zZcAXxcAhZsbD/mSeYc9mVWOwO1EwGQRDwlZSx7sOfZMdP/5tkJMzld3wcSZQRnDLx7QMIsoiR1rA3B9EmUliuqgcREjsHkQvslMt1iCkRzPr6t51dlJEUC+6CQkQ5f81gd3uRFAUDg/7jRxElidZlrXz3yHdpCjShhtLEtvbhWlKGZLFiGJA6MoZ9eoDolj6cMwtpdMlMW1eFFLAT39KH+mIIy/UN6EmV0KPtoOUDa2SvjcmfncZIqXjWVRN9sQfyH6GfK9Z/g6CIKEVOMp1hRIeMbZqfTHcUz4ZqYlv70aNZBLuMZ2UF8d1DRF7owdYSRPJYEa1mcf4fKykHitWKt6gYg/x9jGHoSBYLgiDy8ve+CUBoqJut97Yz74pFHH3pyakQ54qS6djwkumOoJS7yJwIIVhElEIHkWe6sNZ4qGoOgCKhxrIohXayk1nU8RSRZ94MyUGH6OZeAGzNARKvD+UDSAA0g9xgHEMzcK0oR5BFlGIHqdMh4lv6QRZxLysj0xXBUHUSh0YxMhqSz4pS5kQdTZE+EzJDSP6A5DQd5S0CkPR0fr+LJSexOpzY3R7ctgkMAxJZFbdNmRr3wOgBJtITXF57+dSwmllzOfjck2TTKSw2Ow2+BjJahoH4AFWeqou/YiaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMpnfEH0S6g2EYQ8DQuX/HBEE4CZQD794QEkmGbAqKmlE6X6HVuo+az/8dqqrjEhIMxFwY+jCK1YbNoeIoq0RXcyAKKIqVZDyGJCtY7A4QBERBOPcmVfG8NxSOxjKMRdMEHBaqgk6ODUZ49dQY4aocgihQ7nNMjVtc4yWbVOk/HcLhsdC0uITWS8qm2rMrdv5m/t+Q3pXmuZ7neU9q9gWrlY7HQNfPraLCnMuupLyphejYKJ6iYopscaRsGDQVRBFW/x1ppQKhX0aUVARRBHSQBKwNXsKPd6BF8gVptuYAhSsbuO/rh9ByOu37Rpm1tpLCSs/F6iXTr6FoYGTzX1Q31HyfW+t9pE5OAGCp8hDd3IPoVECA9KlJbC1B1OEEmc4I3strmXy4Czlgx9rgI/HsWTzrq0m8MoB9ViGJPcPoiRxGLt+2vSVIYv9Ift4lTqKbe7C1BMicOfcmdVHAyGpYKt3kBuP5YZqBYBHRolnkYD70QbBKU295fSM8BcAxuxDJayW9K0RT+Txku4s0MTLJCINd+7F7fNj8AdKxJKrqYebaWwiNJFl6cyOIORZcfQuHnn+KipY2nv+Pb3DJLe/jtQd+zOz1V9B/6gQ2l4tlN95KLDLJ5R/+BAF7KYlHeonHurEjMG/aBqg19+Xft3RCJTaeYt+z3VPDXvrRCTbc2UpZg//8kQ1AccKZZ4k6XFxSeSmnfxI9F0ACoihgU51YQz78BhgH7kMoaIDEWL64t/d13NXLWH/b1zjw6iTHt4cprQ8yb10pnpf+Amb8OVRUgaGTjMXIpRNYHW6W3nArT3ztn9j/zM+59jNfQFc1vEXFeIuKKaisZqSzg2w6TWFVNcGKKsZ6o6hpg0ymhnV3fRFJVlGzEzz5tS9jGPrU6lTPnINOAuLK1AqKkkT02a6pUB5row/n4tL/9XaNp1UO9oV49fQYX9jUigEc7gvjsMrMqvTSUORibXMRxwajdI+n8NttFLit+aUwDAZCKTTDoMxnf8siGNMvGD0Jj3wADB0W3w2v/weUz+fFnhcvGHVz92aa/c2IKYXDj4/TtKiVQPluimrrOLn91fxIgsDs9RvZ+tMfTO0vdvfzrPvQpxjvClPa2ERhTT1j3Z1T7Z54bQvr7voINqeHGZeuJzI6Agf2UtowjdKm6VS1zcJis2NzOsnlcudmI5DLZBjpbGfJje8lPDLM+rs+Rs/RQzg8XjLJJG2r19N38iiz122k69B+RrvOEiivZPb6jZzetZ2Rs+10n+nC3TifVDSKms0QDVlwlawicOqH+QASgPYX4JJPgGK7qF1h+t0IkoTkUohv6UOLnru2awngXFaG3vxeAiMvE9DOQMvV1M7+GslEHCOnkghPko7H0DWdyNgwnmAhbZduYOdD97Pxo5/E6nCQSSTxFBaieF08d+x5AHaO7+a0rYNr77iCTTVXUFsxHUEUue5zX2S8txtNzRGsqCI1oU4FkLwhHc+hWCV6jk3gL3YgySId+0aYd3kN+57tZuGmWvY81YVik6mZGSQ8kkJSRKxOhf5TIZbf1AAIpGI5BEng2NZ+amcVkknkzyeZhMqr957m0jums/1n7TQtLsHqUOg7PoGnwEZoOEk2pYFhkErkGOuN07ig+LxrfNPFNzGYQFMNYpNpJEWisMqdDyAB5l9eQ9fhcYprPGTTxay58/OomXF03YI7WM7+Z8cpb/JTWu/DawTRfRqWAieOeQJqOINokYm83Ju/zhXAvaoCJAH79ACZ7iioBnosi2iTMTSdyubL2Pzjs8y/3k1haeFbL7BigyUfhte/DTYvNF0OAwdgvB1ECcbPXNwQkvF2eOEzMHwM1n0JXvhbKGqGhnXQcg3IFhBliA2fP11yEqyu/N+iDJWLYf6fgdsM3rlYktEsuYxGKpZD1w2Kqj3EQxm2P3wGjHzAjqSIRMfTqFkdUVJZfXsTOx89S2QszVhvFG+hg1xWI5NUpwJI3hAdTyEIwlQACYCa09n6cC833FqNQ++Akhk8Mrxj6vOdI9vZObKdU9k1tDU3s6rlE6TDUYaVMJ2+JP+67wt8qfqTeAbSlDVOZ+2ff5h4OExxXQMHn3uKmWsvQ5Jkdj/+EDNWrSV0YJD23TtoWLSMSz/011isNjzuIjBAj2RRChwIcj68Mv5KH7m+GJAPBfJuqsNIquiaQbo/SsgCRR7729M5prxMCtliweH1IIgSopi/XxBEEdHInwudM+qockcBGIgPMK6lEW0yWiSLXOYkfWoSe0uQ6OYevBtrSewexjrNj2yTSewcJNsdxbO2Cj2poo6npgJIIB8sYqTy52xD06cCSIBzz93e5NlQQ2xr31TQqa05gOfyGmJb+tDPXe8YKZXI5h48a6oQJIHYq/3kBmLYZxXhmF2I7LdhqDrqZApDM5ADdsQ3nlvIwlR4m+kPR04TUex2LHY7ICAIAhgGoighO5xYrPl748qW2ex6KsnAaQepWGRqesEQEKwSSokTAYFsTxTP+nwgjmCXsdZ6UcfTZDojCFYRcl50u4IWSr/Zhl1Gi2amflZKncRe6btgWT0baoi91DP1nMzeVoCtNUj6+ASxrf141leT7YmiTaRxr6pET+bI9kaxVLoRXcp5belZDXUihSAKyEE7RlY39+F3kayqY5HfIoQkpYIkkFDDeAqLEAQBty3/X0Kx9PkhJHuG9yAKIkvLlk4Nq5k1l31PPUbf8aPUz1tIvS9/vdkR7jBDSEwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZ/oj9QYSQ/CJBEGqAOcDud3hRfj1RyRdiuYshHQKLA6digBaBbV8lWn0PTpeCpFiwub2k41HS8Ti6rmGx2bF7vAiyjMWWL4bRDQ0BAVGSzyuKUDUDv9OCwyLxDz8/htOm0FTi5gfbuzg1FOVv1jchnxtfFAWqZxRQWu9DzWnY3Zb8F+V/QaO/kW+t+RbDyWEoGOfYK+cXQc9cexmyxTL1s2K1UT69hfLpLfkBPbvyb7pGAC0DahosIpIkIkoiAgaCCrLPSmLH0FQACUD65CTy9AD6ueIQp9eKYvuD20X/aAhOCXlOEbndwwjnvsSuxbNIHitaKJMv1iFf6JDtzRd1WcqcRF/qRfJayY0lQTWwtwaJbes/VyCR7285aCN1eCw/nze+IC8KcC7s5I229VgOyWcjN5xEEAUQQJ1M45hVSKYjTPLQKO5VlURf6iE7GMe5qITccAJLtYdsX3QqREXyWxHsMrGt/SilTixeO7GHerE1+bEXBrD21GGd5sNi85Ajg+K1kR6M4LZbUYoCJCfD4Csm+J5qMvEsV3/qa4hShiv/5vPsfuQ+WletYdu9P6Dv5DF2P/4gFdNmsKLiRvRfKHrWzyRxzyq7uJ32J8jps7L/ue7zBxow0hWlYV7x+cNzMZBEOPU0pVULGSi9klD/uZADARZfW8/pXcO0f3sExSqxbF090wrtKC99AULn5nH6WYqGj7L2xgfIvGchVjGJ/JONqO5y+iZh3zPfYfnNt2N3e7C7XOi6jiCIzLh0PYdeeJpkJIynqBhD1xFEEafPT93cBectpqbq6IYBBkTGJWSLjLfAwYJrbmD/04+jqSoNCxbTuGgZ3ftfp23GGuxtBST2DkPOmAogAci0h7HPLMBQ9Td/1/4HnjkyyGceO8qiGj+SKLCjY5yFtQFeOTXK4wf6uWtlHf/1Whd/ubIep0XildOj3DS/klAyy0N7+/jGS+3kNJ3bFlVz18o6ynxmYeevFOrKB5AA2DwQ6YNtX8dTWXPBqB6rB/vAIS6TangsnOHgixmmL/kzSutFhttPA1DaMI3uIwfPC6yZvWETL3z7/5JJJCiorMZfWoanqBhPYRHpeIxL3vsBsskUuqriKy7BX1pO/8lj1M1fxOkd23B4fdjdbrKZDFomQ3FDIxabndj4OInwBA6vF6c/wEP/5zO4CwqJjA0z74prGe06S8vyVTx9z1fAyJ/bJVlm2Xtup2H+YsZ7uvBWT6Pv2FFUNYfD6yPtKGNsTCa28KvUvP5xhNgANG4wA0j+EEiQOjI+da4HSJ+YxD6jALnAA6eewWhYC+4y4v29xEOTKBYrCCKapiMgECivZKSzA5vTjcsfQM1mGevtoWzadAxdI+TKnjfLifQE3+v6Caumrz8XdAfuQBB3IJgfIRlCG+vnXF3oFEEAQRTwFtoJDScQawUMIz8cQFPzI/cdn6BuVgG6pjPcGSab0rj0fc0Mtoc4tfNc8b0AS66pZ6wvRmGVm7Fz10S5jEZkLEXbqgqObOln0VV1tO8bwVfsIDScRFZEDMCqG8hWyQwgeQdYbBK5tIZilRnqCFNUnQ+LcwdtJKNZguVOJgbi2F0Wtj80jiDAshtKSISjTF/iBnJYJTuiKiIFFNAMDE3HWuFm4t6TU0F7GBDb0o+lxovosqCF0khBG3pKzQdNygJ7nx8EwOaRqfX+mmLO4haYexuMngJDywd5xEegbA707ITy+eD6FSEmvyybyt+rOfy/eVzIh2YNHIC2G2H/DyETg9nvBYsTkiGID+V/ibyVULsCurblpyufCyd+nl/GtV+EmkvyYZWmi0aUBSRZRJQF/KUOBk6H8RbZwch/FihzMjGQYPcTnSg2ibkbqomOp5m9vgqH14KhG+TSKqIsIisX9pWsiPl7s18Sm0yTMQpwuErA6sWruC8Yx6k46c8MU1PeQLcywb8f/Hc+PPvDAPQIwzj2neLQc0+x6RN/j8PnQRQlQsOD1M9byCs/+C5tazZwePOzJKMRVt1+J90H92F1OJGtVornliFkBXRNR5AEkASy/bGpABIAdSRJpiOCbbofkXx43kh/DH+T1QzOezude9alqlkEQ8A4F6YoShK6IDBr/RUc840TzeYDFwptJYwJBkU1HiSfFckuo4UzGKqOpcaTDyRpDRLbMUjgPdPItJ+7x5OF/H3QL/WtIApwLh/3lz/LDcWnAhqs9V7SpybPu69Pn5zE1hyYCiCZohlIQRuRpzrR4/nxc8PdqKMJPJfVEN8xmA8y0cG9rgrRKpPYNYTolPFeVoOe0jByOkqpA0M1UENpEEByW1CKHIhW89nYu4msyCgWW/6eywAwEGQJQRBJT06w8vY7ObHtZbxFfhZeaeXAC5O0rVjL0JlTAEwmh/FLxfnnYKKBoIhvhoS0BEnsG8E5vxh1NInv6nqyIwnUpIrkfPN5rJHREB1vhkeo42nkYgfqSPLNYdEs2tnIVNsAqaPjeNZXkz6eDxg2VB2lyo29yEFi+yDZnnz4T244SaYviq3eh+Sxok6mCD/TRfr4BPY5hchBO1oqh2NGIXo8i+ixoMey6LEccpEDS7kL0Xym+7ZK53TsyvmBMIZuYGQ15EI78Z4wxYFGAFy/EELyi/YN76PJ34Tb8uY5vHx6K7LVSvfhA/kQEm8+hORs+CyXVl16MVfJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8n0DvqDqrIQBMEFPAp83DCM6Ft8fpcgCPsEQdg3Njb29i/geQsjgeLMB3LIDmhYny+gzSagYzMWq4AgS8gWC2o2QyoaJZ2IkwhNkopGSMdjWBxOEEUMjPxbU3NZdF077y2tVouIYBj0h1J8fN00Pry6gTmVPj65volFNX6ODUQuWDSLXcbhsV4QQPIGl8VFg6+BmrY5XPnXn6Wwpg5/aTnr/vzDNCxcQvtIjJ0d4xzrD2P8YkUngCyDmoGTT8Dzn4PXv4VixEE0UKz5Yks9qyIo4tQX288TyWCxSyDAoqvr8BU6fodO+MPxrtp3z7Hrk4g1AtKyMrJDCdyXVpLpjmCfWQBSft8RLBLqZL7QAd4s8BVk4c3CS1HIF2VmNERnvkBCHUuhlDoByA7EsbcVkD41iWNOUX56UQBZJNsXw9rgQ7CIpE5O4lxUip7IIVhElFInWiRL8sAono21yNNchH0htJkWpKVelDo3wrkv39tbgyQPjOb/PbOA6Eu9CLKAHLQRe6UPyWtFT6iE7j+NlBEI33uGzI5xFE0het9Z9FcjOCIylt0ZXIdUCmQn6egkgiAw5/KrUawObvj7f6WgspZrPv0F5q69CrU/ceFGjaQuSl+9097J/dfptWC1X1jYYvulN/YCkEtDJg7FM/AffgiPNkbptHzBcVVzgK7D40wMxPOjZjRefTrJeMj5ZgDJGyJ9yLFunF4rcrQbxk4wNO3PiU2OMmfDFWhqDl3TSEYjZBJxREmiZvZ8LHY7Dp+PzgN7GTh94tevmJEPjhJFEAQRw9AobZjG9Z/7Ijf9w78w/8rrUDMZ+k6cIG1JYW3yIbktZHsvPK7m+uJEtvVxunuSo/0hcuqbb7PXdYPTwzFePD7Mnq4JIqkcw5E0//J8vjjqvYuqmVbk4q/XNtJc6uHmBZV8dE0joiBwsDfMh+8/gG7Ag3v7UDWdvV2TfG97FwCqbvCj17t57tjQr1/Xd9C74tjrfIti8bObubruSmTxzX1bERWurLmciqHD+CxpAqU2DANO7oyw/ZEYLSuvBkCSFdTsLxRFCgKGoZNJJAhWVrP4upsJDw2RCIdY/6G/Yv1dHyM6Poquq/hKSjm1cxsTfT20rlxLOh6j58hBbG43ssWK1WZn4MxJNv/nN3nmnq9w+vVtCILIqz/+Hpl4gvV/8Tc0LVlBxfQZjPd2U1hTy5ldO85LfxAkKR/+UFzM/CuuJROPExrqJxGaIBUJYeg6BVU1dJ7uZHzZl6F8Hsy66aJt/j9U74p995fJApm3uLZTx1OgJqCkDcMWpL/9FHuffJTJ/l7Co0MYuo4rEKCgugar3YHT68dTWEhxfSM2p5O6+Yuw2G34SiuwyFauqrvqvPZXV66m1lv71ss02YkvvpuFG8vPGzxjZQV9JycoqvFgscuEhpNUtgQY7oqCkC+w95c6mLOhmuGuCJNDSZxeG4PtYbb85CTBche8cRltwOGX+3D5bfhL3rxuFWUBNatje+PaJ6dTUudlvC+Or9gBQr743eKQmb6o5Lff7n+A3i37b0mdD6fPit2toFgl7O58X/lLHIz1xvAVOTjySn8+vEGARVf5OPHqf9H++sOEhw5wZPNPcEguBItEtjdG6ug46dMh9ETuvIL1N2iRDJn2ENYGH9ZaL3pGw8hpCFaJ8EiKYI2Dkho/Z4ZTPLK/j+9s6eCJQwNMxDPnN1Q6CwJ1kI1DdAiKmkEQ8/eZ8f/BOdcw8oElP7sFvncp7LgHooO/eTr9XJGqrxrGTsOaf4CD98J9N8Ljd8GRByHcCxNnYcGfg9UDG78G0zfBX+6EO56CupV/0AEk75Z99zfxlzgRFQG330ZlU4Chs+Gp/dvusiBbJE7syPf5nHVVnNo1hMUuM9kfZ/hshLImPwWVLiRFxOaUaVp8/jFq8bV1CBgXzLeg0ondJeb7eN77uT4464JrmWvrr2ZD9TosgsTMgjb+duHf0h3pZl31On7S+yCF711D1ZJFjPWcJTQ0iKFqTFu4FDWbJRWLIskKiXCIurkL6D12hP/P3n2H2XHWd/9/Tz297tne+656b5YlWZJ7wR1sU4yBEAg9+T0YUiD1SUgBHhJIgBCbgE0zGIOb3C1X2ep91bb3cnqb9vvjrFcWMt02Ntyv6+KSz5yZ+8zMmZ25Zzjfzx2traO8pZXyxibycg7bcnAMByXqBhuM/tRZ61k8laA4mKLYl0JCojXopmf07Pl+37yhjl/VhSTLSJKKbVvYhoFlWuA4SDgs3HIRkbJKHup7iC0NW5DyTUznDfIrKyDiwnEc3N1RJEVGkiQc2yk9o3Bs7KKFGpt9PpErhTRY6SKu1tDcx+cOTBLYXAp8yh+Zxr+uZu66nj86g7aqEuUdXXjOq8cYTp+1+nbORPL9zP2mBE7RmgsgeUl23ySFk0nST5YCSGSfhlOwSfz0JOZkDr0+yMyPTjD1zUNM33mEfE+czM4xsi+OYY7nyDw3ytTth0jvGMVK/UzwyR+IN9SxO8vrVWYD8Bxsy8I0DbAdZEkCWSaTnKF52QpyySTVbW4u/1ATqquBze/9CNGaOo7uewq7WUOr9qFVeHFsB/ml5xrybHDE7GnWMW2sRAG9PgDMnt8AbAenYKLV+wHIHZzEt7oayX06hMLdGqI4ePb5be55HaDXBQhuqi89T/iZvrw1mceYKD3Hyu6ZIH9wCjmgo4bcKEEdig6ph/sxx7JkXxzDShlkdo0x/e3DZF4cO/uZsfCayhkWbv1nQkiKpf8fQY66yBgJAoEYAAF36RyWyp8+ZxWsAvsm9rGiasUZbaiaRsP8RfTu3QmU/n+DKl8Vx+PHX8vNEQRBEARBEARBEARBEARBEARBEARBEARBEARBEARBEH7H3jRDEkqSpFEKIPm24zg/fKV5HMf5KvBVgBUrVvxuf+ms6qC6oJAshY9kp8AslgrEdD+V2cdIhZbi8vvJzExhWyaypFBX1w0OFOQcmqYjAbZjlwoaLAfHLhVlZFMJvIEQfl0hrirUhGQm0ga7+6ewAV2RqIt4qA57fu4qGkWL8VNJBo/O4A3q1HaGiVb7T2+Cy0XnmvU0LV6GY9m4/X6ePT7B8Yk0PpfGSMJhNFlgfVvs9I+cHQd2/Bccf7j0Ot6HXt6FXPkxJLlUHGIjIePgagvPBUO8RKv0svKSJsIVXnxlrlf5S3njekMdu7MsRSO694NMtnyAQt18CLvwN3bhyDLBt3djzhQIX99B6uE+1Ki7FIIQchG8pJnsC6PoNX5yeycwRtLojUGKfUnsrIFW6yd3aIrQJc2kHh8ojTDcESZ4SRN4ZHyBegrHUoSvbCH91DCpJwYIXNIEkkPezBB8e2upiOj8CoinKObz7O15CMmr8PyPvsfqq9/Kjru/T9OCZWxcezNaVxDHKI0s6xSsuWILV1uE3IHSyK+ulhDJh/rQqrwU+1I4ho1/XQ3Jh/txChb+8+pJ3HsSbPAur0QasygfLkdp8qLWNZDLJjHsPKmZCYLRcgy7gL8lQvFA/Ix9qoRtfh/9Lo/fSKWPpRc0cv9/7Z8LaHJ5Veq7I2fPbBVgz7ehdhn0PknDHVeiXv0kj8aLlDcG2Xl/71mLJOJQ/UofrM0WmruDsPYjZB0Pp/Y+wsJN52MZBhIOuttDMZ/HLBbxRaJc9//9AyoasqUw3neKqrYOVE1/pdbxRxTMYgYcFcWVJzk2TqiqBkmWsW0LSdEo5HKsueY60qlpkCwC7eXYaZPsC6NntKU3BsnsGCEWcnHSLfFEcpJVTRGCXp3nT03x3RcGGE8VeO7kFG9d2cB7z20mWygFlVSHXMzkDPYPJbEcB02RiPl0uqpK4S2G5dA7laW9wk/OsJjOFNncVUHUqyNJ8NUnT3LXziFuWNWAV3/jdTneEOfeygWw5oPw3Jchn4RQPSQHWZ1J8fVNX+LpiV1ISJwT7mTJ9/4IkkN4lP9i89XbefA7U6SmCji2Q2VlHVf8yYc5+MyzNC9fxeCh/S/bUHD5fMzfsJkH/+v/sXj9hQTUCNMnB/CUBRk+epi2Fat57q7v4tgW5Q0tTA30oWoaocoqsjPT6B4vlmkwNdA31+zYyePUzVuIWSwwPdxPPt+NNxTHH40SHx1G190YLwtEaVi4mLaVa0lPTzEzMkzDgiUkxkfJJOOU1dTx0Nf+g1wygdsfYPO7P0A+EGZ4/RdwF9xEbBtJlklOTTB+8gT5TIpoTT0Vza2o2iuEDv2ee0Mcuz/LAfcr9O3UCi/a/q+DO8SUu4XtX/8K7avWobrcRKpqyMRncBxwAhCtrce2bdxeH75whPjYKL17dzLee5Lu9ZvoWreBjy3/GBvqN7B3fC/zyuaxomoFQVfwldfJLKDu+BKLQg9S/v5/JT4jzRXi17SFyGcNajvDFPM2vrDO4WdGWH1FCz07Rll4Xh226TDel6K8PoBtOax+SwuDuycIqTLnXtHCs/f3YhZtsski4QoPvfsmgVKY2pq3tHDwyWE6VlUC4A1qVLUEqGgK4A2Wzv2yIuMPu0qhJn9A3ijHb3lDAFUqkk6YdK6uwjAsVl7axNHnR6nvjmLPFgAf3D7Mphtb2P/wV5noO8nKK65hz4P3sfU9HwBNxRzJIqkyaq0fWVNwACXiAlnCu6AU3lc4Fkf2qAQ2N2DnDKyZAlq9H8mtMGNYbHhfM54KDw+fSrJnME59xMtoIoeqynz3hQE+sKn1dICkWSiFfaRGwRuGYA0c2waxdnBeOWTyDKP74JtvAWv2/PzQX0EhBZs+/YsDQqoWgTsMmQmonF/696V7PoDBF6BmKRy9HzZ8ohQ8Eq6ffTP0Si2+6bxRjt1fxhdyUdceITGZw1/mRtFK3+u6q1tRCxblYRdl1T6S03mKOZPutdX0H5ymoiFA7/5JbBs8PpVIlZdC3qR9eTkN86IYBYtIlZfyxiCpqRzrr2/nubtPYBZtgjE3510Rxp0/AScfhcb1LOrfxze7/4hn8uM0BDvosJcz9Uwej27R3hql7MVbWeoOsa5jKwXdy41t15J1TJqbFxIcHaJvxsJyLGL1jSBJeIMhwpVVtK8+h2htHbH6RiQkiqk0ttvF6PGjtDQtx3HJGAMptPogkiaTOzh1xv5xtUdQ/C6cgok5kUNtCmAUfv/DHd5Qx69tISsqGEUURcXlDqPaKo5mYxgGLo+P86211K9pp9rdgkv2MZ4q8BeP9PC2lQ00+XSaolGc4TRKWEer8JE9MIlnfjl2uohvVRWJB3rJ7BwjEHHh7ohQ9Kq4WsKlsLFaP9uTGbQLaun0uCiGXUxVufE7kPOqnHAshlJ5nt8zyZ+3hynsnTxj9bUqH5ErW5m+8yjYpV0ZvKAJxX/2vZ0ScVHsOx0O7O6IkDsw254sIeky5ni21G6tn+JAEiXkRqvykd4+hJ0pBQQUTiawLmwsLb9vEms6j3dNFYpPQ/ZpqIHf3+dnb6hjd5bsmOC89IwWsB3cmg/NcSF7FNLTk4QqqnH5/Di2hWXnKG8w8AZaUFo+g6YqfGX/BIvrwjQpKk3Xd2IMJHG1hskfmsa3ohJMG9mvkX56iNBFzdgFE8mj4l0cQ1IVJJeMVuXDs6QCO1XEsRy0Si/uzqWYU3kUj4oSdeNqC1M4OnPG+kt66brgXVGJ3hhEUmTknwmvmJtXlrCLFrn9pePW3RlB9igk7j2FM/vcoHAiTvi6DqyZPLJXw9UYxE4XMafyIIGkSKhh92v2fQglOcPCrZ7Zl7NzpRC5oreIjUXAVwZAwF16PpPKm3PzHpg8QNEusqLyzBASgKbFyzi56wXioyOEq6ppDbdyMnHytdoUQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAE4Q3gjVcR/AqkUsXVfwOHHcf5t9/1+vxKVA/ICjg2SBKM7IGG1aB5YO2HCD3xJxRu3kt6cgrHsfH5ovhGPLgIISHhTfiQBgy8wTDxqSEOP/0Eg4f2U1bfyJILLkXzeCnILmwb/LpC1jAZT+UZTeapDLrJFCwGZ/LUR7w/dxX79k/x4NcOUNEYoJi3ePG+Xq78xFIiVb4z5nN5Sm30jCRJ5E2CLo2MYeFSZRQJesaSLKqfLfgvpM8sRgMYP4JU6YDj4Pbr9PWlaa5w411WgTGawRjOgAS+NVXIQZ2YRy2NKPr7mdfwpjFm+im2vo2mR94Kaz/MdHYNveMxtFgdj9/RUxpJU5VYuLaKJc1BpJk88buPgSTh31CLFFAJv62D7AvjeBaXo5S5ye2fxLeuGvfaGjI5A/9buyjM5MGjkA8qmAWToXSO8pVuclKC8YYB6tq6eeSnX8cXizJ46ACpqUmWX3Ylx3c8y6orr0d2y9iajdvtp7q9i759u+lYfQ5Hn91OTXc301P9LOu+DF9ZNan7+pFmf5DvFK25EWIdq3SwvTRKMoDkU5FdCnKNFyTQG0O4uyLIPg1rJo+7LYxa4cUYyeBx+QmGYwT9UbSAG0MqYDcaeJoiZJ4fxZzM4V/hJROW+cMqM359NC4o48pPLGXoyAyaW6G+O0qsLnD2jI4NB+6Cwz+BVX8EqouaqXu5+l0XklFjnNw9zsxo9oxFvH4ZltxUCi95yaK3lkJIbBtmTmGkJzh2+Bnq5y1kvPcU5Y2NPPGtb1DMZuk6ZyMNCxcTIoJdNLAzBitWXIbpNrFME6MAtungDenYWRNzIosvU6CQHuGnX/47NJeLZZdcQXljG2Y+R/+xI5iFPNVtnYTKK8klEyQmxnE3BZA0Gc/8KMZwCmOodF71rqwkf2IGJaCjSRKD24Zou6KR4xNpfrBziGWNYTJFE8t2+PxblzAUz5HIFnn3OY3815OnUGWZ8WSB/3n6FMm8iSpLvPfcZppjp68TigzvXNvIfftGuPWHp4Mv6qMe3rWuiWTOwKW+ckGTQCnIZtOnYN5bIDMF86+C+CDyw3/N8o4LWZ4aLRV733Pd6WWsIpWP3cC1N3+DVP8ErnCI0JPvQDrv07RfuYoiboIf/zOstIoLH8G6KuYv3oRkwIK/2YI9XcBOG8gBnZycoWP1Oh67/WtzzQ/3HGHD229B93jY/9hDtC5fTbC8nINPPHLW6k8PDhCsqESSFVqWejn+3CRPfuu/AdA9Prbc8n6GDh9A1XQWb72E4y8+x+Ch/UTrGkhNTRIsryRUUclDX/13CtkMAGahQD6d4Mlv/zfp6SlUTWfzLe+nceFS7v33f2H4yKHSh0sSl3/8VjpWn/PafT/Cr8508C6vxBjJYIy81LerRg5qMDyNE24gn81hWxaDRw6y4rKreOy2rxIfG2Xd9Tdx8PGHmBrsp331OTQtWc7o4QPse+h+8unS6O3b77iN+OgIm2/5Yzb7N7A+sRQSDorkoqCnkEI6+s8W4CYHYKYXfaaXxqE1xK78PumhQdy6RbD3O5ht78CoWY1tyRCA9pWLmBnJseYtLZimjW06dK6p4tjzY0yPZKgpb6VpLIO5Z5xY1M2l17TxwD0nKav1o2gynaurUHUZSZYYORknmyyCJLH0ggaal5YTEMWXbziR4j4iM7tB94FZpLm+kjZXlkL5SkbHZbxBncEjMyzY4GXoyD7aVq7l2I5nOPfGdzF87Ci1NfOwJZBdCsZIBksCvTFA5Np28oenybw4hhpzo9X7kSMunIyJY9ko5W4Uv042b6JN52kJ+SEr85ZKN5pl8d87BljVHOXISJJ41mA4nqP2pfu64T2QGoZiCnyVkBqDtq1gmeCJ/vKNHjt0OoDkJc99BZa/G0K1P3+5WBu8/S545t9h/Z/B3m+fPc/oPvBXgxZ4WQCJ8Lvgj7jxR0rnHH/IRWY0TVm2SG7HGIXnhtm6sY5hC+KpIsWcRW1HmJO7J6huCzN6eJoVayrxTObwelTStkOszk+05vSdTLTaT6TKR+OCMkzDxusFfeoAhj4fp7MTO6fgNJ9DWz5DoyJhSl62/fgEUyOlvrY3mOGqy99JeOdnqB/eVQrWufY2WHAVzPRzaPIkD339vwlVVLLxHe/FFw6z9rqbeP7u7yNJEnXd8ylkMgz3HMYsFuh57mkAutZtYstb3gv1ASTHQa3w4FkUIzcbEuVqD6OWeyieSqBV+ZD9GlgOR8ZSlAfcp//OhNeWrOLgIEsyelrHfHISx6PiKBLeTVUUXRaOadGq1LN71CKZm+THu4d4/6Y27t03wtqWKAcliQIWW1dVos3eTykeFUmWkDSZ8FtacSwbNeLCdKuM1XhwKQq3HxgmOxOnqyrAZ7cd5v+9bQnfePQoK5uifOPpU1i2Q8ij8fnrF7O8JYpZEUIdy2KOZkEC/8Y69PoAskul8iNezJk8SkBHrfTiZE3UKm9p3lnepRWoQRcZSkGRdtFCditYlIIgXgoIAJC9KpKmYAyXglxfCiB5ieTA5Ff34xgWoQubST85iKs5jJ02kLwq7vYIep3/dGiV8JpRJBlkifT0FPsevp81m6/HfHKKYspEr/NTvqgeU7HZ/+gj+CNRguUVuP0B8ukMbeogu7M1qIrC//nhfrJFi9tvXkFZV4jKhWUY03nSsoTfsAme30jhVAJzKocS86J4VWS3H9mtIJe50byvHGqqlZ0OpA5d1MT0dB5zIgcyBM5rQKsPUP6BxaiVXpTZMAo15sG7spLsC2Nzy7paS8+9JFVGbwxijGRK4b4SeBbFUAI6xf4USJA/OHuebQ7iGDZ6c4hCfxLjVBKt1occduGkjbl/HctBq/Ci1fiRFHHMvhryhkXUd+YxYedLQTFZuXRPFXCX+orB2e89ni5wau8E8fEc9xceA2BZxbKz2m5ashyA3r27WFJ1KW2hNl4cfRHLtlBk8bxHEARBEARBEARBEARBEARBEARBEARBEARBEARBEATh99GbIoQEOAd4B7BfkqQ9s9M+7TjOfb+7VfolVA2QwCqUitWlQmm0aMsATxg2/yWqKjMzOobu8RFwQrgWBMntGCX74ukffPs31jEcP8Lh7aUfAqemJhk7eZy3/OmfY7ijxLNFyv068byBBJTPFl96dYWQRyVbtF5x9fKZIgOHp7j8I4tJTeWRFRlf2MXkYPqsEJKXZA0TlyqBoxDPG9gOOIBllQYizRUtdFcQRXWVRuh+SaIPWVPQvSrOS6vjUimeSuBfV1Ma/VWRKI7nkOzSqPCKKr9yiIDwuimYNj9KLOBdNxxHTlkUbYe6BplCssi1751PfiqPr9KDqslYfSnys4UK/nNrwLDJ7RxH6Yji3VLP2ECak+kiC65u59knhhjtG2T+uTXs+PYugjE3C8+r4/APR7BMi5UXN5DPysgKlLevxvJA16ZrMQoJutadR//+PQQiUdpWrqN/z25aV64hMT5OU9cyIpU1qKgEouW0Ll+DR/VTVlHPYN9hmucvw/f2JhTbReTGTqyZAkrIBbaDY9hErmvHKdogg29tDcXeBMGLmij2pzDGs7iag6S3DxE4r57MC2O42yPke2Yo9qUInt/A1O0H8a2oIrdrAmMoXdqJEoSuaUWKOuwaH6c+WPk7/EZ/fymqTG17hNr2yC+e0RUA3Q/5ODz9hbnJ3qsa8C5exIYbO7n3S3sxjVIoTddyP+XBmVIAxOa/LC3nDoPqxrHBnhxEfuQfMCOdlNU1svPeu9ny3g9y35f+BZzSefG5H36HhasuIPPUCPmXjcIevKiJjMvgvq/tJZ8xuOBtHXgOTM6NUKwHdd79yS/xvf/8S57+7re45s//lp9+4Z8pZEohDZIkc9nHPkmkrg7N4yYdnyQUqsBJFPCtrkZSZSRFwpjIIkkyyYf6kIMulp3fQCZhko+o3LGjnzt29HPrRV18/uEedvfH+fCWNv70+3v53DWLifpcGJbD/zzdy5VLawl6NGzb4cd7hlk6GzzVVOalKugh5NX4vw8cOWN3D0znCHs1rlpaiyKLoqJfyB2EhjWnX08dB8eEzDjs+w5s/D9nL5MYxFvsw+vug/v+DtZ8EJ78HEyfRHeHqT7vbiYfz4KZJsVxfGuqkYI6uSNjZHeOzzUT2FRH6/y1BG4uIxKoQvXrJKbHGZ04geTx0r5qLWaxgGVaVLd3oWg63es3gu2w/7GHiDU0Mr79FOWNreTiCXbed89c28Vchv2PbuOSD/8Z+Uyavdvupf/gPqDUnxk/eZwNb78FzeWeCyAB6Fi7nl33/4T0dOlvxjSKbPuvL3H1rZ89HUAC4Dg8+j//RU1HN/7Ir1B4L7y2VIn8wRl866qRlNlz0EwByQYijRBuQi6q6B4vlS3tHHziEcZ7T7L66rfy7PfvmDsGdt33Y6aHBmhfvW4ugOQlBx57iLVbbyB1x/G5wlwl7CKwpQFG0lgBHSRwTAdJk0E+B96yE1lJIxkJNFc90ZYYOCbFhk1Mf38Qa6Zvbv2j75xPzq8hSZDPmux/bJAVFzaw4ZJGzOEMhZ1j6N1RzAovub0TSI/187b3L8CxHay0QXW9n+HhDJYmY+RsLn7/QrxhjVC5F1UTxXlvSOlxMDJgG1C1GDk7QdQ7A1M/IFK5koqbl3Lw2UkmBwwCZTEs00BRNWzTopBNk03EcSkuZr7fA5aD7NNQYh4kTQGPQuDiRpychVbnB8Nm5ns9c8eu5FIIbm0gce8p8oDeGESt8HBJY5DLL1uInTPJ+DTuODmBJElMpwvIRppQYgDJKoAeLIWnxHuhrAW8ZRD+BSEiL9FeIQzHHQRF++XL1q2Aq/4TillIj5wdPlm1EMKNUNH9y9sSXlNW1sCczGFnDNwuGSVlkHhuFCXkwr++huJgiqZKH2pXBWnD4fCucbrWVTN4eJr1S2NkfnKS2TsavEsr8MY8ZA9NYhds5LCOnSiiuBTUjIEmS1h5k9RMOVqVC2vSwRhOI2k5cgdKRelIsHFLAw9sG6BhfpRItY8xvZW+BT/B5VPR5QLlPpsAUHSX8+JDjwKQGB/j2AvP0rx4GY/895fntu+x277KhpveTbCikud+cOfc9CPPPI43EqK8vhWfHqCYzFMIZmh42wJ03YOdKjDzvaNzgaehy1pwwhpeXeWpY5NcsaQGj/5meUz2JlbMoqoaTtxA6bdxr6kGCSRdwZkxsRUTSYKASyebzLCxKUpbxMvnHunhgxtb6XS7UWyb/qyBZTnkgUDETeqxAXwrq5i+8yjObLiH5FEJbm2AsMZfPn2C/uksFy6oors6wF9fMZ9EzmBrdyX/su3o3Ootb4xwdDRF3rT5Tmac5edVs8jvwevT0co8pT4GoFX50F7+/CykUPb2eRSOz1AcyeBui5QCGYo2ekOAYn+K/NFpQhc1kxg5iWM6uFpCyN5S38OYzqPG3Mi6wlk5IhI4loNTsPAsKCO7bwLvknISD/aCWbr3TD3aT/kfLcLVEHwNvzwBgNwU+WSSB//zi9zwfz4Hj6fwzIvh2DaKT0PxeHApMvsf3Yaq6VS3d9C9/jzUWAxbC7JKGuBEqJm8YfGfNy2lbzrLI2NpljRGqAi4+dHuQXTL4a1rGjgmuXErColchnlhL7VehVjUg+I5+7ptZorY8SKyX0UNla73erWf8vcvwowXQAIt5kF2qVg5A2smj60pqNHScRc8vxFXa5jCyQR6fQB3WxjHtikOJPEsLkcOaEgBHdWtknlmBCtewNUWxn9uHdZ0DmSJ1OODWNN5UAcJnteA3hLCnMwSv/sE/nU15LcPYY7NBvVIELt5Pu5OcS/3asgbFp6fuedw8qVzYcZMAODTwgDolE4yD955lOFM6Zz2dPfzRF3VDL+YJbw+fEY7kaoaQpVVnNq7kyUXXkpruJWCVWAwPUhjsPE13CpBEARBEARBEARBEARBEARBEARBEARBEARBEARBEAThd+VNUV3hOM5TwJuvglmSS6Ejig6SVPpvSYZH/hqMHEr3O+jbt5v5m89Hww0Z44wAEoD09kGWvPsinrv7O3PTcskEifFRtEgduwcSbO2uwKUoqIrMsyemqA17SBcMFteHaa8M8OyJSSqDbprKfMizheD5tEHr0gqOPjdKMFYaIXPsVIK2FRU/d3NcmoJjSyDZ1Ea8aDKYtkPBsjk+nmLHqWlwItyw7qNIT35ubjkn3IysgMujoblk3D4NijayKpO47xR2tvSDaHdnBLvGhxZyEa7w/LzVEF4nAZfKu5uryd7Rg6sxiO7TyO4eJ3RpM6n7T+EOu1ALQYyxLE6hlC7jWRij2J+i2JfE3R1FThVJ3HaIyOIYq1rDFHeOsW51Jc6iMoqmTfv7F2AnC8ghF/Wb61BUCcmtUhjJ4K0PYMzksUZMGqoqyA2puENhgtH1WMkirtYgmekRvE4lZQ1XY+4rEu1qIv3MMO41OuWnYljJItH5dfiOSGgVCrkDkxiyhN4QJP3MMKGLm0g9Pohe5wdFIn94mtAlzcTvPoJ3SQXp7UMYYxmCWxtJbuvDt6qK9PYh7FQRNeIiu3MM78pK0s8Mgw1KyIWxY/T0TnQg/dAg7hvrqWttpzn2ygE/wuukaiFsuhUeuPX0tLJ2qCuN6FrXEeG6T68kMZHDpVtEJ36KW26DbX9eChVxhTBW/Q1meBOFgxb5o+Polf+Mv36SMlvFF4kwfurEXADJS9SsTPJlASQAqUf6KWteQGI8h+ZS8BcssrMBJABWskhxd5wLb/gw2773HwwdPTQXQALgODY777ubi/7kT0nPTGPkcgz2HKTW20byoT7sVKnAWW8O4l1RiaTL5PZPItX5CQd1pq3To2r/ZN8wGzrKeejQGIZlc2oyy5/9YC8//MA6ToyneMfaRn68Z4iGqI/xZJ7rV9Rh2zYf2dKGS5UJeVROTWTIFs4OvaoJuVlQG/rNv7M/VDP90LAOeu4vvVZcIKtgnx4pnaXvgIM/gkN3lwrHFR2mTwJgLfgjph8ugmnPzZ55boSyd3Yzta3vjI9KPTlEWVM3wWNe8sMjIEH0kma8Lj+90wexTIP6+QvRdDeVLa2EKirZ/8g2ZFlmwXlb8UdjNC5azrFdCuHy5FnH/9CRQyzcfAFlNfU8OhtA8pJcKolZLOINhVE0DcsoHZeBWDnxJx89a7ekpibPmpaNxzGLxV951wqvHStvo/h0kg/2YadL36WrJYRe6QUk0u5q7EyarnM24A4E2f/IAwBIknRGCA2URtief975Z31GrKGR4t7puRAHACtewBzNoIRdGLlsKSFPguyL46Ui3Z1T6E0hlHAIp7+AUwQ57EX22FgzLwvNMx0yTw4SbgsjuxT8YxkaL22icCoOqowecyOvqyDxneN4F5ejlnswJ3JYvUnM8SyOA4WpHFXzytBrQ3SsrhLBI28G0RbofxZOPAobamHPHdD/DCgabtui1rGpuvjfMEaPU37d5fz0f+5g7bU34vL7GDl2lOLKLTgHUmA5oMqE39qBZNqYU3lkVaFwLI6nI0LmqWHUMs8Zx65TsCgOpFArvZhj2VL/uSNC4t5TBC9oRHYp6EdmuHlRJTtHkhwYSvDelhmkyR5w+UFzAAeaN5buMWtX/mrbXL0YQg2Q6D897fy/Af/Pvw88g+Yu/a/rUujZBqcen213CTRtLPW3XKLP+7tkJgtkd45RHEjjagyS70vg5Er9NP+6GpKPDhC6tBklqCNZDgHDYtWiMqxEgbbNdUx989AZ7eV2j+NqChL/0XECm+tJPdCLEtJRIm4kTcYYyeDujODkLeyMSerxgdL900Mv63M4YD89zDkXN3L8yAwS0Lt/EhyJ4zvHaVxQRnVbiFonQaRaR3OfDstx+/wcfIV+weCRg5TVnV343L9vD4VsFgmob1hA7FiU9FOnAFDK3AQ3N5B8uHT8p58aItDYhU9XqQq46Z3M0F0j+q6vObcfx54i7KnArC1QPJUo3at4FKI3dKGPpPHHKjCOZbkk6ZD7dg+tQY07L1qInSyQeuAk5mSORW9pJfNUH4HLW8gfmgbDpnAiPhdAAuDkTIyhNGHbyxVLqtFkhV0DM6iyRF3EjSRJjKXiAFQGXaxuLuOKxdU8cnic7+0cxLIduqsDfOi8di5uDiP9knBFLeZBi539PKvsHfNKz04sG7Xcg17rx8oaxO8+gZ0q9WX1xiDu7ihquRdzIofs1+b6VACyu9SvUCu8GGNZioPpuQASoNSX2TkmQkheD54I08N7uebDnyXkKiPfKGMlCqjlXtRKH1a6gCLrvO9jX0FyyZiTOax0EVdNBGsgRXJA46o2lYU3rcCVKLLoSJoLNZnJIiSSBT4VCWPum0R6eoyKFRVceOdOLm0v55wqB3tXHxO6TOiCJlztYeTZ/mahN0H83pMYA2mUiIvw5a24u6PggDGSIX7PCcypHJ4FMfzn1pG49wTF3hSoMv4LGsl2hYmEPfiWVJBpCZIomFTnTGbuOIw1mQcJfOfU4qrSmP724bkwp8LxOAB6Q4D80ZlSAAmA6ZB8qI/oTV2kHh8EQPZppwNIAByI33uSivoAsvdXCEMTfqHcK4SQ2LMhJMnkBBISXsnPwKFp7rvtIMjgrfJwxeXdRGo93P7jT7Ewu4bHvnUESYbudTVntNW0eDmHnngEyzTojHYCcHjqsAghEQRBEARBEARBEARBEARBEARBEARBEARBEARBEARB+D0l/65X4PeapAASyAoobhg9gGMVwcgBoBcmKWQz+Pxh1Hofdu7sAm5skIyzJ2seLwBtFX6KhoUsw9BMjhVNUQ4OJ3GpCm0Vfrb3TDCSyPHCqWmePzWFM1scrOoy6Xie6rYwsiwhSVDeECCfNs/+MGAmk8OjKrh1GcOCx46M84OdQ4ynisiSxOHhBP/9VC+HRrPcKV1C9qrbsdd9FOvyLzG18QsouowvrKO6FIo5C0eVyO6ZmAsgAcgfncHJW8TqAqhi9OHfOY/hUHikH6dgoTcFye4eR28IUDgex04beLrLSD89jBUvoM4W2agVXop9SZAl9LoA6aeG0Cq9OEWb1P296JVe0vf3knl0AJflkP7uUeREkfyjA+Qf7EUp2qS+exQta5J5pJ/M/b243CozdxxBliTSTwySfnwAvdxD/K5jqBEP2RdGye4cx9MaJvGTk2gxD7lDU+QPTeOZHyP5YB9qpY/CyQTGYBp3V5T0U0Podf7StmQM9IYg+UPTaLWlaTigRN0YIxnUMg/GSKk4WvZrWPECkkvBmj12lYA+V8TsWPZZ+9FKFXDJLlrK/a/PFyf8YkvfDjd+F875GFz2ebjhTihrm3s7Wu2jeVGMmq5K3F3nQjE7F6pgrPm/JMZWk96RIv3UMOZEjuyBLJOPhqgNRQjGKnH7zi66ffl57iWOYePkLdw+jXOua0NK5M+axxhOE9TL0HQ3Ri531vuFbJbUxDiOafHw179ManKM/OHpuQASgOKpJE7ORNIVlJgHSVMwDRv7ZbVqBdNGU0rFdMrskNt9U1lGEnn8LpWIX+OTF3VxxeJqPrq1jXK/TtCrEfO76KoqFbj984NHuWLJmQUqbk0WASS/qYZVkByCSHPp9c7b4IK/LRWO+2KYaz5Mfv5bsedfjdN5Kc6Sd5T6GrMsd8NcMePL2flX6mc42BkTd3dZ6bUDiQd78bXGaKxeQLSmngOPPcy9/+9zTA308eg3/pORY0cYOnqIh7767xSzWYZPeGlZXE51WwNuf+CM5n3hMN5QhOTUBIp69rXdG4rg8vjZ9M73IsmlbmkumSRQFjtr3mB5xdw8L+lavwl/tOwX7U3hdWLKEvmemTOKZQsnE1ipIugBbMXD9jv+hxd+fBc4UN3eBYAsn307ououZFkmUlN3xvTu89+CNZI9a35zpgCKhJOzcAoWTsHGM6/UT/EsKie3dwLZpSB7VHIHJ5Hdamm09p9hzeSxZ/Jknh0B0yH+4xMoPp3p7x7FGM6gyCq+VVVkdo3hmVcGEkiyRO7AFHqtH2M0W+rzPNaPZDlntS+8AVUtgIa1sPZDMH6wdO6FUnikU+rXKTMncPfeT9PUfbztTz+KL1qNJximsrmVvp69c+dW38pKKFjE7z2FOZUneX8vrpoAMz84hqTLmK90zCWKKH597rVj2aXiecvGKVoU+1PoaRO/4VAVdOMqzkB6HGS9FARiW6B6INIC4bqz2n9F0RZ4x4/g8v8HG2+Fd/0EOi/59fddpAmuuw3e8xC8+35467dg3uUQbfr12xJeVcZACmywEgUKfUnMkQxqmRs5oGNO5fAtr0QJ6tiGTf54HDNRJLGtD8d0sHPmmaEGs6xEAUmTwXKwEgVc7RFy+yZQQi4kXaFwKoni13HyJjivfF/k5EyCIZ2J/hQzo1nKagIc3znO8osbMYsWh58eYejoDEefm2TZJdcCoLpcRGvrcc0+/3g5TyCIJxA4a3pt93waFyyhZfkaKrQm7LHTgVPWVL70dxd2AWDnTHomM3z8u3t4vne6FGQlvC4CWgRJlTBHM+T2ToDtELqkhfjdx1H9LuIPnMKaKpB+arh0/CFhTeSI//gE5mQOV0uI3KEpJEXGMW2K/Sm0Gh/m9Nn3VVaiiJYxODCUYvdAnAvnV9E7mSGZN4h4NVrLfdy4qoGLF1SzdzDObc/00l4ZIDIbinB4JMU9e4eYzhbOavtXpQR03G1hPJ1RtKgHvT5Afv/kGX32Yl8STAfv/BieBTGib+vCv7EWV1eEyPWd6E1BJF3GKVrIwdm/t5/xSvcAwmvAF8UbCFJe0YgxmkUOaqhRN3qNj+S2XmRFIf34IHaySPxHJ0ht64e8Q+qnfSTvHyR3IE7inn7aszbBn/RiH4tjHJom9uIEHSfS5B8ZwJzIYRyYwvlOD7dduZB3VYTRHh7Ems5jjmaZ+uYhiv0pAIypHDM/Oo4xkAbAmikwdcdhjOE0xliGydsOYk7kSteGmQKpJwZKASQApk36vlNk+pLctWuQ254+xd17hvivR08w/WBvKYAEwCmlgxsjmbkAkpcUjsfRKn0YQ+mzdpVTtEthbdLPeWYWL2AbZ08Xfn25oo1b/9kQklI/NRmfwOsKs/9Ignu+tAe/IiE7EA3r1HdF6SueJGdluWrzBdR1RXjizh4mB8/8PpsWL8Mo5Bk6cpiOSAce1cOeiT2v1+YJgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIrzMRQvJaUlRQXaD5SqNVSyBpfpyqRQB4vraWtpVrUCQN2ZFQoi5k35kjPypR91yBzEvq5y9i27hOMmcQdmkgSbhUhXTB5KtPnuTkZIZb1jcjIRH0aEhItFb4mckWOTCUKDUiy+hujeRUFn/UhSeoY9sOivbKo7rmiw5eXSFbtPjUD/fz7ef7+cm+ET71w/0cn8hQHnCxrjXKwtog1VU17Padw77Oj9JT/RbilheXR8MyLLLxIoefHWGkN4Uxmjnrc+zsKySuCL8Tdt7EnP2OHLNUEKDGPBij2TOmmZM5tEovsk9jNr+gNN9I6cfqro5SgRqqBHapKMazKEb66WFgdiTU8Szu7iiZF8dKASAhHWMkUyosOjgFgBJyYY5l0ap9FHqTpz9nOIMc1DEmS4WdWq2f4qkkyKUR5gH0+gCFE/EztyXqxhif3ZbZQghJlefef2lb7IyBEiwVhzp5C9mn4eQtFH/pb7XYn8LVGi4to8mlqoyXcXeX4ajiVPuG4QpAx0Vw/l/Dilsg1v7z5y3vKJ3Dg7XgCpLPtaKVe+dG+n2JnTKQxybYuL6NstoGPIHTI09Lkoxa5kbSzzwGtBofSkBj/oYanvnBcdSKs8NLXE0hbMvGEwpRP2/h6YNy1qItF6H7AvQ8/zQAmUTiFc+rVryApMq4zqlhtC+JJIH0sgP1skXVPNkzSW3IjWE7XL+inq++fTkzmSIjyTxuWeHe/cP0jKX53guD1JeV1rUy4MajyeSKJnsHE7hVmZvXNdFU5uWctjK+ctNyOqvEKNy/kdoVGG0XwYJrQfNAYgAe+iushvUk3/pjHqv/IM8Vm/jvqQV81vdpnIXXQbR1bnEldQilTD+zTQnkgI7sPTMIRClzl86BzpmjqNvpIi7Vy55t93L8hWfpWLueI89sP2tVj73wLMWciW07PPPDMda99WNEa+oBiNU3svmWjyDrQUZPHGPR1ovPWLauewGJqRB3f2GYE3vLecv/+Qcu/einqGpfx+qr/wjN5Z5dd4nVV99AVXsHV9/6WaI19SiaxoLNF7Du2htQNTFq9hvBjGP9nHNQDscXJZPMMXK8h/jYCD/5t39g3rnn4Y+WkZycoLKl7YxlVl5xDb7yGhbc/HHOeef7ad94IfPf+VH2SVVIC84OndFrfDgF6/R12HHm+gGSVjr/OqaNMZ5Dibgp9iVRy88uaHd3l4EuY45nUcKuUoiTLIED+WNxCv1JXPOic/P71lSTOzw91z6U+kVO1nzFwmDhDUjzwOK3QseFsPSdsOTGs+eJNELrZuTMBPLUXvYWKilky2hfs4XDzz2Oe2npmHS1hSkcj+OZV0b2xdFSH3cmDw6oETd6zdmBdK7WEMWB5NxrSZORAxpO0cKxHeyCBabNzNPjzHe7kY08tGwGXxnIrlLfYHQv6Gf3I36hWBssfxec9ylo3vDrL/8SbwTqV0HjOgjX/2ZtCK86czqPY9mz924ZrJlCKYTEX+oDKDE3TtFGMmxkVcbJGqhlHuyciawrqJVnnh8llwK2g+RSSiElL3FKh6CdLKBGXNgvnXMVCUkp/ftyarWPvAOKJmPbDoWsQajCQyZeZLw/xfwNtTz/k1Ns/+4xDmyXuPwTf83Gm25h/yMPUN3ehfKy672qu+hatxGAms7uuemRqhraVqzhwf/8AprXjznyCkF/Y6WgSQDv6mo++VgPybzJv27roXf67KAr4TViOziGRb5nZm6SJIE1mcfOm7hbwuT2T8y952oNYyULOPmX3+Mn0JsCULTRKr3kDk3h7oyc9VGu5iD5iIuoT2NjR4z33PYif/qD/Xz8u/u4a9cQqZyBqkjc9kwvfVNZnjo+xT89cIR3rm2aa2PHqWmMVwjo+Y03v2hRmA2QeDlzrHQMajEP7rYw4YtbKL95Ab6lFbjqg5S/bxHIEp6uKHrz2YGPvpVVr9o6Cj9fwbSINbaC6ZB+dhitwguShDmVQ/FoWMkCxcE0dt4shYsB6uwzrZfojYFSAM/LuNsj5PeMnzHNyVs0u3Wih+NnrcdLfz/mdP6MtksTHYzxbCl85GXheHpDgELP2W0FcxafvecAn/3JIf7v/Ue4prsSpzd5xjySLiNJZz83lv0aVt6cCyg+YxlVRg7qpWvGKzwz8y2vPCOQTfjN5Q0Lj3ZmCMlL9ySJ6QkU72YODmToWl3F+fOj+CWJ+GTpuNk1tguAFdXLueA989HdCk/ccXQuzBygYf5CZEWhd98uVFllYWwhe8b3vD4bN8ucmCD99NPE77qLme99j+S2bRT7+l7XdRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQfhDcfaQ9MKrR9HAFwNJni2G1CAxiHHJ59G2/xPS8YdpmLiPdEMTfieMo0Hkug6SD/dhDKXRm4IEtzaS9Vhc/JFPMtF7An9lDfusGP/82AjvcVw0lfn43ouDNMe8LG+MEvZq/PHGFg4OJ/nGU6dY1hjh0SPjtJb7WdNSxt/89BB/c8V86n0uJBmmh7PsfnAAAFWX2frueVimjfIzoQkF0yJdMDk4nCRdOLOo8vZnevmHq+Zz6aJqBmdyPHp0Arcms7wxQn1Y5eB0mvOaysinHJ64s4fUVJ7pgTTr20IUD06f0ZZW9RsWwAmvOlOVUJtCmCcTcwW8xcE0enOQ3J4JJFUGWQLbIflIP/51NShlbvSWEOZYFiUYPqM9SZFxjFLBkOxW5wJnHLv0g3bZ87Jps79xl2QJ2zozFMQxbKSXj+wplYqMZU/pdOYULSSPWiocnl1vazqPVuHFGMmU1nt2W1wtIbI7x8EuFdcV+1MEL2ikcCJBoS+JuztK/vA0skdFCepk94wT2FRPYlsvxcEU3pWVZHeOETy/Cce2ybw4RujSFtLPlEZr9nRH0ZqDxI0CMzNZ6iJnFzwLbwKXfR52/S9WTkFSnbnj/uWk8lZiZeWEo51c+cnPMNJzmGIuR233fByvQuRtnaS29WGMZXG1hQlsqsfRJCRJopi3KEjgXV1FdscoOKDV+VErPNhBnaqOa8llPVz+8U/xwo9/QDGXZdH5l5CcrqUso6LPjgx/6uCLdK9djbXzzBG65QovicUK999+mI5VVeDAwHSW5Y0RrllWS8G0+fj57SysDXJwKInkONy1e5AHD45RFXTxb29dwoKaMPGswZbuSoqmRVXQTcClMp0tcmAwSV3Ew7ee76ci4GJ1c5TpTJHayNkFSMKvJid52ea6GCk5wjnX3oUx1U9B8eCqWcRV3zrFSKKPT1/SRTJvcvszfXym210KzLn8i/D4P6IcvZPoJTcx/VOwkkUkTSawsY7UEwOEr20n9cgAxvBsP2NTPfEHTuGqPx0YI3tVHNtBKvNy0QdvJZMYJxCNsWfbT85aV5fHS/OScu7/8n5Mw2ZqUKZ12XtZfJFCrK6Ce79ylGBZkaVbuzj6zJOc89a3k56ZxhsKU97Yxr7HS0VPoyfSPHAizRUfXcy2/z4EwIorbwU7ieb207GqE7fXT9PiZbztbz6HUcjhC0dQVBFA8kaRlKCuK0Jmx9gZ0/WYgyV7yRdUguUVJCdKhZUjx3voPvc8AmUx2lefw8zIINNDQ9R0dOGLVfO9EwYxv4t8YB5TTW1omkwya/JTPcsVa6rI7RgDWcK3ohLJr2Gni8geDTtl4Rg2xkAKvTFYGq2dUgGkVu6hcDyOuz1C7tgMoctbSD0+gJO38K6sBFXCniqdQ8+qr7QcMCwUj4p/TTVyWKewd3JuxPeX+hxalQ9mpLOCBYU3uEhj6V93AIws7PgquEOw9a8hWEcKL4dDF/IvezV2DB7hw+ubuSAQYtO7PkncSRO+phXHdnAsB8kpJTM4pj3X73SKNsZ4lsDGOjIvjOJYDv5za+fek70q/nU15E/ECV/WipUsgARalRdLlhg+maBg2rRf3YEreQLyZik4RI9BIQ25mZ+3ZcIfILXSizGYxhhK42oOkp3Oz96v1aLV+kohN6aNY9inM8hsB8eyMadyBM9vJPPcCIUTcbRqH4Etjcz8oAcnZ6JGSwFhxmApiLE4mEaJutEqvaSfH8W/sZbQBY2kXxwjdFET6e1DWMkiaq0f1lbjiWioukxZrR/LsKlsCjLUM0P7ikr2PTqAYztEq300L6rAKGpMDmvobi8v/uQu1l57I7lkAiSJqtYONLeL/gN7WbBpK/M2bCGbiJOJT5PPZtn6sb/gqweL/FlHJeahM5856M2h0rVgawPfy2c5Np6ee+/4RBrh9WFLYM8eU9b0bFjM7AEpKRJOvnRfT7b0/MkuWnPhoFAKXdAqveSPJ9Abg6UgqBNxJFkmcH4D6aeGwXHwrajC8mn8x/FRYhV+Pv2jA+Rmn0sAfOu5fr7y9mX8/X1Hzli/gmlj2vbc66X1ESK+Vy8oQXareOaVkX5q6IzpesPZgVVnvF8fQK8PYBsWxliW8DXtZJ4ewnEguKUBV8vZwSTCqy+RNTDUABIKWrkXczyHEnFhpQwkr1oKelKluZA6ODPzEQCbs8KaHMsuPTt72bEH4ACm58xwCQBltr8puxQklzIXxPsS2audFRry0t9OceDMEJxpyeHc9goeOzqO48C39g3xyYYAzrH43DxWqgiKNBe8BoAEwQubSD7cR2BDHcltfXPr4V1aQfbABJFr2kncd4rs7glClzSXnpkli/hWVOE/txZJOTvYRPj15Q0Lt3bm83w7b+I4DomZeSA30h3ROe9d3Yx9fhc+IJkpAvDC6AvU+mup8pWCjNZc2cpj/3uEnh1jdK4uTdM9Xmo759G7ZycbbryZxeWL+caBb5AupvHrv/jc9duwMxlmvvd9kj/9KfmDB19xHld7O2Xvey/Byy5DkkUQtCAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIwqtBhJC8llQ3+KtLhWzpcQiEwRNioqjzF9kPc9m5n2BJWwOtVTUUJjNISBBQCb+lFcd0kFwyjgz3HJ7AdGqZiVWgODCVM/jrK+ZRMG2iPhe3XtSJS5M5MZ7mlnOaWVwX5oXeaf7ikm4ayjxoqsJoIkfBsHmhd4a9gwka51VhFm369k/Nra5ZtHnx3l6qWkP4gq4zNsW0YSRZ4JUULRtdlRmeyjI0kyPq05EkODKawqsr9Exk2NoSIzWVJzVVKi4ZOZUkvqycwHQBayQDikRwSwNa3Wv3o2Xh1yO7VQrzytDTRXIHpgicV0/6qSG8i2JY9QEyO8cInt9A6vFBnIJFZvc4yvmNOOtqkHsTqDEPSlCncDKOu7uM/KEplIAOEhROxEsBH4emS8UGskS+ZwbPghiZ50bAcpA8KvmTCYJb6jEG05iTObQ6P8ZgGt+qKnIHJskfmca7rBQEIrsUZL9Gdu8EgXNqSD7cj5UsoNX5yR2eInRJM8kHeykcj+NbU03m+RE8C2O4WkKknx8heEEj2V1j5A9PEbq8hdz+SbTGIMH2MIWRLJ5LmpENm5xhEXpHF8VkBimgYVWkKfgKTDZPUFPfjVOUcXdFkDWFwmQGy1fk1IzKZ3/wAt9410oaykQQyZtOcgSWvRO3UUHikSm8yyrIvni6yF5vDKK1NIBfRwVq2jupae+ce78wmsb2a4SvaisVJ2sSNqVRhMsbAgDs2zXBkvYQ0Ru7sHNmqZDZr3PXNw6TT5fCebbc3E2s6e1oHpndDycpZOMkpiQWbdhCz3NPMTU0wKjaT3l9BdZADmRQl1ayY/ckR3eXRlje+8gAdd2l0cE/uKGZqYxB3gGfrjAcz/P5h4/x3nNbePBgafv+8eqF/Pujx3nmxOlrxV9e2k1F0MWjRydYUh8mb1r80zWL+Iu793NqMsszJ6b43LWLaCsX5/PfRO9Uhul0gZmcQVVZHeMBL6ZvPpmCSTplMpIoXUfTeYOR+GzBpqyC5gYnBJf+G7gCuOQ0nhtqeOZYgfVtMVTDxht2ISkyocubwXCwDYvk9iFCFzYx86PjAChBHf+GOtAVnn10kP5jCbrW1vHEnYNsvfl8Trz4/FxRnKKqNC1ZS2a6gGmUplmmTc+OBAAXvLca23SwLchna2lYuIKd996Fy+2ha/35DPXoePxnFgiZhsPFf7yQR24/xO5tSQJRN1tubidUcbqg0hMI4AkEXtPvQfj12Y6Df5mb4qgfoz8NMgTOqUCPZLAzKVTJYe217+Xhr/8zlmFw9Nkn6Vx7Lo9+4z8BkBWV8qYWujZfznMzEvNrNFyqzFS6QHd1gILhULBsFAmeNy1WL16AxwHJcXAMG8WnYectlLALczyL7FXRG4OknhgkuLUBYzyL4tVQQjqSKuOq8pM5MEH48laQIfX4AN5FFaSfGsbdGaHYn0L2a3PFo+72MHJQx5wp4F1WyfT3jmKOZkGV8K+tIX+01I9xgOB5jcja2YWiwptAqA62fAZW/REoOvgrmMkU+PKhKF/bfgooFcN/6alTLL5uKUpSRpZ1Xnh6mJqmAAtbQiS39eFbVUV6+xCyT0VyKWT3TeBbXknmhVF8a6rRG4IU+hLIbpWyd89H0mTsokWwKYiVMZC8GmY8h2dxOcePJ8ilDJJjOWzbC5YJxQwEqiAxAFULwTZ+t/tNeEPR6wJYM3ncXREkl4LeGKTYlyT91CD+zQ2428NgO9hZE2MkjRLUKY6m0VtCs0XyDt6VlfjW1+BkTexMgcC5taSeGCR3aIrghU2knx3Gu6wCx3KQVRlHkQlsacDBRq7yEri4GaNgIV3cTGosS1lbiFOSRSpvsOT6NmaOxolV+7Adm0LOxBPQScdLzxvaV1USH8sx1jdGfCTL6ssv4bHbP8dTd96O7vGg6Dqx+qX4fB5aV6zhoa/+B45TOlevue7t7HIqmUxAS7XCs1aRxYtisH8SHLA7wjzrh+yyCLsmEnz7uYEz9l11SITovV6Sqkk44sa3uopiXxLHsEFTkAMaSBLGaAb/yioSD/QCkNs7QfDCRnwrq8i8MEru0Ol7fMxSUGT4La1YqSJ6eZCy9jAAQwWTD9yzn41dlbRV+uf60i9XNG38LpWCWTxjujIb3lDud/GJCzpwv4rXdkmW8K2pwhhOUziZABn862vRG4O/fGFA1hRcdQFcdQE8C2Ol9XWLR7yvl5BH5WhcIaCCf2MdiXtPEtzcgFbhIf30EO7OKI4DasgFEqXzT84sBZXMlM51xf4k4avaTod5ALkDU7g31JJ75PS5SS73kNBlZuZFqOxNlsJLAMmjMlqmc9czp9jcGiOytYHEvafmlvMsLUeu8jI4ncPdGsQ5kQQgf3Sa8E3dGN/rmQsLsdpC3DMep6Xcx2NHS8vfc3CUj9y0Eu9YFis5+7fhOLgaQhQlCVdzCMdx0Gv9KOUeQuc3YYykCV/VVtrevImVKODtiKA3Bom9b2EpcEiB2PwyJElCCepIigiMeDUYlo1pO3h+5jxlZU325g2QW6gKT9DpbgDTwZzM4kMibdoU8nmeG3mOK1qvmFuue201B54Y4vkfn6R1WTnqbLuNi5fx1J23k56Z5pzac/ja/q/x1PBTXNR00au+TY5tM3PnnUz+x5expqdxL1hA+Z9+As/ChWh1dUiKgjk9TW7XbuI//CHD/+eTxH9wF7Vf+DxqNPqqr48gCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCMIfGvEL9deS6obRA/DIZ8AySkXCm/+SSNNWHjuR4rET8FEzyscbwPFKYEizI1Q6SG4ZZIchSeKieVWcmMzyNz89xJHR0kiV37xlJR5FBhl6p3KkCybNMS+L6kKk8hYb28uZyhb56vZepjIFLl9UQ2dlqSB8cCaH16+TSxcJlXtYuKmWSLUPo2AxcGSaQsY8K4SkJuJmz2CcRXVhNEXCsE4P4fmec5pxazLZosXXtp8iXSgV50V9Om3lftrKfewcjVP1MyNbPvyjk3SvqGD5xU14wm7UMo8Y/fINZDhTZBIbp8xLRcyNpEi4Lm4mE9YZqnRhporoHpXUxfW0+t08MZZAc4o8s3+Y5RVB1pd7KKypJqzJqAEdX62fwkiG0FWlkXpdXVEkt0pm1zjhK1rI7BgFGXwbasnunyB0URP5YzMUhzOErmojt3cCz7IKXPPKKIykCV3XQXE4jRx2E2gKYkzn8V3RipUoYKoy/rd24qSLqI1BlKJF3gb31e2QN8lYNsqVbeQsm8KicgLra4mniozWB6lpCLLjySEaF8UYOjZD3+EZ1lzRwrNfP0TH6kqGjsZJTuVYdVkzu/63h7VX1TE6lENRovQPnyRQFkGvkrHyBlS6SKcldB8UDZsXeqdFCMmbjaTA4Auw51vo1WsILP5bCtMSwQsbMadz6GUG7mYfiv/nj4rtqvKTH06Vhj6WAUVBkmxcIS91nToLN9Wy//EhTNOmrcUmVutjeDjLsz88ORdAAqCoMsd3xc9ou6zaR8Yf5cI/+wxjB3YzlR4lurWLU+MKCyoDPPnTXro31NC2thrHcXj+nhPkUgatlX48msxUzmRX3wzLmyKU+Vw0x3zs6puZaz+eM84IIAH44qPHuK1hJV94+Bh/ekEHTx2bZH17jG/cvJK8YRHy6NSERQHnb+LF3mne/T8v8K33rqKjMsDBoQS3P9NHV3WAda1l5A2Ta5bVcdeuQb75bD+3XtzFXbuGkFQd9v4Adn2z1JArCJd/EYIBPvhoL/oTx/nixd2c43GjhDQo2Diyg+zRCGyqJZE28F/WgjOZwylYmDJkXArH902hu5W50bNfuNfg4g/9JSPH9iIrCrVdS5gYCuAP22dti+ZS8AZ1XF6V5FQex9bZ/UiAlpV/gizDwWeydK/zc2L3yNwyqi4TrvAQqfJxzf+3nFzKwO3X8IVcZ7UvvPE0BGzU3bcTKxvG6tyIJBmoFVNI3/0IUrCB8IIPkdPXcvWnPkd6agSXzw9ymA3vqGf85AFiDQ1E6rr5wotT1Ea9FEw3d+0c4FMXd9M3lWUqU6Am7GUsXaCl3MennzzGuZ0VuDWZjko/5QWZfc+O4PVpBAI6UrmPCo9G6IYubLtUIGmnDQJtYbAcHNMiUOvDcUqjuvvOraMwnCZ4STOYFk7WxL2gjNyBSUKXtyAHNNRKH65KHwDl71tYKiLVZHAcPPPLkHQZJexG8Wq/2y9D+O3ISimMBJjJFjg8kmJNS5S79wwzkSoVDpcHXHhDOmmrQFRVsC2HnQ8P4r1coeXCJqzxLKFLmikOp4lc1YYxlUfWSwXyxlgWq2Cit4RxCiZ23kQyZUzLAcdBDuuomkJWl3jqoQH6Dk0D0LqiDPfgQ/DQX4JtlUJSLvonMAvYrjAHB+P0TWWQZInFdWHqIqLP+YdK8Wr4VlRhTOVxsgbu7jKwHSSXglbmQVJljHQRJrO4O6JYqQKh85swJ7OoMS+OBLJHxTFsZL8OpoUS8RC5qYtizmRoIk+8OUxDpY9d24dYfVkzpw5MIckS3oDGU7cdxTLO7Bts/cACnp+IE/HpHMwUqap0s2c6waKWMAtbwxx4bJD6rigDh6cxiza1XRGOPDeKYzsMHvNy2cc+y9Dh3eheL+Hqbsq1JKHd3+I7mbdxyUc+Q5mdwR0uI+OLURzJ8cihQc6fV0k2pNG3NMpEuYYE/LR/iofu7efO962hvNzHXTuHyM+u6+K6EKuaIr+Db+wPU15xMU2CcJmP6LvmYcUL2NhEbugit2+CwNYGrFSR8LXtWDMFZI+K5FbQFpURbgnhFC3kkI73bZ2kU0WIunA5DmqZm96iwWTOYNuRMRRJZm17OY8eGWcqU6C13MeJicwZ6zKRKnDzOU3867aeuWn1EQ/d1UG+cfNKOisD1EZe/fsbLeYl+o5urOkCqNLc3+evS4SPvP6m03kMx+aEY1OryoSvacPOGKVg6ctaKY6Wrv+5Q1OELmslt2+cwsk4wctaKJxMYPan8HR5yVR7kW7oRD40haIrTDX4GTRNai9txD2SxVPhpVDrY1JyoNqD9q55JI/PEAq6eb5Y4Nbv7iJTtPicrnD/e9ZQ9s5uzOk8pkfl8WQG+cQkH//eXv5oeT03Xd2KUrDoc2x2ZLMU15XTJMkUFYn7R+MMxQ3y5ulz96aOCj77/En+7sZOKi0J2aWgRN3YqSJqpRcsByWgo0bcAGhhN1ZbCElTSuGAhoUky3PPeWW/jvoLnpsIv52cUQqU+dmwpCM9cfoKEmb+RVpb2nFOmhijGbAh6NHI5EyeP/kMOTPHhroNc8tJssTaq1q554t7OPDEEEu2NgDQvGQ5T915O337drPk3E1EXBEe7X/0VQ8hMUZHGb71U2Sfew7vmjVUfOyjeJYsOWs+rboaz/z5RG66kfhddzH2t39H7/VvpfFb/4tWVfWqrpMgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCMIfGvFL9ddSMQOP/W0pgATANuGxv8N149K5WYZmR2F1e73gBSOZA0cCXUbz6GhTGbKmxUgiPxdAsrIxQnXIzUzWYGfvNC5NIerTOTScJNZeTsijMpoq8oFv7aIw+wPyJ3sm+dsr57OkLkj7bBhJsMzN6itbMPIWD33jEPm0QbTaS+eqs3+k69U1OisD9Iwm+fJNy7hr5yATqSI3rKpnUW0QlyLz9PGpuQASgOlMkT2Dcc7rjNE3maOrOky40kt8LAuAYzvYqoS3OTQ3oqLwBiLBF/f1c01bJbKqkMPhhdFpOlwhvrOjnxtXN2CaJhnL4kQ8RXWlj1zB5JzOCv72p4fYO5NhRWUQPVtkftjFkSPTtC0pZ9dPe1l1cSMnTsSZmsqxfEsDO3aN43h1OuuD9OyfxFUTIL9vikBLgExI4csvnOLWze30jKWIeDRCrSE0bOSIC0mTsPMmBdOiSpHJKzKKJuMYFsf2TNCyvJIjT41Q1x0h21tkZiRD56oqxo8naFpYxujJJIfvOo7uUVmwoZa+3iSBKi/P39vLqsuasG2HYt5i9RVN7No2wMpLm9j7yADxsSz13VGev2eY5Rc18uzdJ1h+USMvPjCMbTmsvLSJU49PMtQzjKrLfHpTLXnb+eX7XXhjyU3Cnm8BII88h3fqKlzLPwI1m1COfQqOHILQv0Bj8y9sxl0TwEwXcEwbyaWgekrFN7pHZdVlLTQvLieTKOCJedCqPPQ+O0o2eXok7lidj2C5m6qWIKMnSyMYxxr81C6K8cxkij+/e4TKYGNplU8c4wMbW2lWYNnFTTz5nR7iY1k8AY3117fjC7u5+8gQa1rKGU/kiQVcPHRwlD+9oJOBmRyrmqM83jMBQLZonbUtyZw5F0R1bCzNdNbgq0+cYkVTmPesb8Elzue/kXi2yF/cfYCcYeFRZe7cMcB3XiiNgP3sySkeODDK/7mok9UtUUzb5sd7hnEcmxtW1kPq+OkAEoBCEh7/B3xXfZ3FdSFay/34K33sNC1aCiZhn45LU5jI5fn8k/1cUhtlbNsQdU0hALSsRfvKIGW1PqaGMniDOqomk5go8Oj/FvCFu1l2QQODR9OEKzTGe5O0La/g+M7xuVVYfUULFU0BrvnkciYH0mguhRUXN/HCvb2YhsX8c2uo7YwwMZBm4NA00RofG9/WQaSqFPDgDbrwBkX4yJuJNzuE9PxXUIoZFO6ARdfDoT0kVv89/eZqRo4XqWqReeb2MbJJk3BlkYWbNCyrEX+sBtNWyVk+7j94imzR4kOb2/jHaxZyciJDwbLpqgoyHM/hdcmMxHM0V/i5c0c/ly+uBsehvimGpCjs23462GblpU1Uh3R0t4rmUdixfZjhY3HWv7UD27QpJoo8fddxFEXGMm18IRdb3z2PmrYwALZlo7eGwHLQK3xnbK/i01F8opDy913/VJbbnunliZ4JPnReGyGvhipLdFcHuOW2F/mLC7sISCoLN9UxduoQ23/Sx66IzsJ11TTPL8Oq8JBGQm8JMT6d49nvHWPdNe3sf2yAwSNxXN5S/3OoZ4ZQhRezaOGPuQksLaM4lmPkVBJJlmhcWU73fBvpzr8qBZAAWEV46C9wrv4aU94mnj00iUdTKZo2Tx49xicu6KQy6P7d7kDhd0ZSZPSKnx9Eo/l1NL+OmSmiZN2Ag6sjgqwryPqZfblcqsj9/7mfkROJuWn186LkTYd559Zy6PlR6rui5FIGgTI3i7fUs+uBvrl5O9dUMeOGvGnzvRcHOK+zgqeOT7K4Psy+sRT/sHeYz2zupEJVQZFQVIlizqSuK0IxZ9K8uJLx/izDp+ZTzJlUNsvULZxixr2Ed9dVUSxoDB6eQlUlApU5lrb4MZfUMBTPMZMrUtVZjlrh5dvP96EpMl+6YSnLGsK4NIUffvAcjo6m8LsUFtWFxd/M6yhbNBgsqtRnTXDg2Qf6mBrMIKsSF9wyj5qYB4IuZLdMPurCKFg8/0A/Q0fjlNX7abu0kd5MlqBb485Dg2SLJtetqEeXZWIBF+//9i5uWd+MX1cpWhYnJtIMzmS59eIuvvbkSYYTedyazK0XdfPY0TE2d1Zw60Wd9E1niXhL1/cPfnsXt7175WsSQPISxaOh1Irwsjcbt6ZRMPPUhlz0JgpUFiE7VcBThOd/fILVWxvwyBLaykoKhkNuXS0nd45z/OuHqJ8XYcUFZSjDDzBhXMRJR8KzKEyr28XgU6P07pmkuChKx4Za+iWLnX0zfP7hHr76jhWsvu05/mRTKwOD49yz93SfN1u0+OquAcaTeZ7omaRo2WztrmA0WXrm/NWdA/TliyyuC/O5B4+yvi1GS7mPv9zRj2E5dFb6+fNL5/HX9xxEUyQuWlDF9SvqKfe7qK8MIMunA6N/XtiepMqo4dPnUFk8F3hd5Wef3Xhedg0f602ypydOWMsxOvMkocpz4aRJ/ngcgGDIzUAuxZMDT+JW3KyqWnVGm/XdUeq7I7x4fy/d59Tg8qiUNzThDYXp3buL+Ru3cF7DeTzY+yB5M49bfXWuobkDBxn8wAewMhmq/vZvCF977Vww6s8jyTKR667D3dFB/y3vof+W99B0x7dRwuFXZZ0EQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRD+EIkQktdSMQVG7sxploGVmQZKRQ0bOmJnvK0FzyxuCLpVZrIGiZwxN+3G1fVoikQqZ9BVHWLfYJwX+2ZY0RjFxqHMp/PgofG5AJKX/M9TvXzphqUUzdIPkzW3ilSweOjbh3BmwxGmR7I89q0jXPWny/D8zAiVC+vCxPwu+qYzfHRzO3URDwGvzqHhOIYFI/Gf2VZgLJGnKuhhNFHggaFptt7cxeDBGSb6kjTMj9K0MCYCSN6wHC5eWM2f33cY24F3rm2ktsxDMm/y7nOaGZrJURPx0DedpTrk4V8ePMqF86v40e4hrllWx8LaEIlcEV+1l3vHZvDP89MWcaG4ZXp2T5BLFRk7laSQtWiYX8bw8Rn2PTfKqb2Tc2vg6Zlh/vVtjKcLJLIGPeMZnuqfZmVTlDt29PMn57Xx9e0niflc3LqpjbHtgwzvmQKgdUU5Cy9u4q4jIyRaNcJ1bpSUSk1IR/OoJCezDPco7Hm4VOSfSxkc3znOyitbOJLMsqillXDIzdRIhqPPjVDfGeTy99Tiyp+gdeMBMtE16IUhCqsasTMnuOqWMlLZAuffWMf0eJHx/hRDPXEAzKLNiW2DbPijea/7tyj8ljLTZ74uplGe/QeobYfhnaVp/spfqSnV/8qBCm6/Rn139Ixpa69spbotzODhaSpbQjQuiFJeH+SSDy5ieiiDZdm4oi76cwX6p0vBTmPJwtzyX3nyBFe+u5xHbjtEYqJ0bs6lDB657TCXf3QJ//VkL7Yjsa61jD+5YzfvWttE0bSoDrlRZZkFtUEODCWpDLhxqfIZ15M1zVEqgqXrQ1XIzfSRIo/NjNNVHaB3KktnVeBX2h/CmZI5k4Jp86/XLSJZMPn+zsEz3h9J5MkbNkdHksyvCfLAgVFGEgUqgi7ITp7d4OQxjGKRv7i0GweHnX0zdFUF+cbuQW5/rh9VlviPm5Zx5ZIa2isCrO6uIDGeRfeoRKq8aC6VKz++lNR0AUWTqWoNcXL3OJl4kbYVFUSqfczfUItlWBQLFrIs0bWuisxMgWC5h8qmIJquEqlUiVTOhjcshNZlFTi2gz/qRpYlqlvDZFNFXG4Vt18UYL6ZGY6My8yfnhBuIBlez9HMOvoPzdC1tprt3+3BNkt9zvhYlr2PDFDXFaWuK8KpfROMxJS58KOvbz9JV6UfVZHZ1TfN3907yr9et5hU3iTk02gAjo6muGJxDQ4SBU1m/fVttC6vIJso4A+7CZa78YVduGeLJbfeMo9soogkSciqRCFrcsn7F3J85ziyItG5pprK5uDcJsiKjF7284v4hd9vmYLJVLrItkNjAPzrQz1z733phqXEcwZ9yRyfe6GH/7l+CRf88QJO7RxH92jo9X4OzaSpLMooiszz3+0hXOVl7dVtHH1uhMEjcQAKWZOdD/RxwXvn8+K9vcw7twa5wsX39w6xsilK69tbiflc/NVDR9iSHCiFWr5cMYNjWTx0PENzmY99QwlcqkpzzM+RkaQIVBB+KdWnwy8JVPIEdLbc3M3JvZP0Hp6ititKRVMA1aUwHM9SsbKcB3omSRVMlrpCtC+IcFFTgGyiiBbQGJIt/uLew1y0oIqQR0eVJZI5g90DcaqCbq5dUc+pXJ7t8Txta6PURT2M9qRoWlgGDhQyJvsePd0v6t03RbBsPqpLobbWi5EqUMyZJNOlEBR5qkhD1ENrzEc8V+TTPzzAR7a0cfmiWppiXg6NJBmK52gp9zOvOsi86uAv2HrhNeNIFGwHx6ey54cnSU3mWXpBA7pHJR0v8sSLJ1m4uY6p42lkReLJO3twZjM9pwbS8NM+8uvL2NMf5z3rmzkxkWYmU+Tv7zvCv1y3iJjfxdBMjqGZDJcvrgWgYNp87oGjXL2slqhPZ2NHOcsaIly+uJrBmSxv+Y9niPl10gWTvFG6/8kZ9s/bAuEPmCxJxLwqqiyTKVo8MZ1icyzEY7cfITWdZ8+zw9S0RcjECzQvjnHfV/bPLXti5wSWYbNw41XkjqboqvAyM5Dm+d39RGt8nH/LPFIzeYZenODObIIHj5aCHo+MlsJIG2NedvbPnLVO8axB/3SOolU6ZjVFpviy+/jHjkywsLYUOvnU8UkmUgX+eGMrLlWhpdzLB7+9k/PnVXHFkhouWlBFZ5U4N76ZZF4KIZl9xl7MmWz7+gHcmkylu5dRIFxTS4Y+CifiSJpMOOrm8GiSp6aeYVX1qlcMEVl7VRvf+4cX2P1gH2uubEWSZZoWLeXknp3YtsVFTRfxw2M/ZPvQds5vPP+33o7UY48x9Ik/RY1EaPrOnbg7On6t5T2LF1P3lS8z8J73MvTJT1L/la8gyfJvvV6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAh/iEQIyWsp0gKuIBSSp6dpHk7YVUR9aT50XhvrW8t+YRMFw8awLNor/fh0hUzRIuDW8eoKQY/GX/74IEdGUwDct3+URz5+LgXT5pUGCJQk0FWJ6mCpGFj3KKSn8nMBJC+ZGcmSiRfOCiEBqA57qA6fGZTi1hQS2SKXLKrmhb4zfwi/pbuC1ooArRWni9JrmsO/cJuFN4bKgIuCYfOJ8zvx6DLpgoVh2bzQO83Dh8f5yJY2PnLnbi6YX8VMpsi5beV0VwWo39LO3/zkEAG3yvvObWE8WeDQcJKHDo/xifM7SHR42FIdIeLTKf74JON9KYoFi3NvaGfbfx0EQJIlll/USCFrokwU+MeOBg59+wRru6Ncu66DXN7knAv8bBua5j3rm3nwwCh1lswze6ZAglWXNTPRn+KBL+ylc16Etg01FGcKbL/jOKsub+bR2w7TsrR8LvBE1WRWX9HCYM8M48cTZA5Po8Q8nOhPMT2cweVV8UV9nDhiobjqqayqosbagXrkTqRCAqoWwt7vwJa/gh//DdE1n+HFAyvP2qf5yfxZ04Q3uLLms6fVroC+Z0r/Pf8qqF78qn9sqMLL4s1eFm+uP2O6x69T23n63Bw2SkFAP2tTRwwjbc4FkLzEthwyM6XjUJIkEtkiAC3lPv79seMsrQ/j1hXec04zbk1BleE/blzGPz1whJOTGTZ3VfDHG1q4a+cQjWVeNEUmXTDprPQzOJN7xWuP8Ksp8+l8ZHMb+4aS1EW8/LxdGfLqjKcK/M/NK5jOFinzuSiaLfxsxI1TvZijxTKKsk2qYKKrKomcwbLGKLc/149pO/zf+w/zww+sI+orLR2Inll05PbruF/WF6hpC5+1Pooqo3tKAQ+N8185aOflfvYzNJdCyPXajS4vvH6kslbshW9D3vttAOx8hsHgFsgUidX6wWEugOQlyck84UoPifEswcVl/PEPdp3xfmXQzXtuf5Ev3bCUBw+O8c1n+/jgea3kihb9U1nWtpYhSaCpMlGfhq4qtC4p/7nrqKoKwbLTx5sv6CJa5aNxQeznLiP84coUTRT5lc/GssxccEHRtHlxIs1n7jlIZ2WA/LRF777jVIfcXL+inmpTQnOrdK6uxrEd+g9On9XezEiGeetr8IV1pjwK9x8Yo6HMRyJnUG9b7BlKkHDX4VZdYJ4OHcMdJuev56EdYzTF/HRU+EnmTXb0TrNottBYEF4NoXIvC86r4ympwBd3nWRTrgLTsnn0yDg3rm7kX548fsb81y2v4xPnd7BvKM7JiSzHx9Ok8yYxv07ArbG4PszTx6eoDXtwazKjySK3PdPLhze3cdynsNT/NDO+c4nHZdLxs++hTu2don1VBY4DB7cP0b6ikvRMHrNoo8sSLQEPN33rxbmQvql0kX984Cgb22Pcsr4J0xLBEr9rqiKhyhLpokWowkvn2irS0wVC5R52PdDL8kuayacNjIKNqklzASQvmRpM48uG+dbz/dyzd5h/u34x05kiFQEXtz/dy4c2t/HZew7y/13YSbpg4tZk8oZNzrD49vP9vG1VPYvrwsiyRJnfhUdXuGBe5VzwFEDQo9Je4X+d94zwZhDy6QwlsgzN5MkVLTKGRdFxUF0y598yj+mRDEd3jDJ/fQ3JybMDm/sPTROt9lPIm7i9Gvm0SdPCGLIi8cQdR2lbXoGRNXjHgpq5EBKfXnqEPxovcE5bjOdOntmfWN8eY99gfO719mOTvO/cZnrGjgFQtGwUWUKRJSzb4ehYiqNjKd59ThMnJ9L8fxd2URV0s6A2SG1EhPC92aTypfDygLt0b/78T06SnMpzXkuAE6PTuP0BvGUhMkDheBytzk9ZWGcGm0xxhHfXvucV2y1vCNC+spK9jwywYGMd/oiLpiXLObT9MUaPH2NV2yrK3GXcf+r+3zqEJPXoowx+5KO4u7qo/8qXUct//n3dL+JbtYqKT36Ssb/7O6b/5zbK3nPLb7VegiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIf6jEUHCvpeqFcOVXwB0uvXYFsa74MlR08c13r2RVU4So/xePTm3YNo4DYY/Gl25YytrWMnpGE6RyFsOJ/FwAyUtcqkIqb7GkLoxbO/Prfc85zUQ9OtFAqUhY96ponrNzaFw+Fc39q+fT1AZdaKqCR1P40HltBN0qEa/GJy/qpDYsCovfrKJ+N6tboli2zcGhJDLwpUePs7a1jLBXQ5YkVFnm8aPjVATd3PZsL8cm0nzx4WN8bGsbH9jYyky2iKZAV1WAd69rYjpTpKBK/Pmzx/hu/wT7O910vqONwNZqvnlkhGhDKaxm4aZaenaM0rNjFKNgsev+PsIVHlRN5vHbDvP8d44x/sAQH+iu5bq2Sv6uq4HpE6Wwn4buKENHZzi1d5Ka9jDBqIfRvVPsurcPKIUwWKZNIWPgCZaK6xdsqmX3Q/1M9KXIpYqMHE8QLHMzPZwBYOkFjex6oI9dD/bxwj193Pf1EY6oF/K1qs+yd/6tJLuuJ3vun8P4YexzPoY2uZtYlXbWPo3ERCHHm079GtjyGVBmv89wI1zwt9C6GW6+Dy79PPgrfmer59IUzm0r5082taLOFkrPrwnyJ+e2ISsS+iuc491+jY+f08Lmjhh90znetbaRurCbdMHiW8/38/mHevj49/bywTt2URbQKPfrfOGti7nrj9fywY0t2I5DdcjD5q4K/vOJE7g1mXeubSLgVmmMimP8N+Vzq7g1mZ/sHUJXJa5bUXfG+zUhNz5dQZUlWmI+UnmT2rAHx7Y54ZqHvfVvQJkNDAnVY17wTyi+CDtOTeNSFaqCLkzL4cBQYq7N/qks2dkRkwXht+Xzeple8XHMDbdCWRuJhuvp2THOC/f2cnD7MLlU8axldLdCrC6AVu7mw/cf4OW5eDetbiBbMKiLeBlJ5FnbWkb/dBbDdAh5NF7om+aKxTVUhzzMrw6iq8rruLXCH4KoV8e2HS5eUHXG9PYKP0G3iiJLzKsO8uWbluHRFHy6ypHRFL1TpXCw953bQkXAxZLmKAs31TE1lKZnxxjhqrOvlZIs8dT3j5FPG3z/xX4+e8U8nj0xRU3YgyJLfOWmZUyFFlC89P+Bazbc0RPBfMtXeHSmksm0wdBMjuFEnpjfhV9XqYn+4vtMQfh1aYrMJQurqQy6kSWYSBd534YWekZT3HJOEy619PxhRVOEP97YQnXYw+auSubXhqiLePC5VJpiPnRNpqvKT3uln9ZyH6blYNo2XVUB0gWTPUMZRsJd1L7wHoKuacpqzw6BCFd5GTmeIDmZp2NVFc/efYL9jw+xe1s/T//gOGSsuQCSzqrAXGjfE8cmsWyIBX55cJrw2jJsh4Bbw1KhpiNMciJP34EpHGDlZS1kk0UKaZPn7j5BMX92f9UT0BgvlIruk3mT/ukcOcPi/RtbaKsMMJPO88/XLsK0HObXBPnKTctY3xajJuTmI5vb+JPz2tDU08/MvLrKpy/p5n3nNlMdcnN+dyXfes9qmmK+12uXCG8y6byF16Xg0RUqAi6G80UWbqpjeiTLzvv7WLihlt3b+rF+JoQPIFLlIz2Tp7w+wJN39nDgiSF2PdjH4WeG6VxdheZS6Hl+jJAhzQWidVUHuHZ5HQdHkkxlinxsazut5T46Kv3836sX8tO9Q9x8ThO6Ujqu0wUTl6rwhbcuZnljhE2d5US8Gv923WKWNYSpi3j4sws62NhRzpqWMpY1hrlwQZUIIHmTSuVNAIJulcnBFPsfG2TBhlqiskS6MEOoogolcvrap1f7KQ+5KSKBo3Nu3bk/t+3VV7Rg2w4v3HsKgOYlK5AVlZ7ntqPIChc2XcgTA0+QLqZ/8/V//HEGP/ox3PPm0fA/3/iNA0heErnpRvxbtzDxxS9SOHXqt2pLEARBEARBEARBEARBEARBEARBEARBEARBEARBEAThD9WvnjQh/Ga6L4PK+RRmBhmxIkxoVUgFG9NxWNYQ+aWLR3w6+4bi1IS8+HSFv7q0G79bZSSew/nZoWCBx46MM782RNCl8l9vX869+0eYTBe4bFEN82sClAVPF6OpqkwuVWDe+hoOPTUMlArgVl3ajG82nOFX4XLpZPMJasMeHjs6xse2tJM3bWJ+FxWiuOdNbXlTlLBXI5E18bhKRQ+yBP9xw1Jmcgb/dO1C/v7ew+wfSvCRLW1kCxZ+t8oXHznOzeua+PLjJ7hqaS37hxJkCyYf3tLGY0fG+ejWDoqmzUymyP8eHeXcthiLmyK0t7uYGUjj8mokJ/M0LYrRd2AKgIZ5Zez4aemH440Lyiir9TN0cJqZ0SwTAymWXdAIQEVTkBfv6wVKxUzP//gki7fUU8yVfpAvK6UCjoEjM5xzTRvjp5KomkI2WaSuM8J432ywj1SazxvSSU3lMAqlwqdgzE3X2mrMyTwbGlroNes5lDSQg+3kpRkWNsTQYoMsXFTN2DdOYcwWTNV1RahqDr72X5rw6nL5Yd1HoPMiyMUh2gqByt/1Wp2hMuTmY1s7uHJpLTnDoj7qxeXAdCbN8osaee7uE3Mjh887twbHkfA8PE4+K7NuQYS9Uynyps2nL+nis/cc4vBoiqBH5Y/ObeWv7znC+zc2o8oyAbfGaDxHzrJ5omeCJfVh/u26xXh0hWTe4EPnteHSRAjAb8OlKqQLFodHUrxlcQ3tFQEeOzpOZ2WA87rKkYAf7R7kHWubKPPrHB/LEPZqTGYcPvr8Qr5w+U9o9ZskPHU8NggHhwe5dHEtx8eSHBvPsKW7gs/cc3Du8y5dWE25uE4Lr6JYfTvHtA9itbydwqSKZQ6x6vJmLNPBE9CZf24NB7fP9jklWHNlC4WsQdav8MW3LeXRo+McG0uzuauC6pCbAyMpxlN5/C6VTNFiY0c5fpeCS5X580u70RWZhqiXoOdX77cKwq9KVWQiPp23rqxnWUOYJ3smWNIQYV1rGeOpAn9/1UKq/C4mMgX+8/ETfGhzGycn0nP3XkXTYtuhMRau9hDyqTz7oyG61lTTtqKCR795GLNoA1DbGSYxkQPg6HOjXHJRLduOTXDL+mZyBYOygJugS2H/cJJc5AKab9qGOz9J0lXB01N+/vXBQ7z33Fb+8YHDLK4P4XOpvGVpDU1lZwc3CMJvq7XCz9ffuYKBeBa3qlC0LNa2lGE7Dlcvq0WWJOoiXoKeUoCfpshsaC/n769cwIOHxniyZ4LPXbMIy7a5Zmkt9x8YZVNXBScnMoQ8Gl5dxXEc7hkOUNvxL2yIKgSKLiqaAoz3lu7TXF6VhvlRnv7+cRZvrWfvwwMsv6gJSSrd6030p8iMZTmvI8aShgiqLPGvD/XMbYOqSER9ov/zu1Yb8nBiMg2mQ2I8h2VYLDm/gWLWIJM38Ufc9B2YYtXlzXgCOt3rqjj8zChQem61/MoW3vXI6X6tYdksrAmjqjId5T68bo0yr8661jJCHh1ZlljTEiNbNIn6dKTZ+/2Xa4r5+NTF3fzxxlb8LlXc2wi/kFtTmE4XKPfrZIsWh2bSLE7KDByeJlrtY6I/TXljgLI6H/XdUQYOTwOgajKLN9cx1BNn97ZSWG6owkPn6ios0yFa7aUw+/xqZjDNjavquXhBNYvrwnRUBugZS2FYNsfG0lyxpHTP2FXpR5Ulvv18H5+4oIOoV6Mh6mVeTYigR2NzVwVjyQKW7dAU87J1XiWJXBEJCLhVPLo2F3YivDml8qVQJr9L5ck7enD5NFZf0cL053eSyk5R0zYPJewCRQLLxlPWT1nfYaCZrwwVqfnf60sBu6E6iHVARTdUzINAFaFyDws21LL/8UEWb6knWu2neekKjj6znQ1vv4WLmy/mjiN38OjAo1zResWvve7p7dsZ+vBHcHd00PD1r6EEAr/1/pAkiaq/+itOXnoZo3/5VzR883YkWWTxC4IgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIg/DpECMnrIdqMK9pME+BPFdBVea4o55fxuTTCHhfbj0/SVeVH1xSMdBFJkqiPeKmLeBicyc3N//jxSebXB7EdCUWSuH55PR5dpmDadFaFzmjbLNooqoKqS2y6qRPTKBXCuXwqtn12wMkvJMkUTYN3r2smlTfxuhT8bpXGmCh+e7NrrTj94+8Kf4FkziCeM5hMF8gVLW5e10TYq9MzlmJFY4Tu6iBPH59EkmBDe4yf7hvmkxd18ZO9w4wlC7zYF+fkZJbljRHu3j3EBze1smtghgcfGOOzV8xDu6ga72xRWD5j4A3pTA2BZZWOSVmRqGwOsuMnp1hxSRNHni0VImXiBRoXlGEULHSPilGw5gJAevdP0rGqkoPbh0lN56loDDDel2LvowOsuqIZf6QUzjMzlqVpYRljp5KMHI/TvDjG9EiGYq7UjiegMf/cWp7/8Uls20FzKay4phV/SOczDx3lfRtauPWe42QNi6ayEbZeVMMin4faci9lNX68v0a4j/AGoqil4os3ME2Vaa88s1AjqcmEqzysubKVYt5C1WRkGRzbxihYHHxsiHnAopVRCqZDa7mfz12zkMd7JojnTG5/ppeJdIGGg14uX1hNImfw30/3smsgTsClcu3yOgqmRcync8H8qt/Nhv+eaYz5eMuSGg6NpDBMm9aYjxWNHRiGg4XDTNbgg5vaGEsVqJZkQl6VI8NJFtSFaIwFuPQ740gSvHNNmOuW17G4PsJ4qkBtxEdNxMNQPIdHV8gULbZ2V/Dx8ztwqaK4Unh1NcUC7B00saw8sfoAQz1xymp8HHsxTueqShoXLmRqMENFUwDHdjANm6Bf4+R0lmX1EbZ2VTCazHNqMoNpOZzXWUHIqxLxapzXVcGShjBuTdzCCK+PJQ0Rjo+lkIAl9WFmskXcqsJli2rminVTBZOrltXxse/u4f9n77/D5Drr+///eab3tr1rd9WrLcuS3HvFgOnNdEw1JCEJENIhPXz5fZIQklBCKMaAjcFgDC64d8mW1Xvb3tv0en5/rLTSaiWr7Wp2V6/Hdc1lzT3nnHnPzj1nzrk99+vURzxEvHbmlLhIZeGtF9aSH85hCbm48MYG9qzvxjTh8nfOI53IEShxsffVXna+OHo86/Y5qA67+cAlDbQNJvG77KQzeR7Y0UN9xM1FcyJAhJb+OM/v7aNjOM571jTwnWf28c5V9ZT7XFQHnSyqDp74RYmcJb/bzmL36fWxqxaU01Di5Yp5pcTSWR7Z2sOy2gALKv0YhsnapggNwx58ThsFE/bu7yfrd9LTZuWWsMnyq2vJ503SiRzpRJaXfz0aTulw2ph3cQVbn2mnem6IZHR0ErbTa+cf37KUz9+3mef39o/VcVFDiLnlGqOYDkJeJ87BBDabieG1Ybqt9LVGsdmt7N/Yy8W3NYIJL/96PyU1PhZdWsnNn1hKciRDJp3ntUSCocSh99tmYXVjhAtPEvTrdlhxO17/2NdiMSjxKaSCckGqAAEAAElEQVRGTs7rtDCUMIhlclQHnSyr9nPwiQ48ASe5bJ7oQJKGZaV07R2hcUUp1fNC5HMFDAuj5+cOK+lEDk/QwcJLqnj51/ux2S00riilbnEJjctLKa3x8dXL5o89p9NuZXVjCQCXzS0bV09TuZ/rFpUDBhHv+LGngNsxIbTP69Tx9GwykhoNrundPkjn3mGu/cBCnB4b2USKWGKAcHUthmFQcsUgtg3/hP3JLZSYK4E/od+sg2AAYl3QuRES3z+yYVcIyhdzSXgBRsDJa9/ZyxWfvZ2Fl13J3vUv0rZtKyuWrKDGV8ND+x867RCS2HPP0faZu3DMm0v9d7+DNTB54c328nIqvvRFOv/8Lxj62b2E3/2uSdu2iIiIiIiIiIiIiIiIiIiIiIiIiMj5QL84PsdK/ac/mWHVnAh2q8GOrhgD8RzLawIMJDM4rRb+9e3LeXBTJ5vbh7lqfhk3Lq5gXrmPze3D5E1z9MftuQKLqib+iDcdz2JzWvGH3bTtHMBiNXD7nBQKTizW07sC5rxKH9s7RuiPp3HZbBgYlOjqwrNOid9JyaE+fGFDmFy+wN7eGF3DKb764DacNis/W9/KvHIvKxtCDCWyvOmCarpHUty2oorqoJs/v3URB/rj1ITczCnx8LstXbzpgmquW1hBMpOnoSFINFXAG3LQtXeYy98xj45dQxTyBZweG8FyN117hwEwDMAATNj+fCcNS0vwhZ2sfXMTT/9kF3bn6ASj4Z4k1fNCLL+2loOb+1l0eRXLr60jk8pRUu0lUOambnGE1m0DuHwOSmp8tO0YZMHaShZfXo3b52D3+m7mr67k1UcOUiiYlNR4mb+mku2/byMVy/J31zWQtFr58i2LeHBLJ3t6YjgjDpoWlVLudxXpHZPzUTKWYfe6Hl753QGsVgvLr63FxGTPKz30t8e47XMrxpbd82I3K1eUUBl2Uup3sbt7hIsaIvzytXaqQi7uWFvPZXNL+YtfbiaTK3DnFU18+ppmbFYLXcNJGkq8LKo++6vUyqjmMh93rG3gmd199MYzBLx2rNbRkBmfw86SmuC4yWIWi4HLZsXntPLxK5tYVBXgqV29ZPMFYukc8Uyebz+zlzevqMGwGOzrjfPVNy+lIuBiUVXgpJMwRc6E3WZhTqmXWAqGyvKk4lmSsQyhCjfZTAGvzSCXyZNJ57BZLVhdVpbUhVlSF6ZjMEHbUAKbxaA27CaRyXFpcwkGcNc1c5lf4cdm1RWs5dyaW+FnbsWJv+uayn0EPXa+8Z4L+dGLB/E4baRzBkG3FZfdgstWYKAzhsNto3FFGXs39FDYZrJwbRW5bJ49r/YAo/v0RVdUs65rhPqIh4qAk/UHBrBbrcyr8HP53NKx56wv8VLic/Ja6xD7emP8yY0LqA65WFoTxOc6tbBLkXNtTqmXuoiHvT0x3rnKwc7uKOlcgWSmQH3Ew7wKP9FUlnS2gM9lJZnO47BbwGMl25klFc8SLHXzwi/2Yh4KTe1tG6GQMymvD9DfEaek2kuk2ovDaaUy7OWrb17Cz19t55ndfVy9oIy3XlhDdchT5L+EHFYV8tAfS+IOOMCEVDzHcG+SdDJHKpZl54tdXHRLA6lYju6DUXpaosxdWY7bgHDBxsr6EDUhNx+6dA4X1IWK/XLkPNNcHiCZLdAfy5DN54EslfPDBEpSvPpwCxfd1ED77kHKav3s39RHLpOnc8/oWFbV3CBzLyrHtqYSi8Vgw6MHWXRJJTULw7z68EFatg2w5PJqyupPLzQponHY81b0UAjJ1odbqGwKsnBtFeQKxFODmKZJSXkpPPQF3C//D/ir4bqv0VWohV8W2GK8m9vf+4EjG4v3Q+926NkOPdugZzu2nfdzuXsY8sD/+xPme8oINmbg15/E6LiOL5phfrn79wzPe5Zg2WJwhw8N2p5Y/MUXafv0Z3A0NlL/3e9iDYUm/e8SfOtbGf7Vr+n5+tfxX38dttLSk68kIiIiIiIiIiIiIiIiIiIiIiIiIiKAQkhmBIfNwsWNJVx86GqXAOmWAlvaR0hksty4uIxbl1ZiMQyW1YYAuLixhFy+AHDCyZp2lxWLFQyLQTZVIDqQon6xE5vNIJPMYbOf+uTksMfJpXPL6I2mMDEp97vP/AXLjGGzWlhQGWBBZYBffPpSdnRG+ee3LeMrv97GQDzLI1u7+c3mTj599Vy+9vBOPnPNXL799D7ypsmdVzQxEE9TG3azqyvKe9bUU+ZzUu53YRom8z7rZddLXbTtGODGjy0mmchy7QcXseXJNjzB0YkVrdsHmLeqgt3rugE4uKWfQsHkug8upKTWRyqeZe6qcvas72H7c52EKt3cdOdSwpUebMdMvr/mjoV07R1moDPO5e+aBwUTq91KuMKD1W7g9NgY7k2Qjo/+sH/exZW8cP9eABZfXk0+nqP7Nz2Eq7z86fVzCNf5cNg0wV/OvZatAzzz010AXHhjPUPdCTr2DBGp8jJ/TQWx/uTYst6Qg0U1ATz+0asilwXcvLx/gDkRN2saIxzoi9M9kuSeOy8hnslR5nPiPI3vBjl9S6qDLKkOksnlsVksWCwnnrizoCrAgkP/fnx7N06bwZ/cMJ98ocDO7igVARd/fdsS/G47ZX4nBmC3WjBOMhlI5GyV+lykLUnSiSwur532XYOEK7wYNaNBC56Qg/hAhlClh3Ctd2y96rCH6vDo5PCheJpMoYDf6VBgjkx7JT4nt62o5qallRiMP/9KJrMM2KzsfqWbjt1DlNb6Kanx8tID+7j2AwtZfVsj2XSe2oVhRgIWXH0WHt/ZgwWDpjIvtWEPF9QG8RwTLuJ12rhsbimXzdVkSpk5rBaD+ZWjoT4LqwMk0llCbieuY/bzvSNJDvQl2Nkb5eH+OLfUlBAsuNj6bDvX3LGAvtYYnoCD8oYgu1/uJlLtJRXP0r5zCMMwCJaNfpc0l/v5ws0L+cPr8zo3m4ZKfE5KfE56rXHSAxkMS5zWHQNceH09w71JquaGcHrsZNN5+vfECJS6GO5LUbcwzJtqyrh5WSU24/WPl0Wm0tKaEAAjiQzdIykyEbAWHKx+YyNun51AiZtENENsMMWSy2sY7EyQimfp3DOML+xk2dW1xKMZPCEHBzb10/NIC00ryuhpifLK7w7i9Nooq5sYKi1yrGgqC4AZy3Ll5+ZjWAzysRzR7CAOS47G7f8M3a/Amk/CdX8NDg/bnvw6sIBArAIzW8CwHzp+9ZaA93KYc/mRJzBNiHZy8IlnaXv6OcoLHQQ9B7FHD2C+9N9ck89wDcD33jC6vMMHkSaoWg6VK0b/W30h2EbHc+MvvUzrJz+Fo76e+u/9L7ZweEr+LoZhUPnXf83+N7+Z7n/6Z2q+9q9T8jwiIiIiIiIiIiIiIiIiIiIiIiIiIrORQkhmqAvqwxRMk319cdoGU9SE3axqKBm3zMmuFB8s9TDck+TAlj6q54YorfNjGDDcm6SQN8+orjK/64zWk5lvcXWQxdVBAFbWhxmIZ7hhcTn7exNYLCY//fhaYpkc3/3gKl7aP0BfLM2bL6ihOugi6HbgdY3fHZXW+Ch961wKBXPcpKLquUEGuhLse62Xrn0j+EvcrHljI8lYlrIGPzXzQniDLrzB0b5YPS/E8mtqyaTyhCvcBEqPf+Vrf8SFP3Li/tt0QRnRgRRbnu4gOZIhGc0AEKn2UsgX2Pj7DgC8QSet2weJDaYpqfURKteVtuXcyecLbH2mHYD6xRGigynC5R6aLijHajNo2zHA4suqgdGL0l72tnljASQAc8v9ZHIFdnVHiaZyLK0JsbwuSNjrIOx1HPc5ZWqc7kTZq+aX4XHYWH+gnzK/iwUVfsr8TprL/VNUocjrM4G+tjgHNvUBMNiZIJ3MsvKmBmx2KzavhbJ6/7h90NFCupK7zED245x/GQV46df76NwzDEB8KEOkysMlb23GBCw2g2DQTbjRT293jBKfk+ZSL9mCSUXARXOZd0IAichsEHDZCZygb5cF3JT6XXhdVja1DdOazlJlWKlfXEohb+KNuBjuSlDZHMQTdLD+oQOkE6NhkQOdcdKpHNXzQlhto59JBZBMb2VlXgoRN10HhllyWTWp+Oi5fbDCTSFnkk7YaLqwjG3PddCxe4hI1VJKanw4rHpfZXoIeBz43XbWHxhkfy5DIGSjPGBl7poKfvPvG1l0aRWpRJZL3tKM1W7BF3JSWufD4bbRsXuIvoMxauaHaNk2wLrfHGDVrXPo3D3ElqfaWXhJFW6fzsXl9fX0JrCbsOLqOsrqRscACskc6fxB3tWwCUdvGt76HVj+jrF1Ng48CywghpX8SBpbyesEihsGBKppePM7sS++gWd+tpu+1hgAVlueyqoCj5Z8lTrT4P3em/HRgy9/AOvO38KGH41uw+aC2ovJ2Brp+96j2OuaqP+/72GLRKbqzwKAs6mRko9/nL7//E+Ct9+O7/LLpvT5RERERERERERERERERERERERERERmC4WQzGArGyKsbDjzH+raHFbyeRNv0MnLv94/2ma3sPb2JrKZ/GSVKeeh8oCL8sBooMeiquCEx1c3lUxoO5Fjr2rs8jqobnbwti9cRF9rFNOE0jofJdW+467v8tipag6devGvwx9xceNHFvPY97djO3S17jnLSnnt9y0ANK4oxWI1eOlX+wCwu6y88a4VVM2dnOcXORmLxSBQ6qZzzzDV80MYFoOXHtg3Fiy1/JparDYL135gIZEqL2X1EwMqjg4UkpnDarWwtrmEtc2nvn8VmUoWi8GBzX1j98sb/JTU+Pj1v28cfdxqcNOdS2m6oKxYJYqcEyN9ybEAEsNisPqNjax7cP9YYMK8VeWsfWszfreDi+ZM7SRMkZnEMAwWV4eoc7t48kc72Lp1AABPwME1H1hIeb0PX8SF1WaMfZ4O2/VSN6tunkOoQoGQM4XFamH5lbX0tIwQHUjj8Tno2jvMhkdGz7Wtdgtr39zEugf3Ex9KF7lakYkMw+DixggXN47/Ln/rn6yktyUKQGm9n0ild+yxlm39PPRfm8lnCwAsuqwKq9Vg34Ze6hZHSEQz2OwK25HXVyiY7Nrej5vR48zDzO7dXBT5XxyWFMZ774W514091hnr5EB0Ny5bgb5cgfzwSUJIjlI9L8w7v3wxfa0xHv7WTxnqSoLnEizJ67g7cj/mK/MIpC/BYjFoWBph9W0uSo1dcPB58lsfxj7yDA1XgGkbxHjoTmi6CpquhoplYHn9QPUzVfLxOxl58EG6vvIVmn71ABaXQtRFRERERERERERERERERERERERERE5man7ZKTOGAex8sWvsfi5bYOPjbRgYJ15JZBqIVHmZv7qSBWsqTxhAMhUqGoO85Y8vZM6yEvwRJ/lcYWxSSHlDgL2v9o4tm03lefonu0glsuesPjm/GYbB0qtqsNkt+EvcvPrwwbEAEoBNT7RhtVlYdGk1FY1BLFYdBojI1HB5bVisR44n5ywvZdPjbWP3C3mTx3+wnZH+ZDHKEzlnLFaDw6dWDUsi7H65e1xgwu71PQx2xItUncj017V3mJZDASQAiZEMO1/sorI5SLDUjT8ycdK01WaM+w6SmcHhtlG7IMKiS6owMccCSADy2QKvPdbCvNWVOFzKU5aZI1w5OnY1f3XluACS2FCKx3+wfSyABGD7c53ULAhjc1rIZQqsfkMjdqdCSOT1bX26nf54hoqwG6fHPtrYsQH7b96K1UjzRP5N4wJIAJ7teBaA2pCLDkzyw5lxj5u5HLm+Pgqp1HGf0zAMyur93PyJG0jHHiFctokv3/lJAAJ39PPWP72IFdfX0bFnmJ/+eztPvjqPIect7P6hycGNl5B/w/9gXPQhGGmHR/8K/udK+NdmuPdDsP57MLB/Uv9GFqeTyr/9G7ItLfT9939P6rZFRERERERERERERERERERERERERGYr/XL/PJc76sfuh0X7U5imeZylRQTA43fi8Tt50x9eyEBHHE/AwQu/2Hvcz1NfW4x0Iofr8EQAkSlW2RjkbV+4iOhAinQ8N+HxbCZfhKpE5HwTqvCy8sYG1j90AIB8buKxZTqRIxXLEjjFq26LzETBMjeLL6tm27MdhCo8tO1on7BMbDBdhMpEZobBzokhPV17h8km89j8Vqqag4Qq3Qx1HQm1uvgNjfhLXOeyTJlkiWMmxAPEhzIESlyUzwkUoSKRyZWO54gPTezn+ZzJimvrCJZ7KKv3F6EymUniw2le/OVesj4rjRWHQm72PgE/vQPTEuCeA800Xn3phPWebXuWKm8VzUaYLX295EdGj0VTO3fS/61vE3vqKQqxGBgG7hUrCL7trYRuvx3DPn5ss7SugSVXXsuG3/2a5dfdxKqKVTzY8is+cdGdVDUHWXXLHNb9Zj9dd99Hx467sTc1Ufvd72EtKQHePbqRkU7Y/xTsewr2PQlbfzHaHmqApqug/lKoXwPhRjDOPGDMu3YtwTe/if7v/i+BW27BtWDBGW9LREREREREREREREREREREREREROR8oBCS81y4wjuhrazBjzvgKEI1IjNLqNxDqNxDOpmlpMZLMpqdsEztwjAenwJI5NwqrRvdj/sjLqIDR65ca1gMgmWeIlYmIucLi8Vg+bW1lNX7ad81SEWjH4vFoFA4EkbiCzvxBp1FrFJk6tmdNla/sZG6RWFiAynqFkXYv6lv3DLBcgXxiJxIWcPEwImmC8pwekfPsQKlbt7w6RW07RhksCtO7cIwVc0hjLOYqCzF5y91gQEclWEWrvIwZ3kp/ogCZmTm8wQdRKq9DHSMD1qqnheiel4Qm91apMpkpjALJo//YAf5nEnaaVAecMGGH8Gv/wDKFtLi+wIDG77NJQsWjlsvm8/yYueLvKHpDTiGfDxGN+m+GIl//EcGfvBDLH4/gVtuxrlgIfmBfqK/f5yuv/wr+r/zHSq++EV811wz7jv28vd8kD3rXuSx7/wn7/vA+/ijJ/+I3+7/LW9sfiN2h8H8jt8S2f4DhkoWsqPpk7h6DOpKjiooUAUr3j16M03o2z0aRrL/Kdj6ALz6g9HlvOVQtxrq10LdGqhaAbbTO5cs/+IXiT3/PO1/8IfMue9erD7fGf71RURERERERERERERERERERERERERmP0uxC5DiitR6ueaOBdjso10hUObm6vcuwOVRaILIqXK67TQsLaVpZRmXv3MeVtvo5ylS5eGyt8/D7lLek5x73qCTGz66GG9odFKG3WXlho8sJlylEBIROTfcPgdNF5RxxTvn07CkhBs+uhiHa3RCpTfk4MaPLhnbR4nMZt6gk7kXVXDBDQ2svb2J0rrRCY8Wm8Elb2mmvH5iyIKIjKpsDLDypnoMy+iE56p5IZZeXYvFcmQCdKjcw9Ira7jinfNpXF6Gy6vxjJkuUuXlug8uwu4cPW7wl7i47oOLiVRNDNIVmYncPgfXfmAR/pLRUB2708p1H1ykABI5ZZueaKNlaz9r3trMQDJLWd9L8MBnYM4V8OGHaG8fDb2rmj9/3Hrru9eTyCW4vOZyGko8ZIFt//M3DHz/B4Tf827mPvIwVV/9KpE73kfZ5z5H4y9/Qe1/fRPDZqft05+h9WN3kt6zZ2x7vnCEK977QVq2bCK0Nc6iyCK+tv5rdOzbRMtHPkrfN/+L4FvfyuL7vo+rLMSv/2Mjm55owzRNJjAMKJsPaz4O774bvrgfPvU83Pb/g+ZroXsrPPIX8N0b4B/r4Ls3waN/BTt+A/G+ids7hi0SofbrXyfT2krnX/wlZqFwVu+BiIiIiIiIiIiIiIiIiIiIiIiIiMhsppnx5zm73cqiS6upnhcik8zjizjxBDQZVORMOJw2ll1dS/3iCNlUHn+JC7ffUeyy5DxW1Rzi7V+6iPhQBpfXRrBMASQiUhwWq4W5F1VQVu8nFc/hDTnxKYBEzkORah9v/sMLGOlLYXNYCZW7sViVDSpyIm6/g9W3NbFgTSW5XIFgqRunQlNnPZvdyoI1lVQ2BUkncvjCTrxBHTfI7FIxJ8DbvnARscE0To+NYJkbwzBOvqKc91q29vP8z/fQuKKUQH2cfMGkoe0BWP0huPVrYLXT0rqVgLuUYHnluHUf3PcgfrufS6sv5dWnfwfY2WFxc+E3v4n/2msmPJdhGPivuQbf5ZczeM9P6P3GN9j35tsJv++9lH3mM1iDQZZfdzP7NqznmR/9L5/58Dv5k/6v8f5fv4+PRK3c8vd/R+RtbwPgbV+4iEf/dxvP/HQX/R0xrnzX/LEg5+OyWKFiyeht1UdG26Ld0PYytLwIrS/DC9+E5/5t9LFIM9SvhbrVULcWSueDZfz2PRdfTPnn/4ief/0a3SUlVPzFn+tzJyIiIiIiIiIiIiIiIiIiIiIiIiJyHAohEQyLQahCV5MVmQwWi0G4Up8nmT58IRe+kKvYZYiIABAs8xAsK3YVIsXl8jpweRVUJ3KqrHYLkWpfscuQc8wwDELlClGU2c0bVMCOnJ6Wbf389ltbKK2ycuOCh3ni7ieAz9B8zYfh2tGwj8TIMF39e1nYdNm4dUcyIzx68FFuqbqW/i/9Jf7fPITjjf/AnuVvx3fVVa/7vIbdTuQD7ydw2xvo/fd/Z/BHdzP0k5/iufhiHE1NrE7nqG7pxfe5v+evKk3+/T0+/unNKX7Ed3jb5kHe3Pxmyjxl3PLJZbz0wD5effgg/W0xrn3/IiLVpzGO6q+ARW8cvQFkU9D52pFQkl2/g9fuHn3MFYSaVVB7MdRdDBVLwVdB5CMfIdfXz8D3vkd+ZISqr/wtFrf71GsQERERERERERERERERERERERERETkPKIRERERERERERERERERkGsqkcmx4+AAHf/8UV5U8xwLbYxhPD7Mh8GWsKZh32ZvHln3lgV9QMPMsvmh8sMj/vPhvJHNJLv2XRxlpz1J516e5IB3mxZ44mfYYzvrASeuwRSJU/c3fEH7Pexm+/37iL7xA8rXXwGajtKqK7ppqOlIj3Pqcj8TaCrZ4evm3V/+Nb2z4BlfXXc2dy+7kkrcsobTOx1P37OSnf/8yy66p5YLr6vGFzyCQx+6C+rWjNwDThIF90PrSaChJ2zp4+l/ALIw+7gxilM2nfF4T/o/OZ+SZn9D9kccJvOU9eNZegeEKgNMHNhcYltGbxQqG9Zj/Gqdfq4iIiIiIiIiIiIiIiIiIiIiIiIjIDKIQEhERERERERERERERkXMll4FMDAo58ukUuVSafDpNPpPBTIyQHe4l3dtJpmM39O5kmWU7ayIjmIYNY+6b6F9+Jw/9Ks1FDS7s2SR9+1tpeXUdW+//CfMcDYQ8GboeuI+W1i38vuc5ftzcxbWvFVg8ZzXl//UFnI2N3P7sfr784Dbu+dUO3v3hC3B5HadUumvBfFx/9qUJ7c1A8749bHrsd+x66TlWxVIsjjTRvdLDS+0v8vuW33N19VW8Yc6tXPb5ZnY/OsSGJw6w6fetVM0NUT0vREmND2/Qgctnx+GyYVgMLFYDi8XA7rRiWF4nAMQwoKR59HbBe0fb0lHo2AA9O6BvJ/TuxDjwHJ5kN56VWWAYtn0Ftp3Ge2dYwWIbvVltR/5tc4EzAE7/8W8OLzh8x/zXC3bPaLjJWNDJ4W0ebrMcfmKwOUdvIiIiIiIiIiIiIiIiIiIiIiIiIiJTSCEkIiIiIiIiIiIiIiIi58rme+GBTwNgPXQ7noJpJeGuw6i/HlbchNF8HX0EWPsPv6dgmvzNm5bws/e/k1XbD+AGrgLgIB1/9DSfuMvKoN8AP1yVauArn/kawbkLx7b9trX13P3UPv6mrZeff/VxfvUPN71+yMcpqGiayw0fv4trP/JJWrdsZMcLz7DnhReoSUfY2mjjuezTPNnx1OjCBnz6A59lZff17N/Uxyu/PYBpnnjbH/iHS/FHXKdXkNMPjVeO3o5WKEByAHOwlcQLT5F84Smy+3dBOophBTAxjNEaDcMk+NbbcTbUQyEPhdzEWz4LudRo6Ek6CrEu6N995H4udXp1n8gNX4HL/mBytiUiIiIiIiIiIiIiIiIiIiIiIiIicgKG+Xq/6pzBDMPoBQ4Wu45DSoG+YhcxBfS6oM80zZsn88nPoO/OpvdBr+XcmfS+C0Xf9063v/l0qmc61QJnX8902PfC7Pu7TgXVNN506btTaTq+52dLr2nUdOu/0/F9UU2n5lzXNN367vFMx/dpqpwvr3WyXudM6L9nayb3iZlcO0xt/edD3z1spveDU3E+vcap6Lu/O7T9U62h2FTH9KoBTr2OKRnvFREREREREREREREREREREREREZHzz6wNIZlODMNYb5rmqmLXMdn0uqaHmVbv69FrkbMx3f7m06me6VQLTL96ztR0ex3TrR5QTeej2fj31Wuanqbja1BNp2Y61lRs59Pf5Hx5refL65wMM/lvNZNrh5lf/3RxPvwd9RrPnxpUx/SrYTrVISIiIiIiIiIiIiIiIiIiIiIiIiLnD0uxCxAREREREREREREREREREREREREREREREREREREREREREZHiUgiJiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjIeW5Wh5AYhvG/hmH0GIax5RSWvdIwjFcNw8gZhvH2Yx77nWEYQ4ZhPHiGpXzrDNeb7vS6poeZVu/r0WuRszHd/ubTqZ7pVAtMv3rO1HR7HdOtHlBN56PZ+PfVa5qepuNrUE2nZjrWVGzn09/kfHmt58vrnAwz+W81k2uHmV//dHE+/B31Gs+N6VADqI6jTYcaYPrUISIiIiIiIiIiIiIiIiIiIiIiIiLnCcM0zWLXMGUMw7gSiAE/ME1z6UmWnQMEgD8BfmWa5n1HPXYd4AE+YZrmbVNXsYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyLlnKXYBU8k0zaeBgaPbDMNoNgzjd4ZhvGIYxjOGYSw8tOwB0zQ3AYXjbOf3QPScFC0iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiInKO2YpdQBF8C/ikaZq7DcNYA3wTuLbINYmIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiBTNeRVCYhiGD7gUuNcwjMPNzuJVJCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiUnznVQgJYAGGTNO8oNiFiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiITBeWYhdwLpmmOQLsNwzjHQDGqBVFLktERERERERERERERERERERERERERERERERERERERERERKSoDNM0i13DlDEM4x7gaqAU6Ab+Gngc+C+gCrADPzFN8yuGYVwM/AIIAymgyzTNJYe28wywEPAB/cBHTdN8+Ny+GhERERERERERERERERERERERERERERERERERERERERERkakxq0NIRERERERERERERERERERERERERERERERERERERERERERE5OQsxS5AREREREREREREREREREREREREREREREREREREREREREREikshJCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIue5WRtCcvPNN5uAbrpN9W3Sqe/qdo5uU0L9V7dzdJt06ru6naPbpFPf1e0c3iad+q9u5+g26dR3dTuHt0mn/qvbObpNOvVd3c7RbdKp7+p2Dm9TodivSbfz4yYiIiIiIiIiIiIiIiIiIiIiIiLTzKwNIenr6yt2CSJnRH1XZjL1X5mp1HdlplLflZlM/VdmKvVdmcnUf2WmUt+VmUp9V0REREREREREREREREREREREREREZppZG0IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIip0YhJFJ0pmkWuwSZZdSn5Hynz4CIFJP2QSKj9FkQmT70eRQRGU/7RRERERERERERERERERERERERERERETkRW7ELkPOTWTDJtIwQe7GLQiqLb00VzqYgFqe6pJyZsT71UheFeBbf2iqczepTcn7JJ3Ok9w0Rf6kTq8+Bd00Vjno/hmEUuzQROQ/kBlMkt/WT3NyHc24Iz/Iy7OWeYpclck6ZeZN0ywjxFzsopAujx6RNASwOHZOKFENuJE165yDxDT04anx4VlbgqPIWuyyZgcxsgfTBYWIvdgKM7t/nBDFsyneWmaWQzpHeP9qXLS4bvrVVOOoDGBaNG5wvCtk8mf0jxF7owLBb8K6twtkQwLBqfyYiIiIiIiIiIiIiIiIiIiIiIiIiIqM0E0qKItMWZfD+3biXlgJOUjsHwTRxLy4tdmkyQ2XaovR+azMURq/kmt41SOR9C/EsK5uwrJk3yfUlKaRy2EJOrEHnuS5XZEqkdw6Q3D6Aoy4AwPBv9xG8rRlnrb/IlYnIbFfI5Bn67QGsLivOpiCYMPTb/UTeMhdrQN+zMrvlhlLkhzNYPDYKqRx9394EhdHH0jsGKPngYtyLSopbpMh5yCyYxJ7vIPZkGwD54TSGy0ohlsFW5sEW0veTnLr0wWH6vrNl7H5qSz+lH1uGa27opOsWMjlyfSnMvIm9xI3Fo+FYKZ70niH6f7h97H5yUy9ln1iBsyHwuuvlk1lyfSkMq4Gt1I3FYZ3qUmWSHX4PC8kcsSdbSe8bBiC5uY+yjy/H2RgscoUiIiIiIiIiIiIiIiIiIiIiIiIiIjJd6FfvUhSZtiiuhSVEn2qDvIk16MBR46OQyetH7HJGUruHxgJIDos+2YprQWRcnypkcsTXdTP80P6xvhe5YzHOOoU0yMyWT2bJx7PkehMkN/aCAe7lZeR6EwohEZEpl+tL4qz3E3u+g/xACiwG3rVVZPuTCiGRWS29f5j+u7dTiGUx7BYCN83BVuoh15MYWyb6dDuueSEMm85zRM6l3GCK2DPtANirvbgWRIg+0Uo0Z2LxOyh5/yKc9a8/6V7ksPjLXRPb1nWeNIQkN5Jm5JGDJNZ3A2BvCBB5xzzspZ6pKFPkdRWyeaJPtx/TCKnt/a8bQpLtSzJ0/+6x0ArPyvLRYx6F+s4Y2b4kg/ftInNgBAD30hI8F5WTeKUHTEhs6lUIicgkM02T2JNP4rv6agzDKHY5IiIiIiIiIiIiIiIiIiIiIiIiIqfFUuwC5Pxk8diJPT0aQAKQH84Qe76DQiZf5MpkpjKOtzezTPxxb7YzwfCv943re0O/2E0hkZ3iCkWmlmlCpi1GtiN+qAGSG3sxs4XiFiYi5wfr6BW08wOp0fsFk/jzHRQSueLWJTKF8iNpBn6yg0Js9DjSzBYY/s0+PCvKxi9oAdCkM5FzzQA4NOHTvbSU6BOtkBs9DyxEMwzeu4t8LFO8AmVmOc74gmE9+bBqZv/wWAAJQPbgCPH13Zim+TpriUwNwwCsxzkmOU7/Plri1e6xAJLR+z3j7sv0ZpomiVe6xwJIAJJb+rGGXGAbfe+N4/ULETkrw/ffT9unPs3QvfcWuxQRERERERERERERERERERERERGR06YQEimO3MRJ8dnOOGZSE1XlzDjnhcd+OH9Y4Oo6LI7xV5zPD6UmrJvtiJOPK4REZjbDNMkcmDgJKNc/sc+LiEw2w2ol0zIyob2g71eZxfLRLPnhYwIMTDCPOdfxX1mLYdOpt8i5Zg278F9VC0z8XALkepPkY/qeklPjXV05Pk/KAM+qipOulz4YndCW2jZAIa0QXjn3DJsV/5W14xutBu5FkROuU8jkSW0fmNCe3j80ydXJVDEzBZLb+ie053qTWINOsIB7WWkRKhOZ3bKdXQDkurpPsqSIiIiIiIiIiIiIiIiIiIiIiIjI9GMrdgFyfrJG3BPabGVuLB51STkzjhofZR9fTnJDD/lkDu/KChxzAhOWswacE9psZW4sbvu5KFNkylicNpxzgiQGe8a1O2p8RapIRM4nFq8Ne62PbGtsXLst7CpSRSJTz+K1Y/HaJ4TtOJuCFJI5Cuk83ovKcTRMPCYVkalnWAx8l1RhK3NjHifwwRpyYvFqDEJOjbM+QOmdy0m82gWGgWdlBc56/0nXc9T6iB+7rebghMBUkXPF2Ryk9KNLib/Sg8VtxXNBOfaaE/dlw2bB2Rwi2zm+JzvrdXwzUxh2C665IWLdiXHt9movFp8Nz/IyHHV6P0Um3Vh4mVnMKkRERERERERERERERERERERERETOiGZbzCI7u6KsOzBAMpNj1ZwIy2qC2KzT84rb9iovvqtriT3ZBoDhtBJ+6zysXkeRK5PprJDOke1KkB9JYws5sVV6sdhHJ+4YhsFBh8GOBjfZfIEmFyyzGhz7CbBVevFfU0f0idbR9Q73PZ9CSGRmM2wWfFfWkN4/TH4oDYBrSeSsJz4PJ7JsaB1kc9swDSUeLmqIUBOeGCQl018ik2Nj6xCvtgxREXCxak6YOSXeYpcls4TVbcf/hiaGfrCNQiIHgGdtJXYFIcksZgs5Cb9zPgM/2o6ZLYABwVsbGQrayC8OY03miOYLBLIFnHZNNhcphng6R8ICtpAd77V1xJ9oBRMMh5XwO+Zj808MqZTZLxXPMNIaJTWQwgjasZZ7qC57/WMWw2bB1RTE1RQ8redyNgZxzg+T3jUIgLXUhW9tFYbFOMmacr7a0TnCywcGyOQKXHxofNdygv6Si6bJdSYoZPLYKzzYyzwn3b7FbsU1L4xrXviU6jEsBt6LK0jt7CfXmwLAOTeEszl0yq9JisuwGFgvLMO6c4B83+h76JgbwtHgxxYuwxZScKTIlDBG992mqRASERERERERERERERERERERERERmXkUQjJL7Oga4V3/8yLDydGrcFstBj/66GouaS4tcmXHlx9Mke2IEbihATNfwLBbsAQUQCInVsjmiT3XwcgjB8faQm+Zi/fiSgyLwc6uEd71rRcZShz5DPzwo6u59JjPgNVtw39NHe4lJRSSOaxhF/ZSBSrI7GDmTVzLSrE4rBgG5JM5yJ/5D93zBZO7Xz7Iv/xu51jb2qYI33jPSko1YXPGeXhrF3/0041j95vLvHz/I6upDZ98oprIyZimyXA8g+fiSgyrgWExSCWyxOIZgh4Ffcns5ZofpvxzF5IfTGPx2em3QXxDD64n2skBOSB6fR2Oq+owFEQick6lBpLE7ttFYX+UHGBv8ON5/yIcFgNnqUfngecpM1cg+lInmUdaxkJLU1fX0LnaQlVk8o+LbWEXkXcvINeTxMwXsJe5sQZ0LiXHt61jmHf9z4tE06OhfjaLwY/vXMPqxpIJy+YGUwz8ZAeZg1FgNGS39GPLcNb5J70ue4WXsjtXkO1NYFgN7OUeLDrGnzFGklm+/PRuGpvdvPPqWoLxHNm2GH3/sxlLwEHpR5biqFRAqYiIiIiIiIiIiIiIiIiIiIiIiIiIHKEQklni2d19YwEkMDpx/D+f2MvK+vC0u+q2WTCJvdRJetcQ6V1DY+2G04b9Uk0CkuPL9SQZefTguLahX+/F2RjEXu7h2T19YwEkcPgzsIeLjvMZsDisOGonf1KGSDGZuTzRx1tJbesf1+6o8WGLnNlVbVsGEvzbY7vHtb24b4Bd3VGFkMwwPSMp/v4328e17e2Ns7V9RCEkMin6+xIUHm0h1pUY127U+6FME9pk9jIMA3uZB3vZ6L40vr0X19Md45ZJ/b6V5MISPDr+FDmnkgdHKOyPjt3PHoySfaKVwdubaFYAyXkr1hkj/VjLuDbH0x1kGwMwBSEkAFaPHescBTbIyT22vWcsgAQgVzD57jP7WVkfxma1jFs2fXBkLIAEwEznGXnsICXvW4TFMfljwdaAA6sCpGek3T0xHtzURanPwRstTowXusYeK4xkSLzajePWpiJWKCIiIiIiIiIiIiIiIiIiIiIiIiIi041CSGaJwXhmQltvNE2uUMDJNAshyRfItkax+B14VpRiOKxkDoyQ7YwVuzSZxgqJLJjHNOZMConRyRmD8eyEdXqjaXL5AtZYFjNXwBp0TslEDJHpoJDOk+2M4aj345wbwswWSG7uI9eXPONtpnN50rnChPZkNn82pUoRZPIFRpK5Ce16L2WyGNkCZn8S15IS7BUeCvEsiY29EJ/Y70Rmo9xgCjNXwAWQP+ag1YR8bOL5mohMLTM6/nNnDTtxzw3jzZqYuQKGzXKCNWU2KySycOwpTsGE1OkdFxcSOfKxDBa3DatfwQwyOfpi6Qlt3dEU+YKJ7ZjhrPxgasKy2c44ZjoPJxj7KmTy5IfTGHYrtpCCRc8XqUPn/RUBF75oBveaSixeO9nuBKnt/WQORjELJobFKHKlIrOUeez/1BARERERERERERERERERERERERGZ/hRCMktcPr+U/3xy77i2D18+B69z+l1p1WK34rukmtxQmtjzHZipHK75YTzLy4pdmkxj1rALw2HFzByZGGTx27GGRydNXD6vlG88sWfcOl+4bh5sHaD7wX2j/WxJCcGbG7HrqtcyC1k8dgI3NpDa2k/0iVYMuxXv2ioczcEz3mZd2MO1C8p4fGfvWFvQbae5zDcZJcs5VBlw8f5L6vnuswfG2pw2C/Mr9F7K5PCGXTjeMo/E8x2ktvZjDTgIXFdPocJT7NJEplQ+mSOxoZuRhw9iZvK4LijDfm0d0cdbx5YxnFZcZfosiJxrzkoficP/nhfCXuUl/lwH5pOtmKsqCFxbjy3sKmqNcu55Sj3E3DbMowL6LB4bjsip94VMW5TB+/eQ7YhhjTgJv3U+zuYghqEJ/HJ2blpSyQ9eODiu7UOXNuK0TwwVcdT4J7R5VpRh8R5/LDjbk2DoN/tI7xzE4rUTelMT7sWlGHYFMs12jaVeqoIuUukcnkUlJB8+SH4kg73WR/CWRgyHRQEkIlNAxwUiIiIiIiIiIiIiIiIiIiIiIiIyk+lXxrPEyrow3/3gKpZUB6iPePi725dyw+KKYpd1Qpagk+jvW0YnfZiQ2jlIcksf5rFXDRc5xF7qpuQDi7EGR0NHbKUuSu5YjO3Q/QvrQnz3g6tYWnPkM7DW42bw3l1H+tmWfqJPt2Lmj73sscjskOtOkNzSDyaYmTyxp9sge+b93eu08VdvXMJHL2+kIuDkhkUV/PCjq5lT6p3EquVcsFktfPSyJv7w+nlUB11c0hThhx9dw6KqQLFLk1nCgUHiuQ6ybTEA8iMZhn97ALd7+gXiiUymTOsIw7/ah5nOjx5vbuilkCtgaxzdv1pDToJ3LMReohA8kXPN3eDH98YmDKcVZ1OQ2NPto6GWJiTWdRN/tafYJUoR2EvcBN67EMuhQFMj4sL1rvmU1Z7acXEumqH/nh1kOw4d8wyk6f/+VnJ9ySmrWc4fK+tD/M8dF7Go0s+cEg///NZlXL3g+KHN9jo/wTc0joaIGOBaWoJ3bdVxwyQK2TzDjxwgvXNw9H48y8A9O8l0xqb09cj0UB1y878fuph/uno+sZ/vIT+SASDbFiOxoQfnvHCRKxQRERERERERERERERERERERERERkenGVuwCZHI47VauW1TB6sYI+YJJyOModkmvK98/cXJGYmMvgevqsQacRahIZgLX3BBln1lBIZHD6nNg9R2Z2Hz4M7CmMULu0Gcg9mLnhG0kN/YRuK5hLLxEZLYoJHIkNvZOaM+0RHEvLDnj7c4p9fLlWxfx6aub8Tltx70Cs8wM1WE3f3j9fN6/tgG3w4rHocNAmTy54TTZ9mMmMRZMcoMpHDW+4hQlcg5kWqIT2lKb+gh/bCm5VB5X0IHNr+NOkWKwOG0EL63GsyhC4oXjnBu+2o3/kmosHh0TnW/888K4P30BqeE0Np8dV8h1yuvmh9Lk+1Pj2sxsgdxACnuZZ7JLlfOM22HjpqWVXNpcQsE0Cb7O+K7VbcN3eQ3uxSWY+QLWkAuL4/jn64VoltTW/gntud4kznoFU54PFlUFiPdnGCyMDwDPdsQx0wprFplSyt0XERERERERERERERERERERERGRGUgzLWYZv2tmXG3e4pv4I3pbuQdO8GN5kcNsASe8TlCN76jPwNEhJWPrl7kxHJYpqU2kmAyHBXuFl/xgely7rcR91tu2WgxKfJpAPVvovZSpYHFaMdw2zGRuXLtVE7tllrNFJk5ct1d6cQSduEp1biNSbIZh4Ii4yZRPDIewVXl1bnges/kd+PynH+BrcVkx7BbM7PhJ+wqzkcnkd5/a+K5hGKd0zm84LVgjrgkBOhbvzBhHlslhO877bbhtWFw6ZhWZEoYx+l9TKSQiIiIiIiIiIiIiIiIiIiIiIiIy82i2hRSFo86HY85RV9q0WQjd0ojVpUkbMnnstT4cTUf3M4PQrY1YT3Eyh8hMYrFb8V9bh3FUmJOt0oNzjq5qLCJTzxZ2Eb69GYwjbZ6LK7BVeotXlMg54JwTwFZ9pJ8bDgv+a+uw2DWZU2Q6cTYFsVUeCSIxnFb8V9Vh2DQsJqfHVuIm+MamcW2+K2qwHyfoRmS6sHodhN88FyxHDtadCyPYq3Wsfj6xVXjwXFxxpMGA8O3N2MITQ/VEZDIYJ19EREREREREREREREREREREREREZJpS4oMUhS3kIvLeheT6U5jpHNagE0eVr9hlySxjC7mIvHshuc44hXQee7kHW8XJJwaZuQK5gRQYo1e3N6yamCYzg7M+QNlnLyA/mMKwWrCWurAFNZlERM4N95JSyu9yk49msLhs2Mo9WD0K/pLZzRZxU/LBJeT7kpj5ArawE3uZJvSKTDf2EjclH1469lm1hl04yhQaIafPsBh4LizHXuklP5jC6ndgq/JicU7OEKuZy5MbSIPFwBZ2YVg1gVkmh3NuiPK7LiDXl8TitmGv9GL1OyZt+7mhNIVUDmvAoXOAacrMF/CuqcK7qpL8SApb2I1doZEiU880i12BiIiIiIiIiIiIiIiIiIiIiIiIyGlTCIkUTa43yeDPdpEfTmMtcRF5x3ycc4LFLktmGVvAiS3gPOXlc0MpRp5oJfFyF1gMfJdX47+8dlInZohMlWx/kpFHDpDa3A82C4Hr67GsrtQEIBE5J7I9CQbu202uM47Fayf81rm4FpZo8qzMavl4luT6LkaeaIW8iXtFGcEb52CLKARMZDrJJ7OkXutl5LGDmNkCrqUlBG9pxF7iLnZpMgNZ7Fac9QGoD0zqdnODKUYebyGxvnt0POLKWvyXVWP1aTxCzp5hMXBU+3BUT24ItFkwSe0YYPDnuynEs9iqPETevgBHjcKmp5P03iEG7t1FfiiNNeIk8o4FOGr9xS5LRERERERERERERERERERERERERESmKUuxC5DzU7YvQf8PtpEfTgOQ70/R/6Pt5IZSRa5Mznep7QMkXuoCE8ibxJ5qJ7VnqNhliZyUaZrE13WNBpAA5AqM/O4AmZZocQsTkfNCIZFl4N5d5Drjo/fjWfrv3k62O17kykSmVubgCCOPtkDOBBOSr/US39BT7LJE5BiZlijDv92PmS0AkNrST/zlLsyCrkwv00dySx+Jdd1HxiOeaCW9b7jYZYm8rmx3gv4fbacQzwKQ60wwcO9O8ofuS/Hl+pP0/XAb+aFD4/ADafp/uI3coMbhRaaUoUBWERERERERERERERERERERERERmbkUQjKL9MfSvNY6xN6eGPlpPpEmP5jGTOfHtRVi2bEfQ8v5JZcvsKcnxsbWIfpj574PmPkCZsHEzJskjjNpNLWt/5zXJHK6CoksyY29E9ozLSPn5Pk7h5O81jLIwT4FDkwnqWyOHZ0jbGkfIprUJDCZOrnhzFgAyZgC5AY0sU1mt/S+oQltydd6yB9nn9s9nOK1lkH298Ywzel9viYykw0nMmxqG2Jn1wjp3Oi4Q6Z1YjBf8rVeCgkdH80kPdHR/ei+3hiFaT7udTKHxyEOK2TzJF6beD6X2jlwLsuSKXT4nPlAX3xWHAcc7sP5gSQc83nMdSXIj2iMdzpoG0ww2BXDTB0zDp/IkdM4vMjUmgX7ehERERERERERERERERERERERETl/2YpdgEyObR3D3HXPBvb1xnFYLXzx5gW8e3U9Xuf0fIstXjsYjF7d9TCrgcVjL1ZJUiSxVI4fv9zC1x7eSSZfoLnMyzfeu5JFVYEpf+58Mkt69xCxFzqw+hz4rqrFUR8g0zJ+gpq91jfltYicLYvTir3KR35w/CQSW6l7yp/75f39fObHG+iNpvE6rPzT25Zz89JK7FZlnRVTz0iK/3h8Nz96qQXThMvnlvJ3ty9lTqm32KXJLGRxW7F4bBQSuXHtBZe1SBWJnBu2Us+ENkeND4tjfN/f0DLIp370Kl0jKdx2K1+9fSlvXF6F067PiMhk2tsb4wv3beKVg4NYDPjo5Y188qpmXJGJx8T2ai+GU5/BmWJj6xCfvvtV2oeSuOwW/vaNS7j9wpoZtx/NJ7Kkdw8Se6ETa8CB79JqHPUBDKsFR52fbHts3PL2ao1HzAbrDwzw6btfpefQOfM/vHUZty6rmpHnzPlElvSuQWIvdmINOvFcWDZhGYvHhsU1PcekzxemafLEzh7+6Kcb+bvLmrnwuOPweo9EzgnDKHYFIiIiIiIiIiIiIiIiIiIiIiIiIqdt5v3SWSaIpbJ85cFt7Osdvfp8Jl/gq7/ZztaOkSJXdmKGy4rvsppxbYHr6rGGHEWqSIplS8cw//DQdjL5AgB7e+P8w2+2E0/nTrLm2UttH2DgxzvI7B8hubmP3v/aiGtJCZbAkX5oLXXhXlQy5bWInC3DZsW7ugLjqAn/9iovtlLXlD5v93CKz94zGkACEM/k+cOfvsbe3thJ1pSp9vL+AX74YsvYhVef3dPHfa+0zYqrbsv0Y3ps+G5oGA2ZO8R9QRlxmybbyOxl5goYdgNbxZEgEovHhveSKoyjJhX3x9L80c9eo2skBUAym+dP79vI7h59V4pMpnzB5AfPH+CVg4MAFEz49jP7ebVlCHu1B3v1kSA2w2XFvbxs/IRsmbYG4hn+9L6NtA8lAUhlC3zx/s3s7IqeZM3pJ7Wtn4F7dpI5MEJyUx+9395MpiOGYTHwrqnE4jsSzmsrc+NaEClitTIZekZGz5l7jjpn/qOfvsbu7pl5HJDc0sfATw714Y29xJ7vwHtJ1ZEFDAi9ZS628NSORcjrO9Af564fb2A4meXBtgG819SNe9x/ec24czcRmUIahxMREREREREREREREREREREREZEZSJe7mwUG4lle3Dcwob11MMHqxuk5WSHXnSDTMkLghoZDk/csJLf04V5aiqVc3fJ80jaYmND27N4+BuIZvM6p6wv5ZJbok23HNJpkWkco/9QKsl1xDIuBrcKLLeScsjpEJks+mWXksRZ8a6vBamAYkBtOk943grM+OGXP2xNN0T2SHl9LwaRjMMnCysCUPa+c3LoDE48NHt7axSevasLnsh9nDZEzl+5PkXiuncD1DZj5AobVQvrAMOmuOMwJFbs8kSmRG0wxeP9uvKsq8SwrHZ1bli+QT2THLdcTTXOgb/wxr2mOHgcvrZm672iR881wMsNj23smtG9pH+Jyqx17tQ/34pLRz2rBZOiBPdirvDgqvRM3JtNKXyzNruMENrQOJlheFzr3BZ2hQuIE4xAHRnDW+nFU+Sj/9Aqy3QmNR8wi3dEUncOpcW0FEzqGEiyunlnnzPl4hugTrePa0ruG8LynAs+KMvLRDLYSN/ajAtqkODqHUyQyeQDWlAfI7hsaPw6/tR97tRdHub4DRaaMoaQfERERERERERERERERERERERERmbmU9jALBNw2FlX62X7MFWAr/NP3ipOGxSDTEiXTclTNVkNXYDwPHa+fLqsJEHBP8QR5w8CwTexwhsWCLezSFVtl5jEMKJhEnxw/ISh4W+OUPm3I4yDotjOcHD/husyvyXLFtqR64sT2tU0luB06/JPJZ7dZyA1lGHn04Lh2x4VlRapI5BywGGAYxF/qGtdc8sHF4+6HvXbK/E56o+NDuyoCOt4UmUw+p42L5oRpfy05rn1ehR/DYpBY3z1+BYvmhs4UIbed6qCLjmOCHGbaftQ0GB37OoZxVJst4sYWcZ/DqmSqRTxOQh47Q8eElJXPsP4LYBgGhtUyod1M53HOUbDadFLmc2K3GmTzJtlCgWxngsyB8f/vAMvE91JERERERERERERERERERERERERERARAvzSdBUIeB1+9fSl+55FJxR+8pIElNdP3apq2Si+2svGTKnyXV2OLzLwf4MvZWVIT4I619WP3Ay4bf/3GpQSnOITE6rIRuK5+XJvhsOBs0qQJmZmO26ftFpxNoSl93rqIh6+9YzmOQxORDAP+/A2LmFvhm9LnlZO7dG4JlzWXjN2vC7u5Y209Votm28rkc5S4cV9RPa7NCDvx1GhfILOXLezCf1XduDZriRN75fgrylcG3Hzt7ctx2o6cfn/+hvnMr/CfkzpFzhcOm5VPXtVEZfBIGN51i8q5qD6MvcKDrWr8Z9O3tgpbicIeZoLygIt/efsK3HbrWNtnr5nLwsrpO+51PFa3ncD1DePaDIcVR8PMeh1yemrCbr72jhVjxwGGAX92y0Lmlc+842SLx07ghmPGHZxWnPXqw9NNY6mXv3/LMqwWg//b3EFiTcW4xy0BO/Zq7wnWFpFJYZrFrkBERERERERERERERERERERERETkjNlOvojMBCvrw9x95xr298UJeewsrgwQ9jiKXdYJ2YJOSj6wmNTOQTJtUVwLI7iaQse9mqbMTqZpsqcnxsGBBG9bWcubL6ghmcnTUOKhoeTc/AjeOS9M6UeWktjci9XnwLWkBEf1zJsEInKYc16Y0o8uJbFp8vp0Ll9gT2+M1oEEpT4n8yr8+JzjDx+uW1jBbz53Oe1DScr8TuaW+XAeNUFQisPjsPHHNy7gA9EUNquFxdUBqoKaaCtTw7AYhC6rwVrmIbNjAKPCg2NBmIACiWQWMywGvrVV2Cs8JLf1Y6/24V4YwRaeGKx45fwyHvrcFezuieKyWfG7baRzBbzO42xYRM5YqdfJf7xnJd3DKUIeO0trgoQOjY2UvG/h6BhEaxTXggjO5iCGTWMQM8XKhiA/+thqWgeSlPudLK0J4HPNvGFN1/wQpR9ZQmJzH1afA/dSjUOcDy6sC/L9j1xM90ia6pCLZTUhXDP0nNm1MELphw/14aAD95LSCQFsUnw2q4XbL6hhfrmfloE4uYCbQG2A3PYBbOUe3Isi2BXEJSIiIiIiIiIiIiIiIiIiIiIiIiIiJzDzfq0vx/XMnj7u/P56MvkCAB+6dA5/dP18gh57kSs7MXuZB3uZp9hlSJE8v7efj/zfOtK50T777ovr+OLNCwl7z114jsVhxTU/jGt++Jw9p8hUsjisuOaFcc2bvD79+I4ePn33q+QKo1fv/MzVzXzq6rnjJvxZLAbzKvzMq/BP2vPK2ekaTvFnv9jEEzt6AQi4bPzwo6sVQiJTattwkg8/tIkSr5OePSkW7QvwtXesoDqkfiezl9XvwLO8DM/ystddzjAMouksX7hvEyOpHADXLCzjH9+ynMrgxNASETl9B/vj3PXjDWxuHwagIuDk+x9ePRZCYi/1YC/VGMRMlM7mueflVr764HYADAP+5W3LedvKWiwWo8jVnR6L04ZrfgTX/EixS5FzpH0wyZ/et5Hn9/YDEPbY+cFH17CsJljkys6MxWnDtSCCa4H68HTXNpjgT+/byO6eGABzSjz834dXM6dUoTEi54Qxs45RRERERERERERERERERERERERERI6mS77OAj0jKb70801jASQA//f8AbZ3jRSxKpET64+l+fIvNo8FkAD8ZF0r2zrVZ0Wmk46hJF/8+aaxABKA/3xyL7t6okWsSk7FxtbBsQASgJFUjn99eBeJTK6IVclsls7l+cbvd9MXy7CzO8pgIsvze/t55eBAsUsTmRbimRxfe3jnWAAJwBM7etnYNlS8okRmmWd2940FkAB0j6T58UstFI46lpWZaW9vnL//zfax+6YJf/HLLRzojxexKpFT88rBgbEAEoDBRJb/fGIP6Vy+iFXJ+eCx7d1jASQAB/oT3Lu+tYgViYiIiIiIiIiIiIiIiIiIiIiIiIjITGErdgFy9kZSOTqHU9itBlfMK8PvsvHM7j56o+lil/a68sksuc44+WgGa8SNvdKDxW4tdllyDkRTOQ72Jya0T2afNU2TwUQGr8OG8xT6VT6ZJdsZp6D+KDNctidBtjuBYbdgr/RgC7nOeFtDiSyDiSyNpV4urA/R0p9g/cFB+qb594tA+1BqQtvm9mFGkllS2TxhjwNDV2SVSRRL5Uin89z75uVEsib9mPxwbw/7ejU5WGa/3GCKbGccs2Bir/RiL3UzlMjgsFnwOEZPuWOpHFs6JgbutQ8mz3W5IrPWlqMCSA5bd3CAVC6PaZoQz2LtS1FI5bGXe7BVeHQ8NEMMxNMcmyWTzhUYiGdoKjuzbcbTWXJ5k6DHcfYFnoX8cJpMZxwzm8de4cVe7ilqPTL59vVNPB7e2DpELJXD6Tv+uFMqmyeRyRP22Kftfso0TXLdCbI9CSxOK7YqL7aAs9hlyVFePTg49u9b5pXxvsYyah024geGcdf4NO4pcs4oEE9ERERERERERERERERERERERERmnkkNITEM4zLTNJ87WdsZbPd/gduAHtM0l57NtmajMr+Tm5dWsKohwq83drCnJ8bbL6qlocRd7NJOqJDOE328lfjLXTgbAuQTWbxrqvCuqpi2P66XyVPqd7CmMcJL+wfGtTeUTM5km5b+OD9Z18oDr3WwsNLP566bx4q60AmXP9wfY8+0j7WF3jbvjPujWTAx8wX9mF/OuXTLCP3f34a92ouZKVDI5Cm9YxG2M/w+qAw4+dLNC9jTG+f5PX3MLffxl7ctmrTPqkyd+RX+cfcDbhtfedMS/vwXW9jRFeVNF1TzntX11Ef0XsrkCHkc/OuyOjIP7ccxJ4g/lePPygP0zSkpdmkiUyrbk6Dve1vID44GdFk8NlJvn8vnn9jFZQ1hrltWyfLaEBGPg5sWV/LTY64+f+z+WkTO3OXzSvnJuvGfsfevaWBH+zCDfUnmr+/DOBgdfcBmUPaRpTibQue+UDlt1SE3bruVZDY/1hbxOqgOnf55Tjqb59k9ffy/x3YTS+f4+JVN3LykkrD33IeR5AZTjDzVSq4zQaZlBMNmofRjy3A2BM55LTJ1ltcGJ7TduqyK8AkCcDa0DPLvv9/Nru4Yb7mwhnddXEfdNDlvM/MFzIKJxW4lc2CE3u9ugVwBAEdjgMi7FpxVEKpMrusXV7C9K8rbl1Ryu+nAeKQNgEEgdlsj9gvLiHgVHCMiIiIiIiIiIiIiIiIiIiIiIiIiIhNZJnl7/3GKbafr/4CbJ2E7s1LQbeeONQ38w0Pb2dg2TMtAgm89vY91+wdPvnKRZLvj5IfS+C6rJp/IYvU5MNN5sv26Evj5wOe08zdvWsKiqtGJNV6HlX9+2/Kx+2cjlc3xtUd28c0n99I+lOT3O3p433deYm9v7ITrZLvj4wJIAIZ/vZfcGfTHTFuUwXt30fvfm4i92EFuJH3a2xA5E4VsntT2AXyXVGGm8hgOC96LKki3n7jvn4zTYeWl/QPc90obHcMpnt7dx388vgeHTQE7093y2iBfumUhTtvood6f3LCAP//lFn6/o4f2oST/9eRevv7ITtJHTeQUORvDnVGMvUP4r6zFTOYwXDb8lT6atL+QWS65Y2AsgASgkMgRHMnw9YYKPrAvReT5boZaRrDbLHz8qibWNkYAcNosfOmWhcedmCwiZ2ZNY4Q7r2jEahkNkvzkZXO4yeeh4tE2lr7ST2hRCY76Q8E/OZOhh/aTT2aLWLGcqsZSL/99x0pKfaOhDVVBF//1vpVnFEKyoXWIj35/PZvbh9nfF+fP7t/MY9u7J7vkk8onsqR2DZJrj2M4LATf0ITFZ2fksYMUMjpGn01W1of5w+vn4bCOnptdNb+MO9Y2YLFMDL3d0xPjju+8xBM7e2kfSvKNJ/bwb7/fTTpX3D5hFkxS+4bpv3s7fd/eTPzVbuKbe8cCSAAy+0fItJ35+INMvpsqQvx4bi3v3JPEly7gv7p27LHcwwfpbRkpYnUi5xOF7ouIiIiIiIiIiIiIiIiIiIiIiMjMY5uMjRiGcQlwKVBmGMbnj3ooAJz1zEPTNJ82DGPO2W5nNtvRFaVgjm/73vMHeMvKmml5RcNCJoc15CT6+OhVirNAas8QpVVeKJ0eV/eUqbWoKsA9d66hYyiJ12mjPuLBMM7+B7ntgyl+valjXFssnWNPd4zmMt9x1ykkJk48MzMFzNTpTfLIdsfp/dZmzEMThobaY/ijWQLX1WMcZ3KJyGQyMwWwGYw82jLWlt47TPid8894m20DCZ7Y2TuubSiRZV9vjMZS7xlvV6ZewG3nziuauGlJJalsnv29cWLp3LhlHtjYwWevnUdz+fH3jSKnIxnL4ivzMPLY0fugIcJ3LCpiVSJTL9cZH3ff0RjA3D2MY2s/WcDWESe9b5jcp1bQXObj2x9cRdtgEpfdSn3EMxaWICJnr8zv4gs3LeRdF9eTKxSoj+YZ+O4WYHTMYfjACMGb54xOki+Y5HqSmOkCnH6OhZxjhmFw1YJyfnXX5QzEM5T7nZQHXGe0rSd39kxo+95zB7h1WRVe56QMk56S1I4Bhn6xZ+x+eu8QwRvnEH2+Y3RMwaEgt9ki5HFw1zVzedOKatK5AvVhD17X8fva7u4o8WNCaO5/tY3PXN1M4wnGtM6FTFuUvu9s5vDgc6Yliv/6egynFTN9pN5CNFOsEuUY+ZEM8Z/twuxJkAWyHXEc9X7cS0tJbunDzBbo6I5RNSdEwG0vdrkiIiIiIiIiIiIiIiIiIiIiIiIiIjLNWCZpOw7Ax2ioif+o2wjw9kl6DnkdvuNMlAh57GNX2ZxurD4niQ3HTPzIFcj1JYtTkBRFyONgcXWQhhLvpASQANitBi7bxMk6LvuJPwu2iBvjmMdtpS6sodML8Ml0xscCSA6LPd1Gfjh9WtsROSMGJDf2jW8rmGe1X3XYrDhtEz87LrsmxM0EVotBY6mXRVUBPM6J75nbbsV+nPdX5ExYPTaSG8eHFpE3yXUnilOQyDniWhQZd9/ZFCK1rX9cmxnNku0Z/Sz4XXYWVQVoLPUqgERkCthtFuaW+1hYGSC1qXfC46ndQzjq/QC4V5Ri9Wvi9UxSHXKztCZ4xgEkAGGPY0Jbqc+B3Xru9sn5ZJboU23jGwuQG8ngXV2JxaN+OdvYrBaaynwsqgqcMIAEjn+u7XHYsBV5fDe9f5hj06+TG3txzQ+Pa7NVKFh6usj2Jsj1jD8Xy7REx94jS4mT9kIe+zT9fwcis4t58kVEREREREREREREREREREREREREpplJ+ZWpaZpPmab5t8Ba0zT/9qjb103T3D0Zz3EqDMP4uGEY6w3DWN/bO3GyyWwQT+coFCb+aPGihjClviMTKQwD/viGBfhc03PigsVnxzjOxOdjgyDOF+dD3z1XKgJO/vjG+ePaltcGWVgVOOE6tjI3JR9YjDU8Gjpir/ERec9CrL6Jk5Nej3G8SUs2C8zyCabqv9ODYTUwbBP7muV1JjidTH3Ew2evnTuubfWcCHPLvCQyuTPe7nQxm/quaZonPEYAWFQVYHltcFzbH98wn7qw+1yUJ5NsOvZdl88x+p13DMtxAnDk/DYd++/ZcDYHCdxQP3bMZ4k4R0/GjmFYTu08J5cvzIrv2NlotvXd2eDw8Y9pHv/4x+KY+B1k2AzMXAHX4hL8V9VinCeTr8+X/htP58jlC6+7zJXzywgcdY5ktRh86uq5OI4TZjpVDIvluONfFpcV78pyjFk+hnA6zpe+e9iiKj9Ljhm/+tOb5lMXObtwj1Q2TyaXP/mCJ3DcMVyHBVulBwww3DZCb5uHo9Z3NmXOKsXsu6lsHvMEX2+GAUaVh/2XVNBYH8J9nO9KEZkkkxS6LiIiIiIiIiIiIiIiIiIiIiIiIlIMZz4z+fichmF8C5hz9LZN07x2kp/nuEzT/BbwLYBVq1bNqsuLHeyL88L+fqLJHCU+ByvqgjSX+ccen1fh5ycfX8tL+wcYSmRZPSfCirrg62zx7MTTORxWC/bj/Aj9VNh8DgI3NjD4s11jbRaP7bz9sfps7rvnSjZfYP2BAf77qb3URTz8+7svYG9vnDklHlbNiVDxOldKNgwD17wwpZ9aQS6exfDZ2RtLY3SN0FjixXmcK9Eej73KhyXooDCcGWsL3tiALeg869c3nan/Tg8Wp43A9Q30/2DbWJvhsOJsHv0uOJP9ttVicMfaBpbUBHmtZZDGUh9zSjw8tqOHRCZPU4mXFXUhSv0zs4/Plr67vy/O/a+28cjWbi6fV8r71tRjsxgMJbNUBlyUB1wE3Xa++d4LeeXgIPv7E1xYH+LCuhCGJkTMSNOx74bCbkauq2PkxzvH2gynlWyVt4hVyXQ0Hfvv6Tj2+9TqdeC/ph7PBeWYJhS8VlydVaSe7Rhbx1bpwV55/MnD8XSOloE4FsNCwSzw3WcPsLVjmLetrOXWZVVUhxQWNV3M9L472+zpifHTdS08vauPm5dWcOuyKtK5AiG3g/qS0c+be3kZsRc7IX/o7TLAeUkVzogLW8hFwWoQT2fxOqdneOtkmk39t1AwSWRyeJ22sWPZjqEkD27q4P5X21lWE+SDl85hac34MbFcvsDB/gSmafLjj63l1dZBEpk8qxsjLK+ZuvGz47E4rQSuq6f/+0efu1lwLynBFtF+/2izqe+eCp/Txv984CJe3j9Ay0CCi+rDXFAXOuPtRZNZntndx7ef3YfXaeOTVzazujE8LnTn8Gcjmc1TF3ETdE8M5HU2BjEcVszMkSCTwPUNOJtDeJaXYdgs2MInHnc7HxWj7w4mMjy5s5dHt3bx/gtqaGwOkts7PPa464Iyhuf42FNqJxBysfws+paInIITBOWJiIiIiIiIiIiIiIiIiIiIiIiIzASTHUJyL/DfwHeAM7+8oowTTWV5bEc3B/sSRHwOov1ZYqkcpglzy48Ekcwt94+7PxV6oike2dLNj146SH3EwyeuauKihsgZbcs5L0TkjkWkdg5gC7twLY5gL9dEVTkzG1uHeN93XqJw6Le9P3qxhW++byW3LqsaWybbnyTXn8TitGGr8GA96urHOzpH+MELB1l3YIDL5pbgslv5n6f38a5Vdfzh9fOoDL7+RKCukSTtsSSN712I7eAIub4U7kURHHMCr7ueyGQx8ya2iJPI+xeR3jWIxe/AvaiEIa+V3zy3n5+83EpzuZc7r2jiwvrwKW835HFwzYJyrllQzraOYZ7e1Us6Z5I3C2xqHwLgusUVU/Sq5GSGkxn+7P5NFEy4ZVklTpuV7Z0jfPn+zQynctywuJw3rajm+88fxAA+cVUzH7+yCY9jsg8BRWCo3EXojkVkdg6Ax06s3suzvcNcHnRQE9aEWpnZukdS/G5LF/e83EJjiZePXzX6fWrmCmR7EuRHMthCThwOJ+Gr60jXB0jtHsRR7cM5L4Q1MD6wK5svsLV9mA2tQ7QMJLhvfSvXL6qkgMn2zih/95vtHOyP81e3LTnj4EeR2aovmuauH7/Kjq4obruVkMfBR/9vHaubSphT4mVpdYCL54Tx1fkp+8RyUtv6MfMm7sUlGC4r+YE0I/Es39razpO7+3jLhbW8aUUVNeHjhwXJ9LGrK8rdLx/k+T393LC4gndcVEtN2M03ntjDj19qAWBHV5RHtnXzy89cRmPp6BjTSDLL3S8d5P/36G4y+QIr6oL8f+9cQV3Yw77eOBvbhmiIeM86XLGQyZPrSZCPZbFFXNjK3CcM/XPODVF65zJSW/swPPbR8YOaqR3Tk+mrpT/O/Rva+c2mTi5qCPPBS+fw1pW1Z73dZ/b08ukfbxi7/9yePn7y8bWsaSwBRs8nf/jCQR7a3MnbL6zFPugjbxr4StzYyzwY9tFjEEe1j7JPLCe5Y4BCLIN7SSmOBj8WuxVLmfad08UjW7v49cZO5pb7+Pfn9/NnlzVRsTBCtjVKstJNbk6AcruN0pyBze3CplBSkXNEnzURERERERERERERERERERERERGZeSZ7BmrONM3/muRtYhjGPcDVQKlhGG3AX5um+d3Jfp7pqm0wiQGk8wX+/fe7KZhQH/FQF/FMeejI0UzT5N71bfzrw6NXuN/RFeWpXb384tOXsrj69K4am49nGXm0hcS6LmwRF2lzCGdzaAqqlvPFM7v7xgJIDvvG47u5an4ZXqeNdMsIfd/bipnMAeBZVUHw5jlYfQ46hpJ85Pvr6BhKAbC7J8YlzSVcPb+cn6xrZdWcMG+/qO6Ez72pbYiP/+AVukZS2CwGX751Ie96QyNupyb5y7lhmibJLb0M/HQXFEysJS4C19ZhLXfzgyf28I3H9wCwszvKEzt6+eVnLmNB5el/f3QNp9jXl+C+V1opmDCnxEN9xEPXcPKkQT0yNQ70JSjzjU6W/H+P7QZgQYWfO69s5uuP7uSihgifvee1seXX/WA9P/jIaq6cX1aMcmUWiyezuAczRO/dhcVnx0znKQwH6fFDR6lXISQyo5mmyT0vtfD/fj+6n93RFeWJXT08dNfllB+MMfSLPWACFoPw2+fhuaAcz/IyPMuPv69NZHL8dF0r//DQdrJ5kzK/k8/fuIB/+u0OPnlVMw6rhUy+wI9fbuUjlzeNTaIXkVH7+mLs6IoCcPuF1Xz32f284+J6frelk/tfbQfgHRfV8qc3LaC8PoCzPoCZKxBf38XQA3vHPq9vub6WB1Nd/PPvdtAykOBv37QYh81axFcmr6d7JMUnf/QK+/riwOh5+/oDg/zjW5fx03Wt45YdTmbZ1RUd239ubh/mn3+3c+zxja3D/HZzF/F0jm89vY+CCU2lHr75votYWHVmQaKFdI7oM+1EHxsNQ8FmoeT9i3AvOH5wr8VuxdUcwqWxsPNePJPj73+znYe3dQOjffuJnT38/FOXUnsW4UjpbJ7vPLN/XJtpwqNbu8dCSDa1DfPUrl5uWFTOJXkr3nt2kcibJAwIvrEZ38WVR4JIanw4anxnXI9MreFkhv95ah9vu6iWHZ0jlPtdvNAX499+v5vKgIvKIQd/m8gx8Fzn6Peg1aDkfYtwLy4pduki5wHz5IuIiIiIiIiIiIiIiIiIiIiIiIiITDOTfUnlXxuG8WnDMKoMw4gcvp3tRk3TfI9pmlWmadpN06w9nwJIAJyHrnz903WtYyELLQMJvvfcfpKZPAD9sTSD8cyU1tE9kuZbT+8b15bOFdjeGT3tbWXbYyRe7gITcv0p8gNphn69l3wyO1nlygxQKJj0RFPE0mf/vnscEyeLeZ02rIZBPpll6MF9YwEkAIn13WTaYwDs7YmNBZAc9sLefpbXjobrPLGj94TPO5zM8lcPbKVrZHT9XMHkKw9uZ2vHyFm/JpFTletLMXjfbg5/SeT7Uwz+fDfxzhjfeWb8fjuZzbOj68z650Aiw0v7+3nP6npuWlJB+1CSX77WQerQd5Gce3arhbkVfp7Y2cvtF9TwzlV19MXSbO8c4Y0rqnlxX/+EdX66rqUIlcpsNzCQIPfQAcx0nnx/ikIsi+O1Pla6XWRyhWKXJ3JWuoZTfOuY79NUtoBlMM3QL/cemVNWMBm8fw+5/uTrbm9fb4y9PTGuml+GzWLQG03zoxcPcuuyKvb0xMZCexxWC1aLrpotciyb9chQVqnPyaVNpYQ99nHL3PtKG+sPDo7dz/UmGfrV+M9r4Il2PnPhaNjkz9a30j74+p9dKZ6BWJpdXdGxAJLDXj4wQF8shd06cV95dNv+Y9YDsBoG//3UvrFxtn19Cb7+6C5S2dyEZU9FtitxJIAEIFdg8L5d5IbTZ7Q9mV0KBZOekRSx1MTxr9aBxFgAyWHdI2n29MTO6jkNA3zHCcf1HtW2uzvGVfPLKc0bBJ5oh/yhD4QJw7/eS7Y3cVY1yLljNQwCbhuxVJa5FX52dEXZ0DpEIpNnX1+cDy6owP1s55HvwbzJwL27yA2mXne7InIWDJ3LiYiIiIiIiIiIiIiIiIiIiIiIyMw12SEkHwT+FHgeeOXQbf0kP8d5pybkJpmZOHlz3YFBuoaT/PCFA9z2H8/y5v98jgdeayeWPrMJEydjsxi47RODHuy20+9G+ejESRjZ9hhmShPZzxct/XH+4aHt3Pz/nuGD313HS/v7Mc3jXxUw25sgsaWP5PZ+ckPH/3H85fNKx4JIDAMun1vKn9y4gHgmh5nKk22fOHmjMDIa3HP0JLbDLAaYh36Zv7IhfMLXMRjP8Frr0IT29iFN1JBzp5DIYmaP+Z4ogCWaPe5+23GcPn8qPA4ba5tK+N2WLtoGk3zploV0DiWxaIJ00TSWenHbLXz8yiY2tA7yzO5e3rGqFq/TSm3Yjec473/AbT/OlkTOjpEukO+dOHnbnzPZ0xtj3f6BIlQlMjmsJzoPSuXHAsDG5ArkoxkybVESG3tI7R0inzgy4bhlIMHP1rfx0JYuukfSfPnWRZT7neztjVMTdlMXcdNzKNzuc9fNpe5QIImIHNFc5uP6ReUE3XYWVPg50B/n64/uornMxx9eP29suU1tQ+QGUyS395PpjsMxh8tmtkCJMXpcbLcaCv2ZhoYSGe5+6SC3/cezbOs8fpCi32Xnc9fOG9fWXOZlYVVg7P6FpV7uvmERv7h2Ed+6cRELynykjhOS9tyePgYTZxaSmo9ODAYuRLMUznB7Mnu0DiT4l4d3cPO/PcP7vvsSL+zto3DU8YPVMLAdZ/9jP855e24oRXLHAInNvWR7JobrHM1hs/Lxq5rGzYF32S1ct6h87H5t2E0ym6fUasE8NlzUhHxsagOvZfL4XHbuunYeEZ+T7uEUqVx+3PGr/zhD7mYyd8JxVhEREREREREREREREREREREREREROb9NvBziWTBNs3EytyejnHYrzeW+Ce2XNkfY0DrEXz6wdaztD37yGt//8GquWlA26XWU+p384Q3z+NLPN4+1lfmcLK0OvM5ax2fxTJwA7WwOYfFqYvT5IJ3L8++P7+G+V9oAGIhn+MB3X+aBz1w2bqIQQKY9Ru93NmMmR8N1bGVuSj6wBHvZ+AmZS6qD/OwTl/Dkzh6ayrz8dF0r7/rWi9RF3HzznRdQNS9EasfguHWsERcA8yt8XDwnzLoDRx6//cIantrZy5KqANcuPPHnKeSxs7gqMGFCVFVQE0bl3LEGHFg8NgqJo0KobBYKAyl++PYLuO0H68aaq0MuFlb6T/s5cvkCL+zt46frWgHoj2fY2RXlb9+0hFK/86xfg5wZt8NKVdDNZ+/ZMNb230/t40u3LMRqgTdfWMMj27rJHZrkZrcavP2iumKVK7OY1WfHUu8n2xId115e6aPebnLXj1/lBx9dw4Iz2P+IFFt5wMUXb17IF36+aaytMuCk4LNjsVvGBYEZLitmzqTn26+NXWnec3EFwVsayTssfOPx3fxs/ZFj4J1dUe66di4/f7UNqwHLG8LE0zmunFfGxY0RDF09W2SCoNvOV968lF3dUe768YaxINYHN3VySVMJV88v48ldvbyzqYze724m35fCf20d2CxwVPCExWOjLTe67h9cN4+6iKcor0dO7Oldvfz5L7awojbIggo/lzaX8Pze/rHH33VxLY2lXqqCbuaW+3hmdy/zKvxcMa+M6tDoOXk+nqVifR/B13oBKDPg/908h7Yyx4TnWzUnQvAMA/tsYScYjO37AWylLqz+ic8j549svsC3n9nHD144CBwa//rfl/nFpy+jJuRmc/sw/bE071tTz/cPLQNwQV2I+RXjj5tz/Un6frSdXOdo+IjhsFL6saU46088Lrt6Tgk/+/glPLajG4/dyrULy1lWGxp7fEVdkJ5omoO9CVZ47RTiR4Xm2AxsQZ3rzySXzy0l6LKBCT9+uYX3rWngN5s6yeQL9FtM6i2MC+SyBhykdgxg9dixV3iLVrfIrHeC4HURERERERERERERERERERERERGR6WxSQ0gMw/AAnwfqTdP8uGEY84AFpmk+OJnPcz5a3RjmfWvqufulFgDK/U7+7NZFfPGoQJDDHtrSMSUhJF3DSZKZHF+8eQGpbJ6g247TZsV6mhPjzFyB9IFhfJfXEHuhA/ImtjI3vitqsDgmXmFcZp+u4RT3v9o2ri2dK7C7JzYuhMTMF4g92z4WQAKQ602S3js4IYQEYGlNkDklHj72/fW8uH8AgNaBJO///joeeu8qjIEUZk8SrAa5S6uIBu24gBKfk6+/8wJe2NvP1o5hVjaEqQy4yBVM5pX7KA+4TvhaQh4H/987V/Ch/32Z7mgaw4BPX9XM4ipNspZzxxZ2EXnPQgZ+spNCPIvhthG4upbYs+2UXFHNl25ZyM6uKCU+B40lXuLp3Mk3eozukdTYpOnDcgUTq8XA45jUwwk5TS/s7ZvQ1jWUZDCZ5XvPHuBPblpAS38Cl93CRQ1hltcEME1TE9tlUpX5naQuriAWz5LrT4HNwH9ZDbFcgSf293HnFU3s7okqhERmrFuXVdJY6uG11iH6Ymlcdhvvufc1vnXjPIKPt2Emc1i8doJvm8vwr/aMm4SeWNeN98JyuoJ2fv5q+7jtZvIFCqbJX79xMctrQpT6ndy4uPIcvzqRmac65GZrx/BYAMlhL+zr53PXzSXicxDpTRHrSwGQWN9N8IZ6Rp488nkt3NaIM5Xk7o+tYXF1QMdG00w+X+Dulw5S5ndy09JKPvR/63j/2gYurA/TOZRkcXWA1XMiuB023A64flEFNyyumPA+ZjvjpA4FkABgQvCZTmo/sXzcOFtFwMmf3rTgjM9t7BVewm+fz9ADezAzBaxBB+F3LsDqUwjJ+ax7JMU9L7eMa8vmTTqHkvxiQzvffXY/AG9YVsVfvXExW9qGqY94uG1FNWXHhH2mD4yMBZAAmJk80Sdasb93IRb78cdTHTYLFzdGuLgxctzHKwJurltYzk9GWui+ppqKJzsoxLIYLiuRt8/HVqZwppnEZbdycWMJ5QEXr7UN8YPnD3DPnatZf3CIXYkMq29vJvebA5jpPBa/A99VtYw8fBDDYSOoEBIRERERERERERERERERERERERERETnKZM8a/h7wCnDpofttwL2AQkjOUsTr5MtvWMQ7V9URz+SYU+KlIuCiJuTC5yzj4jlhsvkCNquFUu/UTHBoGUjws3WtvO2iOtYfGCSbL3DZ3FIODsRpKD31HyqbmTzJLf0YFgP/VbUA5IfTZA6M4F5w/B/Fy+zitFkJuu0MJrLj2j3HhNCYuQKZjtiE9bM9iRNuu3M4NRZActgNiyt53wObuKo+wsVLQyQw+c6WNv6owcPNh/puXcRz6MrXdRO2mTv02RpXm2nyWusQP3rxIJ3DKb78hkVUBd14HFaay7y4Fcog55i92od3zeikZTNbIPpMO4VYFgbTfGfjflx2K8OJLNF0jm++byVLj7r68alw2qwsqw5w9cIKcoUCVsOgczhFyHNmVwqXyVMX8fCpq5pxO6yYmJgmVAZdfOlQUNk//XYHCyv9rGkMkyuYfOruV7EYBh+4tIGLGyI4TzBhTeS0ZAoM/+4A7kUluFeUgQnJTb2YznLuXd/KP799OV59N8oMlS+YbO+K8sMXDzIQz3DVgnIe29ZN23CSO57cwTduW0xsMMX+VIY3OS3kD4UejNtGIoezxEnQbWcgnhn32IIKP9curDhXL0dk1jheWITLbuGq+WWU+ZxknjgSoJcfyRB9ph3/VbXscpgMWmDfSJTfb+9hQWWAkNtO2KOwiOnEYjGoCXtYUh3kv5/aC8APXzyI02ahPOCkudzHUDJDx2CCV1qGuPeVNrx2Cx+4dA6r5kSwHzqHLxwz7gBQiGVxmfBnty7iXRfXEU+PjrNVhSaGnZ4qw2bBs7IcR4OfQjKHLejEGnCefEWZ1Zw2C2GPg55oely7zWoZCyBx2S10DCXZ9OwQX7xlITs6R/jSzzexrDbIOy6qY3H1aFhvbiA5YfvZrjhmpgCncU537BhXVcjNp65pZl9vnFytn0AefGEXtsiZfx6kuBpKvPzlGxbx7J5+vvbILkp8Ti5rLuHrB3v58+vrMZM5Cqk8I48cxMzkyR5n7FVEJpGC7kRERERERERERERERERERERERGQGmuyZgM2mab7LMIz3AJimmTR0KdlJ0TYYZ2t7lJ+sa8EwDD586RxKvA4+enkTP3+1ja89smts2buumUs6m5/0ScUmcMfaOfzFA1swD13Z++ndffzHey44re1YPHY8F5YTfayF6OOtY+0lH14yecXKtNU1nGJvT5Q/vnEBf/HLLWPty2qCYxMrAHKDKTLtMdxLSoh2jw8dcc0Nn3D7XqeNkMfO0FETjUIeB53DKf7v1Tb+76hlY6nchPWPtrV9mMe2d/Py/gFWzQlzw+JKltYEAdjSPsy7v/Ui6VwBgOf39vOvb1/OO1ZNDDERORcsHhv5gkniybZx7alyNyVeJzu7o2Nt2XzhtLdf6nfy4csb+ew9Gygc+g5YURvkHRfVYpqmrhx/jmVyeXZ2RTnQn+CihjB3/XjD2MQ2r8PKP71tGQAWAz59zVyiySzzK/z8wU9eG9vGI9u6+fHH1nDp3NJivASZZaw+O84lJSRe7h7XPuyxcklTCRbDGPc9LzKTbGob4j3fepHcoS/AZ/f083e3L2Vz+xAD8Qw/3d3DhpYhWgYS3LKyBkeNj1z7UZM5LQZxt5USn5O/eMMiPv+zjWMPLa7ys78vxs/Wt7Ko0k80laO+xENt2DOhjlQ2z46uKK0DCSoCLhZV+vG7FQYms1s6O3rMc3AgQZnfyaKqAMFD/X5BhZ/L5pbw3J7+seU/dnkThYLJh/9vHf+9upmjp9AXYlk6Y2n+rauPJTVB/vupfQC8fGCQBza284tPXUZzue9cvjw5gd5omh2dI9y2vIrXWocYSR45d0/nCtSE3JR4Hfz45VaumFvKnx81tvDwtm7+/d0X4nXaqA27KPPYwGB0UOsQx5wAloADn9PG8tMMZ3w9hmFgL524/5bzV5nfxV/dtpi77tkw1raw0ofVMnr+/OYLqqmLeHitZYjL55XwWusQ33lmNJxk/cFBfrmhnW9/YBXLa0PYqyfunzwXlmPxnNrw/sH+OA9t7uLRbV1cNreUy+aWUhlwMafUi8tuY3F1cBJesRRTvmCys2uE4WSWze3D/MNDO8Yee3RbN994z4UMdqWwHjUeD+BepjEBEREREREREREREREREREREREREREZb7JDSDKGYbg59NN+wzCagfTrryIns78vxpb2ET571A/WH9/Rww8/uhqf08YPXzw4bvlvPrmHW5ZWsqRmcn88vqDcz33r28YCSA67d30bty6rHvsB/alwzgmQW1ZKcksfht2K95IqrCFdIXa2ax1I8Jkfv8qmtmEuqAvxpZsXYhhQE3ZzQV2IquDoFLFcNM3AT3aSOTiCd20V7hVlJDf3YlgtBK6vxzHnxJOYKwMu/viGBfzlA0cmIcVSGd64vJp7XzkSzmAxYF6F/4Tb6RpO8pUHt/HS/gEAntvbzzO7+/iP91xITdjDqy1DYwEkh33jiT3csLiCkK6gLUVgGAa2piDuviTJrf0YDivetVW0GSbXLSofCyEJuu0seJ2+fyL9sTT/8rudYwEkABvbhtnWOYLdZhkL6JFz4+Gt3XzuJxtYURvimgVl466sHc/kiaZyhD12bllaxW83d+FxWGk5zpWzf7a+VSEkMinygHNpKQxnSO0axHDZ8F1WzV6zwFtW1jCv3EdFwFXsMkXOyNO7+sYCSA675+UW/uSGhXzlN9tYUOHnVxs7+PiVjZSWeEjd3IDx8EGybTGsAQe+y2sYHk6xpd3klqWV1ITcbGobJpnNM5jI8K+P7MI04YOXzuHxHd3EUjm++8GLWdlwJHivUDC5/9V2vvyLzWNtn766mbuumYvHOdmn9SLTg2maPLipkz++90hwz4cvncPnb5iP321nX1+MeeU+3rCsmn19McIeB7u7o2xoHT1X89oteC6rJv5yF2bexL2ijIOlTt47p4HP/3TjuOcaSebY3jWiEJJpoHs4yRfu28RTu/so9Tn40s0LuW5hOb/f0QOMBu5d2lzKl+7fzCXNJfzytfZx6xdMeHZPH8/v7eMf3rgE194RQjfPIfpsB4VoBkeDn9CbmrG6tO+Uc+P6xRX87BOXsLVjmFKfkwvqQlgMuLQpgttu5RuP7wFGw3l/+ML4Md7BRJZndvdRlwPLui58V9YSf6kTM5PHfUEZ3lWVpxQIOpzM8Gf3b+b5vaOhTa+2DPHY9m4urAtz67IqLp+nc8LZ4OX9/fxiQzvNZT5+cExfSucKtPbHWRXwYK6qILGhBwzwXFxJrtx9gi2KyKQ49n+oiYiIiIiIiIiIiIiIiIiIiIiIiMwAk/2L+78GfgfUGYZxN3AZ8KFJfo7zTtdwil8dM6kCRie+3X5BDaYJb1xeRVOZD8OA7Z0j9MYmP/vFYbNgMvEHk6cTPgKQT2YZenAfFrsV/zV1mLkCyU292Ms9OCq8k1WuTEOvHBxkU9swAK+1DvFa6xDXLyznA5esxO2wji2X60qQOTgCQPzFTuw1PvzX1OFeUoq9ynvcCRbpbB6HzcKenhj3rm/hT29cgMtuIZ7Js7s7SnnAxdtW1vDw1m6qgi7+/A2LWFJ94jCTXd3RsQCSw15tGWJXd4yasAfrcWqwWQ1O79MgMnmSiQyJx1qwwOi+NVsgubGX0PISmiu8+Jw2FlX5+cJNC1hYdeK+f6zWgQTtQ0nsVgO3wzLh8VQ2z/qDAwohOYfaBhP8xS+3YJpw19XNDCQyfPbauSQyeX66rpVYOscDr3Xwd7cvpWs4xY9fbmFJdQCbdeIeymGb+J6KnImBwSSF3x3A6rKN7oMyeeIvdVG2qoxsyIG+IWVmm3gOZLMYhH12/vGtyyj1Ofjehy7G57TRMZzE2RXHHXTimh+mEM8y8ngLFr+DjisrWFQVZE1TCf2xNHfds2FcuNe961t535oGvv3MPv78F5u55+Nrx8LtDvTH+dtfbx1Xwzef3MtNSytZURuayhcvUjQtAwn+6qhwSYDvPX+A21ZU0Vji5Qs/30Q2Z5JI5/nVpg5S2QKfvXYubYNJPrqsGvOhgyT9dryXVGNYDVI7B6ia46M/VzjuOIbmh04Pm9tHeGp3HwB9sQx/ct8m/uqNiyjxOXhwUyfvW1PPr17rAEYDmmyWicezdpuFXN6EwTTGc12MuGx4VpRh8djIdsaJxTIEC+Zpj2eJnAmX3crqxgirGyNjbTs6R/jQZY3c9eMjgdMF08RynLGm2rAbc+8w6R2D5LoSeFdXYtgtFLJ5rIEjIbjZXAHDAJt1/Gcimc2ztyc2FkBy2PbOKDcuruTzP3uNX911GZVBBVHMZAPxNI9t76Yy6GJHV/S4+7eVAQ/xe/diK3Xjv6IGTEhu68c2NwjVpx9WKyInocMMERERERERERERERERERERERERmcEmNYTENM1HDcN4FVjL6E/s/sA0zb7JfI7z0Ugyi9NmndBus1gIe+z8wXXzeGpXL7/e1AnA2sYIQbd90uswgYsaIvxyw/+fvfsOj6O69z/+npntVbsrrXq3mm25d3oH0wwhISEkBHIhN/2X3ttNv+n1pgdCCpBA6N10bOPeZNmyei+rsr1N+f2xtmwhQ0gwmHJez+PnQWdnzszuzk5jvp8zOD0SuCTBZYtL/73CDdVAT6iokQSZvuh0s55Wj/MaC681A5PJWW17BsMkMuqMEBIjo8+YJjsQIzsQw7GgYFYASc94nHt3DfFgyzAnzQlwZmOQPYMRdg9E+Mz5DfzwkbbpaecEXXzhwkYMzcDrMM8qzJhp9jZtNyvYLQobOkIEPVYcFoVERpt+/QOnzcHrsMyaTxBeDZJqQEIlE0qS6T2yb3Uj0VDk5jPnN+BzWijOe+mFRZu7xrnh5m1MJbKYZIkPnjEHszLKnoFcmJAkQYHbykQ8c9zfj/DCoimVaCrLN9bNY89ghJ8/3o6mG+S7LHzi3Hq+ff9+VtX4uWf3ICurAyiyxJrafJpLPTzaOjJdYCtLcMXS8hP7ZoQ3DEU30JIq6cE46c7wdLvLyBUIu6yzz2UF4fViZbUfq0kmrR45R33Higr8Tgun1Qd5um2MD/xlO9G0itNiYv2ZTaRaxkm1HCn2lUwqk5E0mq4DCgbSjAASAEWSpkPyWoejTMYz0yEkkWR2xvIPmxLHYOENLJpSiR91vXXYZDyL1ZSiO5TglLoALpuJVDb3+9ANKPbacGYMjKyGNq4Re6p/et5kIktSUrju5Cp+ur59uj3fZaGh0PXKvynhX5pIzN6vffv+/Tz5ydP52Nn1yLLE+v2jXLa4hDlBF0UeGxs7j+xvzYpEfdDFzRt7QNXBACOpEt80ND1NZ4mdsWiCixeWiFA+4YSYTGTY2TcF5MKd1y0qoSTPxnvWVPJ/T3ZOT1fosRKKpUlOZjED2lSa2NO5oGpzuQvDMEhlVZ7rnOD3T3chyfDek6tZWR0gldV4bP8of3y2i7cuO/Z1nyTBaDTNRDwjQkhe57Kqzlg0zfIqPzdt6OYjZ9Xx9Xtbp193WU04FBk0A3UkQXQkMf2aRRMpXILwihA/LUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBOF17LiGkBxSCiiH+j5VkiQMw7jjFVjOm0ZJno0zGoPcv3doulBNlmDdohIq/A50IzT94DrApq4J9vSHWVzhO67r4bSaqPTZ+dwFjeweCKNqBksq8yj9NwraARS3BfdJJYQf6D7SKIGlTIy4+Ea3oNw7q23d4lJ8zwvuMAXtSDYFI3Wk4Mw2N4Dis03/PTSVZP9wlI6xGGOxNK1DEfYMhCnyWDmnqZCH941w965BPn1eAzdt7GY8lqGxyE13KMGvn+rEZpb55wdOoqnYc8x1bSh0saLKz+buCTx2E9efUoPdLLO5a5wfPnIQr93MR86qo28iwUQ8w4IyLz3jcTKqhuUYoUGC8EqzeawoKwpR7+8+0iiBucrDRT97drppXomHr1w8l1hKZU7QRUXAecz+xqIpPn7bLqYSWQBU3eAn6w/y7cua+eJdeyl0W7lmTRWRZJZllcf3eCO8uBKvjQ+dOYdUVucn6w9Ot4diGf7yXC9fuqgJl9XEr57spMBl5ePn1HPb1j4ebR3hC2ub2NU3hcOicOmiUpZU5J24NyK8ofjynYwvL0R7uPdIowR6iZN8l/nfCkAShNeaBWV5/PyqxTy4d5h4RmNxeR7PtIX4yDl1DE4l+eitO4lnVK5eWUG+20rMZ8UmA0dlhmhLguTl23Bac2GRc0s8BJwWxo8KEXn7igoeax3ltPoCRqMp4pkjIY0leXbKfHb6jwr1s5sVyv2OV/z9C8KJUuy1UVvgpGMsPt1mNcnUFjjYPRChtsDJKXVBDMPArEhkNYNnDo6xZk6AQr8TeW4A/egwIIvCflUlz2FlU1eSj51Tz+7+KYo9Ns6fX0R90bGvDYVXV01+7vqkNM/O25aVkdUNSr02bBYFv9NKOqvx5YvncteOQb73UBuNRW4+v7aJloEwNovCyXPy+db9ucJ7xW9HKnJgDB8ptpe8FnYnU/zo9v3MK/HQ+AL3BATheEtlVdqGYwxHUvidFgYm43zxoibMisymznG+cvc+zp9fxMfOrmPfUISqgBOH1cSPHjnIsnObKN45sz/XmlJks8KWtjHe88ct0+1PtYX46/UrGZxK8sm/7wYg3zXGKXX5PH3wSFb4koo82kdjlHht5Lusr8ZHILyC4hmNMxsL+fWTnZxWH8RqkvnShU081zWBz2mhOuBk0izhqfWidhwJjZQdJiyF4nxSEF5R0r8R4C8IgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIrxHHNYREkqQ/AAuAFo6UXBmACCF5GVTN4P+eaOfza5vY3Z97SLi5zEs8o5LvtrGhI1dUs6DMy1mNQbK6gSwf/wcbY6kM9+wZ4m+b+6gKODApMvftGeLak6pYWuX/t/qyLw6CLBHbMIjstuA9pxJLqQgheaNbXJ7H/1w6j/998ABpVeNT5zawrMrPwFRyRgGlucBB/nubia7vJTsYw76gAOeqYmRLLtyjYzTG9X/aSmcoV4xW7rfz4TPn8KNHD/LtBw5w07XLCbqtPHUwxN6BML+4agmbOsZ5oGWYe3cPsbDMy5mNQQ4MR3FYFCqPEcJQ6LXz1Uvm8mjrKAUuK9+8v5V3r67k9890ARBOZvnOA/spzbPzgdNr+NJdLQScVt61uooCtwghEU4MZ3MBkgHZ54bBpmA/s5wPPX1wxjQtgxHaRmJ88c69+Bxmbn7vSuaXzg4IGo9l6J9Mku+y8LZl5ciyhFmWsFsUPnluPYYBpXk2nFYThR7brPmFV47XYWFRWR7bjwogO6x9NEaZz87u/ineuqyMSxeV0hWK0zOeK7z8xn2t1BY4qfA72Nw1QUORm4AoOBOOA90wiFS70U8pwbV3HBxmQosCfPyRVq47pQarWWFeyex9jSC8HiSzGl+5qwVFkbCbFR5qGcYw4PzmIorz7Fy1soKmIjc/evQg7aMx7vI7+PlFc8jfGUKPZsksCDBZ6eKkyrzpPqvznfz0HYt5YO8ww+EkC8vz2Nk3xfrWUT51XgMOi8KGjnHml+bmCXps/N/VS9jYMUHveJytPRN8+eJ51BS4TsyHIgivgoDLyk/fsZifPtpGTYEbj93E8ko/VrOJr97dwpcumstj+0fZ2j3Jp85rZP9whFRWY2mFj5Sqs6vKQYNdwd42hZZvwziphMd29rDAIvHPHQOYFYm6oJs9/WG6xxMsr/JjNYtruRNtfqmX3797CRMJlc//cw9ZLZfG2xGK85Ez63iua5x9gxHu2DEAwP7hKN+6v5WGQhdfvngu//vgAZxWEx8+cw63tQzx6YurkbeOonWEyZY6aa92ccuGTv77tFraR2MA1BQ4RZio8IpKZzX+vKmXb9yXC8iRJPjV1Uv5y6Yellb6uGvnIAAP7h3msdZRGorcvG1pGbsHIqxbXMr39vTz5fMrCOwMQVbHfVoZtoZcGOhfN/fOWt7u/ilu3zYw/ffjB0Z516pKTqrNZ3vvJNX5TlTd4I7t/fzf1UsJimv6172uUIzWoQg7+qaoDboAiW/e30pDkZuWwQi3bumjOt/JX9+yEFPAhr5/ErnEif20MlyF4nxSEF5RhnGi10AQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQ/m3HNYQEWGUYxtzj3OebniRLjMUyfOO+1ukRYe/eNcgP37YQRZY4o6EAkyxRF3Tx4/UHMQzw2EzUF7pYUR04busxEknTN5ErYu4ePzKKbM9R//1SmTxW3KeU4VgSRDLJyNbjvSkKr0Uum5l3r67ijIYgoViar93Twrce2I/HZuJrl8xjbXPxdNGXtdyN+Z2NGGkN2WFGOipY58G9Q9MBJAB9E0liaZWvXDyXB/YOY5JlPnTmHK5YWobHbsZhUbhpUw9j0TSrawLUFDhn/FZ+f81yllfPDtKZW+JlbomXnzzaRiytosgSaVWfMc3AVJKRaBrdgNVzAnjsYlsWThyHz4bjtHK0ZUWgSHSFk2zqmZw1XVbLbceTiSy/fKKdH1+5aFbRnd9lobHIzRVLy/jhI20kMhqyBB8/p545QSc2s8JYNM33HjrAHR846VV5f8IRbrsZq0me1V4XdDGVyPJUW4hfvHMJAaeVjYfCyg7rGIszHs9Q5nPQNhJjtQghEY4HA/rSKp/c0clF9UHGkxkefHAvWc3AMAx+81Qn379iIeZjbLeC8FoXiqUJxTJktJnngROJDJ+5fTeKIvHWpeXTxexdEwkuvXcP37lkHufUFTAaTzEZz+JNZvE5LdPzS8DDLcMEXBaeODCGqhsoskSxx8YX7tzLp89vmJ52a/cEH79tJ70TSQo9Vv73LQtYU5v/qrx/QTiRynx2Flf4+NGjB0mrOuc0FfKp8+oB+NWTHSyv8jMcSfGt+1tpLHJz2eJSeicTOC0mPnNfC9UBB2fXBTg4lYDnujhpToDBcAqArGawbygCgNtmIpzMEhQhJCdcVtVRdfjug/unA0gAfvd0FxcvKObbD+znwubiWfMdGInxXNcEfqeVUp/Mjc92k1I1PnTGHFznVvK3JzvZNDBF+xODvP/0OfzwkQOksjqKLPHVi+fytmXlIoRGeMV0jMX51v2t038bBhwYjvLUwRALy/NmTJvRdPYMhNnZH+Znj7VTF3SxtrmYK9bv45ZrVrCgxIPsME9P7zjGdqtIEq7n3Wu9eVMPX183j1++cwk947lrwmtPqqLM55g1v/D6I0syqp7bZ96+vZ85QRe6Aa1D0elpxqJptkeTDOcrJBd4OGthMfklnhO1yoLwxnf8xwkQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQhFfN8a4C3ChJkgghOc503eC6k6sAMCsyDqtCiddGc6mXqUSGCr+Dtc3F/Pm5XgwDvHYziYzGp2/fzXgsfdzWYzSa5uS6glntp9fPbnupFKdFBJC8yWQ1nXhaZXd/mKDbhiRBJKXysdt20TocnTGtbFZQXJYZASQAW44RqrBnIMzgVJK3LSsjpWoossyiCh81BS6KvHY+fV6uiHPNnAB/OfRbgdyyP3vHbibiM38rGVUjo2pA7iF9gLaRGIueVxzisprQdKgKOPjA6bVYxejJwmuA4jSj2EzYLQrnzy+a8ZpJlsg/KnRid3+YeFqb1UfQbeObl83nN091kshoeO1mZEni+w+3ISEzOJlkV3+YT57bgNsm9uOvttoCF6FommvWVHF4F+l3WvjM+Q3sG4zwwTPmUOCyEs+oOKwK0vMKH86fV8STbWOksrO/e0H4T/RMJBhPpCnNs3Pj9n7uaR0lqxm4rCY8dhM7+6aIZ9QTvZqC8B+xmWcfTxVZwu+woBtQ5LFNhzUepuoGf9raxzMDUzy6f4yn2kO8/Tcb2dMfBqBvIoHVrPDRs+pIZ/XpotF1i0r444ZuJAlWHgrJG5pK8v4/b6d3IgnkwiE/8JftdB8VyicIb1Q7e6f4zoMHpsMgH2kd4f49w3zy3AbaRmJUBpzTwWwfPnMOqq5T4LJOB6d2jSf47bY+nugYp3U4QncowdxjFFyfM7cQkywqRV8LdvWH2TMYIRTLzHotklKZSmSJp1UK3DOD9KoCDpqKPLSNRLl/zzDRtMr7Tq2hMuDA47KwpLGAfSNRLl9Sxi+faCeVzW1Tmm7w5btbaB+LvSrvT3hzGoum0I2Zbaqe2wYTGY2C5wVD1hY4mUpksCgyB0djjERSXL2yguKgc0YACcDbV1SgHLX/UmSJJRU+PnJW3YzpXFYTyyr9mBSZ2qCbFdUBEUDyBlKV76Akz4bLaqI+6Kbc5+D5h7UL5hcxGkkxmVL5/jOd/GZDF5u6xo/doSAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgvCmdryrhm8iF0QyDKTJjfVlGIax4Dgv503l8QNjFLot/Orqpdy3e4iMpvH+0+bgdZg5OBolndWxmCROnpPPqpoAw5EUfqeZcFJlMpEl8LwH2f9THrsZn8PMB06v5R/b+pEkeOuycuoKXcelf+GNaSKWRjUMgm4bU4kMN27o5hePt5PVDJZW+vjoWXX8+NGDAHSH4rNCPgD6JxMcHIlhVmQailxcML+IJw6MzZjmpNp89g1F+O3TXQDUF7r4xVVLqCt0A3BhczFBt5WDo7MLizrG4kwlsvidVuJplQ0d4/z2qU7Misz7TqvmnLmF/Pm5Xh7eN8wnzmkg6LbybHuIxiI3HzunHptF4ZrVlQQ9tuP86QnCy1PosXFWYxCP3cz9uwcp9Ni5cnkZY7HU9DQXNhfjtZuPOb/dbGJusYd3rfYxEknjd5qZiGfpmYizuWuCixaUYLcojMcyFOfZX6239aYWiqaRJAi4rPzXKTW0DExxRkMBU4kM47EMN27o4bT6AjKazk0buzmtvgCX1cQ3L53P75/tYiyWZu38YsyKTDSVJatp/PrJDlZU+2ku9WJSjnc+nfBmMRZLMxnLcOXyCoKeUTa0h6gvdHPZklIOjsQ4szGIzSS2L+H1qchrY1WNH7Mi8ci+EYq8Nt6+vAK7WeYzFzSgagaxtMrD+0ZmzHdmY5DBqdwxt8Bl5f+dVc/ft/XSO5HPN+5rIRTLUOix8dnzG8GAtKYTTWUp9dn59uXNzC3xAjAwlWTseeGS8YxG51iMyoAD6flJU4LwBrLrUHCPRZExKRKJjMYtW/q44/2r+d27l7GxY4wfXbkIn8PMLx7v4Jn2EIos8bVL5s3q69y5hYzHs/jsZj51XgO3bOklllK5dFEp80u8+I/TvRPh5dk3FKZvIkFd0DXj+t1ikkims7xzZQW/fbqTT53XwCP7RtjTH2ZVTYArlpbx5bv28olzG+ibiDO/NI/GIjd/ea6X27b2saDEw8+vWkxWM/j9M10zlvnu1VXs7gvzWOsoC8u9LCr34XmBayRB+HcNTiUZi6XId1lmhOu4rWYCTgt/fa6XT53XwEMtw+wdCLO6JsCVK8rZ2DHB9afWoOk6mzrHufnaFbgdlln9L6nI49YbVvHg3mEkCc6fX8Sich+qpnPLDatY3zpCnsPCWY1BMprOV+9uoW8ywduWlrOq1o/XPrtP4fVHkSRMEvzkyoXcv3eY/UNhvvuWBfz6qU5GoynOm1eE22oCSSKezoVDbuuZ4vLFZQyHkxR5xT0dQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAE4YjjHULyB+BdwB5AP859v2lVBhxMJbJ88K/b0Q4Nm/lQywh/fM9yTIqEaugUemw0Frv5/sMHpudrLvVw3UlV/7L/6ESKkc4w4VCKgnIXwSoPNufsYou6oJtoMovNLPOFtU0ABFwWFh4jNEIQYimVh/cN88NH2kirOh88vZYyn306cARgW88kpXl26gtdtI3EyD9G0VfrUIR3/2EzY9Fc4eWKKh/fWNfMVSsruGVzL5ArsCjz2fnBI23T87WNxPjH1j4uXFDCP3cO0DeRYN3iUpZV+mYtY26xh4AzV3TxXOc41/9p6/Rrz3aE+PN7l/OZ8xt4rnOCzV3jnN0UZHVtgKcPhvj1kx38+O2LkSTY0x/GZVOo9DsxNIPRnggjXRGsDhNFtV58Rc7j8+EKwkukpjTmyRYKTU6uOHs+ht9MwgRfvbsFgDMbC3jHigrkFxj13WNTqA26+MHDR35bi8vzuLC5iFs29/H5f+7hF1ctxm073qcTwvNNJjLct3uQnz3WjiJJ/L+z6zl/fiHnN5dwz65BPnbbLuYWe7CaZL7z4H6+fdl85gTdbO+dIqPq2MwS31zXTN9kgls295LnsPDRs+r42K27iGc0FFniL+9dyarawIl+q8LrVMBh5sm2ECfX5XPpohLOn1eISZawmRVkYH6pl8hkmv7+CcJjSfLLXBRWH/ucUxBea6wmhZPm5JPnsHBqXQGJjIrPaWZrzxR7BsI0l3pYWRPg8iWl3LVzEN0wuLC5mCKPjS/euRdVN7CZZb5y0TxOrcknOZLiy3XlYFXolTW+/8gBLm4u5oL5xfhdlllFoD6HBatJJq0eucSWJdg/EmVnf5i3Ly+nRISBCW9AO3onsZtlvnp6PYUZIGuQyTPz3FSEgMvK2XMdnNkYpHc8xp829fJMewgATTcYmExyw6k13PhsNxlN5/pTqmkocnPv7iF29EzRVOLhw2fOwazIOMyKuK/xGpLvsvLAnmE+e0Ejf9nUQ0cozjfPbaTJbGWyLc4ZpW6q1s7lR092cEZDAdeeVM3Ovkl6xhP86MpFPLh3iCuXV1AZcHDzph6ePhhiKJyiZTDCvXuG+dblzRS4rNPhTlcuL2dbzwQ3beieXocvrG3iv06pFiFPwnEx1B/FO5zlx4tqiTpkbusc5fS5QQ4MR/jChU3cv2eI3zzVwbtWVfKh0+egGQY7+qZoG4nyTHuICr+DD585h6FI+pghJCZFZlmVn2VVfobCSQYmk3SMxajOd7KqJsCqmtw1XutQhLf/ZhOJjAbA+tZRvvuWZq5cXvGqfh7CK0ORJRa6XQy3hTnb7CBpMlFT4OQT59RjMcns6JmkPOAgnlJZVunjj892s6TCx7MdIcr94jxSEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBmOl4Vw33GoZx93Hu800lMxwjuXccNZTE3pyPtdpLhd/Bjt7B6QCSw/7wbBc3nFqD02wildX486aeGa/vGYjQO5GgMvDCwQeJSIb1N7UycGByum31ZbUsPqcC6XlF6VlNZ3tvrrDjH9sH0HSDk2oDfPniuTQUef6t96kls2SH4uiRDIrPhrnYgWwRRexvBHpKJTsUJzkaJxDPUOez83jnBD969CBXr6qcNf2mznFOayhgfomXuSXuGa9pusFNG7qnA0iuWVOF06Lwm6c6OH9+EVcsKWNj5zi7eid5+lDB2dHyPTbe+bvniB4a4fPR1lG+dsk8vrVuHo+0jlDgttM5FuWLF83F67Cg6QY3HlV4dNiW7gneWhLgLWEDNakxrCl8eWs3e0eiyBIcHInylXv2cWA4it2s8PkLm1hhtfHob1rg0M/W6bNy6f9bjK/Q8TI/YeH1KBtKkmodJ9MXxdboxzonD5PnlRlpPTMSRx1JIJlkxlMqj/62BePQdphf58VxSpAfX7kYs0mm3OfAaX3hfe9oND3r2LKjb4rJRIYbTq0BYCicIpHRcNlEkMAr6ZmDIb54Z8v035++fTd5jqWcO6+IloEwP3rbQmJpFU2H96ypwmM38/V793FwNMb1S8u4piSAeeMIVUUO5p4yh5ZUmk/fvme6P003+MUT7SyuyMNqVk7EWxRe5wbCKbrH43xidTXaSBxZMxg1S4SdOg/uHebTZ9Sx+e/t9Owdn55n+UXVLLugElmR/6NlquNJkgcmyXSHsdb5sNXlYcqzHa+3JAjTBqeS/HT9Qe7YMQDAJQtLOKk2wE0bu0lkNJ5pD9EVSnDF0lLObCjAZTUzFkvzxIExPnzmHA6MRLl/zzCZqRRLTFbiWUi4rGx6cgCHWeL/Lm/CO5REfnyA8RIH6TofleXe6eVX5Tv56sXz+Nw/j+y3r1lTxX27h2gZjCBL8NGz6kSxvHDCGKpOpjdKYtcYkgz2hQVYKjyz7in8O7pDca75w2Z+flEz7fd00BnNAiBJcO37m7GaFVRN5749Q+i6wRMHxpAl+MTqalY4bBQ5rOxXs8x76wKsJpmO0Rif/PtuANpHYzQUu7l1cx/beqcwKxIfPH0O155UjdchzmlPpP7JBK1DEeaVePjOA/v5zluacRkQXj/Mxs7I9HTNF1XywysX8Ksnu3i6bZT31hfhnUijbRqjvtTLVCzL9vgUlQEHDksRFy6Q6A4l+P0zXfRNJHj/6TX89LF2phJZSvNs3Lqlb8Z6/PCRNs6bV0jFi9xPE4SXIhxK0nJLB9FQcrrt/727kfc8upfJRJa/bu5jaaWP716xAFXVuX/vEI/vH+WLq6q5qK4YeX4Zdw5OklJ1dg9MUV/kfsFl7eyb5IY/bWM0msYkS3zm/EauWlkxfc2/q29qOoDksJ+ub+fspkICxwgFfiHZ0QTJfeNkh+LY5waw1npRXLPDUYRXV2QgznO/3YeazYXWmSwywXdaCaRVCvsTzLcpxIostCRVaiZVnrxkIVm/lctu2c66RaUneO0FQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCE15rjnfqwX5KkvwL3AOnDjYZh3HGcl/OGlB1LEPrtXvR4rrgmuXMM76W1dCrq9KjXQbcVm1mhdyKBYRgEXRbcVhOJjEoqq8/qM/m8h8ufb3wwNiOABGDzvV3ULC4gLzgzLKE7FMdlM3Pr1v7ptmc7xrlzxwCfPt/9kove9LRK9PF+Yk8d6cd7SQ2uVSUvq0hJOPGMrE5s4yCRh3KhBbXAF1YXMZlS2TMcxX2MsIPmUi/vXFlBVcBJnsNCRtUYCqewmWVcVhPbe3Pb54XNxbQMhNENgzlBNz969CBNRW7ec1IVd+8c4Pz5xTP6lSTAYDqA5LC/bu7hqxfPI71nmCcOjHLp4hL8zlyxhSxxzECGK0sD6H85QEbLJTkEd8I3Lqzk8vv3ckZjkB880saB4SgAyazGjvZxlK7sdAAJQHwyzUhXRISQvAlpkTTjf96HOpwAILk7hHN1MXkX1iCZ/rPC+xeS7o0Q+u0ejEPHAznfxppzK3j2oV4AQgfDFM3zMuw0c868ohfsJ5nRiKdVZFmaPv4cLZXV+eKde3nX6krOaggSS2cJIgr/XymGYXDLlt5Z7XftHOTceUWsrPFzy5Y+HmoZIc9h5vMXNPLn53o4OBqjudjDVVjhni6yAHvH8eXb8J86+/sfi6ZRdR0rIoRE+PclMxo/Ob+J/Id6YTRXZBk0y4xeWMm584tQYtqMABKAbQ90U7csiK/o3y/w1eIZJv7eRqY7V5Cc3B3CvqgA3+V1yBaxDQvH1+P7R7l9+8D033ftHKTc7+BjZ9fzwN5htvdO8mDLMGvmBGgbjrKpa4L20RgA9+2Bd6wo53/OqOesjhiJx0eQAKcicfb5VYSm0njv70GPZNAB795x5IzOmNdCgSc3Kr0iS1y+pJR5pR729IeZSGR4fP8oLYO57f8vm3q5amUFQbc4FgsnRronQuh3e6avf2LPDVFwwwKsVd4Xn/FFdIXiZDSdeH+c5KEAEgDDgN0P9lBV76NzKsknbtvF99+6kOYyL9fMLeKs1iiM5Y43cy0y4Uuq+Vv3OPfvGQLg6lWVFHqsPNQyzLbeKQCymsGP1x9kSaWPU+sL/uN1Fl6+7lCc3z7dxeVLSjl3XiEFbguWUJaDRwWQAOx/pJ9lNR6yqs5/zyvBvmWMxJ5DwaQt43iKHTxc48CWZ+Ub97WyvMrHBfOLOWduIRlN54G9Q9x6wyqiKZXBqdSs9UhmNTLa7OsgQfh3pLIaw93hGQEkADvv6eLSxUXcuD0XfpPIqPRNJJmIp/n7tn5uu3A+wft74dA9qLeVOBmoMTOCMWsZh00lMnzujj2MHgrxVXWDb97fypLKPJZW+l90Pf+dO7HqZIrQH/aiTeWWk9w1huecCtxnzA6zFl49yYxK77ax6QASADWrUy8pZB7oBAOygHXrKGdcWE34nk4AzA4Tvz63Cbu4fhIEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCe5/hWP4OdXPjIucDFh/5ddJyX8YaVHYxPB5AcFn2kh+Y8O82lXn585UI+elYd71xZwS+vWsKHzpjDb5/u4r//vI2bN/Xy/StyI/we5raamBN0vegy1czsogotq6MdI9DEbpHZ0x+e1f7wvlFizwt6eNFljiVnBJAAhO/vQn3eQ/nC6092LEHk4Z4ZbbZNw7x3bgmabmCzyJx2VFGXz2Hm/51TT4nXzs6+KZ5qG+Vzd+zlzB88ydqfPMMzB8e5qDkXLtJY7ObceUV47RaeODBKud9BdYELSZL42w2rOGduIesWlUz3PbfIjc08+yH6dYtKufbGLWzoGGc0mua3T3Xx40faSKsakiRxzZoqjq6bWFntg4NT08UfhwX3h1lZ5WdhmZct3TODfPKsZtLRmb9lgEzypf9OhDeO7EhiOoDksPimIdTx47vP07Ma0cd6pwNIAPRQigKrgnLUsUHWYTgyu9DusJ19k7z/L9tY+9OnuWvHIF+9eO6M1712MwGnBVU3+OOz3Uwms5hEsdErSpIkKv2zQxrK/bni9PbROE8cGOPspiDvXFnJRDzL5q7cfunqxiLMO0Iz5tNDKeqk2YFL155UhdNqfgXegfBmUOy1URhRpwNIIBdOVrB7nOo8O8nk7OOirhkzCuX+HepocjqA5LDkzjFxPim8Ih5sGZ7Vtr1nkgf2DnHO3CCHsxgn4hny3dbpAJLD/rGtn9O9Tozeo9o1A3n7MMU+K3okM2N649khnt0xxJ07BnjywCi943EGwykGJpOYFJlEWpsOIAEIeqzYTaJ4VDhx4puGZgQwokN8x+jL6tNhVbCaFIz07GDVZDTLgaEo+4cifP3S+WzoCHHevEJWmq0wduQ818joeHaGmON3YAC1BU7SqsZwOMXGzolZ/R4YicxqE15djkOhoJV+Bzazwpfv2kcmNXsbyKY1dvdMcs3qSjzhLMm9M8931aEEpwdcbOuZpKHQzZbuSTpDcU6pC9BQ5GZppZ9bt/ahGwZNJW5czwsjPW9eIV778c5tFt5M9vRP8eG/7qBvNDHrtXRCxWc/ct116cJS/ryph70DET56UjXF28dn3IOSBuOUpwzmlxwJduoai/HAniEe2jtMz3icyXiG1qHorGX1Tx45N15YnofjqLAJm1nm6+vmcevWfj53xx4e2z9C9Bjn7EfLDsWnA0gOizzejzb5wvcYhFeeWZZJP+98srDKg9ESet7x2SDbF0UJ5ILr9ITKnOEU5T4RmCwIryTDeOEQKUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEF4rTreT9T/zjCMZ49ukCTppOO8jDesYz2MaGgGAYeVMp/Onzb28GTbGACXLymlfSTKqQ1BrlxeQVbTmUpm+e5bmvnOAweoKXDyjhXl1BS8eAiJr8iBxW6aEY5QMS+AJzB7FG1VNShwW2e1L67wYj9G2MML0RPHCGJQDfSUCGh4vTFUHXUsiZbMokcyaJE0swZmNcCpGzgsCssq/VyysJS24SjJrEZtgRObWeYTt+1iMJxiUbl3epT58XiGL921l9++eymtw1GqAk6+dX8rQ+FcYcODe4fpGoszt9hNMquxqCyPb65r5r0n15DRNKoCTlqHIrisphkhOU6LidTzCp7v3DnAWXODVPqdLKv0cdv7VvPIvhGq8h2UeOxIrVOz37yms7g8j3nFXmoLnHSMxadferB9lO+cVMmeB3qn2yQJvKWzQwSENz5DP/aD5sf7+XMjo5MdmV3gpKRULHaFZFTH6jQxamgU2uzTr4diaXb3TdEzkaA0z85dOwd44kDuWPPHDd2c3RTkE+fW8edNvdQF3ZzRWMCBkSPFTWPRNHkOy/F9M8IsVy4v5587Bkhmc0WYbquJixaUEIqmsZpkrllTRV3QxUMtw7is5un9kiJxzI1tKp7hsxc0cvfOAWJpjetPqebsuYWv8rsS3kjMJglLQiX9vHZlIo2W0rAGrNicZlJHBe6V1OXhyZ99zvlSvOC+9QXaBeHlWFnt5+mDMwvc5wRdPLB3mL0DEWryncwJuih0W/HYzbitJqJHnX9qhoElq/H8qx19Io1FmR3kZegGkUSWLz/QymcvaOQzt+/mmjVVfPfBAwAUuKx84tx6vnX/fmQJPnN+I267CJESTgzDMDDUYwRK/YchU4fVB92sqvGj51tBYsZ1Zv6SfH77XDeprEaZz8FtW/uZE3SxNAPPL59XJtJUefO5dk0VkZTKzZu6WVLho6HQzcbO8RnTVhwj9E14ddUFXXz5oiZ0A752zz4AOjNpzDaF7FFhJCXzfUhOM6ucdoyJ6Oz7EOTCzlJZHas5F8i4o3eSyxaW8O4/biaeyfX1x2e7ueX6Vfzlv1bww0cOcmA4ysl1+QTdVr7zYBtfurBJXOsI/7ae8Tjv+sNmphJZzi3xISsS+lGhIvUnF/Pt9iEgd0yXNJ3PL69godcJk2liE1Oz+nSoBqWHgqb3DUa46nebmErk9niFHiu/v2Y5c4JO2kfjM+Yr8R659m8q9nDLDav4+9Z+BiYTXLWygk/9fTfj8Vx4xd829/Kdy5t5+4qKF3xvxzzXNoxj/QSFV5HJJFO1opC+liMBW7IswTG+L0M3kI4Okh1OIBkGuYOtIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIOTIx7m/n73ENuEYLMVOJOvMMA/3GeV0JtNMxDPTASQAZT47pzcGuXVLHz9Zf5BfPtHBdx7Yj0mWOa0+H90wKHD/66LOvKCDSz66iMr5ARweC81nlHHKlXWYbbPzaexWE6pmsKDsyMibhR4rly0uw6S89E1Jcpqm36dkN2Gt8WKp8iC7RWHH64mR1YhtGWbklzvI9ESZuOUAWjiD7JpZACk7zVTV+Pn25c3csX2AR1tHKPXZOKMxSEXAyZ6BCE+3h1hdG+Cx/bnRsk2yxMfOruPceUX84JGDXL6kFJtJng4gOezASJT+ySRX/noT23sncdpMNJd5WVrpJ+CyMrfEwzcvm8/lS0o5tS6fj59Tj3qMB/BdVhPbe6a48tcbaRuJsazKz/87u46DI3GuuXELPQHrrGfxbatKGIim+MTfd/GFtU0zgngWV/jo9UjUnFWKy2clv8JF/dtq6MhmEN58zIUOFN/MACfHwgJMxwh7ejkUpxl7cwEoEpYqD+aSXBGlqzYPSZIINuRRfEklLZEEAWdufxtLZfnfBw9w3U1b+do9+7jh5m04rCYWHrWfX79/FIuicGpdAVlN51dPdjIeO7Itu2wK23unjut7EWZbWJ7HHR9Yw/euWMAP3rqQ29+/hqqAg/99aD9fvruF3zzVyaf+sRuf00L/ZIJ3r67EYVG4rX0UbX5gRl+yx8LWeJJNHeN887JmbrpuGe9aXUXAOTtoTBBeKpMsYy12IVkUrDVeTAW5gkdbc4AnekM83jdBw5W1lMzz4fBYmHdKCae/swHrfxicYAraMRXNHLHbWu/DlG9/gTkE4T+3trmYuuCRcMeafCd5DjNj0TT5LgtXLitnVbWf/3uyk8/dsYePnl03Y/4L5xdjDszeNm1NfswlTmTHzGuv7JIC/tw6DEBXKI5uwOBUigJXbj89FkszEknzx2uXcdcH1rCmNjCrb0F4tUiShGt1yax2x9KXF27mc1r4xrr5VM7xsuY9jfhLnbj9NmrPLePJRJy7dw1yRkNw+hpyKpHFWTI7hNXanM/n7m9l90CYBWUe5pd4kQy4sLmYPMeRY9A5TUEWHXUOLJwYbpuZ5jIv61tHp9s2hyKseW8TRfV5ODwWylcVMv/UEs7QFSK3toFJxlbvm9GP4rfx1GSMVTV+9g6EAVhUnkfvRHw6gARyWX03bujG57CgSHBKXT7beib55RMd3L6tnwPDUV6LROjaa1vHaGw6IOTHW3tYfV0T+VVunHkWqk4rptWp85kLGlnbXMS1y8q4JCmzKCMT/dsBkltHsDX4ZvVpP7R/G5pK8rfNPdP9A4xE0jy+f5QfvHUh3kPn1pIEHz2rjqZi94x+rCaFnX2TTCSy7B2MTAeQHPaDh9sYjc6893Y0c5ET2Tnz/N21pgRTXu7+htg2T5wRq8G8y6rxBu14g3bKVgSJzn3etiSBpcyNOpacbrI1+AlNJREE4ZUjSSLkRxAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQXj9mZ008R+QJGk1sAYokCTp40e95AGUY88lPJ+50EnB9c3ENg+jjiZwLi/E1uBHG5gilp45brZZlolnVMaiR8abV3WDP23sobnMy9lNhXjtL+3rLazycM57m0gnNFw+W26kxGMo89kp9dmYE3dxVmMQ3QCHRWFOcHahz7FMxNJE0yr5k2k851aiRzMYukGmJ4q51Ik2lcbsO75F+cLxp6dV0l0RtEiG8N0dWKq8pA9OAhDfMoLnvErim4dRRxIohQ4862q5tX2En6xvn+5jaWUev7p6GQVuK8mMhtUko8i5bWwykeXdqyu5bWs/A4cegn+qbYzvv3XBrHWRJAjFMswr8bB/OMKCsjzSWY2sbpDvsuJ3Wjl7biENxW6+c99+fv5YO29bVkZjkZv9RxUTvWt1FXfvHOCqlRVMJtLEUyrdE3H+uKELgC9s7uJ/11ZT1B7BrIF9UQHZ9ik+63DzkbeW0ZpOc/N7l5PMaITiGXZ2T/J0zwThjMpnrmvg0f2jfPHRfXxubdMr+dUIr1GmPBv575lHfPsomc4w9oUF2OcFkM3H5/AYT6mMx9P4FQUlYCVvbTXJlnFknw33aWVoqsry65sYSKTYH0pgNytUF+QCStrH4ty2tW9Gf7dv6+fDZ9axqz9XrGczKeS7rNy5c4A5BS4uaC7CMGBusYd5pR6ebBsjndWpzndSGcj1q2o6BmD+NwKqhH+tqdhDU7Fn+u+t3RPctrV/xjSHv7+frm/n25c30zeRYNDnoijfjrMjjF7ipLPIRs9ImMZiN5f9cgOrawL839VLyHNYUDWdoUgKsyxT5BXHZOGl03UdxSzhObuC5L5xzKUu3KeXo06luL6ugv+6fTfGgmLKVvi54p1NOL0W5JexjzC5rQSuaiKxc5T0wSls8wI4mvNRjhGkJwgvV02Biz//10q290zSNhJlLJrm54+147KaeEdTEebuKO6OCOfXlzFW6WJTNMG7VlawrXeKVTV+DCCuauSdU0lswyB6Iout3oepwIGezMKV9VhaxmE4QajazZ2xOG2jMSAXlpfKaoxEUhS4rYzFctd/7aMxtvVMcMOptUQzGvNKPHjtItRRODEs1R4C184jtmEASZZxnVSCtcLzr2d8ESORFAeGo2xoD+FzWkgs9ZBvt/CtLT30TiRxWhRiaZXSPDtmWeLypiIy28Zwn15GbNMQRkbDPr8Ac76Nty0rx2qSqTZkflESRBqMMzml8pO1c9kbTdFc6qW51IvPKX5DrwUTsQyBQyEHi8q8vHtFBX3xDJWXVHD/zkF2ptOsPjAFeTb0yRTZgRi2eQFMBXYyvVHM5W5GKl14Eklu39aPbkBNgZN1i0pY3zrCafUF9E0k6J1IcMOpNdjMCjv7p3jswNisdZlMvLaCRNN9UeKbh1DHU7hWFGGt86E4/7NAN+GVY7ccudaPprPcNzxBbyEU13j4becQaVXjOrvCB0+vpWgyS/JvBzBOLgXNQB1JYG/yY5vrJ9U6gWw34b2wBoqc/GNbP4+0DBOKz94uDwxH+fBZddz74ZPpnUiQZzdTG3RiM888N97RO8megQgApzUUzHjNJEu8c2U5D7eMMB5Ls7zaz6LyPByWI32Y8+3kv3c+iS3DZAZiOJYEsTUF0MIpEnvHSe0NYa3z4VhUgLlgZmCgcHxl0xqKSULTDExmmVBG5Vsb2ljbFMQw4JZd3Vy7soJT39GAsXUESZawzw2AScptX22TOBYHUSeSWCczcOh+jiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDAcQohASyA61B/Rw+xGAGuOE7LeFOwlLnxlbpAN5AOFWa6bCZqC1wosoR2aETJrT0T1BW6Z80/Gk3hsvr59gP7+crFc2kq/tcj+fYfmGDf04NMDCWoXVJAzaICAqWzg0UkSaK5xIssSXSF4hTn2VlWkUeh58WLlHXd4NmOEF+8cy894wn2vG0ZyT0hlDwryZ25Io9MT4TU3nEK3r8QkwgieU1LHZhk4q/7cZ9RDgag6kiHRm83Mhrh+7uwz8/HekoJvx+YIDg0yb27hwi6rcwv9dIznmBbzxTdoTj7hyK0DIb55LkNOKwKTUUevnxXCx67eTqABOCkOQFSWZ1vXTafP2/qZd9QrmDissWlBN1Wir02Htw7DIbEnzb2EM+oXH9KDZcsKiHfZcVrM7O9d5KMpvOXzb1cu6aK8+YVoekGDktuFNjPrW3kR48c5LdPd7Gy2s/7Tq3BODSAa0cozhX37+Xdy8r4iMdL+I52OPRbtO+bYN5pZcTiaZLDMRbW+rCHJMIjBuXLitncMc7+iTifvaCRBaUvrxBPeP0yFzrJu6AaQ9On9+3HQ+tQhK/fu48NHeP84YqFLEsahB/snn49tX8C/5X1dD3Yi91rZe2yIKEqH0Xu3H42kVFn9akbYHBk9OL3nlzNnzZ2U5JnZ/9wlHt2D/E/l85jTtDFsko/37q/lfmlHn77ZCcfP6+BA8MRfvd0F/GMyrUnVXNSbT4uEQpw3KVVjY6x+Kz2w9/feDzDWCRFhd/Bc6EohgSrzy3D6zDzp8cOsqsvzFA4N8L1xs5xOsfiFHo0fv9MJzdv6sFlNfG5C5q4cEExTqv4/oR/zaLIZLuixJ46EoyTOjCJ+/QyLDGNr88tp6DMy8EtI9z38C5qlgRpWFGI92UUJ5qDDrznVmGcdXz3rYJwLIUeG+fPL6LM52Bb7yRfv3Q+S8u95G0YQd02SgagJ0KgxcLCteXYzAoLK/L47oMHOK8mH5+iEN0wiH1hAbJNId0VJrq+F//VTZx9x3bWLSph7doKrr1pK+FkFoA8h5nFFXn8aWM3p9bn87V79k2vz6LyPH71ZAc3b+pB1w2WVPr4yJlzsFvEPlt49clmBXuDH1udDwDpBYJNX6rhSIqv3LWX5lIvtflOmsxW2nYOosajfGFRJXePTFBW6OLGDd387LIFuOI6eRkdbTxJen8S57IiJLNMqm0S2Wsh4LdgS2j47u8hG8n9vpw9ERqXFVJ/RinFovD6NSGj6mzuGmcslubK5eXcsKCcke0hum7ppHJ+AE+ZwtXzS7GZJLQ7O5EO3TtKbB0h0xfFsTAf+9JCWmwGm0bDxDMq71xZybtXw8BUkn1DEVw2MxPxDAvK8vj4ufX86JE2OsbirG0uYl6Jh5bByPT6WBSZ6vyXFrr7asgMxgj9ZjdGVgdgojNM3qW1uFaXnOA1Ew4bnEqyuWuCtKqxqsbPps4Jir02avKdWM0KE7EMy6p8FHlsPLBnmDUleZgn0yQNQDmy34w+0Y+l0oPn/Coc8wKY8h3s6J3kk3/fxXUnVVFf5GZbz+SMZZ89txCAcr+Dcv8Ln19ntdz2s6LaT0OhG7tZIZnVALju5Gr+sX2A/skj9+N++vZFXLKodEYflhIXlkvnTN/f0FIqE7ccJL0/t06Z3ijJlhD5752PyW39zz9Q4ZiiEynat41wYNMIviIHtUsK0A2Y77aTzGrcumtwetrP3r2XZ9+zGjQDI6UxdWc7GOC5oApLiYvE7hDqeBL/gvwT+I4E4Y3PMIx/PZEgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgvMYclwolwzCeBJ6UJOlGwzB6ACRJkgGXYRiRF5/7zUPTDXrG4yQzGmU+O17HsUfZlSRp+uFzVdMZjqQIJ7N89oJG/vhMF4PhFCfX5eOzWzitvoBlVT7Sqo7drGCW4ddPdaHpBqr2rx9uHOqY4sFf7yWdyBWijw/EiE6kOO0dDSimmcWc+wbDvO8v2xmLpnFbTSSyGnMKnNx47QqK8+wvuIy2kSjX3biF7KH1MVxm7PMChO/vmvn5RDJkR+IihOQ1ZjicpCsUx2U10eh3EnmsFwDJJIMEmf4o3gtrSLVN5kJJNINkS4iuUhs/39iNRZH5/lsXsLVnkq3dk8wr8XDN6gp2D0zx9Xtbp5dTne9kbXMRX7tk7oziyetPqeHgSJQv3rk3NyrrqgretaqCsVgGqyLz5+d6aB2K8tkLGvny3S3T8/3PvfuQJDi7KUi538n7T69lW88kTcUe0qrO4wdG+cKFjXzytt38dN0C7t4zOF2Q/1zXBMsq/VT6HfRMJAAwDFgdcJN8qI+j8hnQp9I4gftv3s+Ki6p59I/7yKZzBRyh/hi1p5Vw62icZw6GuO2/V79C35LwenE8i+Qn4mk+8rcdHByNAZDRdBLbnzd6t2aQHUsyORint2WS/n0TlF1WyVZpklU1AaoDTgo9VkYi6elZ3rminLqgmy9e2ESx14amGdjMMilVx++00DuRwG5WuHvXIOtbR7jh1BrcNjPfur+Vc+cX8Z4/bj6c0cOmzgl+/a4lnDev+Li9byGncyzOgeEIQbeV0WiapmI3b1taTtBjxWKS+cd/r+K+PcN84/79mBUJq0nh50+089nzGyjx2nlw78isPu/Y3s8fnu0GYDKR5dO376Ykz87JdaIgSfjXnBmD+MbBGW1GUkW2KoSG46QiKk/c2EpsMre/GR/oYqwnyrnXzcX8MoOKRACJ8GqRJInmMi/NZV6mEhn27R3FtX10xjRGNENRBv7YPs7aBUXM8TtYGVeIpDQsZS7iG478Tmz1PnRF5k9vWcQ7btvBmtp8vvuWZrZ0T2JRZGxmmR8/cpDvvmUBhmFgNckossHblpXTPhojreok0io+h4VfPdnB2U1BllT4cteTgnACvNzwkcN29U3RM55gdU0AS1Tjqdv3YRw6wRzrjfGeK+fQ7ZL49Ln1OMIqz93TxZqzywkuDDL1z4PEnhnIrY9Zxn12BV+6aRO3ntWEcSiA5LDs9hHyTy6dtXzhxNjcNc4De4cJJ7PEJ9KYHhslFct9Z+MDcWoWFyDJsGJxAVS40ZMqSsCGNp5CHUkQebiX1EnFfLq1j0+f18A/dwwwGklz165Bllf58DutPNQyDMCegTAbOkJcuqiUjrFOHmoZ4TPnN2I3K2ztmaTcb+db65qpC752Qkiyg7HpAJLDIo/3YZufj8l97PuLwqtnIp7hM7fv5umDIWoLcvegrj+5mmha4+v37mM8nqGh0M3bl5fztXv38b3L5uMKZUDVc0HUsoTsMKEfuj+b6YlgXhrElJ8LFOkO5QIonVYT/ZNJ3rGinDu2DyBLElcuL6fM98L3ZY+2qCIPp0XhjIYCvvDPPXzyvHqe65xgOJKiKuCYEUAC8K3797OmNp/8Y4SJHD4HzwVAzQxFUYcTqKNJEUJynGmqzo6HetjzZO44Nz4Qo691gkVnVyDJcNPbl6AZBjaric0DUyytzCPaGcbaGZ7RT3zTENZqL+poAufK4ln7FkEQjg9xXSYIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiC8nh3vYZK/LUnSfwMasA3wSpL0Q8Mwvnecl/O6E0tl+dvmPr7/8AHSqs7Cci/fv2IhdYXuF50vo+nkuyzs7g9z86ZeLl5YTMBlBSR29E1S5rPzg4fbpqf/ysVzsZpyBWtNxS/eN8DEUHw6gOSwAxuHmX9qCcFK74z2g6MxxqK5wtFoOjfPgZEY7WOxFw0haR+LTQeQAAyNxakIOpGcZrxrq0E3kBWZVE9EPJj5GrO9Z5LP3bGHAyNRrCaZ/7tiIXMPPZee2DmK56wKIk/0E9swiPfCGrJTKSazGn0+M596uh2A5dU+7tk1yCOtuQLNfUMRKgMObtrYPb0cl9XEimo/Syt9bOwYxzCgxGvDAJJZjSfacsEKqm5w04YePr+2CYdVwWe30DoUJei20nWoIONof32ul119U5xaX8AZDQWMh1PkaRIVfhcXBvKwT+n8b2Ml2369jxqHiR+fWs/P9/eT1gzyDIkb37qIobEkE6EkzjwLi0s9xBiY7t9al4e10oNukll2eilIxnQAiWKSWXJKMQGPhV83NPDdbT3sHQizsCzv+H9RwpvSwGRyOoAEYDyeRTLLOFcWIbssKE4zmCWQJOavLOa5R3uJT2XIV2V2j0RZVROgOM/OH65Zzg8faWNLzwTXramiIxTnw3/bAUBd0MW7VlXyqyc7iaVVqgIOvn15Mw1BFy6riVhaJeCyMhXPUO6382Tb2HQAyWG/faqL0+uDWM3Kq/nxvOFFklkMw+Ab6+Zz3+5BFlX6iKVUfnB7G3aLwucuaOQfW/sByGoGWS133A6nVIq9M4/Zy6t8FHqs3Latb9ZytnRPiBAS4SXRDZDdFtxnV4BmIJtkUt1hkGUiY0ksDvN0AMlh3btDhENJ8sv+9TmrILzW9E8mcdpMoEg4FxcieyxIEmQGY8TNMifnu1itWGg+aQ6b7+yi3TTO8hVFWMrcqKEkpoANU8BOfChGTIZPnVtPWtV5sGWYZEalOt9FNK2SUjWGwymq8p3cdN0KHm4Z4aGWYbrHc0F5ZzUV8ssn2jEMeKotxDPt47xzRcUxi4UF4fXguc5x2oajLCjL46/P9fLxiuLpAJLD2p4YZHK1jzOqA0Q7ooRHkyTCGbAqeC+uJTsYQzLJWMrdpMwSvzu7kWqfA+tZFUgSGLqBkdGIbxshkVURMagnXjKj8bunOlleE+Avz/Wy9vRGumIzQ2O6do6xbG0VJFRMfhvJ1nEczQUYuo4Wy0KtlwdDEX7y9oWMRNKcP78IkywxkciwuCKPnz3WPqO/0WgahyV3jaLpBt95oJWzm4Lc9+GTKfLaDt17AzWcRg0lka0KpgIHsvUEXdccdb9MdphwLi9CsihoUykUmwnJLELZTqSDI1E2dIzz/tNqUGSZz9+xl/edVsNvn+4kdSjgoSLgmL7mqrbbSHTHSA3GcJ1cSvSZAdynlqEnVHRNI1vgZOvOEG6PiTyvlUJPbk+lyBJ37hygJt/FtSdVoRvw4N5h3rKk7AXXTZ1KoY6nkK0Kcwtc/OP9q/mfe1qZTGT5+r2tzC/1kO+0Ej90P+tokVSWrPYvAiqOdS/XJCGZZFIdU8gWBVOBHfllBg8KEJ1I0fL0zODHdEJF13TQJQqzEsmoSjadodlkIZPVUWWJwCmlSGY5t5+QJDAMTAE7lkoPkiKRHY7D/IIT9K4E4Y3LMP71IAGCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiC8Fp1vJ/+nWsYRkSSpHcC9wOfIRdG8qYPIdk9EOab97cScFqoK7TRNhzjx48e5IdXLsRqeuEChkxWAyTKfA4SGZW/bc49rP75tQ2c1VTIdTdunTH99x46wBcvnEs8o5J+CaMYHiv0QzZJx3yA3GqaXdAgS8duf/40AF67mTlBF/vTWQK7Rwm8tZ7JO9vRDhWk2ubnI3tFsdxrRTiZ4YePtHFgJApAWtX56J17ePT8ZrizA3UsSXzbCO5TS0kH7dw6MAleMzdvGqDnUFGkJMHFzcXcvmMAkyyhHioe03SDRFpjdW2A0+sLKPfZ+fH6g/xjWz9nNwVpKHLz9hUVuK0m/rald9a67R+KcO68QkYiaRRZIpnRcB+jmCHgsjASTfOJv+/in+9aQVN3Fq9HJrJ3lP6xJHXLC9m1PvebSidUrFtD/O+6Jtq3jzG1eYLhlMKOh3pw+ayUnVlOR8sEFUsKSW8bwb6oACOrE3k0t37leRbMa2vYfGjZ515ei+25QfRwBoAvzPcTconyNuHliyaz7BuKEEllsZpkrCaZukI3N+3s57K18wnf3Yk2kQLA1uBDdpkp6pji3MtqefDv7dgtCnbpyPFhXqmXn1+1hHAqw+7+MD9ef6Q475JFJXzlnhbynVaq8h0cHInx6yc7OKMxyLUnVdE+GsOiyPRMJLjhlBqGwqlZ62uzKMesSRJenkq/A7vFxB3b+3nL0jLaRqL88ol21s4vZkV1gPbRGD6nZTo07DCzLFEZcHDdSVW0DkdZWOZlXokHv9NCdcBF38SRka9rC5w0l3mfv2hBODanQt66OUz98+D0uZ29OR/Zb2Hf7WPMO6V01iySBLIsdhDC65PbaqJNMqi8vI7U432oY7n9p22uH7/VxBmbxkk/OYpZhlNXl7C5bYrkVBolo2Epc6GnNSJP9BJfEMRsVijxWTk4EqNjLEZ1vpNCj43fPNXB+fOLKc6z4bAoNJd6iadVdvZN4bSaOG9eEVu6J0hldfxOC1X5Dj719934HWbeubJS/L6E1529A2GuvXEL31g3n4lEhgKXBbfTPGs6xSJzbkMQly6RONS2f+84+dUuLCYFU9CBhIGWzJLZOkxjV4QMYJQ4sTX4iT7eh+wy41k3h5TX8qq+R2GmdFZj/3AEXTe4fEkpNz/XiyRBhc9B1/OmlRUZTTUw/Daitx3ANjcAioRsNWGu9mKt93HKqIOfrD/I/XuHAVha6eOa1ZX0TiRQJAn1ecXAylG3s3QDqvJdNBZ7UA7tPzODMcb/1II2lbuudywvwnteJYrr1d9uLGUuJJsCOrjPrCDySA9GWoNHe/BeUI1zVRGyRYQ8nAiabjCVyPCFtU1IEnztnn3UBZ1U+B3TASSQC5L0OSxctaAUZ1Jnz54JgnVeUm2TuE4qxVB1JKvCpMPKwb3jTI0maeudYjyTYd2iUtYtKuGJA2NcsaSMv2/rp+PJXCjptWuqqC1wHnPdMv1RQje1oEdzoT6uk0qoO6OcPMeRfevegQgAly0pxaLIZI4KHbn2pKrpAJQXYsq3Y19UQHLn2HRb3iW1TPx1P1o4d13gWFaI97wqFLfY574csiwhmyT0zPOCDSQJQzdIxbJkkirDXWHKm/xYshK+QgeJf3agp1S8F9YQfbwP/VDIk3NVEXpGw7Gs6AS8G0EQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBOG17Hg/nW6WJMkMrAN+bhhGVpIkMdwX0Due4KNnzSH/0EiqiiyzZ2CK8ViGkjz7C843GE4RimUo8lj5/Nom7t09xFQiw+JyH+1j8VnTJzIaVpPELx/v5r0nV3P6i6xTLJIir9CBJ99OJHSk6HjhmeXkl7pmTR9wWjh3biEP7xuZbrt6VSVzXuBBdwBdN7CaZL592XwcFhNDkRR4rDga8omt75suUgVI7Q1hn+vHUvTC/QmvnuFwio2d49jNCgvLvVxclc982YQig+nyOai7Q6hOE+1uhXt6RrlxYy8Bp4X3n17LLx5vx+ew8O7Vlaw/MIbNrPCjK3OjEXeH4mztnuB7Vyzgjh0DfPuB/RR5bFx7UhW3bOnjoZYRQGI4nMRtNbGgzEvbSGzGus0r9fDx23ZRne/krUvLuGVLH26bmRKvjRVleTjNJh7pGOOcuYV8875WFpfmMfDsCMOtk1RcXM2B54apW15I9+4QAIpZZuXF1Yz1R5loC9P1zBDL1lax/aEeABacUcbGOztAgsLr5mHKs2KxK4TvPVIWpU9lMHaOUr0gQDah4eiaQj0UQAJg3jtB3cLgK//FCW9ohmHwVNsof36uj2KvlV9etYRoWmU4kqK+wEli99h0AAlA6sAknrMr0CMZnL0RGlcUkhe0U23MLOa0WxTsFjsbU+Mz2rOazifOqSfPYcYwcoEBm7smMSkyP3usnf8+rYZyv51ibzGLK/x0jMX49VOdpNUjRUvvO7UGy4uEbQn/maI8O7v7p5AwsJlkJEni0+c18qeNPezsC7Oy2se1J1XxtXv2Tc9T7rfjc5jZOxChbTTKB0+vpXUowvrWUU6pK+AjZ8/hua5xXFYTX183n/FYbp+9oT1Ec6kXt312EbAgHGbTJOKbBmec2yX3hLDU+0hGMkTHU+SXuwj1HTmmN59Rhif4wufBgvBaNRXP0DMRx6RIZNsj0wEkAIrHSvSujlxhOIAO2rODzDujgp2tE6xYXEBqxxiy24zjnCqeW99L5fx8vKMGm9tC7B2IsHcgwhP7x7j25Cp+ur6dr10yD00ziKc1Tq0PsqI6QHcoztfuaWFj5wRziz28dVkZ//dEBzecWsOdOwc5eU4+1QWzr+kE4bVsT/8UVy4v595dg5zdFGReiYe+iQwWm0ImpU1P17SqmL23d7DonEryy1xYHSZGeqLEVhWRL0GqdQJzpRvJYULtikzPlx2MYyn3oHgsaJEMiQ2DZII14BHHohPlua4JRiMpDF2ntshDU5GHPLMJt9uCr9jJ5NCR+16Na4oZbJ+iqdlP3pUNpPaNk+mJYF1WiK3Sg6LIbO2dnA4gAdjWM8n8Eg+ra/y8dVnZdLAvQEOhi5PrCtjSPUk4meGdKyo5pb5gOoBEz2pEHu2ZDiABSGwZxj43gL3J/yp8OjOZC50U3LCA7EicyIM9R44zBoTv78JS5cFa4XnV10uAHb0T5DksfP3eVi5dVMIvr1pERs/dV5MkOJx9s7N/il+9bTGW4RR7H+un+aQizG4LpqCd9IFJ5KADtcLNxHCCvn0TnPL2ej67pYNLl5TQNhLlq5fMY/9wlKyqc+68QsaiaUrz7DSXeXFYZ9/i11Mq4fu7pgNIAGLPDmJr9POeNVU8vG8E7VBYsNUkU1vg5M//tZKfrG+jdyLBVSsqWLe49F+GmskWBe95VVhr80gdmMQ+309yz/h0AAlAYusI9nkB7E2B4/CJv3m5AzaWX1jNxn92TLd58u1kUyrufDs2l5kt93cz/5RSQv1R5visqI/1oidUbPMCJLaPTgeQAMQ3DZN3VSNjDoWKE/GGBOEN7lgDAAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAjC68XxDiH5NdAN7AKekiSpEoi86BxvErVBJ4ZhcHA0Sk2+i0Qmy6qaAE6z/KLzmWSJzd3jLK/0YzPLXLKwGLfNjG4YBN1WrCZ5RrF3bYGTdFbn8iWlNJW4X7hjNYtk6MQmU5z2znqGDoYJh5KU1ufh8FpQMyoW+8zRKaeSWa5cXsY5cwvpm0hQXeCkPM+O3/XCI2IOTiVwWhQymsGX/r4LVTf40im1GIqZTH9s1vTqaOIYvQgngtdu5j2rK3FYTZSaTJyyN4wxGCcFIEvYrqznszu6KRjIclZTkM3dkywsz6NnIsF/n1bLnKCT//rTNsyyzGcvaOQXj3ewfzhKocfKly6ayx3bB3j6YC4EpLrASVrVed+pNXSPx7llSx9Xrajgl0908PtrlvFkW4ixaJqFZV7WLS7FapJ5/+m13LNrkFAszafOayDPovC7c+ax64Eesqk0F55Sy/qRGLoBZ1YF6H1wCABdyxVYpOJZHB4L4bEkzaeVsnN9H/4iJ9FQLsDBMHLTunxWJocT6JpB4+oinryrk0xK5cKTi2d9ZmpvlIXr5qCmNfT7Ome9rkylZ7UJr4DEBOgquN54oS+9Ewm6xxNs7Bznr9evYGv3FD96tA3DgOuWlNHUOXsb08IZZIcZfSDGgrfVk4ir4JCO9BeK47aZmBN0UeixYZIl1EOFSM0lHsZiGSZiGfxOM/1TKc5qDNI9nisGvGP7ACuq/FQGHLhsudCg2963igdbRoinVNYuKGZxed6r9vm8mcSSWd61uoqOsSgOq4n5JR7ed/N2klmNVTV+Tqkv4OaN3fz07Yvom0zgsJioyXfycMswK6r91BW6uG1rH1aTwiULS1BkibnFHn519RIUWeK7Dx6gZTB3Chd0W/nfKxZwesMb7zclHD+2jM5U3+xzO208SWmjD03TWX1ZLVMjCcZ6o5Q2+ilv9GF6KSFFqTBkU7n9uijiEV4DtvRM0D4ao9HvQOsMz3hNdpjQI5lZ83gcCjVz8og+0I3JbyM7mCG5O8Tpb2/g1t+2oGZ03n5ZFc/0TJBWdaJpFYnc9n7fniFkCX71VAffWNfMqpoA1QVO3rOminesqGQonCSZ0Sjx2jEMqA446ZlIiBAS4fUjHSMVD2ORdbx2M4oiE0tryFmdnz7bzpcuqqMoYaDGsuTl29m/aYhQX4xlF0gMdYQ5891NTPRFcPVHibdPYS5xog7FkZTZx4zsUAxTgQMtkiHbGyUZzdBKmKZi7wl4429uk/E0RipMmSWDx1/Er5/u4vz5xZTWBEE3qF6QT8PKQqZGEviKneiqzuLlNcT/dgA9nsUUdCC7zChuM4ojF5a3o3dyxjJWVvsp9zu4aWMva+b4+c5bmtncOUFzmZfT6wuoLnDxxQubeLR1hCfaxlANg1gySzStcfX8YrSu2bc01fHkrLZXi6XEBbI0I9zhsGO1Ca+80XCK1qEo8/zwlgYLa+rzSWd1vnLPPt6zppIPnTGHnz3WzlsWlXB1VSGhrRNEwmnKm3zkJ7IkH+xGsiiYS52gaqTJne6e8vZ64tE0/316LXfsGODkunzSqs6qmpce4qEnVdI9s7dhbSrN0iVBbr1hFY/tH8VikjmjIci8Ei+SJPG7dy8npWr4HJZj9HpsJp8N1/IiXMuL0CJpwnfPviemhk7cb+eNQpIk5p5cgrfATs/eEHaPFZfPis1pZmIwzobb21l8TgWtG4eYsySIw6qQGs197uagg1TL+Kw+teE4Dskg7rPjPEaYzXGRjkImDs4gyC/+/0IEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCE14bj+mSpYRg/BX56VFOPJElnHM9lvF5puoHDaqLM5+DBlhE8djOXLnISDo0xsqeHeGSKgvJKCmvmYLYeCfUwKzLLqwI8czCEIkvs7JvEbTPzsbPqSGZUPn1+A398tpv+ySTzSjxctaKCwXCSnz/ewYMtw/z+muWU+x0zV2bsAOz8G9Lcawn1ZdnxcC9VCwPkl7h47u5OZFniis8sw3LUYMDxlEoqq/NQyzD37x0iz2YhnlH50ZULMQxj5qhu4x0w0Qk2LyXxcfzpFNGMj0KPBYfFzAq7lcxgDGulh+Se0IxVMxU6X4mPX/gPFHnt5Dks/OCRNm47Zy7G4JERiNENMnd18Jm313H9nbuJJrNcuKCEmzZ0YzMpfH3dPB5pHcEw4OKFxfx5Uw+dodz8I5E0bSNRHts/CsA5cwuxmWR++EgbAB67iU+f18iegSnmFrvZNxRh3aJSqgIOQrE0X7tnH5AryvjwmXMIjU9RluijKBohqrvwF7np2p0hNZZiXV2ApnwXjQUudhdMMTmUQFYkZFmif/8kJ71lDiNdEUwWhUQ4g9mqUFjtZbgzgprRsDnNSLI0HVzizLMxNTIMEqStJp5f0mat9WB1j7BNtVFX5YF9EzNeNxU877coHD+JSRjZC1M9YLJB32bw10DzW8BZcGQ6NQNDu0BLgwHkVYDv9THe68RgP5MHDrAgkeB7pwcwSxI/XX8QiyJTGXBgtZmwVFlJ7hybMZ/itaAnspibAjx5ewfzTitlTIOdfZO8549bmErkRsF9x4pyLl1QwqfPb+T3z3QyEkljM8sUuK3s6Q/zdHuINbUB7BaZYm/uOGU1y3hsJiyHArUkSWJhuY+F5b5X98N5ExqJphiPpZmMZ3l43whVAQfJrMYZDUHyHGb+3y07+Z9L5vFUW4hd/VO8pd5OIrSfS2wqJXIKP4Oc0aizMV7CDTdv4w/vWYZZkfnk33fzX6dUTweQAIxG0/xz+wCLy/PwPr8QLR2FkX0QG879ngqawPzC4WTCG1fGJmOtdJPcO7OwzeS3M2+ZlR1PDXDPT3dx4YeaGTgwhdtvxem1Tk+XTacY6WxnrK8HpyePojn1eHx50LsJOp+EqW4I1MHiq8Fb+uq+OUE4yuBUgs6xOH/Z1MvXL5mLPCcPxo4U1eqxLIrXOrMYXIKUJOFKZEjHsmSOGoE+tXmYgnI3Qx1hwrsmuOHkan72RG50+8OXWMVeG3sHwnSMxXnPHzdz74dPQZIMvn5fK/2TR5Z9w6k16IbBooo85FcisCcdg7H9EB+DvMrcb3KyC6b6wGSFQC14Zgf1CW8i4YHc/QAkcBVAZAAkGQoaX3jb6NsM67+GLZvmkgVXEapdya8Tdr71wH4+eW4DH1jqxRFtxWwxyCspZscj40THc78vk0Vh+0O96LrBpVc3oD47CECmK4JkVXCtKibFzGsyc6mL5N5D9yEq3Pz42S5OnVcoQkhebbqGM7SL5U4YGZ5ismcvH64pZntogmTCgeKxsP2hHsw2BX+JE5NFITqRpNamoMdz+1B1NAGjYJgkHJVeJJPMwrI8/rFtAICVVX4uWVjCF+7cC8DGznFsJpl/vH8N80tz3/fAZJLr/7SN3olcIO4De4d5+/JydvRO0dof5ju1eaT3Pu++VYGdE0lxmlH8NrSJ1Mz2POsLzCG8UgzDYE9/iHX+Plw2M9XNLka6NpJOZ/nlWdUMJoY42dbLu9/hY0wvpH/7JAUVLuIuM6XFTtJ35473RkYj0xWBrgj47bRuGKZueSEmp8Ifnu2mOM/Gl+9qoWc8zmfPb8JsemkhDrLTjLXGS/rg1Ix2xWdlNJrmmfYQT7aNUeC2srzKj26AIoHdomC3vISgwBdarsOMtd43696EuCd2fNicZmqXBMkrsrP9oV7CYwl6906QTWtALlTZ0A0CpU5iZhlrkQNtOEF2KI6lwk2mNzqjP8kkoz7Wh2HthYIC8FUev5U1DOjfCvvvhT23wby3wIr/Al/V8VuGIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiC8Io4riEkkiQVAt8CSgzDuECSpLnAauD3x3M5r0eqpjOVyBBNqSyt9OUKyuJhnv7bbxk+uH96uvM/+DHmnXrW9N8FbitPHcw9tH3zpp7p9jMbCzApMoZhcP3J1ai6gc2icHAkOj0qZttIjNahyMwQkuRU7uHfipWksgreYG4T6N41TveuXNFoxVw/Nqd5xvons1nSqsa9u4cAmEjkRvf+zgMHWFkdIOA6VOww0grhftBVePqHyG0P4ADOMVlZuu6vbDfqKI9B7IkuAlc3kR1J5ApHAMfSINYqDwCGqpLp70dPpbGUlqC43S/j0xf+E0PhJA+1DPGztzZSp5mIP+91PalSYMpy85U1qIkp7jo4yadXWplf5mP/+AjrypNUOUu5sDqfcKOHDaMRvMYUwxk7SwIp6s7z0hq2cPEchad6U6w+J0jQKbNjVKdYHeSclUGiS9zI6QiGM4g1O8kTU5N8/CQ/pbY0PUk7cx0R7GP72fqnv9N+aL1ql53Cme96B5vvGSA6kaKw2MmGO/ax8pIaNtzRzr5nBln9llpanhpk1/peTr9qDool9zsIjyaZe1IJNpeZ1g1DrLioml2P9REocyFtBi2rYbGbyCRVDvZEmLuwAHX3GBhgKrBiazah7nuIk7OdTK76FNnRJFooCRI4VpdgKRfb8SsiMQmPfhm2/yn3t80Lb70RZBt0b4B5lx6ZdnBHbh8VHQaz/VAYiQ7+qhOx5i9qfChGfDKN3W1Bkib5+/98nkR4CgBZMTH3k19h3eIS5hd7UQ2dpjwnlrBGdiiOOpLbr9qb88mOJZH9diJFTkaeHWa5x4qqpfn6va3TASQAf9vcx4oqPyZZ4kNn1lKb7wIknmobojjPTsBtxaTItAyGObOhkI+dU4/LqjCZyBJ0i4K3V1w8BMkwZOOw7y6qB7cTaHon+vwGtGwWQ8pwRl0eH1/jxZMd5+NLyykxD/E2czvheRXc8dvfsW8oV5iLJHHZB95HzcaPcHZ+Pbuv/x+eGJ9EMzn46fl51HknKb2wkKG4mzxdRrYqdKVTZFV9xirpsTBq6zPIvY9hWPOIB31kJ3txlVdi97z0bULXdGRFjIb8erdvKsGSVcW5c7tDgQyOpYWooQTRjMFoTxSzTcFkVuhpGadxddGM+due28CDv/jh9N/FdY1cfP11uJGgoB6c+aBYoOWfsPqDR9IZjqJrOuMDcVLxDC6fFV+R60XXWdN0FLHtCf+mZFZDkaEy30EokSXakIetJzId2JfNZDGfHcB4YBQ9oYIi4V5bRctggjlOBfdpZRiaTmJ3CD2SQYpncRy69rI4zTSXeXngw2uoloYxy1k+XB8kYpiIGOW4FBVJz5JKDGHIJj62RGbDaB5lTp0Kp0ZXMsG7FrnJJsYJelMwNpE7L4qP58553MVHgiAmuiGbyBWaWl5CEGQ6Cs/8GJ7+fi7wbfUHoXQp7L0dNDV3vpUIgb8aLG5QzJBXDpkkRAdzv19dzbV7y16ZL0d49STDkAqjaway2ZTbJiKDMNWLYRhMUk08YsfmaiSw7cvIoX1w5c0QmHOkj1QUxttz2+IZXwZ0zGY7RWT58lL47NIqJhNp7vnxr9kfyt0TkWSZ06/5NM/dLaHrBuFQEl+Rg/GBOOlIBisgmWXszfkoeVZkmwlro4/0/kkAzJVuZIuCHs2i5Flpb/bx4H172T8e54L5RbPD1oRXzmgrUjrO5ie2s23949PNC86+AP/iSzh8uzKb0hjpjDDSGaGg3IURz87qyghn0FUNxSRzXkOQkkskIrpBSNe4bWvf9HTNpV5OayigKxSnociNWZHZPxyZDiA57I7tA/zXKdX88okO0tfVYhqOoYZSuWydk0qwlJ3Y63rFbcH/tnrGb27NBbIoEt6LajAXiVDfV1s2GuKMgjhS2slE0sY/f/YDwqMjAEiSzGUfvAFPXh1pw4sVhcmhOAe3jLDq0hqy8SyWQ4G3RzMyGpFQEpvThBKwsmDSycWNQVKJLDdt7OGqFZXUBl/8HPcw2aLgvaCa8dA+tMk0SOA+vQxTsYs/PdXOr57snJ72mYMh7vjAGhaU5b3sz0UyybhPLyc7EMtdF0jgWlOCpfylrbfw0igmha6doenwkSPtEnNPLsEbdJDJatgvqCbxjzZSBybwXlSDFs7kwvIkcK4qJt0ZRo+p2DqfItOmQcOFyIEC9MlJFKcdxULu2kvXQZZz53OGkWtT00de4/D2LOX+U89CNgl6BgrnQfE3YMNPc+eUa78PJjOC8KYxe3cvCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCK95xzWEBLgR+CPwhUN/twG38iYPIRkYj2FWZMyKzK+f6iSRyT0c/PNTbNMBJLJiYt5pZxEZG2Xw4H6CVTWYzBZ0IOi28YNH2mb0mVF1Gos8fOzWXYzFjoyyvW5RCap2pEhYNwzCowlC/TGSsQxOr5l87wrcajeg4PZZCFa5Ge3OjYJotiksOqcc0/NGvNR1g3hanfXeeicSTCWyuRCSycHcw8XxUcCAtgeOTKimMUUiFOsGU6pO4LwqYk/14zm/ElQDyWbCXObC5DCTGRoi1dLC2A9+SKarC9uCBRR+4Qs4Fi74z78E4d+mSPCTcz2Ub/sSUtU7iJt8oB55YtYxz4Uv04L/oU8iVa7m44oVacf94PkUDS23oje/lwZpEZGbe3FpBpcvd+FyH0BxTCA9/Cs46aNcFNuAdCCPgsaP074nwcQBmbevysOSHcU6FaZgy1eQC+ZgqGmktoe4+rRPY2y7EanqNAyzxHiinD/d8yB2j5f88kqmhgfp2Po05fNPx2JTWHpqKS2bhtGyOjse6WXZ2ipMZgmvF1avKyc6oWFSJygoMFFWa6O/I8WWe7tYcGY5Lq8Jly3G2neVo8USXHhDPR17wiy/sIpnb2+nddsYkVovKy6rw+kw0dkd5p+/7CW/cgVLT1+DOdyKf62XULKCPZMqT4xF+Eg6S4VTPGR/3A3vng4giS78OKP5l5Ea9GN3mvD5K/FlE2B2wGQfhPty+6a2hyGvApZcA1bPvwwhGU+Ok9WzBB1BZOmVL1jv2Rviyb+1ISsSBRUu8ot7pgNIAHRNpfWBO7jsLR/glu1DOGWVaikM7lJcJ5UAIFtNSFaZbFqjVTPY889Oms8oxWyXiYxl2d0/NWu50ZTKL59o52Pn1PODh9u47uRq2sfi3Lypd3qaj51dBxj86NBxSZLgl1ctpiJfFBS9Yrqegrs/AnMvgV23QGwEeem15A09A+s/CQ4/LHsvfzi9CWnn92DfnfCOW9F33smUbQGDSpjJwwEkAIbBk3fcTfENv8F+13WYw/0sUiWGR8BBFmuyn2X5S3noniFKlxcipzRsA1kGbWOY5rlIRscwkkmk5zbDWAj7sgsI28vY8kSMkc5+8grHOfvaeRQeChZ7IdHxJJ27QhzcMkJJXR6Nq4rwl4jt6PVqqXMMKWLBu64WPaYiKRJaOIWOxIH1/SDBmstreeqvB1h5cRUOr5lUKksmOsXk8ADRsVH8peVMDOSKhYcO7mespwM3bbnwAm8ZxMfA0HJBUonx3D9vGbiKyKrQsTvCs/9oJxXPEqxys/qyWorn5M0KGpkaTXBw6wg9e8apXVpAWaOfVDRDeCzJWH+M+mWFFNV4UV7iKPPCm4tPl5kfkyhM25hntmJGxjijAjmawelT2P7sndgP5lF6bhN6PEMsNk7GNkXdfA+mA2miW0aQLAqulUVk+mNkS1z0PdyLJEs4m32Umaeon2hFsnmgYz0cfATfsvfgi45AbBj6tsKK98LGX/AWxcplqz+I/MwPYc7ZGN5KpM4sOALw2B/hlE/mCj933Qqdj4MrCBf9BLQMPPGt3G9p3uWw7Doo/hfXWqOtuQASxQxnfwUkBW59Z64IFaD1LrjwBxAbgeE90Ho3nPHFXDDFhh+DIx9WvT93Drb46txyraJg/vUkEU0z1hMlOpHCalOw2K1se7ifOfMdVCv34S4tQdUVBvRlPHVrO5HQCDaXmZOv+Dq1fAlT24Ow+D2gZSHSBxt/DgcfBl8NLL6abP8u5MImFH8FUu9GbIXzGR+xER0P0XjSaeQVFSNJMqHenZTPXUXP3gjP/r2dC97fzJZ7u5iKq5QUO3AtLSK2cRBtewpLlQfvWV7a57pJqTK63YSWVgleWcc9PeMc7B/gv8/yMzgho4vi0FeXmmYsnYeruIZT31lFMhal5YlH2f3oA+RXrmRi2EnD6iIObBwGQJIlFp1TgWyV4dmZXYWbfExOJJhjMiHd10XDgUlkn5XE6aVs89jYRZi3LSsnrWr83xMdSMCuviquP7Vmehd2NM0wkA6FnW2JJ7n4fQvRJpJIZgVTgR3Z/Lz7ZRkNPakiO0yzXnulWKu8BD+0CG0qjewwYcp3ICmzA9qEV9DQHsy6SjTlJ6q6SUb6WHD2BfTu2UnPnp0Yhs7T9zxM4ynvJx7Oko6PEuqPAbDlvm7OfEcdznI32b7odJeS3UQ4o7PgzDIsDgW3bnBdVwp920G+WO3hinPnov+bleyWEhfB9y9CnUgiWRXMBXaGYxlu3tgzYzpVN+gZjzPP6wBJQnG9vHtXliInBTcsyC33BX47wsvjLbBz6tvrWf+n1umAg/mnlZJXZMdsNTHUOYXFahAlTMHlJVhUmXBkjLyLq9CGkygeC7LNBCYZa5FOLFxAzD2H+KgXOWzgdgfQbv0H/kWl2LM7ITEBziBTzjySgTkUDOzAZPfBVE8ugM5ZkAv7lRWQTLmbRdkUWByQCudC0U/9NNx+PZzy8VwQHuTOJbUMcPQ+TMr9k/RDgSdHXZfN2HFLIBlHAh5MIkxMEARBEARBEARBEARBEARBEARBEARBEARBEARBEATheJGMYz1x/592JklbDMNYLknSDsMwFh9q22kYxqLj0Pf5wE8ABfidYRjfebHply1bZmzduvXlLva4GJiMk87q3Lihh9u29bL1Iwsxme107tnDAz//HgCXfOILPPWXPzI1PIgkySy7aB2LzrsIm9NJJptFlhU6wipb+7upzJepKwjQHj7IRDLG2LiPm55MMhTO8NVza3h7gwcjmUKyWZmy+IgNxNAyBpMjCVx5Vjz5NnyOXqJGBY/+7gCrL6tFzWjomoHNZSaTUgkusAJgiqtEx0OYTSa8Hh/pRJLuSIa5VT5AIxJNo1isILlyzxjLCtlIFP/QnfDEd0lf/xyGYsIYHmZMzeeen+/i1IuqKRyMYK/3E988jJ7SsC/Mx7wwj5QUxTowwuCHPoRks2FvbiY7OICeSFJ5y98w5+efuC/y2I57lcdrZdsNR2M4H/p/JM/6Pul4FHvCTmx9P9pYEtvCfCyLfWQtKpquo2cyyCYbqZiKbFIwdBPumEbktoPT/UkWGc9ltSTyzJjNMjoS4eEUik1m2wN9WB0KC84s5cCmYQrKXGQyGiV1NswmFavThWKyYxgQnUghyTKGpmFkh2jf/hyGoTPccZD88kqceT6qa9cg7UuiHZxCzrehLQzy7JODVMyXsdpTeAvcKJYA4VGd/AoTsYkRrHYbqupmuDNBsNyN2a6QTWTxe9IYhorV0Mg67ExOaaRiVmRFJpvKvf/4ZJq2zaM0rPSTTWxnx4N/x9B13IECLv7QxwnLHt76t/188/JmLllYcgK/1RlekQqlE7H9psY6yNqLQJUY2B/GbFWYGk4gmyS8BQ7y/ApWUwKrImO0PoCp9SakbBSj/nyyNZdhuEuYcOWKYWRZpsBRgCzJTMSG6IkN0BXp5Ze7fkkkE+FrK77KGUojqtOPKpmx2Ew4tCFIToGnJFfcCyRjuUIiRdXQZRlDkohPhLAZEkomg5yXx1TCit1tRtPjYOjoWYORrgy6LpOIZHB4oujqCIrZjCwr9LfuYfcjuXAnT0GQxedfjNUTIG1z0fHIXYQHernymv9BH8mQ3DWGZFNwLi9CKnTQPZLE6jARD6cxO8yM+hX6eoYYGI9ze2eC8XgGgN+/ZykLrFlsZoXftkSpLXTx0Vt2AuC0KGQ1A5tF5sb3LOeTf99NZygOQJnPzl0fPCkXSPXqeMPsew3DQAuHke12ZGvu80smE0jjE7ljqpYm6TLjkExMZGLYI0kkqw2bTcE53gqyCSa6oeYM0FJgcoKaJKzmc2BnjO0P9zL/lDDP3f67Gcu1Opy845s/QjYraBkbqbiGN2hBliQmh1M48wyQNDBk0vEsalpHNzL07H6WnQ/eTSaZpHbJClaseyvJeIxkLIrTn4/HH6Rvf5z9z4ZY+65yzFIWzewASScVN8joCnaPhmIyMdSRYv2NbdM1RO6AjUuuLcfpBMw2JD2BosXRJDcpnw8dHUcqN7HJ6/3PPnBdg6neXOGSpzhXJPXqesNsu8+ntj9K2lhEctMo6fYpzEEH9kUF6KrOkFXB6rZgNUcwJJXw0ADJWARfUQkP/+ZnJMJT1CxdwdK16zDZrNjdue/XMHQwm0il42RsOg7FjlWxoWkaumyQ1VRUQ0NDxTqZR3wijWEYuPw6hq6j63asTjNWm0I2FUHLpjCbrWy4e4xUNEvTyUXYHBbGeqM48ywEyt0oskTfgSmKa9x48kCJRZEddgwkjPAkqstPMq1gNetYLJCOZ7FYJNSMioYFqxUMs5m0qiADatZANkno6XRu5HBJBknC6bNjcxy7uNPQddSpKfRYDMlkxlxcNF0MfYK9LrZfLZFAHR0DqwVzQQGy6ajczbG23IjtZjuazUdiSiMj2XOfr6SjqyBJBiaThIGEbKjIVjPpTBxJV5EUM4mIBTWjkYxkSKcyFJSZMdtMOLwOpLhKascYqa0hZKcZ17nlGCVmEtEpzG06yWdHMeXb0SZT6AkV31vriLnNDHZFCBab8ejjaE4LIZ9E/qTG5EAUzZKP35fFr/TlQqmqT80VcYb2w8J3wGRPrsjT7s+NTD+0A/bdBWUroHINdDyWC3o47MwvwePfBONIcCWL3w1rvwdm2wt/sC13wd/fDc1XQCYOJhu0/HPmNHPOhqJF4CmE+z+VazvrK7D+a0emOevLsP5/4Jp7ofqU//h7/je9Lrbd40FNJImNxZDMZjBbUEwymm7krp00A4tVIptIohpWZLMMuoEiqciGjprVsdkMkmkTEgY2txltcgrFaUHTFcZGIZ1SsdpNjA/EMXSDsrl+YqEkmZRKQamddAqe/Fsb6aSKYRjMPyVIVbMVk8WCbPGQjGQxW0xAGsPIkk3nrh1NNg2TKY2EjsnmIZNMY7WAhg4GDOxvYaTzIBaHE4fHS35FMxNDCi7vFAc2PUJRTQ2e/ELK8huZvGk/kiRhCjrQwmlM+Va855YxtX4QtT+Oqd6DfLKfNlMXv9z5S/aN72N18Ur+X/P7qMcCiglcxWB/8TC1V9EbcvuNDvQzFY2QX1YBwNb7/4nLk8dz/7yNheddz/aHVSrnB6hsDiDLElaHich4isIaD8mhKM7nRjHSGtLKQu7JpmiqyKNxwyjpjjAASp4V2WkmckYpl/99B1evquT/nuyYsQ4/eNtCllf5ueJXGxiNHB3uW0r7WJTOsTj//MAaGopeeFvIDMQIP9RFpieKtc6H5+wKLEUiYOmQN+S2C0AiDOMHiOc1IUsAEjd96kPEJ0LUrz6FkvomnrjpNzi8eSy75HN48v2M9Q2jKDEkyUbn7iyTwyne8r75sHsMrSuCucSJ+aQSRqJZMoZBwGvB+PN+0A5dLMlgWVCA56JqbC/zunsiluaSXzzLaCSN1SQTTau8o7mYj5cEyD47hGSW8ZxTiX1eANl6vPPLXxdeN9vu2FiC/l0hZEnC4bWgZlTi4SyyIhEohr1P/IP9zzwOQHF9E+ed+z7ST43jObuC+HNDqBMp7PPysa8sIGlKoJgtZNJJTBYbsYkx1FQKdyAfxWLD0IzcOaehYRgGajaLoetYHHloWhKTyQSKnVRkEpMiISlKLhtEB5PZhWSWMdIqJpOEpmlYLDKZuAqaik1Og57rV5IVkKTcteChoHVJljGQIJvNfTuKAmouLF0ymXLzYeTuLWgaOJ2gakgWM0Y6jWw2IZnMSBYZc6AQYqO582Z3MaRjkA6DPQAWey6kD8BdBGoW4iNgcYE9L3fvLxMFZ2Hus4iNgNkJVncuLNDsACTIxsFkBzUJrqLcucWr55W4cBRRbf+h8d/9jtHv/wD/e6+j8FOfOtGr81r3mrjpIQiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIBxxvJ+AjEuSFODQg4mSJK0Cwi+3U0mSFOAXwDn/n737DJPjqvO+/63UOU335JxnNMrRilaW5WyDbbANmAxL3iUuN+zusxE23OzeS1iyAdtg45yTZFmycs7SSJNz6p7OodLzomXJ4zFhwRgb6nNdujRTXXWquup0dXVN/38H6Af2C4LwqGmap37ftv/QDENHnOhHSmd4X53G++vKSKZ1RFXGGwpS2TqTNe/9CLt/eTeK3cHm932GdCLGvqfvZ8bl68mkU0iyQioSRhwfZU2gAMnr5esHvkaxq5iQM8TRxGP81XXvYJN7Dkoiij4xgR4Oo4+MYC7aSHQkjctno6q1AAQYOBvBPqseSQTZJvLsD07icCsYhkkurbHp/a0Ijz1PbEYjT/73f5COxxAlmTW33UFDeQ2NOZXIqIkhS2i5LOMnjzPceZ7ShiZUtRLFEUBtvAXvOzbC0DDpAwfJGQKxhnXYnTJFQTvukhIi918KqEjuGsLjkplw9+MrLqH4pz9De/EF4s89j2PmLFxLLyPb2fVmDCH5kyWlRhmd8zkGXtyKS/HiryxnsKSDksX1DI+e5vB/PM6ia9/GuX27sDtdlDY0cWLb88zbdDWJyATN+vyLbbkWlSB5bWSPjGOrcjFpm2Bo5Bx2l5vuIwdpW72O3hNH6TrgJVRWxJmdO5m9dgNnXzqPlstS0TKT0zu30bBoKaahM97bTePiZaRiUYKV1djsdmxOFwXlFThsbqSjCXJnJwHQB5IIoz2su6WMhB4hPBhGzerYnBJOTxT0AnKpMUzThSdgUt6QxuY0ScUmcfo9DAz0IoginoIQ48dPYnO5KKlrIJfJYi/3kAxP4CswqZ9fhJbNMtrpZvnNt6OrKqIksufxh6mefQtP3TyPYVn/Ix3NP10j8RFsQjGJ/iyGZuBwK0THk5Q2+sGEvrMRFLsfeeAsPf/1X9gaG3Av+ghqXx+u0AaErJNoRiByJofDKeIJ2Tke7SDgdnIgs4sCp4eZ9ho+u/CzXEYjjniKWCiIkUwRGeoiNjZMQXklAXcCV/cxoqHFiKJIPDKBnstRUFZBdHyMVGSC4op6HMFSkkYOPW1D18JEh+OkE3HO79/DwJlT1MyeR8uKjXiDEpKsMNGnE50YJ1RVzex1VzDScY7o6AjzNl3Njnt+gs3pZOFV19N/7CCLN78dJjWSuwbzOyepEnu6G//NzZzYPsDkaApDM7nmE3MoPbWD6u/+D2Yux22338G3xXo+v7oKr5LFiEQAgY+1BkjqKg1BB19pEijqbUd3uDhbVIdmmKxsKqSh2MNzp0YYjmZIq1b//t9ID/aTPXceYnGSO3fiXrkCpboaQbGjT4wj2O3kzp1HD4eRi4vRmhux79hJ8vBhzFwO+QPvZ7hyLrnhIRxFrRSMn0EZP45WOJPO+BxSsQz7n8iPbu30lBIsr6Jl+UokxUb9rEXYbS46jxxgpK+T+nkLyWXSZGMeZKcTURBIRxVyuQyTg4O4AwGS0UmOPvskhq6z6JobGThzio5D+6ieO59QZQ2SnEFNJtj5zOOkopMsvv4m+vvaMQwdp9uLKIuIUn405PCZMewuN4JgcvXHizANEbvbhmkYjE8OEM+50VUVRdVRDAOj4yjZ7vP4Vqyi/7vfR1h9OdLsNvxNLXgLi3/7nT7ZC3u+DYd+lg8MWvYJqF0BRa1/oKP85yXiasB8bJBcV/7SP9cXRx1J4buqFoeiI4nDhAeGsPv8hCqryCSTuAsKuP5zXwFMMokEkyNDeAqC5BIJJJsCooiAgCTJZHvG6Os6T1lTK66CIFo2gyB4ScVtCIJO1pyk73SU6pYUJ7YcxATq5y9Gy9pJCQLpWAxREsmm0sxdW0EqXoyv0ImUM3DWeknpJpmESiKSIVDkZLQvgaF7MA0PpE3SSY1cyoW/WAB0RvsyONwKikNCSIvYXQ60nM7oWA6nW0TNZDGBTFLF6bFhc0qYps5wxyQunw1JSeLzSxQGdGxlZRi5HOmjR0kdOIipqbgWLgRJwkinyZxrRy4szO8PSQLTRPT7sZeW/hGP+JuLmk6jjY1hhCMY0UlMQHQ4SO3di9rfj2vePJylElLfNjIlV2LYIIqApkqkEykw82GMpmmSTqgotgi5xBieUAgTk0wiTu+xI5Q2t+ILFWKzu3CUiZimiaGm0VWJ+FgU+3mR1M4RlGIXeiLH5D3t+N7diGADyZDwrqlEUCREt4wezZLqCaPPEoFOBPsM9KwdORzFLVbw1M/7CQ+mgF4Uu8T176+ipGYFjJ3Jl2eVzoHoAGRj+SLJzCScfx5m3wJ7vwvNmyE5lp/2SlpmagAJwIlfwrKPQ/GvOR96ivMhOoFaOP0olM6ePo8g5ENQyt97aVq0H1xBSIVfPlj5/4eOvpEhJH+yMl1dmJqGqWlkRDenjiTxhNz0nuqncWEJ0dEk9fOL0XIGmqojiiKZlIHLbzDeHmWsL07DgiIUh4SWFRgZVPEEZRRJRB/RSKtOJFPh2NY+Og6O0baynNHuGA0Liiip9TE5lMTptWGYMNiZpKDUxaKraxEEAV8oxeRIL2d3dVC3YDGyEkNXNYwcjPZ0kopGqZjRhs3pIjkR58izT9Cy7HI8wSCmYZISRTKJGJLdgcsfoGXpSuLhCQBkJUtRlYEku1h45TWIooyJiZHI4ZpdhFRgR+1PYKv2Yqv1M3HXeYxUvlhZPR5BnsiR3pDgPTPfw1hqDJfi4nC0nSIpSEEqAo7ufLBP1eI/4tH906bJEg6vh9T4JKIBTfOWovjcaKoKYhAYpefEBD0nJhAEWHFTE4ef6eGyD7fR45M5WGPDLgo8sPc862cUc4WikO2IItglfGurUCfSGAmVwpTOD94xj/+7o3PaNjxxbJBVjYX89H1LeOBQP4d6J9k0s4ScquNQRL7+tjm/NoBEi2QY//EJjIQKQObEONpokqIPz0Hy2P5Qu87yZmCqjAtlEIsQHxvD4wxy+1f+nUQuxj1f+hSVrW3M33wNNqeXoroSUuFeTjz/DZKTEURJYv5V78TmrKVvIEnPQIrlNzUR9kp87bl2Tg3HiaZVfrG2laILAST2Bj/2xgC53jjpbQOI84qwVXp/580Peuz8y9tms7crTDKrURlwsl6TyD7Te3GeyH3tSO+fiaM5+HvvLssfTkbVGeuOce7AKJIsYnfLpGM5Fl1VS2y8h/bdO1h+4+20LllFTkvDSYPA9Q2Ef34p4CZ1cAQ9p5Fqy5KIRdA0FUNTES987hjr7kKUJGS7DQQBXdUwDR1RkpAUBdlmx1dYxLl9uwiUlCFKEr0njhKsqCIZiVBUU0c2laJ61kqyGYV0QsVml8E0ke0S23/RyfzLi6nyRRHDw5j6hXtLJqDn37sRpfwEQURPJDBSyXyQmteD6HYjBgL5YFdZQY/HER12sNsxc7n8NksyRjwGuk7O14sztx9xx9fhso8AAuz8BtSvg0Xvg8c+lQ8oWfnZ/P/b/hkKGmD9V2Dv96F3J7TdAI3r4bFPQ6AaVnwKXvqv/DYu+TDs/yGUzoJADWTjsOxjUFD7hvcPi8VisVgsFovFYrFYLBaLxWKxWCwWi8VisVgsFovFYrFYLJbfx+sdQvJZ4FGgQRCEnUARcNPr0O4S4Lxpmp0AgiD8ArgeeNOHkAx3nM8X1fZ1c+jJR0hNTtK6cg2z1m1CVVVWv/cj6GqOppnLqC5qI9c+iWCvouWTyzFsErqZo//0CZ773jfJD+cHC66+gY+u/xDjRgTDNFhcshjRFLHlDEy3m9jddzN5190gSfgf2URpg5/Ow2O8eE87kiKy8MoaXAU2wv1Jllxbz9afnCaTzBdNtFxWgsslkG1r5rnvfYt0PAaAoWts/dkPCbzjvRg/vQv/t76Jlk7SdfgAis2O0+MhHY8hCKcZ7a4nFc0x//ICsoePMPov/0Lxtr24hjMs3FyDYZNQh5LT9lX64Ch93mNk1RSXvf3dJA4eInv6NNnTp0ls20bFd77zxh04C9gLUcfOURmtQz+fxL/Rj9s5D/VUiqLCSlbccDtb7/4uuUyay264he13/5hZazdy5JnHScdjtN20DJUotiovZs4g/kJfvt2zEewhG0WLa3j0O//Csptu5fkffBvZZmPm6vW88OPvsujat7Hj5z8lk4iz7KbbeOZ//pOW5Zdzduc2BtvPsOrWO3j629/A7nbTuvxy9j50H/M3X8ORp59g9sL12M56pjwVUzUQ4yaPfOsfCFZU0bRkGc9975ssu+k2tvzwO0iyzIIrr+OFH/0P5U2thKqqObtrBwuvuZEjzz5BeVMrqdgknYf2s+rWO9hxz50IgkhpYzOHnnqURdfcyER/H5Isk0nE6T56CABJlll1+/sIlWfpbe+nprXqjT6Kf/Jk1UF0KMvT3z3B9X81D0M1iY/n2P3gUSRJYMHmGgRJILrleQRA9vkZ+Yd/pPond5I5dozx8sU8/+hZRElg+dsa2XbfGbIXChRnLWlkVn2GyX/7W+qvvBz7mmoyhdXoqSwHnrib9t0vXtyOt335/2NCl8j1dnF8y7OMdJ0HwO5yc8MX/4YSVzX05sgcGEKp8eGodRDJJYlHwux98F7CA/nXx/GtzzBw9hSbP/YZnv/B9xi90A7A6ne9n+s+/1WOPv0YBx5/CEPXKKypo+/kMQA8gRCpY+PT9pE+liKb1DA0kwVXVCNIkByJIXj9qCePo/3H1/nCP/4jzlSA2LZtRH76MwAK3vMe3GtW8x9NGsHH78M5bx5kJvE/fieOps/x0909/NXGZraeGeXG+RUUe3+/0Zj/nESOH2b8k3+FNjyMrbmZ4r/8DOPf/CaZk6dwzJtH0Sc/QfrgIca/9S0AXO/7GEbJbIRZa1HsPpwehfFcgOf/+xxazgBiLF7jY15qJ+PyCvq7orh9l46H01fKrHWb2P/o/dzxhf9EPZcgNzxGXeVM6lbP55Ef/TOty9eQnJwkUFLCmV07mBweZPb6zXhDRaiZFFt+8O2L7e365T2seMe76T99gujICOGBfqraZvHUf/87mpqjaclyuo4cwO0vwOn1cWTXdsa6O7nshlvY+pPvXbyembvxSpouW0kmESE1qbHlR/9DLp1Ccbq49jNfYt/Wp+g6dIBgeQUrVm8k9eIOYjfdyPbHfkl2y2OUN89g04c/Saiq+jfvdMOAfd+HPReuJcIJePKzsPnr4C7OF8hbfmfpeAxzVLsYQPIyM6cjSCKugiwHnnyGuvmLUBSFY1uepmX55Rx99klioyPYXG7O7NwGgCCIbPzwxxFlBaffjyRIiC6B8b4eJMXGaHcnQTWHt7AYQ0sQG+0kOtKP7HDSMG8ug+2dOL1+jAuhZYGyCo48+zgVzTNIhCeonbeQkc52QmXzUfo0Mtv6MbIarjlFpKu8SKLIrgfOM29TNX2nJ9A1E1ESOfR0PtRHVkSW3tjArvvPY5omS2+op67Gi3liHHM0havCy4l9w5TPCDLQHkGxy4z1xqlsLaC6LURRlYfB85M4vQpqVkZOJHAPDyO4XGR7ejB0HWdTExP/8z9oY2P4r78eW2MjamcXUlEhRiKB4HQhRCLoY2O4Zr9GEMSfmXRHB2YqBbqONjiI6HSh9fcRufc+RLebgne+Az0eJ1tYj+ndSGrvEYSVm8mkdGITaURRwFPgYHIkxamXBmlZAvu33sPcTVdxZtd2KppbiYcnqJ49l30P/ZJgZRWFldWEqmvpPXaY41ufRVIUNr//0yjRAnxrq8gNJLBV+5B8NvTRDFQKIIkYaY3ki/2gm7gWl+JoCbLnyTtZcOX19J7cT1nTKjw2CPeqFwJI8tSszoHtca5YPIjsr4SDd4LdAxWL8qEk556BOe8AdxH07YFgI8SHQJTzhZGR7ks7THyNWwCeErC5f/2Olu2w7iv5kePVFDRuhJMPTQ00qVsNHdvy877MFcyPMP8y6UJhvq/stz3EltegJZJkz53D1FTMbBY5GOT8qTSZtMnQwRFWb6zB6JykWDWwRbIMJlXi0Rz7H+9i9W0tHN/Wz9C5KIuvqSOXVomOptn1YAcrbmoglNXRdw2S6Y4hFzuxr6um/0wEBCit81HW6Mfls3Hw6R6al5Qw0hXjwJPdtCwtZeBshKIaLyW1XuLjQwy1t9O0dDmSJBMPj5NNJomOjiDbbATLK5AkhUw8zuP/+TUWXn0DNoeDZCRMLp3v/2o6Q7CyEk1Vef773yRxIYTEV1TMpo98muNbH8NXWMyx555CzWW5/bP/jihAfGvfxX0leW0XA0gu7r/BJK2RUraEd/PvHfnrrXlF82ib/REKul4CXzmko/n+G2p4g47qnw/TNDF0HVfKidadRO1P4ChzIdZA7dyFxMdVZJt44ToXmpaU0ntqgsve1sDjHaM8enSIwWjmYns/39fHR+ZXIboVPMvKiG3rw8xcKGI/HSa0oZI5lX72doWnbEdFwMVfP3iMf3nbHP7P1W3ohokkCuiGyZnhGC+cGeWBQwNsaCtmQXUBDkWasrx2IehkyrTRNFo4Y4WQ/IlLGA40dZLu/QeZ2Xw52skE2fgInpYCNt7+MYa6z9G0dCU9x020TIp9D/+Q5GQEAEPXOfjY3Wz8yN8y0p3CtItMKPDc2VG2nbv0eT6OSREgOCTsdX5iz/RcfCy1b5iiv5iDrczz6k37rXRPJPnyg8fpi+SDwTY2F3FNSkF91Xzpk2ErhORNTswZlDUGGDw/SXIyRyqao7TeR2mDn479nbzvy/8NfRrai5M4Kj3Y5/jJdscuBpC8LHtiAsesEsbi3ZiGQSoeY6y7E09BkGB5JcnJMMMd5wiUlhMoyQchxsfHyCSTSIrCpD9A5YxZnN+3m8LqWgrKKjj2/NMsvOp6Djz2AA0LL2P4/GF8JYsQgMhwClkWyWU1Fl9dxws/O8MVt1YSGDqCkU6DpmGaJoIAiBKCzYZgtyEIAqZhIugGWiRM8qWXsFVV4tmwgZF//Cdy58/jXrUK3/XXIQBiIIBgmph6GlGSyQ0OYiQSULYAN8CLX4flnwLFBe1PQbgDGtbBkXvgmb+GtV/OX+uOnoD73g2XfwE6t8CxX0BiGKoug84X4NFPwpovw9Z/gKe/eOnnthvyYag7/x9c+XWQlDe2g1jePMzfPIvFYrFYLBaLxWKxWCwWi8VisVgsFovFYrFYLBaLxWKxWCxvNuLr2ZhpmgeB1cBy4CPATNM0j70OTVcAfa/4vf/CtDe18PAQdpeL+MQY237yfWJjo2hqjhMvPEv77u1Eh4dQ7DYcHg/VRW1ogynkEjdygQP1XBRbVCQTj/PiT39wsWAX4NATD+OZMNg7tJe/2/V3fOPgN2h0VGIaOsbwcD6ABCj45KeQ7RJD56Ic29qPrhnk0hq7H+wAQ0CxSRx9vpe1725l9W0tbHx/G3a3jOI1SWSzxMaGpz2neDRCwSc+gaHm0DJZ0rEoO+/9GQcef4hd993F0Lmz1M9TOPZCP6msjcQLL1D6wzuRRAmbU2bPI53kEiqCLExrW/TaSMYinNn1IvHxAdxf/Y+Lj+mRCGp/37RlLH84mWgEW4eEfjqBZ1kF8ZcGSL44SK49htfwU+Fq4sqbP83y626l5/gRADzBEOHBfhweD0lPAtGrYG8KkD4+NqVtYyKHlLx0+smlUzQsXMLpl7YBoNjtJCNhqtpmc37/bgCC5ZUMtp/BV1RMZGiQTCJOy7JVHHz8YSRFweZyM3TuDM6CAIJ9anEQQDI5ia5pNC9dya5f3kOospq+k8dJRSeZtXYjL937M3LpNEW19ex96D7qFixm9/33EB0ZxuX303FgL81LV3L6pW30nTxO9ay57Hv4l5TUNjDa1cG5vTsJVVZdDCAB0DWNo88+iZpJ4HAOksvFXuejZNHicOT5XnTNwBO0M9IT4+iWPnTVIJfR2fNwJ4lwFt/mzXjWrWPywQdxX7GJzKlTJAbG2bM9BibUzyvi1EuDFwNIAE7smyQ8puFZfTnGN3+MPtCPbkjEJoamBJAESsuIj49z4ImH0VT1YgAJQDaVxKG60I5EER0ySoUHdJPciQmcdi/x8bGLASQvCw/0ER4cmBJAArDnwXtJhscpa55BKjqJYnew/ObbqWjLF6AbuvqaxW6iQ+ayG+rZ9IGZJCdzjPclefJENd0bPofr458HIL1jO9kTJ5j49ncwEgmMRIKJb3+b3MmT1IecIIiMf+vbjH/ve9iKi7AN9GGXRc6PJnjf8lo+tKoemzz9dWeZLp2IEv5//402nH+PLfr4xxj6P18hczKfq5Y5coShr3w1/76vqjg//w+8OD6LJ38+yBP3jrB7rJFUsIaXtqUuFmYC7N+WItzwEVRXCU0LgogX3mcFUcDmSCFnZd79sX/DGMmi9sfJnI0Q39KHuifM5js+w45f/ARdzfL0t/+ToXNnmLFyDZ5gCC2bpn3vrmnPo+f4EcqaWilvmUEmEWeirxdNzQFQWF2DyxdAcTqJjo3QfeQgs9ZuZOd9d025njn63FNkkwlEUeL5H37nYsFx1YxZ7Hnw53Qe3IdpGkwM9PHEL3+GuvZynvvFnWST+SCzwfbTvHj3j1CzmWnbN01iBA7/bOo008wX6U/2vPYylt/aeF8PSCAo0z9amDmdTCpOeKAPNZNh1713Eaqspff4UY488zhlzS0XA0gATNNgxz0/yReaG4CYH207VFVNZetMMokYsbFRMHQ0NUdFcwuhyhpq58wnm0hw5OnH2fPgL9j38C/Z9ct7yKUSzF6ziYOPP0xxfSMHHn8IQZQokGRST3RhJFXQTLRDozhHUggSKHaJVDSHO+Agl9aRX/G8NNXg7J5hauaEQBAoL3QiDiSQbRKK2wZbe5lb5+PQM70Eyzz4i5yEB5M43AondgwQGU1RPSOIL+TE6VHIyD70WAJ9ZBSlqAhnawsDn/8CqX37yXV1M/af/0XmxEn0ZBIzl0NPpdHHRtEiEfRolOzQ0BtwhN/czEwWI51Gn5zENEzUgQFG/umfyZ0/T+boUYa+/H8QbDbMeJzc+Q5c8+eR02U0zcBX6KSg3E1kOB/YJUoCPce2Mnv9Jg489gAldQ1kUykGz57m4OMPERsfxVMQxFdcwuDZUxx66lHUbIZMIk7v6eNIboVsdwxbpRfRLWNiIhU50CNpBFEgsX0AM6NjqgbJXYOYEZWuI4fIJhMMd7Sj5iKklADxSG7a8wyPaGij3RDuhMImKJkFsQHo3Ab1a6F/PxS3QfvTF0Zj/wYUtuRHYxdf8R5t80BR66XfBQHWfRUCvyEsT8/B3u+C4ob1fwNdO+CWn+WLK1uugk3/CGeehMZ10H8gwN5WKAABAABJREFUv0xxG2i5/LKQL9Sc7IGy+VC5+Pc57H/21N4ejGgUM5nETKdRZRdn9k/gDTqYM7cI9ZHzSLEctiIn5liKqpCDUy8NIMkikiwydC6KYpfQsjp2t429j3ZSWu9DVE2053rQuvOfW/TRNOkHz3PZmkqW3dDAuQMjeIN2JkfS5NIaumZw7ELgh7/QSaDExbEX+nG4NRIT49TMmYehaqTjcUa7OnAXBBnr6aTj4F4G20+jqTliYyPoqoooSWQScSRZxuZyMdrVQbCqip5jhxk6d+ZiAAlAbGyUjgN7KK1vZu+D95KOx9CyWRKRcVJHpn72xHiNqk9JwBkM4DsS4e11N1Lvr+fI2BF6c5P5AuHkGBg5iA78oQ7hnzVd13EYTjIHxkEzUSo8iIqMdiaJoirkcmnmrq9i6Q31bP7wLCqa/bRcVsqT4SiyLE0JIHlZeypL4Lp6TJNLASQXuPaN8t65lZT5HBenVRY4KfTY2HJmjDPDcZI5jXAyi2GYtI/Eecd39/Dvz7bzo51d3Pb9vbx0fnrYo2B7jc8/wq+YbvnTomuE+/uY0bKC5PZBJJ8NpcyNmdSoK51HZdtsnB4vjQuLsDkzTPS/1ueNOMHZQZa8o4kvPHmK4Wh2yqOPDETQZxTgnBEieWh0ymNmTifXF/+dN/94f/RiAAnAeCqH6p0ejiAVWEGjb3Zut8Lx7f3MurySte9uZd0dM5h5eQWpeJb5yzejHYsjKiJKuQdBEEgeHkWp9E5rR3TKqFqWdDzGRH8vE329BErL8BUVk4rH6Dy0n0BJGZ5gEC2Xo//MScKD/UiKjMvvByEfsFNQXomazXJm53aaliwnHh5HzWZxeLycful57C4B0wC7W0bN6Yjipb8PDPRkEd1uBECQJERZRpAVBFEA08TMZjGSSYxYFHVwELWvH6W0FKW0jPTBQ6i9vWCaJLdvJ/KTn+Yb1Q24EFyiJ+IIioIejaFH49CyOT/PuWehdmX+5/F28L3iT049u6Bs7oW21EvXtJC/Bq9clP/Z0EHLTP/5zGPQsDZ/LyI+8vsdbMtbkzD9b2AWi8VisVgsFovFYrFYLBaLxWKxWCwWi8VisVgsFovFYrG8VbyuISSCIBwFvgBkTNM8YZrmqwdR/J2bfo1p0ypJBEH4sCAIBwRBODA2NvYai7yxTF3D0HRio6PTHju1/QXsbjemrqOYdgTNJHMuQnxLL7Hne1EHkxgpHTWbIZdOT1s+EZ7grtN3EclGODFxgh3Du5DcbvTwpZFdHbPmIEki5w+OYnPJrLi5kTW3t1BY6SGdyIAATYtL2P7zdl685yzb7j6L2+8gl54kPj6ON1Q4bb0u2YYxOYlhmpiGzokXnpvyeOehfSi2HKIoYOgGckkJckMTumkwOZLC0E2ySRVEAcn3imJ5UcC5rJCOo/sAyKXTjPUnkAKBi7MIr9kN/jS82fougA0n+tn8qOWiTcSI5b9o7b+ihtTRMaIPdaC8kKNqspHZl23IzyfmTylaLsfZ07sZahxCKnf+hjXlj6uuaUiKMmWaaZqI0ssFPPmXvMsXIBHJF6AJooim5nD5AsTH8/stHBtEXDJ1JHe5xkNXVz4c5OUi+UBpOeN93QAYhoGuqpQ1NdN74igAdqeLVHQSX1ExExdCIgpKyxnr6cLmdJGczL/WKttm0Xn4wIW2p5/yIsODjHR38NLPf4Kuhac9/qfgj9l/ddUgEckX65i6yfkDo0iKyNIb6ln77hbKmwP0nQojl5YiSBKoKqEPfIDow49AsJhU9EK/LnIRHkxOaz+tyYiOfB/WI5Pk0jqGNrUwuHrWPNKxKFouS3RkeniT3XAg+x3EnushvrWP+LY+FL8Dh+HE0F77bVLLTS8+zqZT6JqGpuYIVlQye/0VJMbHcPl8FNc1MN7fg2t+EbyiiEP02RACdl667xxbf3Yaf5GT0zuHMAyTM0cTdNlnY2tswn/ttcSf3zJtnfHnt+CcNYvkSy9d2OE60YcfwQyP47ZLtJX7WNdShM8lv+bzeLP7Y/TdVHgE7dDRi78b8fiU924AbWgIpaQYpa6O7kwZ8fCl/jDSl2HCCE0JzAFoWOihL5Jgy51f46Wf/198gQkaFoaQZZFCu5/iYyGi93YQfbwL0WvD0VIAQOZMBCVnyxf1mCZqNsPCq65n1y/vQc/l2H7PnTg900fYdrg9eAuLkGQZw9ARpFdeUgqAid3lou9kPovOHSggm5r+GotPjGECaubStU5xbT2DZ09PmU9XVWLx6UFOXYcPXBxV/NdSnFOLiV4mO/MF+W8xb7brBkEQOL5vC+7Lp+5je70fPZFDz2nMuHxdvg8IAmBwasdW4LXfO9PxGIrdjiBwYQRsgUw8Tmx8lNp5C7G7PWTTaewOF+P93Wz54bfZfs+djPf3kH5FPzF0jbO7XyIZjVAzdz59J45hszsIllei96emrdc8F8Fll1AcMlouHz4yMZDAeNVI4ZMjKXwhJ2uvrYVtfcSfe/n6PYFnVSXmwRFa5oaQJOHisoZukpjIICCQzehEhlJEhlOMDeeIuaswkwkyJ0+iDgzCq/bJ5C9+gVJchDY2hmAaGMkUktebD99ITH9dvdm97v1XU0FVMdIZ5GABkw8+MPVx0yS1ew+mrqEODJDt60cQTGx2ifhEBi1roGZ1DMPE4ZFIREbJpdM0Ll6G0+vj+NZnKKqtY7D9DFUzZ9NxcC+CINJ7/OiU1fgLitEmM4gu+eJ7fmL7ADIydq+X3MD0YuHMsQmqWmchyjIdB/aAmUZQbBRXT79+blhSiGP8ACCApyQ/0VkA6oW+LDvB1KFkNhTUwQ3fAUOD8oX5sJBrvgHvuBtm3ww33wnXfxuu+Gd410PQes1v3s/BerB74aX/gGe/Cv5ysLnz62raBNkYVC6EHf8B1cvg1nvhtl/CnJvhph/BLXfB6i/CjOvg1nsgUP1bHNw3lzfTude4EExkJPPHX8im8AUkME1CdhFHUwGCIhJ7vpf41j4m72vnqpuacHhlNDUf0CDbRDRVJ5fR0HIG3pATj01AH5t6/8HM6YR8Cp2HxwgPJTENAU3VUewymJC7EPhgGCaSImGzS2iqhKewEDDJZdNIssRYTxdaJkPX4QNgmhi6gSiKSEr+voAoikiynH+fMECUJGTFhmGajHRODecDCA8NkIpHp0yLhcemBJ4B5AYSOGYGp0yzLQ2y/4WH8XtCLPHMo7mgmc8t+hxZLZsPIVGcl/7/E/Fm6r8AxA1s1T4S+4aJb+0j9lwPRjyHTbVRVB3i4FM9tO8bQXFKiJKI3SPjc8iYBtwwr4LKgqnHpsTnwNlWOPU+08sMk1K/gx/csYhPr2/iMxuauGJmKf+99TyFHhuqbvCBH+/nqv96iX9/9gy7zo+TyE691v5/W86RyE59f1aKXbgWlUyZ5l1TiRL60+k3bwZvur4LKA47ei6HkBJwzS4itrUv34+f78EI5yivakG2O7jv7z9B1+GdFJRXTmvD75MpafQjOGQGoxkqAo4pjz9wapi9dS7ibQXTzmvAa9wV/+2lc1ODeg73Rxlq9MErAqNFl3zxM6Pld/NG9N3h7hgzV1ZwYns/L/zsDC/efZZUNIepg02zoQSdxLb0En+hj9jWPpSgE8x8+NMruTaWMxEZAAQEQcQ0DUzDAAREUUS221GzGURRRJREHG4PgiShqyq6ql64Zy+gqzmcXi9aLosgCjg8l34OlFZg6CK6biDLIqIkgCBcDCLxBW0Y6TRIEqYgYJompv6KvmqYoNhAkhCcTgSHHS0SwTQMRKcD8xWfoTLHjiH5fJhqDsFuR5AkTE1DtDtAFBFEEeIX7t95yyB5IWhKlKeGRnhLLz0G+WuDlzkLIJd87cde/tlTAqkJ8JaDMvU1bvkz8Vrnb4vFYrFYLBaLxWKxWCwWi8VisVgsFovFYrFYLBaLxWKxWN4iXtcQEuA6QAPuEwRhvyAInxME4fWoLuoHXjk0cyUw+OqZTNP8nmmai0zTXFRUVPQ6rPb3Y3e5MU0DxTF95EhPMESgpBS724MpQ244hT5xaTRXdTCJFk5jd7sJlJRNWVaSZXyFU5/fVw78f+BwIBUUIBfnHwt/55sIIrQuL2XDe2cgIKBrBvM2VpPLGqSiKvse66JlaSkLr6xh9ppKjr3Qj5bLEKyoYNnNt2NzugAQBJHL3vYO7DmN8Le/g+JwYOgvfyF5KsPQaFtZhl3W8KxdQ3rXTiRRwu7KfwF3YDhFdiCBa0Exvg3VeNdW4b+6jlOnXsI0DLyhQryhMvY92oX9hncAoNTUIAX8v/vBeJN7s/VdAMllRwpM7bu2Ki/ZjihG/NIXu/XOJGX+BhS7nb5TJ/IjXU6MEygp5aXH72LXjnuxzQxMaUcI2MCfP/0YuobT56fjwF5mrs6HmWQSMQIlZfSdPE7joqUAJCJhguWVjPV0Ut6cH709E4/hDRWRiExcfJ1ER4Y4ePopcmtknJvKMFa7GC4bIDI5BIB8IehkpPMclTNmTZmWjsdx+fPbKogCgigSHx8jWJYvqtY1FcXuQM1mcHh8F7Y1jjuQL8xQbNNf6zWz5jJ49jRqNsPk0J/mSNp/zP6rqzpNi4oByCRVmpYUcfXHZ+NwK+TSGo0Li2lZVkru/HnSR4/gXrEc0e1G9HnRXniS5svyRWMTAwlKan3T2neRhAtBOHJJfj2xiB13waVCxshQP5LNhprNUFhTO60NSZZJ7HrFW5ZuEt/ejyjJDLSfoWHhkinzz1i1lmBZBfKr+lPDwiV4gkFMXWfOhitx+fy4CoL0HD9CqLKaQE0lmqzj21iDd20Vvg3VuBeWkBPTLLqqirXvnsHpXUNMjlwqvD93Oo287krk8nJs1VW8mlJdjfEa5/nMiZPMLvdTX+hGNUxKvG/NYoo/Rt8V/D7ERfMu/e5yTZ9JkkAUkeubGJmeY8b4QIqyhkv9taTWh9vTzfaf/5jI0ABD7afpObabulkprvtoNennhjBfUWiWPjKGrebS8obx8mMCgZIyRnu6KKqtZ6Srg/j4GPXzFyPbL/VHUZJpXbEaU9cZOncGly9AqLLm4jXDSOc5DMNAFGWK6xoBSE5GKCibGlAhSjLuYAhREqf0dzWbudjWK9nd08NC/KVlrznvNM4AbPhbEF8xMn2oEYpb84X1bzFvtuuG5GSETDKOFjLwXVGDd3Ulvo01SEEHYokd2WHD5QuQnAwjCAJaLou/KH9OlWQlXwT2CkU19Riqimyz54vebHZGuzo48uyTiKJEsDzflwRJZs8D9wKQjk6Sjk8PeUhFJ4mHJxBFCX9xCdWz5+VDczzT37PFgB3BJjHSE8VX6ESSRcZ64vkCuVeonFFAdCxFIKGivaJYXx1KgmEi2CRkWULXTCRZRBBAVkRalpYSGU6SjOYQLwSUpBI5zp9Kgs0GqvaqQJ8L2+XxgGLDzGRAEBAkCSOTAUlEsE0ftf7N7nXvv6IEkoQ2MoJgtyP5p39mEL0ezEwGubyckS99CZ9DJTKSIhXLoeZ0bC4JxS4x0p2kbv5aEPJhd7LNRio6SWpyEm9h0YVrxQIEQcATCk1Zh6Fq2Mo8pI9dKlQ0szrx53tRnHbkgunvlUJQoW7+IiZHhy/sG4PuExPIgTSX3VSL4pAQRIHGpYU0zJKhcV3+nJWJQbgjP8p669XQtSM/EnvfPlj9eai/HNqug7ZroWZpfp5F74cZ14C3BIpnwPzbYdnH86Oz/zZFkZ5iuOWn0HZDfhT40dP5os2KBfDc38CL/woHfgzX/hfUr8mPLh+ohNLZMOvt+W1pXA/NV4Cv/H91iN8s3kznXsFuz58D7PliXCbDzGk16Do2jiSJKGVuMicnLs5vpjX0PYOUVfuwOxUKylyk4yqeAgcujx2nV2G4I4rkVECefh6y++2MdMdIJ1RiEylC5W4mBuPYnDJljQEgH7Y00hOltCGApol4CoLouo4sKygOO/7iUtRcPjxwpKuDUGUVuq7jKyqioKyS6Ngoss2Ormlk00mK6xqY6OslVFFF4+Kl07apvKkF/VWhTScPvYBtbmDKtNxgAufcInzX1uG5ogrhCh8Rf5hT27fg8vlpj5/j6e6n+a9D/0WJuyRfUOwpzhciFzb/nkfqzePN1H8BRKdC+sgYZvpS2EfmVBgzrQMGi66qpajaS2IiQy6lEh1Js0Z2cofi4MsxkXuaK/n2phn4nDJfe9tsWkq9CIqIrdqHYJvah73rqklKAvGMhtchIwBPHBtCM0zeu7yWj919iD1dYcYSWe7a08t4cnogZDKroRtTC4lFh4x/cy2h984kcH0Dhe+fhWdVJYLyet9q/fP2Zuu7ALph4vT5kXw24i/05cMRAEyIv9iHlBbA0HF6fciKwuW3vw/Hy59lBIHlN72TEl853v2jOO4/zwPLGqmz23jbgoqLmaILqwNEBbj+oSNE508NiRYUEVuV93fe/tZSL7I49fr2QC5L8V/Mo+DtTRTc0kzRR+ZgK3vrhTW+mbwRfbfz4Ch7H+mkfl4xC6+sZd7GKsb644z2xPL3NLf3XwqsMUzi2/oQRAFblTd/72h1JcF3zUAPCWCaOD0eQlU1lNQ3kpiYIDI0gM3pomXZKgxDJzw4gKaqlNQ1UN02G19RMYrdgdMfYHJ4EIfHh8sfoO3ydYz3dqPncsxcs4GuI4eYufpKchkdSRIwDQM9Z2DoJuGhJO6AnbKifLip6HTkQ0Zcrvz/gQKkYBApGERUFESPB7m4CNf8+Tiam0ifPZsPZnzFvSvR7QJZRg6FQJQwTQN9fBwUBbmgACnggc5tINuheRP07ckvuOgDcPrx/M92HxTPhHBn/vfKJTDZe2nnX/YROHEh/LB0DsRH8j+XzITEaD7MZMmH4eTDsPlfwD097N1isVgsFovFYrFYLBaLxWKxWCwWi8VisVgsFovFYrFYLBaL5c1Mfj0bM02zB/hX4F8FQWgCvgp8HZB+7YK/2X6gSRCEOmAAeCdw2+/Z5h+c3ekilYjhCRUSLK8kPNgP5Ava5m++FlXXUbOZ/EjCg4lpy2sjKWwzC1h12x3svO8uwgP9uAMFrLjlXRwXe6bMa5gGh6KnmOv3U/iJTxJ96EHSx46RimYprPIQH8+gqTqaZqCqOnanhOIQyaU1jm7pu9SQAG5/BYnJMKZhMG/TVQiShChKiIqM7g+hDw5itztAgFBVDRN9l7bF5Q/g8HgorfMik4GKCtyKjURSxelVaFtRRmWxE5thIpe40GM5RIeAXOTg3GO7qZu/iMXXvp0nvtWPbpjY5y3E8f73Iygyosv9hzlQltekSTZ866uJ/LIdUzcRXTJykZNsR3TavEZE5eq//BLD7WcpKC+nZu58kpEImz/2l4z2dKI2C7jKy9HaE0iVLjLFWUb6TrDhgx+j98RR1r//o4x0nkfNZrniLz5D34ljLL/ldiJDAyQiYa78xGfpOXqIxTfczGjneVLxOMvefivHtjzNhjs+hi1tx4GTuk/O4/iR5ymb0cpjd/87gdIy2lat5eiWJ5m17grUXI7B9jO0LF/F2V07aF66kvKWGXQdOcjs9VdwfMszzFy9nq4jBzi3dxfzN1/DoScfRc1mqWhp4+zuHSy4+nr2Pngv6XiMkoYmzuzazpLrbmLHL35K+96dLLvpNg49+QjZVJKyphaqZ81lx89/AoAg/b6nQsurOb12ypsKmLNOZ7wvQUldgMhgglRMRRBAlEQy8RyhmbPxYCK73GijIwTvuIOBT32aGV91Y+gldB8bZ9nbG/OFleNpRElg0TIPjq5tZIfy50gpFCKX1jizO8H693+BEy88xNC509hcAYpqGpi36WoMzWDh1Tdy9Nkn0DSVunkLMV41yjCAkVTR1Rw9xw7Ttmoty266jfjEGMW19ZS3tGFzu7n2r/6aPQ/+gsmhQRoXLWXm2o0MtZ+j++ghxvt6mHfFNaSiESpaZrL9rh8h22w0fmIhrkIfWjiDIAnIBQ4mRjrJpUy0XDHxcGbKdngLbLhXLiMdLMF7xRXEnnoaI5p/jUuBAN5Nm9AnJ6dtvzijjWtnlNE9niTktiEIwrR5LK8t6C8l8xcfJNHZi97Xj9rbS+CmtzN5/wOX5nnfe0GW0dpPUb0YxvuntlESyBFYVkEuo1I7W0cQRjnw6FMXH1/69ndwdvdLHN/6DOtv/jCFo9NHsDa1fIGOvTFAOJMPadJyWTyhQnRVJREex+XzESgtZ/B8Oxs/+HHiE+OkYlHc/gDbfvJ9UrFJrvrk5zh/YC+GoXPVJz9Lz7EjpGNRyltmYGg6DQuX0HfyKAcef4hrPvNFdtzzE8IDfbj8AZZcfxN2p4uRzg5W3voedtz9Y3RNo33vLla889288OPvXtzehgWXESgpoWnhZZw7uBfIB/xs/MDHcPl+y5Cy+nXw3idg+DhI9nxhccnMqcEklt+JaZqUN7VgaiZyyIkWSSNIEo6mAKZXwCG4sTkcTPT10LZ6PWomzaLrbmLo3FlOvvg8K9/5Hg48/hDpWJSimjpW3HI7Do/3QuCGjYnBQXRNZbSrA0EAUzewO1x0HNxLbCxf7BUfH6OwqmbatjUtWY6kyHQe2kfzslW4fH7szhAZr4IYdGC8fF6URZwrKji+b5jVt7bgcEnsvL+Dle9oQs3qKHYJNatT0VpAaZ2f/tNhpLEUrz7Da+NplKVleG0Smmpwbv8IK25uwtRNAiVOBFFgoi+Ov8iJoRtoWQM1Z2BqGpn2drxXXIFcXIQ2emm09NBHP0LipR24L1uKmc0gVRRgajqS34e9svIPdVjfMgRFRnA6UaqriD3zDMF3vYvU3n0XCxFFjwelvAItEkGprMCzejWpF57DKF0NAiQiGXwhJ1pOZ+l19YSHxgiUxBnvO0AmHqN15RqOPf80y2++nZd+/lNW3X4HvaeOUb9gMT3HjqBm8kE0507uobl8ybTtU4dSCKMGthkBxKNjGIl8aILgkFDm+HHGAjzxn1/DGyrCEypn/5N9lKwswj4nSUu5QqmzDMGtUdK/C7N4FihOzOqlCJFuMDSEwpZ8UaPdCxv+DkINf7idXTITbvwupCP5cCfFmQ9zKp0NiWFwl0DorRfs9FYkhUIYqRQYBno8gejz4T1zlKU1RQhFDZh9r3F/YTBJ8ZIyzuwZYvFVdfSdDTPaE6Og3Mnad7Xy4s/bGY9mqVlTSfr5S0W2trmFGD4bdpdMNqUx2htnxnIPG97bRvfxceaur8Tukjj50gDLbmjA5pBIRnJ4i0qwObJER4cpUEppWHQZajaD0+cnHYty4oXnmLf5WkIVlWz40McY7+3BMPR8KGlBCBMTNZPB5nSSy6SZs34zx194FkEQmLFqLYXVtUz09eApCJGI5ANXRns7Ed/lRnBrKAMicrkLe5UffTyDqRmINhFPYYidD96LIIoUNtTz4OlvAaAaKicnTrCifh3INgg1gfNPNwj1j0mSJDS7SG4oOe0xI60hSTIHnmwHoKzRT6DMze4HO1jd6Cd3OJ/QJ3ZGmeu18eKHluMv9SBeCFSwlbkp+vAcEvuH0cbSJGcUsM9u8tTjJ7n/UD4U1CaJfPnqVnZ3TFDqd5DVLhWuxzIaa8r9bNjYhkM3GVEEvnaohw+tbsDvtE1/Lh4bztbgtOmWP23psJYPJs2BmXtVaKcJaiKLGFBY+c47iI2NsPehe5m1bhOyYkO222iefTmRX/aiT+aDmRxdUebPK2S3JPCXG5tpKHRzpH+SWFplMqXyrYEJPryxkqLOOHKBHe9l5djKf7uAED2toQ4m0CMZpIAdpdzDzAo/P/vAEv7j2XYGJ9PcvrSaG+ZXYgs4sVVYwSNvJaFyD11Hxjm+7dJNg+bLSskkVPSsBq/unjkDUzfQMypKmRtTl9DGUig2kWBpFcnoBKquYnM6CRSXIdsUMEE3dJovW4EoyZgCCIKAQD68WRAE4uFxRFFCcThQ7HYqWmdR0TITXVUxDJHGxetRHEWYaR2HW0HNaDh9dmx2gcxkiuZ3V2Kb6EGorQXjUui5ICsgCpiCgGCa+cAfMR9iYmZziMEgnnXrCP/0Z1OeZ+GnPgUOB6aiYKoq2sgotto6BKcD0WHD5QrDDd/J3xPIJuCa/4KCWnAX5YP9AIrbQE3Ddf8NrhAEGyDSCVWXQbAxH+Lnq8xfE3tLYfQs3LQyP2+4E955D6hZeNcD+ZASi8VisVgsFovFYrFYLBaLxWKxWCwWi8VisVgsFovFYrFYLJa3GME0zd881/+mQUGoBW4B3gHowL2maf7H69DuVcB/kg80+ZFpmv/06+ZftGiReeDAgd93tb+36OgwXUcPk0nEMHUDw9BRHA48BSEKyio4/PSjzNlwJa5eB6mtg1OW9V5fy0+/91ne8bdfI52Ik00msLvc2AoCPBZ+lv8+/N8X56331/P5xZ+n2GilYrSHXG8vZjqDdPkmYnGB8GCS6FiK4c4YhZUeimu9FFZ5Obt3mBPbBi62M2t1BW0rHGQTMR78+t9fLKp72du++Lf4TpzGuWwZYS3HcEc7faeOM3DmJMW1DTQvW0l5y0x8E8dweN2gZUF2MeFewHhPApdfwTOaRtJNEjsHMZIXivDsEgXvaSHrzvLM97uZGMgwd30VzSPPEfnJnfiuvYbiv/xLJO/vPtLnH8jrXnn/Zum7k8MJ0ve1455ViGmCUuwiNxhHj+ZIHxqdMq//tkbijkkyqRQ2ux3TMNF1jWwiwfEXnqPpsuWM93VjGlAxow09lyM+Mc6eB+8lVFFJcjJCWVMLy295F+noJMnYJDaXG7vDiZbLYXO5MTWNZDTMzvt+ztK3v4NT256ncdFyqqUWUlsuvXYci4oYLxkjm4kjiCLpRALZZsNmdzDQfoaK5lYkWUbXNJKTETzBIIrdSToWQ1Jkcpk0noIQkaEBvIVFiKJIbHyM4to6YmNjyHY7Do+H+NgodreHTCKOrNhwBgKEB/px+QN4AkEyyThnXtpG56EDmKaBbLdz/Zf+gdq2tjf6UP4qf5DUiD9G/w13DaKKLhS7TGwsw8RAgp4TYSRFoGZmCE+BA2+pQsCdwh6owEyMk2zvxYzHyZw6TWbVjfSfi5OczGFzSpQ1+HDrEZTEABgqkdNHcS5YwGh1gMpMHUO9GQ481U2w3EFhhUJRTSEdhydovcyBSQy7w46mqhiahtPnw6l7SP2s59Los4BUYEe42k9/10lObHueTDJB2+XrCZSUUtU2h0xKQNcjOFxutFwWyeEkF4vxi7/9ApKicNUnP0/vyWPMXrOBF376AxoXXYaEQlPRQiYf6wDNvLge1/py2oc7yCTL6D8bITyYL/YTRYGV72niMy+dZ0ldkC/X64i6Tu78eRAEbA0NGKJI2h1g8iMfQB8eBkCZOQv1y3/Plb/sxOeU+fZtC1jZ9IaPSP2WPvequspY/zm0/n7sbh/Y7Sjdg5hjYZTSEgSfD0wTVJWMt4w9O+L0npoEAWYsCTFrmYdub4TQuMQj//K3NF22jImBfib6evAWFlE7dwHHtzwDQMviVSx2bULtnVqMHLiuHi2lMmkfRyp00HvsMKd3vsj8zdfi8vl58r//nWv+8kscefYJvMFCaufMp/fkMUrqGhhsP4MoSRTV1DHe34uh6bQsXUGwoorwUD9Hn32K7iMHsTmdbPjQx3F4vOTSaUzTwO0vIJ2IIUoSNqeT6Mgo3lAIQ9cxTYN0LIYnGCIdj5GYGCeTSmJ3uWlZugpfURHJWJSJ3m6yySSBsnIKK6sRxLfUaPNv6b77q7Tv3UUmEqfO2Ub0qe6L0211fpyXFSMYg0w6y0nHO4mNjeItKkZxOJFEifBgP4rDQaCkDNM0cHh9JCbGCZZXsufR+5no6mCstwc1k2bOxiuZs34z8fAEsqzwwD9/dcp2rLr9fbgDAfY8cC9qJs2stRsobWxBEAQcXh8mIEk+RNlDYiKDPpkl5JSRJQEh6GBgJIVsk8kkcyiOfFbjcGeMytYAiXCWbEojl9YoqfMh20WC4xkSz00NBvRdU4dW5cWQBURRRFMNDMMgm9IY6Yxx7IV+FlxRg2nkz9OCKOB1aAROPouo2MgNDODduIHs+Q60sVEcbW0IDieSx42JgCCJ+VG1ZRl7bS3yG3tt/Kbtv6n2dsjlMJMptIkJBJtC5vRpRJsNuaQEfSKMqaooNdVIoRBmNsu4WMZETMLptaGpBmO9cVqWlGBzyhiGiWBOksvEOLtrO6Ik0X/6JDNXr8flC+DwehEAE4HY+AiSLOP2F1Bsr2LyZ+embJut1odzdiFmUCKrJ5ETEoIooXsMzp3dy+GnHqW8ZQZzN97AqV0aJY0+Kpa6iWQnUCQFU1NIDMUolNM83AnLZjZS4FKoCbmRpbfU+e+P6U3bd38f6e5uzHgcU9UwMRFFEbW3l3SwBo+/nMm7z0yZ395SgLihmngki8OjkEmqpOMaPSfGKSh1UTMrxORImkChHadqYE7mEDwKYymVZFpHUUReuOsss9dUEihxMtYTo6jGR6DUjc0uggDHt/aTSuZYuLkWSRIx9CyGNkZiYhxPYSGKYiediHHg8YcY6TxHzZz5LLzyehKRMIrLDYbG/kcfpHHJcrzBEAD+snIEBDLxKKIoYRg6pmGSTSex2R3oukYyEsEwdIJlFUwM9OErKsETDOIccZA+PE6u61KQpu/qWrbs/CGhqhrU1kI+c+LLFx/79IJP88HZH3xjDuBv70+y/8bHJ8g+OUzmVHjKdM+tdZhlMvf8zSlal5XSuqKMI1v6qC73ULB7MF+A/gqutzXinVuEYp+asby/e4J/ffosx/qjfHxtI//3ufYpj1cWOPnlR5ZxejjG+++8tC8+uaSGdwzloO9CQIoAtpubsbUWEHBNDyGx/Fp/kn0XIJdVmejvxq34Sfyk8+K9TQBkAdstpeCX6D5ykEwizv5HH5iy/K1/8XWMp6f2fQQIv7OJR/vDlPgc/NMTp1k/o5jZFX62nhkl6LLxmQ1NzK4MIIm/3a41NYP4i33EnrsULOVZVYFvQzWiXSaZ0choOiGP/XfeF3+i3jJ9d7w/wSP/eZjMhZA7u0tmweYaJFmkotRB5p52eEXQkuiSCdzWhDGuMvno+YshJXKJC+/1NYwl+7A53bgLClBTaTLpBCPnz9Gxfw9LbrwFT6gwHxBiQtfhg/iKinB6vWi5LJOjI5Q1tCLZSzB1E13V0VUDu0tGkU1ENY0cn0DQciDLIAhINhFFSZHTfeS6exEkGaW6CsFmRwtPoEciiIoNbXwcU1WxVVZiKjJ6/wCmICAHAmS7upACfgRBQI9GsVVUIvp9CD4fpmlihsPI5eU46upe9/3/JvWHuN/7+v4B8c/IxA9/yOi//TvB97+fki98/o+9OW92VsK1xWKxWCwWi8VisVgsFovFYrFYLBaLxWKxWCwWi8VisbzJyL95lt+eIAh7AQX4JXCzaZqdr1fbpmk+CTz5erX3RsmPcJ4hPj6GpyA/OqqayTDa1YHd7eL0jm00LVmBEDRQmnyo52IggH1uCK3QYP37PsrOe3+G4nDQdNlK0vEo0WIRRVL41PxPEc1FccpODMPAQYjv7Ooj4HKwtGkpM0rceB12hESCzqNjDLZPAjDWG2fw3CQb399Gw/wiiqu9JCdzeArsBMqcSLY02XRqWgAJQDoRp7C+HrWzC7O6nEBpOUPnzzJjxRo0XaOgtBxTT+LIDcHT34faVaBncRd2kC2+kYH2KI0FCno4PeVL+mZWJ3NkgiNpAy0nMGddJe6ADefCa3C0tuCcN/fNGEDyJ00HjEiW2LOXimvlQie+6+vRxlP5gnYRXEvLONe5jxd+/n1W3vZeOg/spaJtFqGKKgxVZd4VV+PyBwhVVpFJJLC73YiShL+kjBWywtnd26mYMZPmJSvoOLiXwTOnKW1oIjY+xsCZE1TPmsvxrc/SuGgpst3OgiuvBdNk8XVvx4mX1L1DU7Y7c2AM5xVOnvjB1wlWVDFrzQaOPPUYjYuXIcsy2+/6EbPXX0Fl2yxObHuewbOnuOzGW9j70H0UVtUQrKikfc9OBEEkVFlF87JVONwewgMDOH0+hs63c3bniwTLq5i76UpObHuedDxGy9KVSDYbAjDe04XLH6B+4XI0TcNTUExJ/WXYXCVv7EH8MxFM7+dkYQMBMUR0TGPPw5feevrPRFh/xwwcdi/2QAEAgqcQsdZAPXEGuaQY/8QZnDPqORePk7GnyJap1AZbiOdKOT95ni9NPsdY5z3QCf80698YfsKJmtEZOp9g6DzISpS5G6oIDwnEJjxkY0/QcXAf6z/wMQxdp6NjH62blqK+EMbM6Yg+G57rq9ny+A8oq2+irLEFUZHRc1kKyspJJ0101UYmE0QSTXRNwzRSnNn5IgC6qhIZ7Geitxuby8PcDZs5s2s7zfNXkDoydjGABECPZCGu4/IHSMVNVt/azMhEmpHxNHE7/MW2dvon0/RPpvlyXRm53l7k0jIQINfTg1FTx9/tD/PX3/kh0bPncDhsjIQqUN0BSn2DDMcyjMazb+wB/xOgSArlNW1Q84pQohnzAYi/+CL62DiCPV/kKA0epG5NNeYSjbaiNkaVPs5JCYrkGRx+/nuYpkHnwf0sv+V2tt/9Y4LllYz1dF1stv3gTpZ8/m1ISRV9IpsvilsZoidzBndRAQ/8899R3tzGkhvejt3tIZdKEaqoYtNHP8XJF7aw4MrrwTQ4t28PmCZbf/xdCsrKMQyDU9u3Mn/ztVQvno8rUEAyGmG8p4uK1jaK6+qRZIXd9/+c8EAfGz/8SXLpNLvuvQvDMJi36SoEScLtD/DSL37KaFcnt/ztv6CrGvf/41em7K/V7/kgvqJ80I3b58c9a+4f+AhZ/rdEUaSwqJL4E/1Tpue6orgWFqFPjOEOQjSTxdB1Tmx9lmwqRduqtYz39dC64nIOP/0YrStWM9zRjq5qPPKv/8D1X/gq3bKCpNhoXLSUsqZmcukU/zn2Yz5T8j5Wv+v97HnwXtRslhmr1lBSW4/d5Wbmuk9R2RLg/OEEybgbUTAJj2iIkojbJxCbCLP/8UuvE1EW2PT+mdicMppq4A05iUcyqBmdskY/3cfGObtnBE+BnZW3NNF/JkLvyQkuW19FoLWA3JkIAM4FxThnFqIUOC62nUmodBwe5eiWPhS7xGXX1mP3yBiqiWkY2OwQTA1guNyYhoHvmqtxzpyJZ/nyN+bg/YlwNTdPm+bbsAEjkyF94gT6ZBRRkTE1DdHpxNbaStHYJKaZIyeLuH02ZEVizyOd5NI6c9ZW0LiwHJurkmwyyf7HHqC8eQbZZILCympyuSxnd79E0+KleAJBTu14AdPQmbf+agJXVBN/vg90E7nQiXNmiOiTXfhva+Tn//YlFl/7NuoXLiadSFBcW8e6934Em9tJclIkUOImHLDx9n/cP+35fHxtI9/acZ77ZzbSUGx9LrKAs7Z2+sR58/ADEx1hPBurSWzN90Wp2EWmJYgazuIvcRIdThEZTjPRn2C0Jw6A2+9AkOCR/z5GUZWX2WsqmOxNIDslPH472bTGtZ+ai2mabP3pGVLRHGf2jABQN6+Q2Wsr6To2wcxV5ex+sIPWpWVs/0U7nqCNpTe2oKkikaEcZWVOLn/fBxE1GO/vYbjzHIWVNYz1djF07iwLr7kRl99PJh5nor+XyeFBiuvqOb9/L+XNM8hlUuy4+ycsueEmAiWlHHr6cWRFoaptNk5/hoayCjIOBcMwwWBKAAlA4oV+lt14K7ufvJfT5ZcK8xVRYWnZ0j/U4bK8miRgznMgTzjRRtIgCbhWlTIS66akooVNH2hDkEWiwylMzcyXP79GaWoinCXTn6C8IXBxmqYbfHNrB/u78+/POd2Ytlx/JE1G1Wkr8zG7ws/xgXw/udzvhn2RSzOaoD/Tg7ep4PV89pa3OJtdwe4NkU6GcV1XSeqxfoyEiuCQcF5ZRvv5/dTMmYuua8THx6Ytr+WyTIsRE+DsSJwf7OjiipmlrGwsZMvpUXZ3TLCkLsjS+iDVIddvHUACoI6niG3pnTItsWMA19wibJVe3A4Z9+v7pwHLG6yw0sONn13AUGeUXFpDUkTsHgWXS2bvln6WXVdL+qlezLSG6Fbw3JC/js3sHr4YQAKgjaQwJnL0d56kvKmFib5uHG4vit1OoKSUVe96H7qqMt7TxeTwIKGKapxeN91HD9K2cSNhMUZowQz6tTEWySpiycyLbedDS0wEKQhUQCYG0QFw+sFXDoATcLbMmPLctOIicufOoQ4OYibimKaJYFNwzpyJtGQJeiZD+vhxlKIijFwOdWgQPZ5AqatHKi/HUVn5BhwBi8VisVgsFovFYrFYLBaLxWKxWCwWi8VisVgsFovFYrFYLJY/Xa/3N43vME3zzG+e7c+IAMV1DfSdOMqx558GoLC6lnXv+wiJyAQAisPBoS1P0DJnOaULGjAMg5gxgYzA09/5L+rmLyIZifDiz37ANZ/+AsOkqfXUMpGZ4MzEGer8dSwrW86Hf9DHX185g0RGo300wfGBKB9b20hMMy8GkLwsOpYmHs6QSmbRK6KU1hRj84gU+wNkUwrJsJui2gZmrVmPt7AYQYC+0yfxhYrQz3finDWbaCaBaRjMv+IaDF1HVGxE40l60xolHVuh7XoQRBBEHJETOIvmUT2rAUUQyJyf5NXU0RT2QhfFtT7OHxxFy+rUfGEBwQ3r/9BHyfIaRK+CvKoc9dlLBQtGSiUqwM+rbKxZUIfkEMmKEvX6Qmy3Odj/4sPM3XAlR597Et+VhWz54XeYv/lazuzaTjoWRVIUdDUfPrPine/h4OMPsemjnyY1GWHrj/+HdDxG9ay5lDW3sOfBXwDgDgQpbWjm/IE9zF63CcPQiY6O8NQ3/4Orbv0MXn36iK0eT4imy1bQcWAP7Xt2csVffAZBEomNjVLZNguHx4Pd5aF1xeUMnTuDKMlIssx4Xw8z12yg6/BB1GyG8b4eEuEJrvr0F5jo62XXfXex4MrrMXSdgbMnGe3u4Nq//GsMXeO573+T5GS+WKmidRatl7+XnQ8NUVR1HRPDGh1H46y+LUN57R/+2P3ZkWwU9+5DariRjoOvyr4yYeh8lJbLSqdMdgWLea7mKGaZA7tiMJ7bR7AkiKqr6HhQJIWgM8gS5xL+7fJ/4/HOxxlLj9Fkm0GmeJT6eUVoqoGsiHQfGwcE9j/Zzc1fXMRQxwIG20/jCxXy1Lf/L56CEDabk4brFuKQ3Ih+G6OxbuasuwLJZqNixiy0XA5d18jEE9QvnImhG4SHkhi6geJwMt5zgJKGJkKV1ch2O+cP7qVu7gL2PHAPTUuWo2bSCKKAHp0eCGJkdMoX1lEzx4bH7+BIJsPHHp76Vr20PsgBVymBYI7K8f78OT9UzfueGOedS6rolb0c9tXzf59rxzDPYZdF/mpjM9/d3okiTSufsvwePMuXkzpylGxXJ4LbxeS8OtqlAcpCZQwYHezo38G97ffy/Q13Ymr586mm5jj90jZW3fZe1GwGQzcYPp8f7d00DB783j9w3cf+mkR3EtXIMNh9iJblqxAkmZlrNnBy2/M8/l8dbHrXJygqqsLUBE4d3oqay3DihWeZtXYT1bPnIisK5/btIjI0CIDN6aJx8VJGe7tJjI/iLgjRf+YUZY0t7H3w3ovPqaK1jcH207Tv2Un9gsWEKqroOLSPvhPHkBUbmz/+V+y458ccfe4J1r3vo6y69Q52P/gLDE1j7oYraV664o0/EJb/lWBFFenBScyUNv1BQ8cTiiFO7iNRcjX7H/klxbX12BxOdtzzE9bc8QFio8PMWLmaka4OTmx9lvjEOO5AAZlkAm9hIY2LlxGbGCc8NIhSGWJ+5SJUTCSHk+s//xUwTUwBtJxKdMLHyR3tmIYTT8BBKprD6VPY92g3a9/diiiLnN0zPHUTNZPwUJLIUJJUXGXO2koKSlzYnBKTIymal5TiDTkJlLh4/ken8BU7WXVVLXJWQ20qwLeyAodbQQ45EW3SlLYdHoWZqypoXFiCpuoggCDki0clWcDuVDDNcszLFiLarVHoX2+iw4F70SLcixZhZLMINtvF/e/x+VCkbrKjw+iSl2C1j+oPtqE4FBwe28U2mpYso6SugeRkBEGSGO3qIBGeoHbOfERJZry/F28oRGRwgPu//jfc+vF/xrexBjnoQI/nMDM6cqGTVHSSmjnzyGbS6LqBoWqM9XTRc/wwDQuXEp+YpLhlPZM2gSKvnbFXhHzJooBNFllcW0BLiRVAYvnNQg1B1Eof9pYC0E2MpIpkl1FK3UhuhehImj0PdxAsd1PW4Cc8lKTj0Fk2f3QW1316HtmUhmmYKE6JA090k71wfnd4FFa8vYFUNDdlfV1Hx2ldVsacdVUIAtTPLSKb1nB6FeasqWLbT8+iqQaCKLD4xkrCdV3Uxn04fX4MXWPg7GlKm5opaWhCQKDn2BF8hcX0nz6BKMmUt7RRPXse4f4+XAUFzF6/iR333IkoydTNW0B5Sxt1S1YSKgwhnH8G78AJRsuuBNE5bd8YKY1IVy+zrrgKu2+MYXOCAnuQtzXdyMzQzGnzW/4wEmOjxDMRlMUyQWcdpmDS1XsYT6iIXDqFzeNmtCvG/se6Wfa2Rk7uHmLDklIyuy+FkIp+G0Kxi8loFl9Gw+O4dItTeEVOg12e/nllSW2QIp8dj13h2+9awOGeCMOxDPV2O6+O49VjWUx1epCJ5c9bsDhI36lBckoGZbMft+nBtMHBvU9Q1trMaFcH3mAhgpnvjP6SUuZu2Ixss+OuKyQb1DHCmYvtmfOKuOdsPtjpmZPD3Lywkn+6YRbnRhOEPDa6xpKIwm8fQAJgZvQpQRMvMzL67/7ELW86wTI3x2JJXDYb58dTfPnBg2xoLuJTG+sxPTYK3t2KFkmhSlkeuetrrLzm3dgm1WntmKpBQXklg+fO4vL6GOvuonXlaiYG+jn67FOUNDQi2+zINgdaLsvg2TP457fwdHInC4rnk031Mv+lbyPe8K0p7Qriq87BDl/+328gu1zIc+dizpmDmctN+6wkORw4m5tJ6zpGbx/2pibkqmocjQ2IivK/35EWi8VisVgsFovFYrFYLBaLxWKxWCwWi8VisVgsFovFYrFYLJYpXu8Qki5BEG4Dal/Ztmmaf/86r+ctI1BSxvkDe5l/1fXMXLsJQ9dxerzIdieiIHHVJz9H36njrLjyNnJPjxIbzhfQ2yrcSOscXPPpLzA5PITN5cRXVEJWdlHpqkMRz9OX6GdZ2SpsRjl/8aMw4aTK97Z3sqimgFNDMf71prm4bDLiaxRcAEiyiD/koqahesp0u8uNaRqse/9HiY4MMXj2FIauUVzbgM3tIXD99QCUpFP0HD1M5+GDSIqMI1DIXq2EHx0cY/vbP4jrsQ9DKh+0ojdt5p4zSeYuOEFfrJerZq0mcyo8Zb16rZ+TT/eiZvNfhq9sLcAVsIox/1gK3HZO13jw3lCPciaCEXQwWePBH3SwrK2UYwOTbE5IaFv6SJgQkN3c8M4v0hc/wzWf/gKCrHDdZ/8P0ZFh0rH8qMK6qlLROpPqWXMJFJcwa81GcukU3UcP0bricoIVVRSWV2MzHKy5+QPse/YB9jx0Ly1LVzJ73SY0NcdI53kqW9qomTOf4dFO/KF5GBOXijQFp0xWTFI9aw61c+cDMsNdWUIVLmJjo5iGga+omGwyzWh3Fyve8W4ANnzoE2y/+8fsfeg+lt9yO6IkkU0mCZSWkZqM4PQF2PyxLzHam2XzJ75KLhnGXRDANEFE4PLb38dYbze+wmLcBUEGz+5j8ZX1RMdtDJ3Pf7lflP53BSOW31L5Aop2/DuTxUuQ7dK0h20O6WLB8Su5FTc/OfsTrm+8HkVU6In1UOGpIJqdOlr6wtKFLCxdCMBId5SqNo29j14KO5m3oYpQhYsb/2o+ujaMzS6z8KobyKQSrH//R9EzKqFEMekHhsiKAggCvmtKefaJ/6F1+eUkJsOIsowkycxYtQZJFpFkkZJaP6aZL4xXnC4MTaVu/iI0NYe3IEQ8PEHrys2IosbMNRtIRScpnVNFatvU4npHUwHO4ksFHqU+Ox9f28B3X+xEM0waitx8dHUjWU3jxqfHgJfPu6MsqinAY5dI5jT+47l2TDP/SFYz+M6LHdy2pJoZZVYx9OtJUBTcixfxUuEEXbEuamwSTpwcGT9Co7+R8fQ4AHee/AHvWfYueg/tBWCsp4uxni6u+9xXSEUnqWqbQ9+pYwCUN88go0k4GmsQkpM0VISIh8cRTJi17gqqZ82l2FdD7rFRMol8YefimZtJr9LQFQNJllFsdrLpNFd/+guEB/pQHE6cviomh4cwVBWb14eazTB77SbO7tnBylvvQM1k8BUWISkKx7c8g5pJc3bXduZfeR3jvT1APkCl++hB5m66ip6jR7A7XSy+/iaal63CNI388rJVQPRmFyyvYCydg2adbPsrzqGSgC2zH2nrB9Hn34HpD7L0lncT6e/G0HQq22YTKKsgk4gzeO4s5c2tKDY7ks2Gv6iYTDLB9rt+zDWf+RKmoSMX+RixxSgXynlg6Flu8K6l98RRMEGQXGRSVQx3dXP5rS3s+EU7M1dV4A7YOfBkF8FyN6ZpYnfJSMr09wTFIRGs8CCPpXnqf46z5Lpa7E4bil3k7N5hCis9qGkNV8DOyssrEJ/rwUxrmIC6uBT35tppASSvZHfJ2H/FRy9BEBCsAJI/uNcKebHX1mKvrf2Ny/qKivEVFRMZHiIZiwMmiUiY6NgohZXVlNTVE6qqobSxBaXQDSMQvvcs6CYI4F1XBSV2yhtnUNLQwNmdLxIoLaOorgE1l6Ooto4jz32T6vnriKZU/v66mXzl4RNMJHO4bRKfWNfIWDzLZzY04XVa50TLb0exy5iSyMRdJ9En86Eh9tYCAtc3kkmoyIpIeDBJeDAJgCgLJMJZhjuieEMOguUeMLkYQAKQSajomjltXaIgIIoC/WfCzFlbyeRIiq5j46y8uYntv2hHuxDgYBom+x/so+wOnYHCGI5DHShOJ5qWY/DMKdzBIP6iUnbe+zNu+vLf03PsCFoui7uggKq22fhKivEGi8gmE6y89T3k0mncgSBlbbMpKrsQOljSBtv/ndDZZ4it+AlIQv61eIGt2U+mppwPPNFFfVGAty34PMsbQpQHXH+oQ2F5DSZQHKgm/dgg8bEuAMoqK3HMLiGjpsjEbBx5tg/ThPZ9w7QsKyNX5kJyKcj9cTS/nUmnwrYfnqS00Y8ctNNcGwBAlkTev6KObWfHANjXFeb+2xbSORLn7tPD6MDfXz8Tjz1/Pq0qcFFVkD/+uf44aeHCBl7gml+E5LsUTmWxvEx22PFoPuJPdhJP5T+nzZm3CrHczfHdzxMe6Kd27kI2fPDjeEOFZFNJoqMjnD70Ig0bFiP12pEjArQEUas8pB4YBcDvVKgrdPPjnV0Mx7IkshrfuGUuAZcNUzPQolkEUUAucPz67Qs5kQJ29MlL98xEt4Ic+vXLWd56ir12esMpZEnk8qYinjo9StdEiu/OqyH9XF9+Jlngqus+zYjWi39BJandU+8dmQGRVGeYRHiMXDpJcV09vSeO4S8qpmrWXPpOHmPxtTdiAmMDPbStXU+FPcayR74Ak73gLYOb7wRv6bTt+338us9Kkt+PZ+lSWLr0dV2nxWKxWCwWi8VisVgsFovFYrFYLBaLxWKxWCwWi8VisVgsFovl9Q8heQSIAgeB7G+Y98+CIAjMWHE57Xt2cmLb8/iLS1l0zQ0UVdegq1l2/PynzFixGq0jiTacxr2sDMljw8zpSFknPaNjaFqOWO8I46rE3zw9icl5vvfuhUgJL3/zxGlSubGL6zs3muCfbpjFFze3EnDniyQMVadpcTHn9o9enK+k1ockC4SHU9S8xmC/giiQiUfZ+qPvkEvnx4EVJYmrPvV5imvrALA7XTQvXUFl2ywGw0m6UxJj58f4q43NPJ2WWPW2+wlmepGcfsY9LfTuGGOmEUUzNeIFWTzrq0juGgLdxLOynFGncjGAxOWzseiqWhwuq9Djj6mhpoBTSpQxr4QiizQVeygvcFEedLHQbmP0m0cuzayZpJ8eou2Tay4WQhRWVhEdHeHgEw+TiEzQvHQloiSx+/57mL/5WjBNdt13F/7iEmrnLKCkpB7zQIrc2WHKnKXcfOvfcOTscyhuB7LdznPf/ybFdQ1kE3FEUcJdGsK1qpzM9jG07gRymQtldYhjR57j0JOPXNw0T0GIWRs/jaY2UFYHyaiApNhoXbGGx7/xz+TSaewuNwuvvYHCyhqGO9pxB0M4fX72P/oA0dFhcpkMdfOWUD3jZpSIkwAe1LEEo8leTh98kYHTJ1l83ds5/NSjRIYHqZ49F4fHTmzkBULlIdpWLqG0/jeP9Gn5HXhLYN3f4I4M0bQoSN/p8MWiMVEWKKp57ZCMYkcx1zdez6Odj5LIJbim/homs5MUiUW/clW6ZnBqxwCzVldgdynk0hpndg9RO7cQQRij6/ABdj/wczBNFl37NhLhCRYuvRYhZuLbUIOR1RGdErlzceqaF7D9njtpXHQZdo+HRCTMomtunLI+QRAIlXtIRTyMdHeQmBgnl0njDRZSPWce+5+I4wnI1C30gZhAqHXgvqyU5MFRRIeEb301SsmrCipNmFXm48fvXUxa1Qk4FY72R3j7gio+fHk939/RiWlCud/Be5fXcn4sTk4zLwaQvGwypdJW7sOYXodqeR1ohka5u5z72u9DQOCGphtQdZVwNh/gpZsm9qIKNn/is5x68XkEUaJ56Up23XcX473dNC5Zxpr3fAiby4VNs+MYS+NyBcg5K8g6JlBzafpOHqd9704WbL4R42ASI3FpNOTsyQj26iLGRIFw7172P3pf/gFBYMOHPoHTU4dkc2LoCsmhXfiLS3n629/A0HWal65Ay2WpbJvF1h/9D5GhAZbffDsDZ09RWFOHYrdfDKcC0LJZRFFi7hVXXRwlOVDy+hYuWf7wPMUhtMvdCLJI5nQEOeTEu7GKjE1g/Pr7+MZJN+tjdlRHNXKphyqnRu+uraRjk4z39RKqrKLj4F4a5i9hqPMcg2dOcW7fLhZefQOeghCnch0klAEmo5McGzvGguB8ys0mSgoqwCkzMJHj8K58uM3kcIp0XEWxSyTCGQpK3cxdV0k6qXJyxyDzNlTzws/OXNx2m0PCG3QwdD5K17F8Aem+R7u54kMz6To2QfveEURRIJ3IserGBuQdfejpS0X56f3DuGcXIjUXvLE73fKGKygtY+bKK2nf34Wuy3iCKjvv/RbB8jKKaxuQFAXBFIhv7bsUemBC/IU+fHINDcoc2o/u59CTjyBKEhs+9AlmzFyJHBW59pq/wuX14jYSCMC3bpvP4b5JZFFgYDKNxy7TXGIFf1l+e6ZukHhp4GIACUD2TITs3Cj97WGW39TIS788h6GZiJLAws21nNwxQFlDgFRUpbBSoKTOx5x1lRzb2g+AIHAhoMR9MbwEYNbaCkRFoHVRMT5Vp9AtU764BEMUpoSYAJgmBPVi7jz/Td5r24iZSlJYWY2nIMiJbc8z0ddLSV0joqKw+t0fYMuPvsORpx9n+Fw7C665ATWTJlvextnBCHY7NDZUU1z1iuuGYD3cfh/pgSy5nRH8V9aR3DOENpFGmVEAqyvpDSd4z7JadMOktdRnBZD8EXiCIdTdEfSxzMVpWn8SoyfDuNiDzduApIiQ0RnvSzA53MmS6+oprPJwciBJ394RUtEcdrdMYYUHKa6S7o1hjKRAEFhS7ubnH1rKqa4wV6AgPNBJqWqwbnEpjpUVeIpe+5grZW5Cd7Qx+WgH+mQW17xivOurEX5FuK/lz5vbHSD1QD/GK85z2SNhXI1uPAWFnNu7m67DB1j/gb8gHh5n64/+B0PP3/c85HiUVe98N6fOb6N1zvtYWFXBLz60lKFoBlnM39+dXRmgMpljbWsR86oDaJMZ4i/2k9w7jCCL+DbV4FpYjPQrAsokr43Qe9qIPt5JtiuKUuUlcG3Dbwwv+V0ZOZ3cQAJtOInoUbBVev9g67JMNTCZ5vxogkKPnQ+urGNjWwlLPE7k+9rxrLsQPm6YCKpEdfUsDDmLc24R6RPjSB4F5/pSdm25F19JETNWrSM6MszkyDDeYCGSouAtLKSitQ1vQZBAeSXVM+dcWvl7HoP0BHjLwVf2x9kBFsub1atvplosFovFYrFYLBaLxWKxWCwWi8VisVgsFovFYrFYLBaLxfIW8nqHkFSaprn5dW7zLc9TEGLBldcxc/V6JMWGrCgYuk5sbJSGBYs5u/NFyior8FxeQbYjijqQuLhsy9sW02f20es2+dGpNIPRfCDIj3d2MacyQCqnT1nXrAofLWU+/K/4Aror4EC2Syy+po7wYIJAsQsTMExwel475MMdCHJ+356LASQAhq5zYutzNCy6DFl+Rfs+P9UuD+dOjyKKAuFkjqDbRszfTFHjAgBKgH+8oZSzkTNk1RTCniipzizuBcUgCKQOj1Lx9iau+tgctKxOoNRFUZVVZPfHZpNF5lW/dkGtHs9N+V0uduGcEcRI5DADdgRBAMBfXMJVn/ocj33jaxRW17LrvrsormsgHYtyZtd2EAQaFi7BEyqE01n0M/n+b6Q0Ms+MsPCd13Kudx/JyQiSojDW3UXr8svZfveP6TpygKPFT7Lm1g9QcHkFh7Y8jvtMAUeeeXzKtiUiEyhKhCPPCQRLm9n1YDsAdrfMhg/9HYY+TjYFNlcxex/6LkPnzrDsptvYff89F9uYtWYjRTX11NltJJ/r5eVSqZLGckYKyhngJIrdTmR4ELvbTUVLGzvv/dnF5c/t3kpV678Bnt/nkFh+FYcXpcxL5kQPy9/WyMRAAlkR8Rc5MfXX/sJ3hbeCo+NHaS1oZTIzyZNdT7KxZiMhZ+hXriaX1ll0VR2Hnu0hFc3h8tlYeGUtGCbjgz3sf+yBi18wtzmc+IpLcDo85CYjpA5eCoLyrCwnqFSi5bL51wFw1Sc/h2x77XNyMhrhpXvuRNfyxU2dh/az5j0fZOUtMxntPIfDWU7lytUY+yZJd0bxLC/HzOrEnutBDjmRvZdGjW2r8NM3meaLDx5D1U2W1BbwuStaCHrsLG8I4bJJOBUJuyzyb8+cpSec4v6PLkMSBfRXJI4Ueezs747w5LEhvvGOedgV6bc7Vpbfis/uY9/wPio9lZiYnJ88T52vjhPjJwBYW3oLw0f3cfaRuyhvaqVt9Sqe/943MU0DgPP7dnN+327e9ZX/i/HMJPp4jAQxEMB3cz2TE33oqRw9xw5TXFJHfX/ztG2wGy4kJXIpgATANHnxJ99n00c+yVhPHyX1jbQsXcXxLU+j5fL5c2d2vghA87JVlDe3EhkawFdUwvKbb6e4rp6H/+0fp6ynpKGJUGU1Zc2tf4hdaXmDONxeJs+OYyR1vKsr0aM5Ju8/T++6cm595hQQ5YoFIutnlNA97iN7/gimadJ3+iSh8kp23/9zAPpPnmDWuo3UzF1AYXUNJY3NCLJA0F/KL49+l/3D+9lUu4mNqeWkHm6/uP7iMjcLVpXT2xVjYiBB9cwgVW1BFIfE/CuqSUby/XPu+iqcHoVrPzWXjkOjOL02iqq8TI6kUewSC66o5vgLA8TDGaJjadIXrnc6D48xY0UZkmbgmhFCK3GTPjEOWv68qMes/MU/F74iNzNXNRMZSWGaJmvf90ViowPYnCJaLkF2Ip4PlPTbcc4pRJBEMmfDmDmD1PYh6tbPZa/9frRsloBSROb+QcxM/jOd+uIEa943k6/u7WJ/T5jNM0tpK/chSyJeh0yR1yrktfz2zKxOtit28Xe5yImzLX+dW90aIp3IsWBTDQWlLqJjac4fHGHmqgqS0SySLNB/JkJpvY95m6qpnVNIJqlSUOyioMzNlR+ZTc+pCcZ6YtTMLKSiJYDTYyMxnib6WCcvl+PbG/zMW1XOkR2DF7dDEAUkr8mXZ3yVka40/tggJ1/cwuz1mzm1fSs2p4tr/vKviY4MI8oy1/7llxjt6kC22Tj0xMOMdJ7nys/8NX+/Ix/MZt8Z5vFPraSp+NL9A1X3EX/wKHo0R7QzjmtWCOfcQg77JdoKHFxX5WcylcPjkLHL1jXsH4OWzaH3pkAWcM4qRA460MbS5HrjHDz1CCtueTcNi4o58cIAAMFyN4PtEbwFdjoPj6FmddwBG/PWV3PwmR6a63yEf3kcM5e/FhbsEgs/PId5lSEm7jz5ckYlmb3D2AocsOa1Q0gEScTZGsJW6cNUdSSvzQogsfxKRjKHOpAEARwtQZRyN3osR2Ysxva7f8SqW+9g+90/Zv+jDzB341UXA0gA1Eya8f5e4hPj2OL5ewUFbhsFbhv37u/liw8cp67Qjdch8w+Pn+bvrhO5LiuS3D0EgJnTiT7eiVzoxNka/JXbaCv3EHpPG0ZaQ3DKSI7X+08Bl6RPjBO579L1uVLppfDdM5D89l+zlOX1UOZ3cv/BfsoDTr721BnWtRazoqkU7+VVJF4auBSUIwsEb21l8rEulDI33pUVGCmNSGKE+oWLOPzs4zi9fsZ6OvEVl1BQWkZkZJhkZIKimnmY4mvcIw7WArVv4LO1WN46TCuExGKxWCwWi8VisVgsFovFYrFYLBaLxWKxWCwWi8VisVgsb2Gv9zePdwmCMNs0zeOvc7t/Euwu98WfM6kkkiQTnxhnrLcbaaMbxeEisX1gyjLxp3uRb2vmSw/tmTJ9Z8cE86sL+NCqeu7c1YWqm5T7HXx6fRNZVUe1SShSvlAiVOGhoqmAl355DqdXYaQrxvxNNbx4z1lWvK3xtTdWEMll0tMmZxLxfHrJq9hkiStnl7GgpoBUTqfU58Bpm1rMo8gis4raCGRdmCcHMDWDxM5LxUhmJEvd4tJXN215k5ICDhABA1wLihEkkfjOAVKnxvFfUYegiMgFduRCF1Vts3nX1/6T41ueAaB27gIOPPYgAPM2XkXfqRNU1s7EaNemrccYz3DoiUeYsXo9qz74KXbd+W3a9+xk9bs/wLl9u8CEtJxl9OQeju94liU33AwI09pxeSXWvrsZl+9SyEM2qfH8nQMsvqaObHICJXGO6tlzGTp3BrjUzxddcyO9J45SVTOL5AtDU9rVzsdpXreM47uewzDyBU8NCy/j1PatU+ZTsxmGOtopqqn7XXa35bdUN7eIrT89QyqaRdcMyhr9LP8V5zmn4mR24WwG4gMUOAuYY5tDra+WmYUzf2X7NofE/ie6Lo7onorl2PdYFzf81Xy0bBYte6kIPZdO03XkALMa15DtmJzSTmLPMGXvbaFq5mxymQwLr76BunkLf+V6R7s7LwaQvOzUjhcIlJaz5YffBuDGv/wbnIdAj2RJjPVfnC/bE8XRfKlQxKFIXD+vgsvqgmRUgzK/42KAiM+p8J/Pn5uynoqAk8qAg399+xy+8vAJ0qpO0G3jL9Y08I3n2jFMk3AyR1nA+Su33/K/N6twFqPJUXrpBQHK3eXEcjE21W5iU/W1HDnuILXrHkzDYODsKapnz70YQPJKckwiPf6KcAQT0luHqF87l5pAG20LVrNny/001s/FOB2fsqy93IM5OsCrqdkM4/297H3wXgRR5OpPf5FsMjFtvkwihu5wojicyHYbu355N42LlrLxQ5/g+JZnkGSZxsuWU1hZQ82ceRfDqyxvTXoyR/rwKEZCJddzqeg9lMifM25dUk2hR0GRRJpKvPSO+QConjmHvQ9dCrqJDA2w4+472fDBj7Hz3ru4+tNfwNA0asuauLr+auYUzWGm0oL2wMiU9RtDSSpnhiifW4ggCBRWeXD5LhU8Ot02CisvFagHyzw4vQrP/uAUB5/qAcAdsDNzVTmty8s48EQXpgHFNT7UhMrCFaWE/HbMySzx3UNIXhv+K+uIPdeDmdGRglY4xJ8Tp9eG05u/pkylvfScypL6/9m76zi5zvve459Dwzw7s8wrrZgsS5ZlljmmmJ3ESRrmpJRym6R0c5ve3qbtbdM0zInjOGbHzExi5tUyDOM55/4x8q5XsyKL5d/79ZLleebQ7D5z5jlH8/s+owUUXiR+Xhv6bA2jzjceTOZb3oDirlx6K9vL1LZ1khwawNGvUcpPFCTbJYvya/3MbwrRFPGQK5b5+oMbGEwVWNgc4j1z63E7jl3xsDi9KC4d96wI6Wf24F4QQ3VqpJ7dg2tGmMYZEew6D+vWjpIezTO4M0V9Z4hcssjO1SMM7w1G1XSVKz8zl5ZZk0P6QrUeQrWTQxxKQ1mSD+2Y3LYlwexbu1n32gCFTBndUDnz1mbiLS6aQo20BS1e+e1qYq3tjPZWxq/FXJZiNo2qavgjUV6865fsWjP5FtOeNW/SFJ7O7tEchbJF71h+UgiJNVbETOwNzSxbZN8YBGD6rdOJB1xki2XKlo06xXWjOD5y6SRGh4Pg4k7sbAm7ZOFo9eOsiZJ+ZogNzz3JBR/6Ai63zvZVw8Ra/ARjHnq3JmiaGWbbG0PMWNbAi3dvZfb5jShbE+MBJFAJ4cm+OYA6ReBC9tV+vEvr0NwGY9kiZdOmxj85JEHzGYBRta4Qb5crptFaPfhmxCnsTqGoCnrERagxQk1zG0O7dhCM12LbUCpWB9YVslkcLhea5qZ/Z5JBzcbr0Hh8wyANQRfLOqKk8iU29qcYGc2RXZep3sbm0QOGkACoLn3K98LRVE4USNy7dVJbaXeK4p40bgkhOeamxX0s64zyo+e2c/OZzXidOqbfwNydngggASjbZF8bwDUnSn71MKXeSp8yzvGxdvWTZMdGcXq99GxYT7i+kXKpzEt3/ZK5F13GSF+O2g4JZRLiHZEwEiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQp6Cj/Q3kc4APK4qyDShQSQKwbdued5T3c8oznC6cPj+6w0GpkMcR8GKOTRHAkCvjn2JmXgWFxa0Rnt20kU+d34kNuHWNntEcf/SrlVw+p45PntdBR8yHqiq0LYiiGQrDuzN4Ag5GejNYps2aZ/fQPCuC0zO5uKKYy9I+fxGrH/vdpPY5F12C7nCwP7WBgxdgNkQaGYgOU+7PTmpXvVJQdyox4m4it3Qzdu82NL+D1JO70YJOvItqGfn5eijboCtE3zcD14wogWiMeGsHM5dewLS2M2l8/3Qst4U3EMXripDPZYif1U1hcwJHgw8rVya/dhjcGigK2dpu/vLFHLe+94+Y0eCgq7WBBZe+Bxsbw+HkmW0/BmDb668w89wLWPPEIwDMvegygrV12HaRmqYCvnCE1jlRdqwerrwQBWradJ798c8Y2LaBhZdfRceiJYz19xFrbScx0A+KwsD2rYRvqMMsj1T9LHz+KJphoGo6mlF5L0353WL5wvExF4p7uPLTc0kMZFE1lWCtB4dz/7ObT49Mpz3YzmBuEI/uIeQKHXD7pUKZ1ukhwnVecgWTvm0JBranSI8ViLW24YtESY9U+taqxx5i6fW3oFhTFDeWLRxOF9f/6VexbQvDeeBzp6ZN9RoUhnp2jj96/ZH7OK/xRszRycVNenjqcJC6YHV7d62fP7m8m//90AZsG3xOnX947xz+48mtvLR1mC9f1k3Y62Bjf4p/fXQTqUKZszujBNxSoHe0hV1hbui+gf5MP4qiEPfEx59LF8pkhnYx4vEDfQD0b91C8+x57Fqzcny5SGMzVsHcd9OY6SJa0El+R5JIrJbWGQvIxLN4xzyUe7Oggu+8RhzNfmp9TeiGg3KpOL6+Pxojn6qETNiWxcqH72fW+SsqwVBvM+Ps83nlnjs555bbefPh+1ly3U28et9d9G3bzPKb3k+ovgF/pIZgvPZo/ujECaI6dbSYGytdmtTujrh4/1ktRD0OumoD4+01La14/EHquqZP+flo2zaRxmZcXh/uQJCwK8zVnVfTn+nHk3KQyK2uWiccceGbU3NIx2vbNquf6iFU66G2PcCutSNkxgrk0yUijV6WXttJeizHGWfVs2B+lPRzPSQe2I7q0Qlc3ELige0kH9mJd0kdmteBo8F3mD8xcTpYtTvBvzy8gY/ObGD9j3q56APLGEvuoWH2NHS3Cywb27Ip92VQMhrO6SG0iJMZ8fNZ89KjqPnqQs7ySIGGbj9fu2/tpPbuOj//88w23rekhahPinnFwSmqgndpPaW+DHrYRerxXXiX1lEezpO4YxOKW2Pm+U0YnUGmTQszli3Ruz3FcE8aT8BB88wIxXyZV+7fTl1HEMdBCtjtko1dqg5Eczo0bv7zMxkbzqB7IFrnx6lX+rChqdS2d7Dt9VfxhsIEYnGSgwPYloVZLu3NP51iLK0ovJVdpqkKsX0CJBRNAU0Bc/Lnizfo5M1dY/zTQxtY1ZPgstm1fPK8DjrfFmAijg/N0HF0eGHIIvVUD6pHxy6ZeObFWXHLJzEMJ2a6yPSFMWI1brauH0XVK5/fde1BGqaFMBwa5ZJFtMGLvXqoah/loRzuebGqdr3OS0mBB1f28o3frSdTMPnk+R1cO7+xKoxEiAMpW0U8y6MUXk9i1LhJPrYLI+7GZcW44WN/Tc5K0j33bEp6mVwmWbV+vK0D27JJjfro6e3lb7bsJlM0+ZurZ1EbcHHfyj2EPA6+fFk359UGMYYtygOT76XqNSdHGKhdtrBy1fe17SmuR8XR5zQ0zmqLYmgqvYk8iWyRb766k780vFXLmokCvrMbyL91XxQw1RLYNrWd09j62kvMWH4u7kCQF37zC7rOXMbInh6mLz+bwEnS34Q4Zcg/BQghhBBCCCGEEEIIIYQQQgghhBBCCCGEOIUd7dSHK47y9k5bumFQzGWIt3VQ09IOpoqj3lVVKOOeH2PEofKBpS38+MVKsbnb0PjcRV00hN38/fVz2TKYplC2uOeNPfzy1crswb94eRe7R7N86/Yz8DkNnE6D0d4sTq/Ouud6yadLTFtcKfq1zOpvQ6qqSjoxxmWf/hKv3X83ZqnI/EuvxOU58gJL1WMQuKSVkZ+tH3+tjrYAel31F6PFyUvRVNzzYhgtfkZ/tgEAz6I4yUd3QdlG9Tvwn9NIbt0I+c1jeObGaGybiX+Nh/Ldw8QubyO3epjyWILZF55LbuUgtNg4WvzkXhtA9TkIXNFGLgZc80X++Kkh0oUyzw14uPa8boLhyTNvt89fxEu/+SWDO7YRa21n6XtvxuH2sOml51j12EMAaLrODX/+t1x4+wyGdqXoH8pS8ugk0z0MbKu8htcfvJfm2XOJt3cy96LL2LV2LcO7tgCQzA0TaPJg7s5N/BycGn1DW7jggx9Hd/p5zxf+jDVPPszsC1bw/K9+Or6c7nRS1zn9WP5KxF4ur4GrPXjIyxuaQYOv4aDL2SWTqA1zi2XsTSMoc2oIzKuha1Ecf8RFKNbFpZ/4PI9+779I9PehOxwE47U4a3xkXBp2fqL4xtEWAE09YKjT2zXOnIOq/RrLnCjqWXTFVTzxo++NP96++jWuvPmLFLclx2cB12NujMZDP7d6nTofWd7OedNjjGaK1AVd/PPvNvDA6n4AvnrvWvxOnQ8tbyORKxHxOrhpcRO6KrPIHyu13uqADp9T55J5LWy338d93/ga2DZbX3uJpe+9mfiMuexZ+Qo1zZVQnERhAJfCpMIH9/wYY/dvQw860ed46PQsZvfQena4d1N/Vidlu8yOsedYZF1LpL6R93zxyzzyP/9BZmyUUG09Cy+/mqd//oPx7eUzaQZ3bOOyT3+JV+/9DZZlcebV1xPvnMa5t32Yh/77m+RTSRwuDzf/9T9i2zaBWBx/JHocfoLieOnLFlGX1aPsTo8Xoeu1HvRaL0tsJ2e2RXAZE4FKnkCIjsVL2PHm68y+YAVvPvzA+HPuQJBgvJbuZeeQTSZonj13/Llaby2Ww6Q4L0buzcGJA9AVnIcRBFIqlYk1+Vm7o5fhnjTTzqwlnymRGs0Tbw/Q0urC5dVI/Ww9AL5zGvEsrmPku2vIPN+LZ14N2dcGcLYFcXVHKgXv4l1l92iWD3/vJYYzRdb1pvjKrdNQ9FE8Xi+G6iL12C5KA1lc08N4FsUxs2V85zRS6skQfzZK861fwE6VYdXk7fqW1DGrwcvC5hCv7xoDoKPGu/czeSMz6gJcMkvCm8ShMWIewh+YycgP1oGuonoNCi/24V3egLMriIJC+qGdFHelcHUEmHFWA8mz6nD7Dba8NojTozPjrDqKufJBQ0j0iBNnV4jC5rHxNsWpocfcuKNuAtGpC5fru7rp27KZnvVrueD2j7HhhadZ89TjdCw8g5Fd25l1/opJIWuKqhJtbOK9QQ///nKOr1w9i87YxHi31J9h7KFt+JY3kH6qZ7zde0ETWb/B7f/xDMm9hfK/fGU324ayfOdDiyVU7zjTHU7sjEl+4xiBC5op9qZRnRpGnZuGfCeKrqH2Zsi/2o+zN8P8aSHsuJtwk49kX5b1z/cy76JmUCA1UiDUGYKtiUn7cDT5Kfdn0GpcmEN5oNIn/ec18WpPgs/+9LXxZf/23nV4DJ3blrYczx+DOMXZlk0ulSBQHyL9wh5C13ZimxblwSxK3o31bApnv0VgehTmNrD8lttZ9/TjaIbBwsuvQlE05q64hsSwj6G+UeIBF72JHFsG0/zgue0ADGeKdKPh+fkmHBc2k980Nh7sodW4cXaFT+BPYIIedOJZECP7+tvG55qCXuvZ/0riqDJ0BWwbQ1XwuXQ+O6cB50iR7Mv9k5bzzI9RHs1PrNceYHv/G7QvXMzgzu28cs+dXPWlP8Xt99M6dz59Wzdz5tW3UtdRf7xfkhCnvrcCVxW5XyCEEEIIIYQQQgghhBBCCCGEEEIIIYQQ4tRzVEJIFEUJ2LadBFJHY3vvBoqioOkGT/7ke1z8kU/hCLkYvXMTwSvbyb42QHkkj3tmBO/yBp7uT7J6T5I/uGQ6RbNS1Pmvj27i7M4oHTEfUZ+TR9b2jweQvOXZzcPsHs0xo65STFPT5OOB/1o1/t3HNx/dxaLLW3H7q4vgdacTs1hkzfPPsOiKa0CF1Y89XPn/I1QezZN8eDuBi1qwTQtFUymP5rGzZYgc8ebFcaQoCnrIhdHgo7gzVZmkulzpo/5zGkn8bjuYNlrQieo20GtcBBY3Yi+wyLzaT3FXisClrSTu24oWdGIXTDIv9gFgZcsk7tnKtsua2VNy8tFz2lnYHGJ+c4iwt7rP1nVN48a/+Bov/vYOhnbtINLQhOFy07d54/gyZrnMkz/+Djf95d8T7AjwoQdWsX04yzfOdk3a1q41q9i1ZhW6cxrbVjcza1mYjS88y5rnHmfh2ZfjCviwtubQ4k70ZWG8So5VT2XZtTbFzGUuWuYuxxd2cOXn/4h1zzxBMF7L7PNXEG/rOGa/C3HsFXenGPvJ+omGJ3cTO7+JJ1/so2txHMPlon3hYm5p/TrZxBiK6mTX6ldQalUCl7aRe3OQUn8GZ2cIV1doyknd9yeTGOOKz/4+W197hXwmzfSlZzPa10ukoYE9GyrFdrPOuZDc6iH85zVh25Xvt5uZEuX+LI76Qy/MdxoasxsqIS7r+5LjASRvSRXK1AddfGFFF/mSxT/ct46z2qPUh2RG3OPJZWhMW7CAW7/ydTa88Sa204XeMp11WRcdM84m9dgvWHXHTwnXNXDZdZ9De6OElSrinlMDqkJ5T6byZzhP6KYuooUG4k2VMJ6x0hCvP3QPLfMW0DxrDpH6RqYtORunz0espZ0H/+P/UC4Vx4+lY9GZbHjuac648lraF5yBZhi4vJU+F2tu5X3N36CUzxGIxXH5/Cfk5yWOvZ7RLKG+NLUXNGNbNoqqYCaLFDaMsCWkcu60WNU6hUyGl+++g4VXXMsFH/o4W197iUhjC9OWLOPJH32X0d4ePvCP/4KmTy4MVw2NwKWtqB6d7BuD6DE3oSvaD6vIcWR3lqd+vnHSuHjeRU00TA9h2RbuTImx+7aNL5+4bxuh66fhWVJL9qV+3PNqQAEt6JQAknepbUMZhjOVc2FvMs8n71vFp5bEuDWuMvK7DdjFSoFwbuUQZqqId3EttmlTHsphpUuYLycZaR8l/p5mCs8NgWnjv6gZ5/QwLR6Db962gHve7KVQthhMFfjmo5sAWN0zJiEk4rDoLgNHow8zUaA8nMc1K4IWdIIFo7/djJWs9OPCulHMwTzTL2rmnv9eA0BqGJ7ZvZl4WwBf2HWg3aA6dULXdpJ6cje51UMY9T6Cl7dhxA58bnb7A5z13psZ7duDVS5z8Uc/y2h/L4m+Pdz/7//MVb//Z1z5+T9mw3NPoTkcTFt8Fs/d8VPOes9N6BdP5+zOGhx6JeTKtm0yL/dT3JSAgkXg0lbskoVe58E1PcIbO0bGA0je8vL2EXaPZpnlPvQQQ3HkQrFaMmODuDpCJB6Y+Lw16juxsyaWbZJ6sRcrW/l9FVYNow3lUa7roG9rgtr2IK8/vJP5FzWjqgoZr4Hv/CbsNwZAUfCcUUthW4LCljF8yxowVvhRNAWjzosR9/Dk/burjukHz2/nmgX1eJ0SSCMOTU1LK1uefB6/K4j/3CasfJn00z0Er+pg9Debsfeeb/IrhzDGfKTsQRpnzKa2cxrpkREijZ08/N0BTLOf8z48g3W/6eGa+Q385rWJAKVLumpoWTOGlSmRfGQHvnMaAdCjLpztQfSDnJuPF0VX8V/ciurWybw+iF7jInRFO4YETh83PWN5fvD8Dt4zr4FanxNjZ4bM2mECl7SSebUfu2ThW96A6jNQyjr+i5pRNBUzW6S9eTFP3vFdejdtoHHGbNyBIP5IhK4zl7Hw8qvxBOQzUoh3xK4O/xdCCCGEEEIIIYQQQgghhBBCCCGEEEIIIU4VRyWEBPgpcBXwKmAzubTaBqTyfgqqpjLnvBU89r1vceuH/g5zIEfivm24ZkRwtPgpbE3gTpeoDTh5Y9cYb+ydCRugM+Yl5JkojPA6tartuwwVp64CYJkWI72Zqu89rn+ul/kXNeEJOCe1B2viRBqaMUslXr77DnSnk/mXXEmkqemIX7eVLVPuz5F8eMekdves6BFvWxx/iqLgXVJH9s1BsEAxKjNslwazYNqoXgPf2Q0kfrcd/7lNZN8YwLMoTnFLJTjBNm0wbVzTQuRWD0/euA3xnMnPX9pF2GvwviUtUwaQAGi6QcvcBTTMmIVZKjLW38+uNauqlhvt7aGYz+HwO6kNuNg+nGWH6cMbjZMZHhhfrn3RmQzsUEgNFXD62phxzqVsePYR4h0d6D4X+mwNUzGJmjN5/CcjFHNlZp0TYM+6X/L6/Wv3HpODG//q72iaMeso/bTFiZTfOFbVpq4fIRr3MLIniz9SCeHwR6L4I1F2rR+mpqUdczhH4p4tuKaF8SyMU9yVYuy3W4h+bM4h71tTVZ7+2Q8wXG4Mp5PHvv/fnP+Bj9A8ax4D27fSvnAxy9/7fjI/3kF2tDD5GN3v/KPepWu4DJV8yZrUPpQu8s1HNwOwvCsqM8efIJpu0DhjFqvLYb74izfIv7gdqATQ/M+1VzBT19n80nNs2P4ii2aswNkeZOyerZjDE7MelweyWKkS2kO58Rm1Q04vl93yefLpJACBWJx8JsMbv7uPWGs757zvQ6x96nFK+Rzdy84lOTjAZZ/6Iv5oTdUxKopCuL7h2P8wxAkX9Djw9GRIbhib1O5oDTCju3HKdWzLwun14guFeOYXPyLe2s7WV19ieOcOOhadQfuCTxJpmHrsaUTdhK7qJHBhMzg0NNfhnesGdqSqxsVbXhsg2uTDKtnk1gxWrZNbPYj/whaKO1KUh/P4L2xGj0sA07uVx1F9/fWD10f43PULGCtumtRe3JbEe1Y95YEs7rlRsq/2U+7J4prtR+/yE1zYDLaN9rZwyNqAi/X9Ke5+Y8+kbU2rlTAncfg8Z8TJrhxEr3GjKKCHnVjZ8ngAyVvKQzk81j4nRxv6tiSo7wwddD9GzEP4ui4Cl7SiOjXUQzw3q5pGtLF5/HG9bxrlQp6LP/YZbNPk0e/8P0L1DVjlMk/8+DvMv/hyhgYGuW/Q4H1LWyYOtWxR2DoGQHFnqhKUCXjOrMU7Pz7l+9apq7iM6nZxbOlOJ0aNj9RjW8fbHNNCldARRUHBHg8geYvZm8GbMwnVennxt5X1FBS6l9aSzZs8+ehuLrt5Gp6dSTLP7xlfP/3sHsI3TsOzsG58W/F97oEB1Add6Jp6LF6uOE15AkEaFs1GTzoprBnD0eTH2RnCHM2PB5C8pbQzzfybL+OhX/wbPevXsODyq9n0qk0xX7kGGxktUChbjOVKRHwO9iQq12zzYn7UV8ewATtvknp0JwChaztPmgCStxhRN8GrOvFf8M7G5+LIxPxOtgxm+JeHN/LjWxeSe7GPUm+GUn8G96woiq5ipks4OgJkn+8j+/rEPdDsRTC4czvTlp7N/EvegzfUQLhe7tMLcaRyFHmtU+FiCSMRQgghhBBCCCGEEEIIIYQQQgghhBBCCHEKOirfBrZt+6q9f7cfje29W0Qbmtm+8nXSoyMoDg1Hsx/XzAh22ULRVRRDRXGozKrz8Ynz2vnvpyqzw/qcOv9w/Vwi3omiiem1fi6YHuOJjRMFk394STetkcqMk+p+CilcXgN1itnbNV0n3taOZZY5++b3ozuc+Gvi1DS1HvHr1vwOtKATM/G2QnkFtFB1EYg4NTgafMQ/M5/SYJZQvIvUUz3Y5UpogWdBjNSTuyoLKmCOFTBTRbSwE3O0gLK3+1nZMqrPwMqUAHBOD+NsDeBxa/z7xd2EWoLUBg9eYKEbDnTDQW27j1I+X/X8jHMuwBsKo2oaX7p4Gh/4zkv81yvD/O11n6J284skt21g+tLldCw+l9/+yxYCMRc7VufJZpZw9i3LUDQbly+Aw5mnvrMOGz+x5iyN00s4XMO8ds/a8X2Z5SL3/9s/8YF/+Bc8wdCR/ZDFCad6p/jIdOsU9imOG3/K6+DFuwtceJ6GszOEozWAbVq4Z0WxSxaqfugFbumxUZKDA5PatrzyIld8/g+Zf8nluP0BNN2BOTNF5rneScs5Gn2HvJ99tUQ8/OEl3fz9/evG25Z1RNgykAYg4nXwJ5fPwOuU4qITaV5ziHO7anh4XaWP1AVcNLS2Mm3xFzj3ttspmQ5Kzwxg5MpoPgfeM2rHxxq51UOgKOMBJAB2wcQ/5seYVwkV0QyDxVe/l64lZ7Fj5RuM9e3his//AZqmU8xl8QbD+CJSoPRu1xB0MdwaQNsnhKTUGaAjXhmPlksl+rdupm/zBpweLzUtbcw+bwWvPXA35XyePRsq55rkYD+Lr76OhukzD7hPRVPQpigiPhQO1xSF6B6DnauH6ZoXRQs68Z3TiOrSsO3K+8IqlLGKZYJXtKE4dIx6L6oUrr9rdcX8XDO/gbvfrISEzK338VcX1KG5jPGZ5Yt70uTXDFeu6xQFTBsrZ6I4NYyuIK7mZgLRqT+nHbrGp87v4KVtw/TtvW66ZGacM1rDx+01itOHo95H7BPzMEdzlJMlFE1BNSpjUaPRh3t2dHxsUHZPdX489LGeoqvowSO/tm+eNRd/TYznf/kTCtkM/Vsmwn1yqRRGyzy+ctY0anxOLNOkf9tmxnr3EO9qpLQnM/n4O0IATK/1ccnM+PiYCeBLF0+jNeo94uMVh88V8pEsTxTlehfGMZNFFMvmrRsFeq0Hz/zYeP/UfQ4scyIgcXBnisGdKZZe004+XeLNF/pY5FCqAky0yOR7CedMixHxbmEkUwniMTSFT1/QhVOXz3VxeKKNzRRDeUq9OVArY0bFmLjW955ZhxZ0YNug+lwsu+l9aIaPNx/N0rMxPb5czq706yc2DPDVa2bzl3etxrLhmZ4xrm33o6wfm7RfvebkDMJT1Hc+PhdHpjPm5WvXzOavfruagm2jhhz4poX3jkMr910Vp4pdticFkOh1HoItQS780CdwemOM9vmp6wycwFcixOnj/xnPcO/NGq1DI9Se6IMRQgghhBBCCCGEEEIIIYQQQgghhBBCCCEOk1QNn0CBeC25VJJysYjq13HPq8G2bPSwB0UD/0XNqD6DoNvBF1dM5z3zGkhkS7REPLTVTC6Sifqc/K8b5rG5P0nBtAl5HEyP+1DViYCRUNyDN+QgM7Z3tmMF5l/SjMvrYCr+aA3+aA2lQgHN0FHViWKMYtmkZNrvqPBcCziI3NbN8E/WYaVKKA6V0HunYdR6Dntb4uRhxDwYMQ+2beNoCVAezJJ7cxDFqe39oruGXawUuefeHCJwSQuJB7Zj5U20qIvcuhGCl7WSuH8bzq4Qqlsn+fAOFIfK0rMb0MfK5DeMYNR50Q6xqK2uo4srPveHPPGDb5NLp5ix7FwWX3U9qlbpy0vao/z6U8tYvSeJw6GxaNHv0eDXMZwubNvmko84eeKnGwjG3Gx9fZDh3SpLr/Hx2r3/ykjPLtyBIJd/9g/omJdk5xuvsOi8q1l+9Qd4/al7ySbGAEgPD1MqFA5wlOJU4ewKo7h3TcxorEJ5ZgRt3Si+SHWfDNd56VjYhOIzcM4Io/srhUfYNv4LmrCNwwghGR6uahvr70VRFPzR2Hibb1kDxZ40pR0pUMC3vBFHa6V4JFMoYWgqjsMorFNVhVvPbOaM1hCj2RJuQ6Mx7GY4U+SmxU2013hpCsu5+0SrD7r5xk3z2TSQJl8y6Yj5aAhVitIUbwi/rpGda2ENZ3HNr0HzGrC33tOzrI7SWK5qm2pWI1LXQD6TYdWjD/LcL39CuVSkY9ESzv/gR4nUNx7PlyhOAT6XgTWnhnxfluLKIQAcMyN4F8TxBir9ccebr3HXN/4O9s4CHIjVcuXn/pDXHrynanuFbHW/fItdtiiP5kFR0CMuFLU6UO9gatsDeEIOsm8bF89YVs+Ld29l9sIY3jNqKWxLYJdt7JJJbu0w4Ru60EIujOjJWfQpji+XofK5izp539IWCskxrFVP0jAcJJ8eRfMY2JaNsyuEYqhoISdWyaS4I4nTqeHoCFLoCFHjNygNZFAMDT1cHbY3qz7InZ9eztbBNC5DY1rcR9Az9bWbEAdj1LgxatyUk3lya0fQQk68Z9ejOjSK25OVwLyyhbMM7XMjbFs1AoAn4KCuM/SO9mkmC1i5MlrAieo+/HsHvlCE4T27q9rz6TQLFsymIRogm0iwa81KHv3uf5JLJbnw+o9S39aCuT0LCngW1+LsDJIrmbgNnb+7bg43Lk6QL5o0hFw0ht1o7+BzRBw51aXjPruW1F2VwF0UUBwqqt+BlSsTunEadq5M4v5t42NXf9mioa26OF4zNIJxN9vXjTDjpi4Cbg1HXSXkSQs7cTRMDnzqrvXzy0+exRu7xsiXLOY1BZnTEJy0zEAqT7FsURtwYewn2FcIAIfXBXNqyK0cBE1B9TtwdoUwaj0UtiYovVwJRnJ2h6k9o5NCssjs2X7yKYvh3iyRBi/z5sT4ecdSPA6NuqCLmfVns7onQdTrwBf0UkiUKPdmQFXwX9SMcQRhoyeCmS5iZUooHgPdL2OZY8Gha9y0uIlFLWEcto3/bA+FbWOoTq0ShKMoaCEnuUQSdBXKFlrYSWFRLW/+rh/brqF1bg2aoeL0GCf65QhxWkhaw3yzf5CMOnqiD0UIIYQQQgghhBBCCCGEEEIIIYQQQgghhDhsEkJygjVMm8m6px7HLJpYpoWqqSQf2IaZLKLXeghd2wkx8Dp15jeFDritQtnkt2/2cufrPdT6nXz12tmcPz2OQ68US0QbfSy8tBWHW6vMxA3UTlG8sS/DOVFcb1o2L28f4T+f2MxgqsiHl7dx8cw4Ee/hzXLpbAsS/+xCzGQB1aOjR93jxyRObYqiYNS40UNOaj46h2JPGi3kxBwroO0tNLCLJuln9xBY0YLq0QnfMB1zJIdVMol+eDZW3mT0Z+sBCFzcSuqJXeMzGet1HqIfmIVxCLO+6k4ns869kOZZcymXivgjUXTHRF/VVIUFLWEWtFTPKK8oCu3zY0TqvWSSRfZsHiMYU3j9/v9mtLcHgFwywfbXX6G5cTZnuC/GvHOEFk8rLTd8mUce+R8Gd26l++zz8IYjR/xzFSeeo85L7JPzyG4axSqYKHVehnJlus+qIzRFiJJmqHSfVU9hZwLVqTN23zasZBGjzoP/klYU/dAL2WKtbVVtXWeehaZPLgwxYh5iH55DaTiHoqsYNS76M0Xuf2YbP395J501Pj5xfgcLp+jz+9ObzPPvj2/m8Q2DTIv7+No1czirM3rI64vjI+hxsLht4lzTm8hx38pefvnKLrriPj61vJ2uOh/2SJ7kA9sxxwroMTeBS1tRpvgI951Zh6Kq9G5ez1M/+d54+9bXXiJUV8/5t39kUjiZEJZl8+ZYlvvVPLffOo1i2eSu7SPYL2zjA0tbafbCUz/9/ngACUBysJ/k0ABdZyxl8ysvjLcrqkqksWnK/ZTH8qSe2E3mpV5QFPznN+E7uwHNd3jFjOE6L9d9aSF7No2RHM7jdOusf64XTVcJx92M/GIj5kgeANVv4Du7kfymMfwXt5IezWM4NSnOexfbMpjm//xuAw+s7mNJe5gvd2RI5Mr47Qi5TQOUByshOqrXIHzzdGzbJvtCL8VdKdxL68jPjxGJuEjdsYnChlEUl0bwqg4882Kojsnn1oaQezxYSoijQQ+40LwOUo/uIHBZO+mne1BdlQBIABQ498bp1LQG8PgdNEwPE6n3Hnij+7Atm/yGEUZ/vQkrXcJo8hG+flpVEMRBj9XhYO5Fl9K/ZdOk9ulnLachWrmXsWfjOvZsWk8ulQTg8Tu/Q/eZ5zL38hXUTevGDDl5eOsw33pqC4aq8ukLu+iKefnG7zby4Jo+2qMevnbtHM7urJkU4iqOD8c0P9qKMMrGIjhVSpvSZF8bqDypQOCyNlSPgZUpAZB6bBfRT85j+tJaNr7YD4DTo5MJapz9oRloeRO9bKMli6Se3F0JZjy3EbtkwT5ZT11xP11xf9Ux5UplHlk7wNfuXctYtshtS1r4+HkdNEv4ojgAI+KmGHBS2pPFSpfwnddAeTBP6dk9ADjaAmh+B4mfVu51uRW4/JpORtw60UYfik9noD/Bt5/eis9p8JkLOrlqXj33ruplxV1ruH5WLTec20VT3IuvzndY9xNOtMLOJKO/2kh5MIcWdhK+cTqudxhuJQ7MoWvMbAhUxp5vDFDsyeBs9jN61xbsXBlHZ5DiPAX/h9pxOX3kgfRAlq4z6ilky/RuGGX5TdNP9MsQ4rRxSaqPC7M5NjrXnehDEUIIIYQQQgghhBBCCCGEEEIIIYQQQgghDtup843l01AxnyM5NMiZ19yAmS9A2Sbx0HbMZGVG9nJ/ltE7NmGmiwfdVqls8f+e2MyvXt2NadnsSeT5xI9eZe2exPgygRoXwRo3r/9uJ0/9fCODO1Joh/ml9VW7x3j//7zIkxuHWNub5Mt3rOTB1X2H98L30kNOnC0BjBqPBJCchhRdxdUVxr+8kcgt3WgBB9mVgwSuaEOLuLAKJrZl45oVxdURxLu4Dv+yRtzdERSt0h+MBi/FncnxABKAcl+WwuaxwzoWf7SGcF0Dtm0zsmc3yaHBQ143GPfQ0BXiyk/NY/pi73gAyVviLR34tnkwd1WKTa1sGevxBMsuvImupefSteI6VE3ynk4XjjovpfYgL25J8MtvrWbdc734wy60/cyMraoKSslm7K7NWHvP7aW+LMkHtkPZOuT9ZlNJzrr+Vtz+AJphMOeCS1BUlWxyrHqfbh1nkx9HnRdbVfnRCzv42r1r2dif5oE1fbzv2y+yoS95SPtN5Er86a9X8tj6QWwbNvan+fD3X2LzQOqQj10cf6Zl891ntvN3961jY3+a+1f1cet3X4KyxdhdWzDHCgCUB3OM3bMVVddwX1KH6jdQfQah6zpx7i0KG9i6pWr7G55/erzQV4i3bOhL8Xvff5mMDV9+ehPX3fEG339lJz94bgef+vGrJNI5cslE1XpDu3ey5LqbmH7WclAUAvE6rvvyX08ZvgSQWztM5oVesADTJvXYLgpbxt7RMYfrvMw+t5FFl7XQMC3E2Td2ccufL8banhwPIAGwUiXKg1msVJE3HtnFT7/yIr/9l9fp2TCK/bZQFfHukCua/K/713Pfqj78LoOvnd/N9ldeIBpvwi6Y4wEkAFamRO6NAVIP78DKlgle00nRaxDrCpF/bg+FDZUZse28ydgdmyj1pE/UyxLvMuWQA+/SBkq70zhbAuRWvu36yIbU/duYe2Ytc85vOuwAEoBSf5bhH63DSleCI0q704z8aiNWtnTY2+o8YynLbrwN3eHE6fFywQc/RqSxafz827tpPYXM5PfOhpef5sn7f4AS0Xl2xwif/slrvLErwcs7RvnI91/mha0jPLC6D9uGrUNZfu/7L7OhX8a3J4I7GiQVSLDB9zqJkf6JABIAG9JP9+BZEJu8UqHMebdM5+LPzaX+mhaC1zRTSBR55rvrePInG3AN58i+0AuWDaZN+ond5A/jHsKqXQk+/7PXGUwVKJk2P3x+Bz99Yad85osDUnQVR3sQR7OP1BO7Sdy3HXN0YjzpmhYm+0r/xAo2ZB7cTthr4Au7eHLjEF/4+Rus6kny/NZhPvi9l3hlxyh/ddcaRrJF/ueVXVzxy9f47e7RUyqApJwoMPyjtePjI3O0wPAP11Iayp7gIzu99Y7mKO5M42wNkHxkJ3aucl+1uCWBc6PCcHIPRZ+Dta8O8MRPNvLagzvwhpyc/74ZBA4h9FkIcWhMKv/GoSiHfg9YCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQ4WRz1by0riqIpitKgKErLW3+O9j5OF5pu4I9EWPf0E1imWSmQKE8uajBH8uPFwgcykMpz52uTwxFsGzYPTBTjDO5Kc/9/rmS0N0spb7Ly8d288chOLPPQvwT58o4RTGvyMX7rqa2MZQ8elCLenRRdxdkeJP65BYSum4Z7Tg3xz86n7vcXEbi4BT3orFpHj7pABS3kovS2Qs63lPozh30cI3t6uO+b/8T3fv9T/OhPvsDapx+nVDz4e+stnoCDUF0Yp3dyEZ5hOjC3VR9PLNRKqXQ+G57LMZCofg3i1JQZK/DAf61i6+tDlEsWu9aNcv9/riT9tuKifZmJApiTz5vloRz228J1DiYQjbHy0QeZsfw8Fl1xDXs2rUfTDV6997cM7ti23/V6Ezn+5+nJz+dKJuv7Dq3Ics9Yjtd2jk1qy5cstknB0EltTyLHD57bPqktWzQxU0Xsojmp3UoWMVMF1g09T//sQVy3NeA7qwHNYwAQjNdVbT/e3onTffgFyeL0tnUoTdmyaa/xsrpnckjNlsEMPQWd2RdeMqldUVR84SjP//pnWDbc+tWv876/+wYdCxejqlrVPmzTIvd6dZBYbt3IER27021Q1xGkdXaUQMxDaXd1EER5KIfSEeLVB7ZTKpgM7kpz97+9wbCERrzr9CVyPLyuUkT8hSVtvPijDYTq2hjo24aZrB5blvqyOC+rw3dNG+7ZUcKtQfSyRW71UPWyU4x7hTjakkM5fvWvb/L0i/1YtZ4pgxWsTAntCPIWysO5yv2Nt7f1ZignDv366y2+cIRlN9zG+/7+nzn/gx/j1fvu4md/+Ue8/tB95DNp/DUxok3Vt50WXPoeNKeLHz6/o+q5ZzYPMS3uG39cMm22Dsn5/ESxyibDe3ZhWEb1c5kSivNtYwJdRY+4cXoM7tgxyB88tQF32Wb9r7eRGs4TiHkw9wY8vV1+zfAhH8/qPdVhe796dReDqcPvv+LdxRHz4FveiP+WNvQVEaib6NP2FPde7YJJ/4ZR+nck+c4zWyc/Z8MTGwZpDE0OhPj5yzvJlSZf053MzNECVmpyAJVdMDFH5f10LO0czVIazGJPEX5bWDdGOFDLxhd7ee2hnRRzZRKDOR7+7hoSQzIWFeJoMt8K3rclhEQIIYQQQgghhBBCCCGEEEIIIYQQQgghxKnnqIaQKIryeaAfeBi4b++fe4/mPk4n2bFRArE65q64jO3b30T1VhdcKA4Nxa0fdFtuh0ZDqHqmwoB7Ypsje9LsW1+09tleMolDDxBxG9XH4nVoaKpyyNsQ705awImzxY8RdaN5HWgBJ4oydb8x4l6i759JeTiHszM00d7kI3BJK45GH8We9PiX6cumxXC6QKE8dSFGuVTihTt/wZZXXgQgn07xwL//M4PbJoo8UsNDbHzhWV6861dse+MVcumJkIZSIc/grh3sWb+W897/ERRl4tSpugzUcHWQSioH/duy9Lw+BDn5ovHpIjGUIzU0OXAkM1YkcYCiYXWKoB3FpaEFHIe831hbG0uuvZHtb77O2qcfp33hYmJtHbz+4N089F/fJJ+ZumhS1xQ8jupCfod2aB//HoeGd4r1/a6Dfy6JE0dXFFxG9e/Y9Oiw72lXVyjrJdIjIzz2s2/x46/+ASO9PezZtJ5NLz2Hqmk0TJ85vrjD7WHZDbehOw69/4p3h7fGnPv5aAcUZp93EQuvuAZ3IEhNcyvn3/5RwvUNNM+eR8+61dzx939FYT/nMwBUBUeTr6rZ0VDd9k7Ytk2pP4OzO0zgklZcMyLjzzmnh9mweZRSYWKsYZVtxvoklOndxu3QqPFVzoFRSyU5lMcTmsWO9avQWl0AqD4D/4oW/Bc141leR97MMJzuQTUqn6mqU0ePeaq2rfmrrweFONrGBnLk0yW2rhrmV99aDbVeUEExVPznN+Ff0ULwynZUxzu/XaT5qvuy6tFR3+EYUlFVtrzyIo/+z38w6/wVzL/sPST6exnavYua5jb6tm7inFs/SLi+AW84wnnv/z26zjwLVYGAq/pYvE6N/D7XjsEplhPHXiGXxTJNWubOJ1EYGr9L6WgLELiklcBlregxN1rUhep3EL19Jnqscu/L59QJeQyUoYl7WumRPFbEVbUfo8nHaKZApnDwIMi3zvFv1xzx4HFWXxcJsa90YpjB1G529qzijTceRF8WAl2tDJL1yQNltcaFp8aNayTP385v4YtL23j77VW/S6+6zzUt7sM4he7Bqh4dtH2OVwHVI+fcYyldMCl2BdH81eczLeZiZGgPlrlPYJMNgzslkEuIo0kd/8e4U+e8LYQQQgghhBBCCCGEEEIIIYQQQgghhBBCvOVoVxF/Eei2bfvQpxh9F0uPjmCWiyiKgrexBtVt4F1WT+b53vFlQtd2oE9RQLEvl6Hx5cu6+fzPXh+fcPiMlhBzG4PjyzimCDPxhhzoUxQq78+ZbWECbp1kbqJw4w8u7cYvBTviKFI0BffsGoxGP2a6iJ0rU+rL4GwLknx470zWCoRumMZQm5dvP72dR9cNsLgtzGcu6GJWQ2B8W8lcicG+PjY+/9R4W31HNwvOugJHykl5JEfJKPHir39JXbyTsBJnbPMeBndsp3PxUtY9/QTeUJhnf/EjCtkM4foGzr7l/fijMUK19dS0tJIKp8n/fCOYlTef2hViw/bK7MnugIPAFEVM4tTkcGmV743vE+hkHKAgzaj14FlaR/bFvkqDAsEr2zFqvQfdXz6TYcebr5FNJkiPDLPshluxFYXU0CC2ZaFqOv1bN5EaHsLlrS7Crw24+fLlM/izO1eNtzWGXJPeIwfSEvHwZ1fO5C/vWj3edtW8embU+Q9pfXFi1Ifc/PFl3fzVb9eMtzWF3KwzS8w8t4n0U7vH24OXt/Pyi/ex5olHALAti8HtW0j09/PiXb+imMsy+/wVtC1YRKCmltqOTmqaW4/7axInv5l1AS7sjvHi1hGumFPH79b2Y+4dlK6YGacj7iXkDjPrvItomD4Ds1giMdjPI9/+DxZf9V6WXHMDT/74uyT6+4g0NE25D0VR8CypI7tqCCtdmVFdi7pwzayEhdgli/JQDjNbQgs5UZ065aEsKEqlgPkgxY6FrQmGvrcG9oacuWZFcc2OonoNHHOirP6n16rWMSSU6V2nLujmK9fMrlx37S0mXvVkmvNv+zgDhR5qzq/HCLhJPrgdu1TpS/pMPwOB7cSaWnH5fKhunfDVHQx+dzWUK+8TZ3cYLewkvy2BnS+jBZzocfd4cIkQR4vDPdGninmT++/czKVXd+KwbFKP7cLKVM6vuTVDRG6dgR52YSYLlIZyqIaGGnRgjRWwTAsj6pkyWE+v9eA9q57MC3vvbSgQuq4LPXzwextTyWfSrH3qMc646r2seeIRLNNk6XtvYWjnNh797n8xf8VloMCym95PtLGZeFvH+Lq3LWnmwTV9459JTl3lstl13PFqz/gyK2bEmVl/aONjcXRlRkbQDJ3U8CDBmXX4rm+htCqBHnBOXPurEL6pG1dXEM0/EfB40cxavvfsdlSPxpJLm+nsCKIUTFS/g/TmUezU3rFC2MlYo4e7XthJwK0zuz7AotbIfsN0F7SEmVHnY31fpRjf0BS+fFk3Pqfc9xIHN9rXy/3/9k/MvfASYtM6yXhzRGa30b91Cw23ziD5261YqRJqjQvvFe1kfruZbLJEGLgp5iJ+/jT+4olNuA2Nc7pquOuNiXOV16Hx4bPb0Q8xVPRoKI8VKI/kUB0aesyDup97H+XRPOWRPKpbR49NjF/0GjehqzsYu2vL+LKBS1rR49VB2uLomV7nZyRv4s+aOKeHKWzcGziiKxjnhFD0MVStOnDENUWImBDiCNhv/WUfeDkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEOIkdLSr5nYBiaO8zdOW4Xbx8m/voG/LJs6/7sM0zOgATSFwSSu2aaF6Kl/cVvY/pfy4lbvGeHbzEH94aTeFsolTVzmjNUJ9aOJL3bGWANEmL8O7M5UGBZbfOA33FLMi7k93XYBffGIZT28aZCRT5PzpcRa2hA73pWMVyhR3pyn1Z9ECDhxNPvTQOytIEqcmq2hS3J2i3JdF9Rk4mv1VRWl6yIkecmJc30Vpd5rB/1458aQNY3dtYeNljfzkxZ0A3Luyl5e2jXDXZ5fTEHIzlCrwjw+sQy3mmFnbwGjPTtpmL+Ts2TdgvpAib/cw4OkncFsXsxvPwXw2CZaN1xHFdVU9j3/vvxncuY1pS8+mkK28b0Z79/Dsz3/E9LPO4T1f+GNUTYNuJ/ZHZ6MO58llyqzbOMb61wYAWH7TNDxTFOaJU1Oo1sPCS1p4/Xc7x9vmXthEuM6z33XMZBFHkx896MQuWyiGiuLVKY/lD3re2/b6yzzyP//B3BWX8+q9v5n0XNOsuSy84irWPP4ITndl/7ZlU+xJU+pJozhUHM1+rp5XT33QxdObhmiJuDmnK0Zr9OABKADZoknQrfPly7vJFk0cmkrYYzCSKRLySL8+mV27oJGmiIdnNg3RGHIT9RrU2CpGo5fwLd2UB7Moukr2jQHOOPNyNr30LOnRYTrOWMLKhx8k3NBEMZcFYM2TjwLQtfgsPMGghJCIKWkqXDu/gZm6QShZ4m/aaunzqGzFZGl7DSG3A7Nc5vk7fsrWV1+atG5mbIRMIkGkoQmX/8AhR456H/FPz6fUnwVVwajzoIdcWMUymRf7SNy/DWxQnBrBK9tJ3LcNu1gpvgu/d/8F8GamyNhvNo0HkADk1w4T/eAsXNPDKLrKOTdN49Hvrxt/vn5aiJrm6gAocfq7ZGYtd376bLKpIsa8KG5Xmad+8p8k+vtYdvVttK+dPh5AAlBel6LzhjMovDaMFczhaPbhaA9S+/mFlAZyqG4NNIXC1gSpR3ZiZcuggH9FC/5zGlEl7EYcRZE6LzPPrmfdc5WAkJHeLIM2xHYkxwNIAIo7UhS2JbAKJsM/XIs5ksdzRi1WrkR+7QgAesxF9AOzqsL1NLdB4LJWPPNqMNMl9KjrkAL49kd3uoi3taOqGumRYZbfcjv9WzbSt3UTWBZvPvzA+LJXfv6PiATqKe5KY2ZK1LlVvn7dHF7bnSDg1on5nXzvmW388WXdKEBX3MfCljBRn3P/ByCOGafXy45Vb7Lq0Qd57f67cfsD3PixvyFzV9/EQhaM3b2F2s8vnLTu3MYgP/7YEuJ5G9eOFMlfVYJBFbdG5LYZlZAn26ZkqPRtHuVsp85d28dY35vCZWjMbQpNeUwtEQ/f+dCZrNmTJFM0mV7rY2adhNSIQ7P1tZcoZNK88rbr9xnnnE8xmyVVGqHjtgvYs2aETMGkZfUwVnLivMtgnvO0Gr64ogunrvEnv17Jf77/DHoTeTRslng9GL05MgMFHE1+jNr934c4Gop70gz/YA1mogiAd2kdgUta0fYJ2i3sTDL8g7WVzxAF/Bc24zu3Cc2to6gKnjNqMRp8mGMFtKADo84rIWvHWEvEQ2hnEs2lUfbp4//WYDT42NG3hpingYgnRuwGWLthjC2rhwnG3dS2SeCsEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhKg42tVMW4EnFEW5Dyi81Wjb9v85yvs5LdiWTTZZyWwJhONYqSJWqoTmNkBRsLJlcquGcTQFUPYzQ+tbNg9m+OlLuya1LWkL8+0PLSbornw5PBB1ceWn5zG4I0U+Wyba4CXWcvhfLp5ZHzjiWYKzbwwy9pvN448dHQEit81EP4xAFHFqy60eYvSXG8cfG01+am6fiRasLv5SDQ27bFE1aWDZwsqVJzUNpApsHUzTEHKzsmeMX7/Wg1NXufiK95H8/jeYv/hyzKdS48tb2TJWfwHzmcTE7IRFi8JDA3QvWM7CpVfg0QI03z6LV5+9h96tGwBIDg9iWSaqpuF0aMQ6wtABqZEcbUEXdbOiRBo8xJrlC/ynE93QWHRZK00zwqSG8/gjLmKtfgzn/j9OrUyJsTs3Teq/qt8g+qHZVSEkpaFcJRzC0FCjBi/f/WsMp4tiNlu13WxiDJc/yIqPfoZALA5AYVuCoe+shr0zvasBB7GPzeWC7jgXdMcP+/VuH8rwtXvWMZguTGr/7ocW0xGTwvuTWcBtcGF3nAu749z/Rg/2cJ5YUANdI/NyL8WtyfFlywM5llz0Xh779f/QeeZZ2KbJ9jdfq9pmNpVg00vP0zhjFg6XzFwtJlvXlyKaMfE9vINSsRK+EHNqNN4+g1i40l+scpn0yHDVuunRETyhMPMvfQ/RppaD7kuPutGje7dZKFPYkaQ8ksMuWmgRF+ZwHrtgknpsF54FMTIv9VHYOEp+0yi+JfVTbtPKm5SH8lO0l1H0ymzzXYviBGvcjPRmcPsdxFv9eKcYt4jTn9PQWNgSZk1PggcjNjdOC7Huqcp51e3yYQ5W9yVltNInAfRaDzUfno1R68Wo9VIezZPfPIZdtPCd3YCVLZN+oZfUIztxdYZwtgeP6+sTpzeHW+es6zrpXBQnPZonUOMmVu9h9NmeqmXNbJnCk7swR/KgKuhhJ8lX+8efLw/myawcwj3DwkoW0IJOjFoPiqGhuQ20jtBROWZd11l8zY1sfP4ZXF4fhtOJ4XKRSyarlg35ahn6wVrK/ZXxswuYdXkLj2WL5Epl/uvJrQA8t7USpPLv71tIzC/n8hPFGwqTHhkaf5xLJckMDGPss5ydK1dd+wPMbgiSfXOQ0Ud2YjR4cc+MYpsWpZ407jk1pF7qI/t0Dx7AA3x0ToR/GE2xayS33xASgMawh8bwsQ14EKen1PBQVVtmdIRF73kvu1a9QeDyAEVFwZcqojy1u3oDvVmaZgX4sztXUbZstg5luGFRE4UdSQa/vbISrgOoHp2aj8/DUf/OA54OxCqaJB/eMR5AApB5sQ/XrCju7sjEctkSo3dvwbMojuLQUBQwU0VKvenxzwDV0HC2BODgw3xxFBkuB2O/3YxnfhzbslFUhfzqIVrnz2bkB5VgRRU487wmZl3QSLDWSzAm1/lCHE2KXTln2/v+m4YQQgghhBBCCCGEEEIIIYQQQgghhBBCCHEKONohJDv3/nHs/SMOwO0PMP2sc3jlnjspO0xQFcxkkdybg8DEDO52yUQ5QIE7QCpfqmpb1ZMknTcJvu37w4Gom0D0xH6huDySq8xS/zbFrUnKfRkJIXmXKCcKJO6b3AdKu1MU96Rx76eYVws5UQx10qzyqs9ge6G677v2zqi6Z6xSAFooW/zNy3k+/4E/xeeLU2ZygZ2dKVcFnKhunSZfN5mHdmMyiheNC5d/kIdL32Zw13YWXnYVulHdX/0RN/6IfGn/dObyGrTMih7y8laqWNW/rFQJe58iusLuFEPfWT3e7uwMMv+cK3jkJ/+PUF110fycCy+mdcFiInUNlW0WTZKP7hgPIAGwkkUK2xIY8XdWQJctmVUBJABjuer3nTh5zcnYqI/sIb23a3iX1IEJxR2Vwl27aNIybQG3fOXrOL1efvW3f8GZV9/A+mefnLSdtnkL2b1+LYpy4GA08e5UKFm0785iFyc+p+2CCetGoKtSqGi4XHQvO5eBbVsmrdswfSbeUJiG7sMLuLEKZVJP7Cb1+N4gPhUCl7SRfrYHK13CTBRQfRNlzMVtCdhPCInmc+DsClHYPDapXa+ZOB7doVHfFaK+K3TIxyhObwOpAj96o4dndnn40jmXsPnh37J9wxvUtV+FuW1ygJhiqOP/X+7PUupJo4crYWRmokDq8Z2YI5XPXC3sJHBRM8lHdmImqj+HhThSnoCD1jkT41nbtvHMrSH1xOSCeEe9l/TeInnVa1Aem9wf9bgHxbQY/I83xttC13XiPbMeRTu644W6ji6K2QyG00kxl2PbG68x/azlvPnwA+PLKIqK1wqQ7J8cAhB+oZ/Lz6/l609u2XezbOpPVbWJ46tj0RK2vf7K+OOsnSGouidd12hRF1qg+vrbLluYYwWMOg/OtgDJh3dUnlBAcekU1kwOPzNWj3DjxQ0My/WMOEa6zjyLTS8+O6mtZe4Ccokxpi9bjqap1O4NF0uMFSjtTk9adrTOzardCQJug5FMEbehYZs26Wd7xgNIoBKom18/fOxCSHJlClsTVe3mcG7y42wZz5wa0s/uqdz7oPLZ4JoVqVpXHF920cR3TiPJh3dg500AHK0BFGvycrlneqj74kIcEkAixDEkKSRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIU496sEXOXS2bX91qj9Hcx+nE8uyiLW2s+jKa3jziQdQHGqlOHIvu2CSfX3gkGZK64r7qtrOm15Dje/kC/Wwy3alKHUf1hRt4vRkly2sbHXRz1T94i16jZvo7bPGi4m1oJPw+2eQdkw+jV02u3b8/dAWnQhe6EsW+Isnhxl0VIecqD4D9qmR8yyKk3lkcliJ+XySpStu4uKPfYb2BWcc+EUKsZcWcVX1Ly3gQAtN9EWrZJJ6ZOekYJLClgRtdXNQVJUNzz/Nue/7MJHGZvzRGhZf9V6yiQShWBzd2FtgX7YmzVL8FjP9zgvsWiMemvcJ1VEUaK85NoVO4ugrj+TQHt45qd4h81Ifru7w+GPFqRFoqaVp5mxGenaRSybY8uqLXPzRzxCubyAQi7Pkupvo27KZxVddh+F0nYBXIk52bVEPzswU55vRyeelWFs7Z15zA/5ojEhDE+fc+kF0p4vaji5c3sM7t5T6sxMBJAAWpJ7ajWdBHKgEOZipif079s7GPhXVqRG6qgOjsTKGUBwaoRumYRyjwk5xemgMuzE0hW3DWdYG5zDt8hvo37WFwgwLvaEyDlUcGv6LmsntUwj/9muf/Kax8QASAHO0gJkuoQYcaGE554pjT1EUPGfW4Zq5t2hcU/CvaEGv9eCaXhkzWJkSemjytZR7bg2ppyYHl4zds5XS0OQQnqOlaeYc3MEQtm1RyuewTIsFl12FNxwh3t7J9X/2FTSrOsDVTJco5U0Wt4WrnpvTGDomxyoOXayllWU33kYwXkswXktez+C7vgXVU/ldajUuIrfOQJsitFY1NLSwE/fsSgjCOBuS92/DMz9WtY7Tho6YfL6LY6OuYzrLb7mdUG09gVgtZ11/Ky6Pj+d//TOcnsn3bt1zomhzo5X7BSqUFsW4ayRF1O9kLFukLephTkMQ27Ioj+ar9mWOHbugMtWj45wieE/bJ9ha8WqUB7LjASQA5YEspf7cvquK40yLusivGR4PIIFKEKmZKU2+R2XZk4IkhRBHj0T4CiGEEEIIIYQQQgghhBBCCCGEEEIIIYQ4lVVXZxwBRVHuoXparwTwCvAt27arvzH9bmbbrHvmKeZfcjnx9i7MKWZiLfVnoWiC68C/qqXtEb5wURf/74ktlC2b7loff3DJdJyGdqyO/h3TQk5csyLk146MtymGihH3HGAtcTrRg048C+NkXxuYaNQU9AP0AUVRcE0PE//cQqxsCc3nQAs4+L24m4UtIdbuSdIZ97GoJUTIUylOmtsU5A8umcY3H91M2bKZWe8n2BogeGU7iQe3g2Wj+gwcrX7CN05n9DeboGyjuHXsgALmPqczC5o7ZuPprgEgm0wyvHsH5UKBSEMTwdq6o/2jEqcBR72P4HvaSTywHcxK/wpd14URm+jvdt6k2JOuWtdKlLn1a/+b1Y89TGp4mGU33MbuDWtY+/Tj+CM1LLvxtvFlVY+Bb1kDiXu3TtqGc+8sy+9EPODin2+az+d/9jr9yQJuQ+Or185mdkPgHW9THF9Wrjyp6OgttlkpMlKcGpFbujH2KSbrWb+GXCrJRR/5NOmRYWzbpmPREuIdncfluMWppzHsYXR+DHPz5BnTvWfEJz02S2WiTS0sv/V2nC4PicE+1jzxMB2LFh/2Pq10dfCSnSujOFRUr4H/ohYSd28BwDU7imta6IDbM+q8xD46h/JYAdWpoUdlNnBxYB01Xv711oV8+Y6VfOvVEeY2dPLPf3IZRilNOVTEYfoxvG6KGzOUdr/tc14Fo3ZiHFDcmazadnkwR/CKdow6uUYSx4cRdRO5bQblkTyKpqBH3Ciagv/cRoq7kpT7c5SH87jnx8i9OQhUruPZt27ZtLEy5eodHAWqpmE4HLx8z50sve4mMmNj2Nhc8vHP4g1FyIyNYvptUBWwJq7lnGfGKbk0rl/YyO6RLK/uHENV4KPntLOoJXRMjlUcOncgSLSxheW3fpBysYjb5+d3D/wXF9/6SXy+aCXA8QABu0ajD0pW1d1Iu2ShOCbfE1MDDiKNfqY3h47BKxECIo2NNHTPxB+L43C6yCUTWJZJIBZH1Sb3R0fEjXVpC5vbfAxmCvxsQx/zWsM0h9387xvnsbg1QsvecF3fWQ2M7to4aX337Ogxex2qoRG8uJVSXwZzOA8K+JY34mjyT15O1yr3rvdR7sscs2MTh0bzGpQGpvjdDOVQDA27WLlPYDR40SMSeifEMbF3bGJV/ZOpEEIIIYQQQgghhBBCCCGEEEIIIYQQQghx8juqISTAViAG/Gzv41uAfmA68G3g9qO8v1OaP1rDvBWX8vj3v0VycIDrf++vMPZZxj07iurdt7VawO3g8yumcfX8BrJFk+aIm4jXedD1TgTVoRG8sh3V7yC3cgij1k3w8nYJIXkXUXSVwIoWVLdO5rUB9BoXoSvaMeoPPhuxHnLC22bgjnqdrJhZy4qZtVXLBt0OPn1+F1fMqd/7vvAQ8Tqwa7w4p4exc2W0sAs95MTR4MfR7CfTN8zmtS+h7Rig1hPHyk4U0ClODaOmcoyp4SEe/va/s+31VwBw+wPc8OdfpbZj2pH+eMRpRnXreJc04GgJYKWLaGEXjvrJMyCrXgPP3Cjp53ontSfNIdY89DRnvOc67v7G3/HGQ/eMP3fxRz+DwzW5QN49rwa7ZJJ+dg+qWyd4WRvO5sn7OlxL2qP89nPL6R3LE/IYtEW9KIrM5XmqUANOtIgLc+RtOXCagrMjhKM5gF7jwqiZ+PyNtbbj9gfIpZLMvegy7v+3b5BLVkIlWuct5NJPfgGjpnpGeSEcukrN7BpSZZvcE7tAUQisaMHZMRGENNKzm6d+8l1G9/QAEK5vpHvZucy96DKc7sMfB+oRd1WhuRZx4ZoZwXtmHapTw6j1oKgKWo0b7SChflAJdHJ4Dj72FgJA11SunFvP7IYAI5kidQEXcZ/Bqsde59Hv/hfYNigKN33pa7iXRCmtTqCFXASvaMNomPh8ds+tobBpbNK2XXOieObWoOjqcX5V4t1MdWg46iZfkxm1XmIfn0d5OIeia2ghB/7zmrDLFopTI/X4LuzCROCZ4tbRQ/sPjDhStR1dqKrKs7/4Me5AEG8oTMusefziK39KuVggGKvjug/+KeYLScyxAp4z6/CdVc+t4UqB9YLmEDtGsjh0lfYaL0795AtufbfRHQ7WPPkI2954FQCn18u57/swetSLI3rwaxlHjQdKFoqhYpcmUnFUn4GzO4yrJ01h8yh6SwDXRc3UtQRQVbmeEcdOqLae4V07ePi//hWzXLmntPDyq3EHqgNCG2M+fF4HO0ay/MO82v2el1wzwgSv7iD1xG4UQyVwSSuO1mMbDmrUe4l9ah7mcB7FoaHH3Kj7hF2rhoZnfozEPqGqru7IMT02cXCa34l7Tg2ZFybfZ9LqXRitPkq70ri6wwQuakE7hH97EEIcPntv+Igtww4hhBBCCCGEEEIIIYQQQgghhBBCCCGEEKegox1CstC27fPe9vgeRVGesm37PEVR1hzlfZ3yFEWhZ/1akoMDALzxygOctey9WK+ksUsWzs4g/vOaULRDKzwzNJVptf6DL3gSMGo8hK/pJLiiBcWhoR5CUag4vehRN8H3dOA/vwkc2iEVBr8Thl79vlB0taq4TlEVjLgHb1gnpnSy/pknabpsJuXHRjETxcqMyTdPx4hWQh/2bNowHkACkEslef7XP+eqL3wZ3XlyBgCJE0d1qDhb9l8gpKgK3rMbKA3lKWwcBV1BO8PPa2/cy6bXn2fG8vN5zxf/hNWPPzweDtE0a3bVdvSAk8CFLXjPqEPRFdSjVEhfF3BTF3AffEFx0tH9DiK3zWDkF+sxh/KoXoPwDdNwtgdRpii+jDQ0ceNf/T27165m+5uvjQeQAOxY+Tp7Nq4jICEkYj80j0FoWQP+uTWAguabfA5a//zT4wEkAKO9PaiaRtuCRe9of3rcQ/QDMxm9YyNWtoxe4yJ8ywyczROf+84WKagTx15r1EtrtDK2HNq1g8e//+1KAAmAbXPnv32V2/7+/1B7cScYKpp7cr90dUfwLK0j+1IfAJ4ldbhnRSWARJw0NJ8DzTcRLKJ5J/4/evssRn65AStZRAs6iNzcXQmJOkZqmlu5+a//kTVPP8ZY7x7OuOo6Hv3Of1IuFgBIDPbx43/9I97/1f9DTbwR1eeYNOYJehzM8xy7kBRx+Aa2bR0PIAEoZDJsfP4ZZp5z4SFvw6jzVvrirzZgpUpoYSeRm7txNvhw3DodK1tGcemoDgmdEceeoii8cOcvxgNIAF5/8B66zz6Pxu6ZVcsfynlJ8zrwL2/EPS+Gok4+Dx9Lut+J7j/wPS733BpKe9Jk3xwERcG3vGFSEKE4MRRNwbUkRr4vhbk9DbqKttjHYw9+l7NuvJW62m5UjyHjTSGOpfFLQvvAywkhhBBCCCGEEEIIIYQQQgghhBBCCCGEECeho131H1MUpcW27Z0AiqK0ADV7nyse5X2d8sxymZ71E9ksW1e9wshADxfe+DGaps9GD7tQnYf3K+pP5tk2lMHj0OiM+/A6Tt5wD0VT0QIS1vBupqjKSdcHDMNB69wFtM5dAIA5s4iZLqJ5DbTgxLEm+vZUrdu7aQOZTJrdQ3mS+TItEQ+NYQluEIfGqPHgubqB/mf3UCrleePJHzPSuxuAzNgIHQsX0zB9xiFtSwsc3YKkHcMZesZyRDwOOmM+DClSOaU4m/3UfGIeqeEcacVmwO+g5QCzsMZb2/GFI7xyz6+rnhvp2X0Mj1ScDvaM5dg5nMXn0ul0eHG/bSy6a+3KquV7N2/A6fa8o30pqoJ7VhTjCwuxsmW0gLMq+ESI4y2XTGCZ5UltZrlMIZNGa5t63KsHnYSv7sS/vKHyOOKWglBxynB1haj93ALMdAnNZxyX67vaji5qO7oAGO3bMyngCirvucHeHUQ72tk2nKE3kScecNJR40ObIoRNnFjJocGqtv6tmylkMzhcrkPahqIouKaHiX92IVa2hOo3xoMTFF1DCxw8fOStMYzfpdMZ9+IyTt77aeLkls+kySbGqtozY6OHtH7vWI4db42nY5PH07r/5AtR0sMuQjdMw3dhM4qioEdcMo45CZRMi/78GCuT99OxdDGlcoHXn/gBo717mH7uOdRO6zrRhyjEaU/Zm0JiIyEkQgghhBBCCCGEEEIIIYQQQgghhBBCCCFOPUf7G/V/CDyjKMoWQAHagc8oiuIFfnCU93XK03SdaUvPpm/LxkqDotA0bQ7BYLwyS9phFses3ZPk4z98mZ6xPADvX9rC7188nZqDzFgpxOnMLluUhnPYRRM97ELzOUiPjtC/bTO5ZJJIQxOx9g4MY+pCDi3gmDLQIdbWXtXWdeYyfrNmlK/evwHbhhqfg+986EzmN4eO9ssSpxjbtikP5bBylSJ5PTT1edkR9LJ504tseeXFSe2h2nrG+nsZ2LaVcqlIrKWNWGt1HzwWnt08xKd+9CqpQhlNVfir98zkliXNuKUo75RRLFvctWGAv7hrFSGPgy+d0cKFLWHi9X708OTCzlIhz8C2rWTGRulYtIQ3H75/0vN1ndOO56GLU8zK3WN89PuvMJguAPDJ8zr49AWdhPbO7N591jnsXrNq0jqtcxey+ZUXqGluI1Rb9472q4dcEDrwMrZtUx7OY2VLaAFHZR0hjgF/NIbD7aaYy423OT1eBiw3tSUTpzG5EL48nMPMlND8Doy493gfrhAHZOXLlIdzgIIec6M6pg5y0ALO4xYuObx7JwM7tqEqKvG2DjyhME0z57B73epJy4Vq63l4bT9f/MXr5EsWDk3lf984j6vm1aNrUhx/Mok2NlW1dS5eijsQnHL50nAOa+95c9+xrB5ywn6utQ7kzV1jfPQHLzOULqIo8KnzO/nU+R0E3Sdf4IM4+flCESKNzYz07JpoVBSCsfhB1121e4yP/OAVBlOV8fQnzmvnMxd0jY+nD6ScKGAmCqhuA73GhaIcv9Al1dBw1Mo45mRRLJvc8epuduwZ4YaFF2Bm8gyP7SI5OABA4BD6ohDi6LFO9AEIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDvwFGtILZt+35FUaYBM6iEkKy3bTu/9+n/ezT3dTqwLJNQXQMdi85k62svc9ltn6NmV4zcz3aTU3bjO7sB/4XNaL6Df9E8Xyrzfx/ZOB5AAvCTF3eyYmaci2bUHsuX8Y6ZuRLloRyKoqDVuNFcUtAuDp2ZL2MOVYo7tagLzW1UL5MtkX5uD6nHdoIFep2H4I2dPPTD/8v2N18DwBeJctmnv0RtRxdun/+Q91/X2c2ym97Pi3f+Asss0zRzDq3nX87Hv7t+fJmhdJGv3L2GH35kCf4pjk+cuqySiZUuobi0Kfve29lli+zKQcZ+sxm7ZKH6DKIfmImzrbqoznA4OefWD5IaHmJg2xZ0w8E57/sQnmCIu77+t8TbO9CdTp775U+44nN/SGP3zMn7eqvIPlWsFNlH3Uf0OvsTef7gl2+QKpQBMC2br9yzlkWtYeY1hY5o2+L42TKY5s9+s4r6gItvndVJ6PEerCf6GfDoRG6bgWtaGKj0n3XPPMHD//3vqJrGio9+mqZZc9m9dhWqpnHmtTdSP637BL8acbJK50t8/YH13D63nm63k4wC317dw3nTYyzvqgGgY9GZ7Nm4gXXPPA5UQkkMp5Nnfv4jyoUC1//ZV4k0NB71Y7NNm9yaIUbv2IRdNFG9BpH3zcDVGRpfxiqYWNkSqsdAdU5dZC/EoXD6/Vz2yd/HHM6jlCFjJWmcOYdn+hUCQxmm1wcAsC2b/LphRn65Ebtgonp0IrfOwDU9fIJfgXi3sy0bM1nAKpgkH9xOft0IAO75NQQvb68KfTie+rZu5nff+jcapneTGRvl8R9+mxv+/Gtc9Huf5L5//SeGe3aiO51c+MGPYwbr+P3/eIGYz8nnFjQTVhR6elPsaAzQGT/06z5x7AXitSy+6npef/BuzHKZ2s7pzL7wEtSiTTmdR/M5UHS1ct5cO8zIr9523nzbWPadSmWLDO1I8A9L2tleLPHvr+zkP5/YwrldNZy9dwwjxOFwBwJc+onP88B//DOJgX4cbg9Lrr2RbGLsgOul8yX+9t51tIbcfHJhE1vGcvzguR2cNy3GOdNiB1y3sD3B8E/WYaVKKIZK8JpOPPNqUJ1yr/XdaPNAmoff2MNfNsVwPpEB00EoMo+m35tN/9gOytkCxZ4Utmmj17jRPHLPUohjQbFtAGzsE3wkQgghhBBCCCGEEEIIIYQQQgghhBBCCCHE4Tsq30RWFOUi27YfUxTl+n2e6lAUBdu27zwa+zndjPbuYWT3TmJtnbQvOJOGbDvFYgr/ihawbeySRWFXCs/M6EG3NZYr8cK24ar27UPZY3HoR6w8kmP0ri0UNo4C4JoZIXRN5wktaBKnjtJAluybg2Re6AXbxntWPZ4FsarZ40s9aVKP7Bx/XO7Lkn5sN2axEqgw67yLcHq8PPDv/4zT6+Pc932ItgVnYBgHD/5x+/0sfc8NzFtwMXbRxFHn497tY1XLvb5rjNFcSUJITiOl/gyJh3eQXzeC0eAldFUnztbA/pcfyDL6q4289X1zK11i5GcbiH92AVpgoq9ZBZPyYBa/GeLmP/47xpIDOJwuQnX1bHj+aWaedyGrH3+YYi7LjOXnsWvNSmo7OtH39lfb2ltk/6uN2EULxakRuW0G7hmRd/xahzMF+pOFqva+RJ551ROWk86X6E3k8Th0GsNHFoAijlx5JEd5rEDAtOiq8fKZ+U0EH96FbVY6o5UtM/Kz9cQ/vxA97CIx0M8TP/wOnmCIpdfdzMpHHyJc18Bln/4S0aZm4m2daLoUsYmpjeVKfHZmPe1P9WEmR0BVmHt2Iz358vgygZo4l3z8syy+6jpSQ4NsevkFnv7p92lbcAbBeC2716065BCS8liB8kge1a1hxNwo+v6DQ8pDWUZ+sQHe6vuZUqXvf24heshJsSdN4oFtFLYlcLYFCF7ZgaPRd2Q/EPGulertIzDgo/yihaPJR8PMLjK/6WVZwcSZVyl7nOhBJ+WhHMM/24AWdOBZ3gBAYUcCPeZCl89QcYKUR/Okn9tDbtUg7rmx8QASgNybQzg7Q/iW1B9wG7ZlUx7KYaaLaAEnetSFoihH5fgGd2wl3tbOhueexh+t4cxrbmDHyjdYfNV13PLV/0VycACH20Oorp5XdozQFHTxzfmt+J7YA2WLOR4dZ1sNSAjJSWVkz24izS2c94GPUMhmcLg8+AsBBr61kvJwDs+8GP6LWsCyGf75eihPPZZ9i5kpUh7KUTBt0l6dYNCFdz+ht3bJwlo5RPeDu6BsM8drsOjimXzwd2vpSeSOy+sXpyfLLHP2zR8gOTiAZZmousaaJx8l3t6JNzQ5OCdbLLNnLEfZsrl1WowzevJozw2hRF3cfPlsdmeKB9xXOVVg5OcbsFIloNKvx+7chOrScE0Lo54Eoc92yap8NuTK6BEnekju/x5LfYk8X5rThPOe7Tg7gjg7gtg2eFRwdQUw1toM/OINAIxmH5GbujHinhN70EKcjuxJfwkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEKeUo/Ut5POBx4Crp3jOBiSEZApmqcT2la/Ts34NK97/acxEEdeMCKnHd4Flo3p0jEMtgLRhSVuER9YNTGpur/HuZ4UTK7dmeDyABCC/boT89DC+ZQ0n8KjEqaKwLUHq0YlwkdRju1ADjqoQkvJwvmrd4uYELdPnMrxnJ+5AkFfv/Q0A2cQYd3/j77nlK/+LpplzDnoMZqpI4uEdZF/qA0CvcXHu9V1Vyy1uDROWGUVPG1a2zMgdGyntSgNQ2pVm6LuriX9+IUbN1AXD5li+6tvmZqKAmSqOh5CUkwWSD20n+2rlHK7Xuom+byZGbaVPm6bJMz/7wfj6r91/N0vfewvlYnE8hGS8yH5vUZ5dMBn5+XpqP78QPfrOiplrfE7qgy56ExPvJUWB+mD19jYNpPjru1bz/NYRgm6Dr14zmyvm1OE09h8MII6dwtYxhn60DjtXBlXh389twhvxYpmTO6OVLVMeyaOHXZQKeUr5HGdefT1P/fR7mKUS/Vs2sf7ZJ5lx9nlc9ukvcvSGjuJ0E9FU1FeHMZN7iyQtG/czvczqnDlpOcPpxLZt7v2/X6dcqiy7/tknaV+wGH9N/JD2VdidYvgHa7FSRVDAf1ELvnMa0PYT+FUeLYwHkLzFSpcwkwVQYPiHazATlWMpbEkw/IM1xD67AD3oPJwfgRAAGFknmed7AXDPipJ4cPv4c/kndpPzOfCf04iZKKCHnbjnxUg9sQtMG8Wp4aj3SQiJOCFs2ybzUi/pp3twdgQp7kxVLZPfMHLAEBLbtMi+OcjonZugbKM4NCLv68Y94+DBqgdTKhbZtXY16556rHIs6RRP/fi7XPLxzwHg9gdw+yeCAeN+F3+4qAXf73ZPhAFmyxR+s5lygx89JOf4k4Wqajz94++SSyUBuPIDv0/mjl3jn93Z1waw8ibepXXj1zpvsbLlvefTSqBBabByTVTaXbles6aFeKDTw4JZtXTFq++vlQYypH67ZaKPZEo0Pt3H7y1ooikk52Lxzo0N9PG7//rmpLblt9xOqTA55HPbUIZ/vH8dv1vbzx9d0MV7dxexN48BYPfniD2wk/rfm3XAfVmpEubYPuGhNpT2ZFC9DlwdwSN+PUfCypdJP7eH5MM7wAbVZxD94CycLfsPcxVHpi7opna4BPNqwIbk3oBmPeoifF0HIyvXji9b2pUm83IfwSvaUdSjExomhKiwx/+WGBIhhBBCCCGEEEIIIYQQQgghhBBCCCGEEKce9WhsxLbtv9n79+9N8ecjR7JtRVFuUhRljaIolqIoi4/G8Z4sLMukZ/0aALyeEO6ZUdJP7QZrYlbX5EPbKacKB9oMAE9tGmJJe5TWaGXWQkWB9y9tYX7zif2i+VRs2ya3driqPbdhdIqlhag2Vf/Jr6pu06YoLDOa/fT3bKVlzny2vPxC1fO7160+pGMo7kqOB5AAlIfyaC/28/Xr5vDWd/brAi7+5upZ+F0SQnK6KI/mxwNI3mIXTMqD2f2uowWq+6HqNVC9E2EOxR3J8QASgHJ/jtSze7D3Ft4N7dxetY1NLz2HwkSBiJkoVhXl2XkTM3XgWZMPJB5w8S+3LCC0N0jH0BT+7to5TK+dXMCXK5r8r/vX8/zWEQASuRJf+sUbrO1NvuN9i3eunCww8suNlQASqIRBPLkHn1OvGvkpDg1770krUBOjoXsWlmVilkqTllv//NOkhoeOx+GLU1XWxOrNVDXr6VJV21h/73gAyVu2vfkqkYbGg+7GzJUYu3tLJYAEwIbUozsp7ane91u0gAP2qadTXBqaz6A8kh8PIBnfR7I4ZZCZEIckW+lsqt+Ysh9lXujFypXR/A48C2KkHts5XmhvF0xGf7OZ8qj0P3H8WakSmZf6ASgP5TDqPFXLONtDB9xGaTDH6B2bJkLxiiYjv9hIeSR3xMeXTYyx4dmnJh+zaVIuV3/OALRGvcwLeKrCAK1UiczokR+POHpKhfx4AAmAVwlUhYfl1w2juqYey6q+SiijbdtkXu0fDyABUDeN0V2Abz66kULJrNq3OVaYMjDyyo4osxtOvvtp4tSx8flnqtp6N63HF50IZSqbFt95eiu/W1s598ZVdTyAZJxp402XD7gv1WOg+qrvOymGWglFPcFKvRmSv9sxEfaTLjH6m81Y2anP3+LIdcV9OBwaRq2X3KqJ6/jycJ7cywMYTZPv6eTXj2AVDtzPhBCHT9l74pMQEiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQp6KjEkLyFkVRahVF+Y6iKA/sfTxLUZSPHuFmVwPXA08dbMFTjdPjRXdUitPHBvaAXf1lRHO0gJU5+JeA73lzD//00HrO7ozyhRVdfHHFNByaQsR78s3uqygKrumRqnZXV+j4H4w4JWl7Zzmu/L8T/4oWXLMjFPekscvW+HOOJh+eM+Ljj1WvQfCKduZfcSW6w4knWF1U5A4cWqFRqb86dKKwZYzrZtZy3xfO5ZefXMZvPnM2c5tCh/HKxMlOcWqgV390qm59iqUr9LiH4BVtEwXwukr4pmnooYl+XOpJV61X2DQ6XgTij9ZUPe+P1qAZE4VGqt8B+8xaqzjUKYuRDsdZHVHu+dw5/OKTZ3H/F87lljObcRrapGUGUwUeXT9Qte62of2HAohjx8pMMRM2QK6M/8KWib6oKfgvbiG/99fp9Hi55BOfw+2vnpHa5fGiOxzH7qDFKS+JhRp3V7UXXFpVm8NVvZzT7cEXqT7X7cvKlintTFW1m4n9h/YZMQ/Bqzsn9f3IjdNR3DqUTPwrWvCd04hi7D2/K6BOcdxCHIrs3uAuu2CieibGB6pHx39BM96ldZSGsqhBAzXorA5IyJQwpwjvEeKYMxS0kBPFqeFeEMfZGUKvnQgiMZr9uGZUX8e/nZksjoeqvsXOlTFTR96ndcPAHageo3gOcP2mTxFCpXoNkooUgp5MPMHQpMemUgkLURwqvnMb8a9oIXBpG4pPJ3Rt18SdTE0hfOM09GjlusouWRSmCLf1D+VZsyfJYLp6rDBlYKTfQVdriIBbwkTFOxeM11W1hWrr0fWJfjWcKXLfqt6Jx4VSZXy6D2Pv2MJMFshvHqWwPYH5tgAPPeQkfMO0iXsVCviWN5BfN4IWPPH3hctTXJuWezOYWQm9OFYcuorqMaYMFilsS2DUeSe1OTtDqM7939cSQrxD9qS/hBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIU8rR/nbp94HvAX+x9/FG4BfAd97pBm3bXgeV4IrTTShexzm33s4TP/wf3nz+IWYvuBBHS6Wwxy5ZKIZKfkcC1XvwX9PC5hBPbxriZy/tGm/7woquY3n4R8Q9t4bc2iFKuyqF9462AK6ZBy5oEuIt1swwypuDKLqCd2k9yYd3VGZKViB0bSfexXUouormcxC8uhPv0nrsgole40YPu2hrWkhteyf9WzZz59e/gm1Vgku8kShNM+cc0jEY8SlmBu8KoSkK7VkLK2tiuC1s2z4tz1/vVnrERfDyNhL3bh1vc8+PoU/RH95OjbgI3zwdK1uuzNTtmFzcbjT4qtZxTguPF4G0zl2AJxgimxirbE/TWfremyeFQhgxN+EbpjF656bK+0FXCN/UjR6tLvY/XM0RD82R/b9Gn0ujrcbD9qHJ4TxRr4RWnAia14EWdFaFMlgBB5qmEL65GytbQvUZWBpYwYnfU01TC4bLxbpnn6Jv0/rx9vNu/yj+aOy4vQZx6lE8Bv3Laon/bjd2rgwKFM6MU/Tr1O+zbKylnYYZs9izfu142/m3f5Rw3b5LVlM9Okazb3wM+ZYDFVgqhorvzDqcbQHMVAk95ACXRva1fuycOb7dwBVtJO7eiu/cRvTYkZ87xbtTv1sjvKQW4+V+0BS0gAMrV8Z/UQtmooBdMMmvH0WPuTFqPZWAhLdVpakeHe0IA8SEeCc0t0Hw8jbKiQJWukSpN1MJaHJqaB4do86L5jvw2E4P7A3Fe1sQieLS0PxH3qe9oTAXfujj3Pt/vz7eFmtto65z2n7XSft1Suc14nm6B6zK58HwBQ04DxAgKI6/mqZWZpx9Huufq2QPb9+9kpltZ+KdEyf1+C6sTCVsoTyUJXBpG/GWRZipIlrQgRHzjF9vK4aKc0YYZ2cIxamhKGAVTHp0cA9reKcosNdrPQTf007i/m1gg+LQiNw8HWdYxgHiyMy58BLWPvUYpUIeAMPlZtZ5F01axufUmV7r58VtIwB89809XLC8E/8ju8eXcXaF0Ou9FPsyDP9wDeZI5RrPOT1M+L1d6HtDel0zItR8bA6FzWMoikJu7TDOrhBG/eSwiRNBD02M093zY5X7aSrYRfMEHtXpz4i4sNNFXLOiOBp92KaFoqkURjPYjon7lHrMjW9ZPYoq9y6FOPrsvf+VGBIhhBBCCCGEEEIIIYQQQgghhBBCCCGEEKeeo115UWPb9i8VRfkzANu2y4qiyDeK90NRVeZcdCk1Le1QsMhvTeDsCJL83Y7xZYLvaUc7hCLuq+Y38NTGQc7rjmFa4HfpXDwjfiwP/4gYNW5qPjSb8lAOqHzh+VBepxAAibCD3NVttLoMkr/YWAlcALBh7LdbcLQEcOwNddBcOlpL9YzZbn+Alnnzue1vv0H/ts04XG7qOqcRaWgaX8a0bLYOpulL5qkNuOio8aJrlZllHS0BPGfWkX25r7KfqAv/eU0k7tlCbtVwZQOaQs3vzcbVFT6GPw1xPCmqgvfMWowGL+ZwHtXvwNHoQ/Psv7CyPJilsG6E7GsDezcCgUtbMaIu9GClYMjRFsCzME729coyetyNf3kDilYpAok2tXDz3/wjfVs2US4WqG3vora9c/KxaSqeBXGMRh9WqogWdKLXuA8agrNjOMOukSwhj4OuuBeXcfhDg4jXyd9dO4ePfP8VimYl1OeKOXXMbtj/zPTi2NECDiI3T2fox+sqYRCqQvA97eheg/yaETIvTMy27b+oGUfUA2/7VQVr4lz9pT+hd/NGsqMj1BykwFcIgNqAi80xN/3XteMvmJQdGi8lMlwbrw5Z8kUivOfzf0Tf5k1k9vax2o5D62Oa2yB0TRfDP1yDlSqBAv4LmzEaDlxgqRjq+NgAIL95lOwrA5R6M5Xn3TrBqzqIfWoeetyDamj725QQ4976DA17HHTu/QxtiPv4ntbH+Vc102KA+73NOG0da7BAbs0w5kilGFn1G0Rum0Ho+mmM3bUZTBvFqRG+pXu8oFiI401v9FLsSZN8aPt4OI7nzFo8i+IHDSCBynV9+Ma9oXhlG8WhErmlGz1ydAIdOs5Ywi1f+TqDO7bhCQSp65pOoGb/9z2aanzcHR3DuKiegKXQZ5tYhsUKl8GLW4fRVYXOuI+QR+5FnEgun4/zP/gxupefT3Kwn0hDE6GGLpJ3bRsPIAHIvjqAe24M94wITBGsoCgK7plRRn66HnOsEtSgBhxoV7bw510zCU/xe1YdGr6zGtDag2TG8hS9OomoCzkLiyNV1zmN2/72n+jbsglFUajtnEaspW3SMl6nzpcv7+b277xEtmgymC7w07EkX/7YHMyhHGWnRjnuwXZpZB7ZOR5AAlDYOEphW2J8zKAoCq62IHrQSXk4h2tGGD3mRnWc+NAlo95L4JJWyiN5yiN5cm8OApB6bBc1H5mDs13uGxwL5eEc5t5wyOTDe/+tQVMI3dZNqsYgNr8W27QwajxoAfkcFOJYUCb+yUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhDjlHO1vImcURYmy93t1iqKcBSQOtpKiKI8AdVM89Re2bf/2UHeuKMongE8AtLS0HOpqJ5TT7aF17nxKQzlyYwMkH9056fnEQ9txdUcqs0QewLS4j4+e28Ef/epNCmULXVWIeh201ngx9oYmnGw0n+OQCpneDU7Fvnsidcb99Bgayd4MdmGfnCMbzFTxkLajqhr1XdOp75pe9Zxl2dy/qpc//OWbFE0LQ1P43zfM45oFjWiqguZ3ELq6A9/SOqyShRF1U+xNTwSQAJg2Y7/dQuxT807rkJ13W/9VnTqujhB0HNryZqIwEUACYEPqyd2VGY33hpDoASehazvxLW/ALltoUTe6f3KfiTY2E21sPuC+FE3BUeeFukOb7fjlbSN89Acvk8yXURT4/EVdfOLcDnyuw5+t/uzOGu75/HK2DmUIug1m1PmJeJ0HX/EEOp37rrMzRO3nF1AeLaB6DYwaN4WtY2Re7J20XOqpHqj3sEex6a7zj7cHamIEamLH+7DFIToZ+26hZLJ1MM1X7lmLadm4DJVv3rZwv4XdgZr4AQvHD8TZ7Cf+2YWUR3KoLr1SYHmYoSGlvux4AAmAnSuTXzmI64ZpBwyWEkfuZOy/78RL24b52A9eGf8M/cJF0/j4ue3UBV18/KJprO9LsqpQZlrER6umkdmSGg8gAbBSJXIrhwhd3YmzxY+ZLqEFHRg1B77uEyfO6dJ3D8QaK5J6bOekSsnsy/24ZkQOaf23QvEcTT7MVKkSihc9enEOhsNJ08zZNM2cfWjL6ypXLGhgY1+KnrE8M0Iu3A6ND373RTb0pwE4tyvKP14/j6bI6f3eO9n7ry8coWvx0vHH5bECpV3pquXeChfZn/ymsUnLWMkisd483fOnuuVYMZgr8o/PbuE3r/cA0BBy8T8fXMwsCVQ8KZzsffdAYq3txFrbD7jMGa0R7v7ccrYMZPA4NWbW+Xlmxyhfunfi/u6Pb19M29axqnVLPWlYVDupTQ+7TrowM9Wl4z+vkdy6EUZ+un683S5ZjN23lZqPzkFzn37j7xPdd0sDORQgv2byfcrUvdvYdmUzge4YXueJD6kR4nSm7h1UWxJDIoQQQgghhBBCCCGEEEIIIYQQQgghhBDiFHS00yn+ALgb6FQU5Vngh8DnD7aSbdsX27Y9Z4o/hxxAsnc7/23b9mLbthfHYqdW4azq1cFQq6dFK9tY2dK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",
-      "text/plain": [
-       "<Figure size 4416.75x4320 with 324 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g = sns.PairGrid(df, diag_sharey=False, corner=True, hue=\"Continent\")\n",
-    "g.map_lower(sns.scatterplot)\n",
-    "g.map_diag(sns.kdeplot,warn_singular=False)\n",
-    "g.add_legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lastly, we are going to create a loop that will allows us to visualize all the comparasions between the variables and the GDP through the years."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(0, len(variables)):\n",
-    "    df[variables[i]]=df[variables[i]]/df['GDP']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
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-        "yaxis": {
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-       }
-      }
-     },
-     "metadata": {},
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-   ],
-   "source": [
-    "for i in range(0,len(variables)):\n",
-    "    a=px.line(df, x=\"Year\", y=variables[i], color=\"Country\", width=800, height=600)\n",
-    "    display(a)"
-   ]
-  },
-  {
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-   "outputs": [],
-   "source": []
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-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/IQR.webp b/IQR.webp
new file mode 100644
index 0000000..7d38a71
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diff --git a/Logos/chart (1).jpeg b/Logos/chart (1).jpeg
new file mode 100644
index 0000000..19246c0
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diff --git a/Logos/chart (2).jpeg b/Logos/chart (2).jpeg
new file mode 100644
index 0000000..84bd840
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diff --git a/README.md b/README.md
index 73a8811..07a35df 100644
--- a/README.md
+++ b/README.md
@@ -48,6 +48,13 @@ Therefore it is being utilized to get rid of all the outliers that may come from
 
 2- **Substitution of the NaN values**. The developed Nan values´ treatment has been a mix, between the linear interpolation and backwards filling. The linear interpolation is a form of interpolation, which involves the generation of new values based on an existing set of values. Linear interpolation is achieved by geometrically rendering a straight line between two adjacent points on a graph or plane. On the other side, the backwards filling will help us to arrive to those values which have not been fullfilled with the linear interpolation.
 
+3- **Scaling method**. The escalation process has been done dividing each value by the initial one of an indicator (value in 1990). Considering the start point as 1 (initial value divided by itself), each result will show the growth respect to the initial data.
+
+4- **Removing indicators**. 
+- Those indicators which have 20% of missing values of its total have been removed because a lack of data shows unreliable results.
+- There are some indicators which represent exactly the same through different units, so, we are going to select only one type. For example, in monetary cases, indicators which are expressed with current US $ has been selected. Then, which are showed with the percentage and the total value, we have programmed to selct which ones which show a greater value.
+
+
 # Run the application
 ## Dependencies
 Dependecies are automatically managed by Poetry and there is NO need to use external dockers for running spark.
@@ -71,10 +78,18 @@ Poetry will take care of:
 - From `scipy` import `stats` and `shapiro`. Scipy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems. Extends NumPy providing additional tools for array computing and provides specialized data structures, such as sparse matrices and k-dimensional trees. Mainly used for statistical calculations.
 
 - The `plotly.express`  module (usually imported as px) contains functions that can create entire figures at once, and is referred to as Plotly Express or PX. Plotly Express is a built-in part of the plotly library, and is the recommended starting point for creating most common figures. Every Plotly Express function uses graph objects internally and returns a plotly.graph_objects.Figure instance. Throughout the plotly documentation, you will find the Plotly Express way of building figures at the top of any applicable page, followed by a section on how to use graph objects to build similar figures. Therefore it will allow for interacting graphs.
+
 - `seaborn` is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Used for the correlations matrix. 
 
 - The `requests` library is the de facto standard for making HTTP requests in Python. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. It allows for downloading data from the websites.
 
+- The `functools` is a module for higher-order functions: functions that act on or return other functions.
+
+- The `ipywidgets` allows us having interactive widgets (sliders, buttons, dropdowns...) with which we can control and customize the display of our data.
+
+- `warnings` to avoid warning messages when showing the notebook.
+
+- `dash` and `itables`: both libraries can be used for making interactive tables designed for viewing, editing, and exploring large datasets in Python. At the begginning, we started with `dash` (which is a scratch in React.js) but as it is rendered with semantic HTML, we looked for an alternative that visualizes inside our notebook, so we found `itables`, which have a similar functionality.
 
 ## Running on local
 To start the execution of our code, you can directly run the notebooks on Visual Studio Code opening the files .ipynb, or with the command `poetry run jupyter notebook`.
@@ -86,11 +101,8 @@ To start the execution of our code, you can directly run the notebooks on Visual
 - The Net Migration indicator measures the difference between the number of immigrants and emigrants, so the number of people entering the country minus the number of people leaving it. As the difference is measured, it is necessary to see the original data to draw the conclusions correctly. Taking into account whether this net value is positive or negative and extract conclusions according the type of correlation. 
 
 - Something similar occurs with the indicator Direct Foreign Investment. This one shows us the    difference between outflows and inflows, so, if countries invest outside more or less than which is invested in them. Again we should see which are the net values to extract conclusions 	correctly. 
-
-- After treating the data, we realized that the indicators: Mortality Pollution and Gender Equality do not present sufficient data for analysis. Therefore, they have been eliminated. No conclusions will be drawn between this indicator and the countries. 
  
 
- 
 ## Repository setup
 The code is divided in several notebooks that need to be excuted following the corresponding order, which coincides with the one described above in the main steps section.
 
diff --git a/Visualization of correlation (Global).ipynb b/Visualization of correlation (Global).ipynb
deleted file mode 100644
index d51c683..0000000
--- a/Visualization of correlation (Global).ipynb	
+++ /dev/null
@@ -1,2997 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization of correlation (Global)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook has to be read next to the following document (url), and is also complemented by the following notebooks (url)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import ipywidgets as widgets\n",
-    "from ipywidgets import Layout\n",
-    "from ipywidgets import interact, interact_manual\n",
-    "import plotly.express as px"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= pd.read_csv (os.getcwd()+'/Data/'+'GoldenDataFrame.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "columns=['Country','Year','Exports-Commercial services','Renewable electricity','Employment-agriculture','Employment-industry','Employment-services','Exports-G&S','Fertility rate','Foreign investment','GDP','Education GExp','Workers high education','Literacy rate','Net migration','Mortality-infants','Health services use','R&D GExp','Ninis','Suicide','International taxes','Alcohol per capita']\n",
-    "clist=df['Country'].unique()\n",
-    "countries_by_region = {\n",
-    "    \"Europe\": ('DEU','FRA','SWE','GBR','ESP','HRV','POL','GRC','AUT','NLD'),\n",
-    "    'Persian Gulf': ('IRQ','QAT','ARE','SAU','AZE','YEM','YDR','OMN'),\n",
-    "    'North Africa':('DZA','EGY','LBY','ISR','TUR','MAR'),\n",
-    "    'South Africa':('SEN','ZAF','LBR','MOZ','CMR','NGA','GHA'),\n",
-    "    'Asia':('BGD','IND','VNM','THA','IDN','PHL','KOR'),\n",
-    "    'Latam':('MEX','BRA','ARG','PER','VEN','COL','CHL','PCZ','CRI'),\n",
-    "    'Pair':('USA','CHN')\n",
-    "    }\n",
-    "\n",
-    "all_countries = {}\n",
-    "for region in countries_by_region.keys():\n",
-    "  for country in countries_by_region[region]:\n",
-    "    all_countries[country] = region"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following cell, we have defined a function that will allow us to calculate the different posibilities of relations: cuadratic, cubic and logaritmic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def multcolumn(frame):\n",
-    "    for u in range(3, len(columns)):\n",
-    "        name2=columns[u]+'.^2'\n",
-    "        name3=columns[u]+'.^3'\n",
-    "        namelog=columns[u]+'.log'\n",
-    "        frame.loc[:,name2] = frame[columns[u]]**2\n",
-    "        frame.loc[:,name3] = frame[columns[u]]**3\n",
-    "        frame.loc[:,namelog] = np.log(frame[columns[u]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Moreover, we want to know the correlation between all the variables, so to acomplish this, we have created the following loop, which will help us create a new dataframe where we will have: the indicator, the type of relation, the value of the r^2, its behaviour, the country and the continent."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Type</th>\n",
-       "      <th>GDP-R^2</th>\n",
-       "      <th>Behaviour</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Exports-G&amp;S</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.954928</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Health services use</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.916594</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.911725</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Employment-services</td>\n",
-       "      <td>Cuadratic</td>\n",
-       "      <td>0.883525</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>Cubic</td>\n",
-       "      <td>0.875820</td>\n",
-       "      <td>Negative</td>\n",
-       "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Suicide</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.922759</td>\n",
-       "      <td>Negative</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Renewable electricity</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.912560</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.879177</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>Cuadratic</td>\n",
-       "      <td>0.867314</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Literacy rate</td>\n",
-       "      <td>Cubic</td>\n",
-       "      <td>0.840397</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>444 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      Indicator       Type   GDP-R^2 Behaviour Country  \\\n",
-       "5                   Exports-G&S       None  0.954928  Positive     DEU   \n",
-       "7           Health services use       None  0.916594  Positive     DEU   \n",
-       "4   Exports-Commercial services       None  0.911725  Positive     DEU   \n",
-       "10          Employment-services  Cuadratic  0.883525  Positive     DEU   \n",
-       "12           Alcohol per capita      Cubic  0.875820  Negative     DEU   \n",
-       "..                          ...        ...       ...       ...     ...   \n",
-       "6                       Suicide       None  0.922759  Negative     CHN   \n",
-       "5         Renewable electricity       None  0.912560  Positive     CHN   \n",
-       "2   Exports-Commercial services       None  0.879177  Positive     CHN   \n",
-       "14           Alcohol per capita  Cuadratic  0.867314  Positive     CHN   \n",
-       "10                Literacy rate      Cubic  0.840397  Positive     CHN   \n",
-       "\n",
-       "   Continent  \n",
-       "5     Europe  \n",
-       "7     Europe  \n",
-       "4     Europe  \n",
-       "10    Europe  \n",
-       "12    Europe  \n",
-       "..       ...  \n",
-       "6       Pair  \n",
-       "5       Pair  \n",
-       "2       Pair  \n",
-       "14      Pair  \n",
-       "10      Pair  \n",
-       "\n",
-       "[444 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "multcolumn(df)\n",
-    "demo2=pd.DataFrame()\n",
-    "for i in range(0,len(clist)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == clist[i]]\n",
-    "    cor=dat.corr()    \n",
-    "    cor.loc[:,'GDP-R^2'] = cor['GDP']**2\n",
-    "    cor.loc[:,'Indicator']=cor.index\n",
-    "    cor[['Indicator','Type']]=cor.Indicator.str.split('.',expand=True)  \n",
-    "    corcolumn=cor[['Indicator','Type','GDP-R^2','GDP']]\n",
-    "    corcolumn=corcolumn.loc[corcolumn.loc[:, 'GDP-R^2'] >= 0.75]\n",
-    "    id=corcolumn.groupby('Indicator')['GDP-R^2'].transform(max)==corcolumn['GDP-R^2']\n",
-    "    corcolumn[id]\n",
-    "    max_df=pd.DataFrame(corcolumn[id])\n",
-    "    max_df['Behaviour']=np.where(max_df['GDP']>0, 'Positive', 'Negative')\n",
-    "    max_df['Type']=max_df['Type'].replace(['^2','^3','log'],['Cuadratic','Cubic','Logarithmic'])\n",
-    "    max_df['Country']= clist[i]\n",
-    "    max_df.drop(\"GDP\",axis=1,inplace=True)\n",
-    "    max_df=max_df.reset_index(drop=True)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='Year'].index)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='GDP'].index)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='Unnamed: 0'].index)\n",
-    "    max_df=max_df.sort_values(by = 'GDP-R^2',ascending = False)\n",
-    "    demo2=pd.concat((demo2, max_df), axis = 0)\n",
-    "demo2['Continent']=demo2['Country'].map(all_countries)\n",
-    "demo2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we’ve loaded the data, we can start right away to create widgets. These widgets are essentials to add interactivity to our visualizations. We’re going to use two widgets: both, multiple selection widgets. To create these widgets, we can use `ipywidgets` library that is available for Jupyter Notebook.\n",
-    "\n",
-    "The first widget that we are going to create is the multiple selection widget. We can do this by using `SelectMultiple()attribute` from `ipywidgets`. With this widget, we have the option to visualize the R^2 only in particular selection of indicators instead of all.\n",
-    "\n",
-    "The first argument that we should specify is `options` , which should contain the list of available options of our variable (in our case different indicators). The next one is `value` , which should contain the variable values that we want to display as default, and then `description` is for the text field to describe the name of the widget.The rest of options are just visual details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "398af352ecdb4804a66d236c0ee701b7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(SelectMultiple(description='Indicator', index=(0,), layout=Layout(height='80px', width='…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "coloraxis": "coloraxis",
-         "customdata": [
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
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-          [
-           null,
-           "Positive"
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-           "Cuadratic",
-           "Positive"
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-          [
-           null,
-           "Positive"
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-          [
-           null,
-           "Positive"
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-          [
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-          [
-           null,
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-           "Cuadratic",
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-          [
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-         "geo": "geo",
-         "hovertemplate": "<b>%{hovertext}</b><br><br>Country=%{location}<br>Type=%{customdata[0]}<br>Behaviour=%{customdata[1]}<br>GDP-R^2=%{z}<extra></extra>",
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-          "DEU",
-          "FRA",
-          "SWE",
-          "GBR",
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-          "NLD",
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-          "ternary": {
-           "aaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "baxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "Exports-G&S"
-        },
-        "width": 700
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique_tri = demo2['Indicator'].unique()\n",
-    "tri = widgets.SelectMultiple(\n",
-    "    options = unique_tri.tolist(),\n",
-    "    value = ['Exports-G&S'],\n",
-    "    description='Indicator',\n",
-    "    disabled=False,\n",
-    "    layout = Layout(width='50%', height='80px')\n",
-    ")\n",
-    "\n",
-    "def graf1(tri):\n",
-    "    dat=demo2.loc[demo2.loc[:, 'Indicator'].isin(np.array(tri))]\n",
-    "    a=px.choropleth(dat, locations=\"Country\", locationmode='ISO-3', \n",
-    "                     color=\"GDP-R^2\", hover_name=\"Country\",hover_data = [dat.Type, dat.Behaviour],projection=\"natural earth\",\n",
-    "                     color_continuous_scale='Reds', width=700, height=500, title= dat.Indicator.unique().tolist()[0])\n",
-    "    print(tri)\n",
-    "    a.show()\n",
-    "widgets.interactive(graf1, tri=tri)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To wrap up, we can create the second widget that is exactly the same as the previous multiple selection widget. The purpose of this widget is to enable us to choose which Continent that we want to visualize. Below is the code implementation of this widget."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b312134fcc9347939c0556c6f008abc2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(SelectMultiple(description='Continent', index=(2,), layout=Layout(height='80px', width='…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "customdata": [
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           "Cuadratic",
-           "Negative"
-          ],
-          [
-           null,
-           "Negative"
-          ],
-          [
-           null,
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-          [
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-             0.8888888888888888,
-             "#fdca26"
-            ],
-            [
-             1,
-             "#f0f921"
-            ]
-           ],
-           "sequentialminus": [
-            [
-             0,
-             "#0d0887"
-            ],
-            [
-             0.1111111111111111,
-             "#46039f"
-            ],
-            [
-             0.2222222222222222,
-             "#7201a8"
-            ],
-            [
-             0.3333333333333333,
-             "#9c179e"
-            ],
-            [
-             0.4444444444444444,
-             "#bd3786"
-            ],
-            [
-             0.5555555555555556,
-             "#d8576b"
-            ],
-            [
-             0.6666666666666666,
-             "#ed7953"
-            ],
-            [
-             0.7777777777777778,
-             "#fb9f3a"
-            ],
-            [
-             0.8888888888888888,
-             "#fdca26"
-            ],
-            [
-             1,
-             "#f0f921"
-            ]
-           ]
-          },
-          "colorway": [
-           "#636efa",
-           "#EF553B",
-           "#00cc96",
-           "#ab63fa",
-           "#FFA15A",
-           "#19d3f3",
-           "#FF6692",
-           "#B6E880",
-           "#FF97FF",
-           "#FECB52"
-          ],
-          "font": {
-           "color": "#2a3f5f"
-          },
-          "geo": {
-           "bgcolor": "white",
-           "lakecolor": "white",
-           "landcolor": "#E5ECF6",
-           "showlakes": true,
-           "showland": true,
-           "subunitcolor": "white"
-          },
-          "hoverlabel": {
-           "align": "left"
-          },
-          "hovermode": "closest",
-          "mapbox": {
-           "style": "light"
-          },
-          "paper_bgcolor": "white",
-          "plot_bgcolor": "#E5ECF6",
-          "polar": {
-           "angularaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "radialaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "scene": {
-           "xaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "yaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "zaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           }
-          },
-          "shapedefaults": {
-           "line": {
-            "color": "#2a3f5f"
-           }
-          },
-          "ternary": {
-           "aaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "baxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "North Africa"
-        },
-        "width": 700,
-        "xaxis": {
-         "anchor": "y",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "GDP-R^2"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "Indicator"
-         }
-        }
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique_tric = demo2['Continent'].unique()\n",
-    "tric = widgets.SelectMultiple(\n",
-    "    options = unique_tric.tolist(),\n",
-    "    value = ['North Africa'],\n",
-    "    description='Continent',\n",
-    "    disabled=False,\n",
-    "    layout = Layout(width='50%', height='80px')\n",
-    ")\n",
-    "\n",
-    "def graf1(tric):\n",
-    "    dat=demo2.loc[demo2.loc[:, 'Continent'].isin(np.array(tric))]\n",
-    "    a=px.scatter(dat, x=\"GDP-R^2\", y='Indicator',\n",
-    "                     color=\"GDP-R^2\", hover_name=\"Country\",hover_data = [dat.Type, dat.Behaviour],\n",
-    "                     color_continuous_scale='Blues', width=700, height=500, title= dat.Continent.unique().tolist()[0])\n",
-    "    a.show()\n",
-    "widgets.interactive(graf1, tric=tric)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.12 ('base')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "db2f77d7771d8a0072cf3f8e7f9965a22a9c65bf4f8ee5135b3d479c9967abaa"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/WDI-Complete code.ipynb b/WDI-Complete code.ipynb
index 0431e37..f310345 100644
--- a/WDI-Complete code.ipynb	
+++ b/WDI-Complete code.ipynb	
@@ -4,7 +4,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# EXTRACTION"
+    "<h1 style=\"text-align:center\" >Phrontistery - Data Driven Decisions  </h1>\n",
+    "<h3 style=\"text-align:center\" >The Big Three</h3>\n",
+    "\n",
+    "<center>\n",
+    " <img src=\"https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/capgeminLogo.jpg\" width=\"800\"/> \n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The starting point of this project is to check if this hypothesis is validated: there exists a relationship between GDP (Gross Domestic Product) growth of a country and social and economical indicators such as population growth, fertility rates, investment in specific sectors or prices. To demostrate the validity of this assumption has been used the correlation as a decision making tool. So, through Python language and the availabe libraries such as Pandas, it has been developed an algorithim that allows us to extract reliable conclusions, being able to take the data from the database files, perform statistical computations on them and get useful numerical and visual results to make conclusions.\n",
+    "\n",
+    "This tool has been developed for all available datasets, but in our case we have extracted data from World Bank, updated on June 22, 2022.\n",
+    "\n",
+    "The steps of the project have been as follows:\n",
+    "\n",
+    "* [Extraction](#Extraction)\n",
+    "* [Integration](#Integration)\n",
+    "* [Normalization](#Normalization)\n",
+    "* [Categorization of variables](#Categorization-of-variables)\n",
+    "* [Correlation study](#Correlation-study)\n",
+    "* [Visualization of results](#Visualization-of-results)\n",
+    "* [Spurious correlations](#Spurious-correlations)\n",
+    "\n",
+    "\n",
+    "Along the project all steps are explained, as well as, assumptions and special terms that's important to take attention.\n",
+    "\n"
    ]
   },
   {
@@ -24,16 +59,100 @@
     "import numpy as np\n",
     "import glob\n",
     "import os\n",
+    "from zipfile import ZipFile\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "import functools as ft\n",
-    "from pyspark.sql.functions import concat, col, lit, split\n",
     "import ipywidgets as widgets\n",
     "from ipywidgets import Layout\n",
     "from ipywidgets import interact, interact_manual\n",
     "import plotly.express as px\n",
     "from scipy import stats\n",
-    "from scipy.stats import shapiro"
+    "from scipy.stats import shapiro\n",
+    "from scipy.stats import pearsonr\n",
+    "from scipy.stats import spearmanr\n",
+    "from pandas.api.types import is_numeric_dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This step has been depreciated due to security features added to the World Bank website. However, the data is still obtainable by openinng the `url`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following cells we are going to import the data from the [World Bank website](https://www.worldbank.org/en/home), Data sections, and decompress it. So later on, we can process it in the integration, and subsequent parts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To download the data, we are going to use the `request` library as just to be sure that we do not run into any inconvinient we are going to use the `stream` and `verify` to have a simple and cleaner download. Therefore, at the end we will have the data downloaded in our working directory data by design. (It can be obtained with the following function `os.getcwd()`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = 'https://databank.worldbank.org/data/download/WDI_csv.zip'\n",
+    "r = requests.get(url, allow_redirects=True, stream = True,verify=False)\n",
+    "open('WDI_csv.zip', 'wb').write(r.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we extract all the files that are contained in WDI_csv.zip , into the default directory in a new folder called Data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zip_name = \"WDI_csv.zip\"\n",
+    "with ZipFile(zip_name, 'r') as zip:\n",
+    "    zip.printdir()\n",
+    "    zip.extractall('Data') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we are going to delete the WDI_csv.zip."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.remove(os.getcwd()+'/Data/'+\"/**.zip\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Integration"
    ]
   },
   {
@@ -441,13 +560,6 @@
     "df"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# INTEGRATION"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -470,7 +582,7 @@
    "source": [
     "FILTER 1: BY COUNTRY\n",
     "\n",
-    "From the almost two hundred countries we have information about in the worldwide database, we have decided to study 50 of them, grouping them by geographical and economical similiarities. With this, we can keep in our dataframe the selected countries."
+    "From the almost two hundred countries we have information about in the worldwide database, we have decided to study 50 of them, making an initial grouping by geographical and economical similiarities. With this, we can keep in our dataframe the selected countries."
    ]
   },
   {
@@ -885,53 +997,90 @@
     "df3"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BronzeDataFrame=df3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normalization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Taking as reference both works of https://www.pluralsight.com/guides/cleaning-up-data-from-outliers and https://careerfoundry.com/en/blog/data-analytics/how-to-find-outliers/, for normalizing our data we need to start computing the outliers and removing them from our dataframe. As there is not a direct function of pandas that performs this step, it´s been step-by-step code, where we begin with the computation of the quartiles, then the IQR (Inter Quartile Range) and finally the upper and lower limit."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "FILTER 3: BY INDICATOR"
+    "##### IQR explanation"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As there are lots of indicators that have very similar meaning we have decided to select some indicators to perform the study (**Indicator group** = *Name of the selected indicator*):\n",
-    "- **GDP** = *GDP (current US$), measures the monetary value of final goods and services produced in a country at a given period of time.*\n",
-    "- **Literacy** = *Literacy rate, % of people ages 15 and above which are able to expand one's knowledge of reading and writing in order to develop one's thinking and learning for the purpose of understanding oneself and the world. Government expenditure on education, total % of government expenditure incurred on education service.*\n",
-    "- **Migration** = *Net migration, difference between the number of immigrants (people coming into an area) and the number of emigrants (people leaving an area) throughout the year.*\n",
-    "- **Exports** = *Commercial service exports (current US$)* and *Exports of goods and services (current US$). Exports term is referred to the goods and services which are produced in a country and sold to buyers in another one.*\n",
-    "- **International trading** = *Taxes on international trade. This one reflects the amount of money that a government collects thanks to all kinds of taxes (on products that enter and leave, customs...)..*\n",
-    "- **Fertility** = *Fertility rate, mean of total births per woman. How many childs have born during a year per women.*\n",
-    "- **Healthcare** = *% of people using at least basic sanitation services. Amount of children covert by sanitation.*\n",
-    "- **Employment** = *Employment in agriculture (% of total employment), *Employment in services (% of total employment), and *Employment in industry (% of total employment). Amount of people employed in these three relevant sectors.*\n",
-    "- **Renewable energy** = *Electricity production from renewable sources, excluding hydroelectric. The units are KWh.*\n",
-    "- **Mortality** = *Number of infant deaths.*\n",
-    "- **Outside investment** = *Foreign direct investment, which is the net inflow of investment to acquire a lasting management interest  (BoP, current US$).*\n",
-    "- **Pollution** = *Mortality rate over 100,000 population attributed to household and ambient air pollution and age-standardized.*\n",
-    "- **Alcoholism** = *Total alcohol consumed per capita measure in liters of pure alcohol, taking into account people who are 15 or more years of age.*\n",
-    "- **Tech adoption** = *% of GDP which goes to the research and development expenditure.*\n",
-    "- **Workers high education** = *Labor force with advanced education. % of total working-age population with high level education. It measures the probability of having a good job according to the studies.*\n",
-    "- **Optimisim and pessimisim** = *Suicide mortality rate per 100,000 population.*\n",
-    "- **Gender equality** = *Rate of gender equality in a country between  (**1=low to **6=high). It assesses the extent to which the country has installed institutions and programs to enforce laws and policies that promote equal access for men and women in education, health, the economy, and protection under law.*\n",
-    "- **Education** = *Share of youth not in education, employment or training, total, Total number of young people.* and *Government expenditure on education of total. *\n",
+    "The interquartile range (IQR) measures the spread of the middle half of your data. It is the range for the middle 50% of your sample. Use the IQR to assess the variability where most of your values lie. Larger values indicate that the central portion of your data spread out further. Conversely, smaller values show that the middle values cluster more tightly.\n",
+    "\n",
+    "To visualize the interquartile range, imagine dividing your data into quarters. Statisticians refer to these quarters as quartiles and label them from low to high as Q1, Q2, Q3, and Q4. The lowest quartile (Q1) covers the smallest quarter of values in your dataset. The upper quartile (Q4) comprises the highest quarter of values. The interquartile range is the middle half of the data that lies between the upper and lower quartiles. In other words, the interquartile range includes the 50% of data points that are above Q1 and below Q4. The IQR is the red area in the graph below, containing Q2 and Q3 (not labeled).\n",
+    "\n",
+    "https://camo.githubusercontent.com/a5f6cf8164048f8c28f9b00b94e1264480c8c3b20a1b3d0bdca47083f3a86a19/68747470733a2f2f69302e77702e636f6d2f7374617469737469637362796a696d2e636f6d2f77702d636f6e74656e742f75706c6f6164732f323031382f30332f696e7465727175617274696c655f72616e67652e706e673f773d3537362673736c3d31\n",
+    "\n",
+    "When measuring variability, statisticians prefer using the interquartile range instead of the full data range because extreme values and outliers affect it less. Typically, use the IQR with a measure of central tendency, such as the median, to understand your data’s center and spread. This combination creates a fuller picture of your data’s distribution.\n",
     "\n",
-    "To acomplish this, we use the function `isin` that will allow us to only select the the indicators afromentioned, that have been compilied in the list called *indicators_list*"
+    "Therefore it is being utilized to get rid of all the outliers that may come from errors when creating the data or from unexpected years."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "indicators_list=['GDP (current US$)','Literacy rate, adult total (% of people ages 15 and above)', 'Government expenditure on education, total (% of government expenditure)','Net migration','Commercial service exports (current US$)','Exports of goods and services (current US$)','Taxes on international trade (current LCU)','Fertility rate, total (births per woman)','People using at least basic sanitation services (% of population)','Employment in agriculture (% of total employment) (modeled ILO estimate)','Employment in services (% of total employment) (modeled ILO estimate)','Employment in industry (% of total employment) (modeled ILO estimate)','Electricity production from renewable sources, excluding hydroelectric (kWh)','Number of infant deaths','Number of infant deaths','Foreign direct investment, net (BoP, current US$)','Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)','Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)','Research and development expenditure (% of GDP)','Labor force with advanced education (% of total working-age population with advanced education)','Suicide mortality rate (per 100,000 population)','CPIA gender equality rating (1=low to 6=high)','Share of youth not in education, employment or training, total (% of youth population)','Government expenditure on education, total (% of government expenditure)']"
+    "Firstly, we compute the first quartile (Q1=25%) and the third quartile (Q3=75%). For that, we have grouped the data by country code and indicator name, so we get the Q1 and Q3 values for each indicator in each geographical area. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001B1CA01DBB0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped=BronzeDataFrame.groupby(['Country Code','Indicator Name'])\n",
+    "grouped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -954,180 +1103,135 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
+       "      <th></th>\n",
        "      <th>Date</th>\n",
        "      <th>Value</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>Indicator Name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>5334</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>4.795977e+08</td>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5335</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>3.747657e+08</td>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5336</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5337</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5338</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1768492</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>7.900000e-01</td>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1768493</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1768494</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1768495</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>4.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1768496</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>85000.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>20377 rows × 4 columns</p>\n",
+       "<p>59239 rows × 2 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        Country Code                                     Indicator Name  Date  \\\n",
-       "5334             DZA           Commercial service exports (current US$)  1990   \n",
-       "5335             DZA           Commercial service exports (current US$)  1991   \n",
-       "5336             DZA           Commercial service exports (current US$)  2005   \n",
-       "5337             DZA           Commercial service exports (current US$)  2006   \n",
-       "5338             DZA           Commercial service exports (current US$)  2007   \n",
-       "...              ...                                                ...   ...   \n",
-       "1768492          YEM  Total alcohol consumption per capita (liters o...  2000   \n",
-       "1768493          YEM  Total alcohol consumption per capita (liters o...  2005   \n",
-       "1768494          YEM  Total alcohol consumption per capita (liters o...  2010   \n",
-       "1768495          YEM  Total alcohol consumption per capita (liters o...  2015   \n",
-       "1768496          YEM  Total alcohol consumption per capita (liters o...  2018   \n",
-       "\n",
-       "                Value  \n",
-       "5334     4.795977e+08  \n",
-       "5335     3.747657e+08  \n",
-       "5336     2.466000e+09  \n",
-       "5337     2.512000e+09  \n",
-       "5338     2.786733e+09  \n",
-       "...               ...  \n",
-       "1768492  7.900000e-01  \n",
-       "1768493  3.400000e-01  \n",
-       "1768494  1.800000e-01  \n",
-       "1768495  5.500000e-02  \n",
-       "1768496  5.100000e-02  \n",
-       "\n",
-       "[20377 rows x 4 columns]"
+       "                                                                 Date  \\\n",
+       "Country Code Indicator Name                                             \n",
+       "ARE          Access to clean fuels and technologies for cook...  10.0   \n",
+       "             Access to clean fuels and technologies for cook...  10.0   \n",
+       "             Access to clean fuels and technologies for cook...  10.0   \n",
+       "             Access to electricity (% of population)             15.0   \n",
+       "             Access to electricity, rural (% of rural popula...  15.0   \n",
+       "...                                                               ...   \n",
+       "ZAF          Women who believe a husband is justified in bea...   0.0   \n",
+       "             Women who were first married by age 15 (% of wo...   9.0   \n",
+       "             Women who were first married by age 18 (% of wo...   9.0   \n",
+       "             Women's share of population ages 15+ living wit...  15.0   \n",
+       "             Young people (ages 15-24) newly infected with HIV   15.0   \n",
+       "\n",
+       "                                                                    Value  \n",
+       "Country Code Indicator Name                                                \n",
+       "ARE          Access to clean fuels and technologies for cook...      0.00  \n",
+       "             Access to clean fuels and technologies for cook...      0.00  \n",
+       "             Access to clean fuels and technologies for cook...      0.00  \n",
+       "             Access to electricity (% of population)                 0.00  \n",
+       "             Access to electricity, rural (% of rural popula...      0.00  \n",
+       "...                                                                   ...  \n",
+       "ZAF          Women who believe a husband is justified in bea...      0.00  \n",
+       "             Women who were first married by age 15 (% of wo...      0.15  \n",
+       "             Women who were first married by age 18 (% of wo...      2.15  \n",
+       "             Women's share of population ages 15+ living wit...      4.90  \n",
+       "             Young people (ages 15-24) newly infected with HIV   85000.00  \n",
+       "\n",
+       "[59239 rows x 2 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "BronzeDataFrame=df3.loc[df3['Indicator Name'].isin(indicators_list)]\n",
-    "pd.set_option('display.max_rows', 10)\n",
-    "BronzeDataFrame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# NORMALIZATION"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Taking as reference both works of https://www.pluralsight.com/guides/cleaning-up-data-from-outliers and https://careerfoundry.com/en/blog/data-analytics/how-to-find-outliers/, for normalizing our data we need to start computing the outliers and removing them from our dataframe. As there is not a direct function of pandas that performs this step, it´s been step-by-step code, where we begin with the computation of the quartiles, then the IQR (Inter Quartile Range) and finally the upper and lower limit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### IQR explanation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The interquartile range (IQR) measures the spread of the middle half of your data. It is the range for the middle 50% of your sample. Use the IQR to assess the variability where most of your values lie. Larger values indicate that the central portion of your data spread out further. Conversely, smaller values show that the middle values cluster more tightly.\n",
-    "\n",
-    "To visualize the interquartile range, imagine dividing your data into quarters. Statisticians refer to these quarters as quartiles and label them from low to high as Q1, Q2, Q3, and Q4. The lowest quartile (Q1) covers the smallest quarter of values in your dataset. The upper quartile (Q4) comprises the highest quarter of values. The interquartile range is the middle half of the data that lies between the upper and lower quartiles. In other words, the interquartile range includes the 50% of data points that are above Q1 and below Q4.\n",
-    "\n",
-    "When measuring variability, statisticians prefer using the interquartile range instead of the full data range because extreme values and outliers affect it less. Typically, use the IQR with a measure of central tendency, such as the median, to understand your data’s center and spread. This combination creates a fuller picture of your data’s distribution.\n",
-    "\n",
-    "Therefore it is being utilized to get rid of all the outliers that may come from errors when creating the data or from unexpected years."
+    "Q1=BronzeDataFrame.groupby(['Country Code','Indicator Name']).quantile(0.25)\n",
+    "Q3=BronzeDataFrame.groupby(['Country Code','Indicator Name']).quantile(0.75)\n",
+    "IQR=Q3-Q1\n",
+    "IQR"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Firstly, what we have done is to change the name of our indicators, as their original denomination is not easy to handle."
+    "Once we got the quartiles, we compute the upper and lower limit, with a basic mathematical expression."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1151,396 +1255,100 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Lower limit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>Country Code</th>\n",
        "      <th>Indicator Name</th>\n",
-       "      <th>1960</th>\n",
-       "      <th>1961</th>\n",
-       "      <th>1962</th>\n",
-       "      <th>1963</th>\n",
-       "      <th>1964</th>\n",
-       "      <th>1965</th>\n",
-       "      <th>1966</th>\n",
-       "      <th>1967</th>\n",
-       "      <th>...</th>\n",
-       "      <th>2013</th>\n",
-       "      <th>2014</th>\n",
-       "      <th>2015</th>\n",
-       "      <th>2016</th>\n",
-       "      <th>2017</th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "      <th>2021</th>\n",
-       "      <th>Unnamed: 66</th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to clean fuels and technologies for coo...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>16.936004</td>\n",
-       "      <td>17.337896</td>\n",
-       "      <td>17.687093</td>\n",
-       "      <td>18.140971</td>\n",
-       "      <td>18.491344</td>\n",
-       "      <td>18.825520</td>\n",
-       "      <td>19.272212</td>\n",
-       "      <td>19.628009</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to clean fuels and technologies for coo...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.499471</td>\n",
-       "      <td>6.680066</td>\n",
-       "      <td>6.859110</td>\n",
-       "      <td>7.016238</td>\n",
-       "      <td>7.180364</td>\n",
-       "      <td>7.322294</td>\n",
-       "      <td>7.517191</td>\n",
-       "      <td>7.651598</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to clean fuels and technologies for coo...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>37.855399</td>\n",
-       "      <td>38.046781</td>\n",
-       "      <td>38.326255</td>\n",
-       "      <td>38.468426</td>\n",
-       "      <td>38.670044</td>\n",
-       "      <td>38.722783</td>\n",
-       "      <td>38.927016</td>\n",
-       "      <td>39.042839</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to electricity (% of population)</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>31.794160</td>\n",
-       "      <td>32.001027</td>\n",
-       "      <td>33.871910</td>\n",
-       "      <td>38.880173</td>\n",
-       "      <td>40.261358</td>\n",
-       "      <td>43.061877</td>\n",
-       "      <td>44.270860</td>\n",
-       "      <td>45.803485</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>18.663502</td>\n",
-       "      <td>17.633986</td>\n",
-       "      <td>16.464681</td>\n",
-       "      <td>24.531436</td>\n",
-       "      <td>25.345111</td>\n",
-       "      <td>27.449908</td>\n",
-       "      <td>29.641760</td>\n",
-       "      <td>30.404935</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>384365</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Women who believe a husband is justified in be...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14.500000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <td>1.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>384366</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Women who were first married by age 15 (% of w...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.700000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.418352</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <td>0.625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>384367</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Women who were first married by age 18 (% of w...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>33.500000</td>\n",
-       "      <td>32.400000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>33.658057</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <td>1.375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>384368</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Women's share of population ages 15+ living wi...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>59.200000</td>\n",
-       "      <td>59.400000</td>\n",
-       "      <td>59.500000</td>\n",
-       "      <td>59.700000</td>\n",
-       "      <td>59.900000</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>60.200000</td>\n",
-       "      <td>60.400000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <td>49.850</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>384369</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>18000.000000</td>\n",
-       "      <td>17000.000000</td>\n",
-       "      <td>15000.000000</td>\n",
-       "      <td>14000.000000</td>\n",
-       "      <td>12000.000000</td>\n",
-       "      <td>9700.000000</td>\n",
-       "      <td>9600.000000</td>\n",
-       "      <td>7500.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
+       "      <td>-22500.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>384370 rows × 65 columns</p>\n",
+       "<p>59239 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       Country Code                                     Indicator Name  1960  \\\n",
-       "0               AFE  Access to clean fuels and technologies for coo...   NaN   \n",
-       "1               AFE  Access to clean fuels and technologies for coo...   NaN   \n",
-       "2               AFE  Access to clean fuels and technologies for coo...   NaN   \n",
-       "3               AFE            Access to electricity (% of population)   NaN   \n",
-       "4               AFE  Access to electricity, rural (% of rural popul...   NaN   \n",
-       "...             ...                                                ...   ...   \n",
-       "384365          ZWE  Women who believe a husband is justified in be...   NaN   \n",
-       "384366          ZWE  Women who were first married by age 15 (% of w...   NaN   \n",
-       "384367          ZWE  Women who were first married by age 18 (% of w...   NaN   \n",
-       "384368          ZWE  Women's share of population ages 15+ living wi...   NaN   \n",
-       "384369          ZWE  Young people (ages 15-24) newly infected with HIV   NaN   \n",
-       "\n",
-       "        1961  1962  1963  1964  1965  1966  1967  ...          2013  \\\n",
-       "0        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     16.936004   \n",
-       "1        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...      6.499471   \n",
-       "2        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     37.855399   \n",
-       "3        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     31.794160   \n",
-       "4        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     18.663502   \n",
-       "...      ...   ...   ...   ...   ...   ...   ...  ...           ...   \n",
-       "384365   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN   \n",
-       "384366   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN   \n",
-       "384367   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN   \n",
-       "384368   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     59.200000   \n",
-       "384369   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...  18000.000000   \n",
-       "\n",
-       "                2014          2015          2016          2017         2018  \\\n",
-       "0          17.337896     17.687093     18.140971     18.491344    18.825520   \n",
-       "1           6.680066      6.859110      7.016238      7.180364     7.322294   \n",
-       "2          38.046781     38.326255     38.468426     38.670044    38.722783   \n",
-       "3          32.001027     33.871910     38.880173     40.261358    43.061877   \n",
-       "4          17.633986     16.464681     24.531436     25.345111    27.449908   \n",
-       "...              ...           ...           ...           ...          ...   \n",
-       "384365           NaN     14.500000           NaN           NaN          NaN   \n",
-       "384366           NaN      3.700000           NaN           NaN          NaN   \n",
-       "384367     33.500000     32.400000           NaN           NaN          NaN   \n",
-       "384368     59.400000     59.500000     59.700000     59.900000    60.000000   \n",
-       "384369  17000.000000  15000.000000  14000.000000  12000.000000  9700.000000   \n",
-       "\n",
-       "               2019         2020  2021  Unnamed: 66  \n",
-       "0         19.272212    19.628009   NaN          NaN  \n",
-       "1          7.517191     7.651598   NaN          NaN  \n",
-       "2         38.927016    39.042839   NaN          NaN  \n",
-       "3         44.270860    45.803485   NaN          NaN  \n",
-       "4         29.641760    30.404935   NaN          NaN  \n",
-       "...             ...          ...   ...          ...  \n",
-       "384365          NaN          NaN   NaN          NaN  \n",
-       "384366     5.418352          NaN   NaN          NaN  \n",
-       "384367    33.658057          NaN   NaN          NaN  \n",
-       "384368    60.200000    60.400000   NaN          NaN  \n",
-       "384369  9600.000000  7500.000000   NaN          NaN  \n",
-       "\n",
-       "[384370 rows x 65 columns]"
+       "                                                                 Lower limit\n",
+       "Country Code Indicator Name                                                 \n",
+       "ARE          Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to electricity (% of population)                 100.000\n",
+       "             Access to electricity, rural (% of rural popula...      100.000\n",
+       "...                                                                      ...\n",
+       "ZAF          Women who believe a husband is justified in bea...        1.000\n",
+       "             Women who were first married by age 15 (% of wo...        0.625\n",
+       "             Women who were first married by age 18 (% of wo...        1.375\n",
+       "             Women's share of population ages 15+ living wit...       49.850\n",
+       "             Young people (ages 15-24) newly infected with HIV    -22500.000\n",
+       "\n",
+       "[59239 rows x 1 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "BronzeDataFrame['Indicator Name']=BronzeDataFrame['Indicator Name'].replace(['CPIA gender equality rating (1=low to 6=high)','Commercial service exports (current US$)','Electricity production from renewable sources, excluding hydroelectric (kWh)','Employment in agriculture (% of total employment) (modeled ILO estimate)','Employment in industry (% of total employment) (modeled ILO estimate)','Employment in services (% of total employment) (modeled ILO estimate)','Exports of goods and services (current US$)','Fertility rate, total (births per woman)','Foreign direct investment, net (BoP, current US$)','GDP (current US$)','Government expenditure on education, total (% of government expenditure)','Labor force with advanced education (% of total working-age population with advanced education)','Literacy rate, adult total (% of people ages 15 and above)','Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)','Net migration','Number of infant deaths','People using at least basic sanitation services (% of population)','Research and development expenditure (% of GDP)','Share of youth not in education, employment or training, total (% of youth population)','Suicide mortality rate (per 100,000 population)','Taxes on international trade (current LCU)','Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)'],['Gender equality','Exports-Commercial services','Renewable electricity','Employment-agriculture','Employment-industry','Employment-services','Exports-G&S','Fertility rate','Foreign investment','GDP','Education GExp','Workers high education','Literacy rate','Mortality-pollution','Net migration','Mortality-infants','Health services use','R&D GExp','Ninis','Suicide','International taxes','Alcohol per capita'])\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Secondly, we compute the first quartile (Q1=25%) and the third quartile (Q3=75%). For that, we have grouped the data by country code and indicator name, so we get the Q1 and Q3 values for each indicator in each geographical area. "
+    "lower_limit=Q1 - 1.5 * IQR\n",
+    "lower=lower_limit.drop(['Date'],axis=1)\n",
+    "lower.rename(columns={\"Value\":\"Lower limit\"})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001AE50FA37F0>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grouped=BronzeDataFrame.groupby(['Country Code','Indicator Name'])\n",
-    "grouped"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1565,96 +1373,83 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Value</th>\n",
+       "      <th>Upper limit</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country Code</th>\n",
        "      <th>Indicator Name</th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <td>10.0</td>\n",
-       "      <td>6.400000e-01</td>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Education GExp</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <td>14.0</td>\n",
-       "      <td>5.130000e+00</td>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <td>14.0</td>\n",
-       "      <td>1.449997e+00</td>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Employment-services</th>\n",
-       "      <td>14.0</td>\n",
-       "      <td>3.830002e+00</td>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <td>100.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <td>12.5</td>\n",
-       "      <td>2.997499e+00</td>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <td>1.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.011100e-01</td>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <td>1.225</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <td>12.5</td>\n",
-       "      <td>2.292500e+08</td>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <td>9.975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Suicide</th>\n",
-       "      <td>9.5</td>\n",
-       "      <td>1.025000e+00</td>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <td>69.450</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Workers high education</th>\n",
-       "      <td>10.5</td>\n",
-       "      <td>1.712500e+00</td>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
+       "      <td>317500.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>978 rows × 2 columns</p>\n",
+       "<p>59239 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                     Date         Value\n",
-       "Country Code Indicator Name                            \n",
-       "ARE          Alcohol per capita      10.0  6.400000e-01\n",
-       "             Education GExp           0.0  0.000000e+00\n",
-       "             Employment-agriculture  14.0  5.130000e+00\n",
-       "             Employment-industry     14.0  1.449997e+00\n",
-       "             Employment-services     14.0  3.830002e+00\n",
-       "...                                   ...           ...\n",
-       "ZAF          Ninis                   12.5  2.997499e+00\n",
-       "             R&D GExp                 8.0  1.011100e-01\n",
-       "             Renewable electricity   12.5  2.292500e+08\n",
-       "             Suicide                  9.5  1.025000e+00\n",
-       "             Workers high education  10.5  1.712500e+00\n",
-       "\n",
-       "[978 rows x 2 columns]"
+       "                                                                 Upper limit\n",
+       "Country Code Indicator Name                                                 \n",
+       "ARE          Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to electricity (% of population)                 100.000\n",
+       "             Access to electricity, rural (% of rural popula...      100.000\n",
+       "...                                                                      ...\n",
+       "ZAF          Women who believe a husband is justified in bea...        1.000\n",
+       "             Women who were first married by age 15 (% of wo...        1.225\n",
+       "             Women who were first married by age 18 (% of wo...        9.975\n",
+       "             Women's share of population ages 15+ living wit...       69.450\n",
+       "             Young people (ages 15-24) newly infected with HIV    317500.000\n",
+       "\n",
+       "[59239 rows x 1 columns]"
       ]
      },
      "execution_count": 14,
@@ -1663,17 +1458,16 @@
     }
    ],
    "source": [
-    "Q1=BronzeDataFrame.groupby(['Country Code','Indicator Name']).quantile(0.25)\n",
-    "Q3=BronzeDataFrame.groupby(['Country Code','Indicator Name']).quantile(0.75)\n",
-    "IQR=Q3-Q1\n",
-    "IQR"
+    "upper_limit=Q3 + 1.5 * IQR\n",
+    "upper=upper_limit.drop(['Date'],axis=1)\n",
+    "upper.rename(columns={\"Value\":\"Upper limit\"})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once we got the quartiles, we compute the upper and lower limit, with a basic mathematical expression."
+    "Thirdly, we join the three tables we have (main dataframe, upper limit and lower limit) by matching country code and indicator name.."
    ]
   },
   {
@@ -1702,84 +1496,147 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Lower limit</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>Country Code</th>\n",
        "      <th>Indicator Name</th>\n",
-       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Value_x</th>\n",
+       "      <th>Value_y</th>\n",
+       "      <th>Value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <td>2.190000e+00</td>\n",
+       "      <th>0</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>97.1</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Education GExp</th>\n",
-       "      <td>1.026766e+01</td>\n",
+       "      <th>1</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>97.3</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <td>-4.905000e+00</td>\n",
+       "      <th>2</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>97.8</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <td>3.131501e+01</td>\n",
+       "      <th>3</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Employment-services</th>\n",
-       "      <td>5.269500e+01</td>\n",
+       "      <th>4</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>98.2</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
-       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <td>2.684375e+01</td>\n",
+       "      <th>1225413</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <td>5.828450e-01</td>\n",
+       "      <th>1225414</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <td>-2.623750e+08</td>\n",
+       "      <th>1225415</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Suicide</th>\n",
-       "      <td>2.203750e+01</td>\n",
+       "      <th>1225416</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Workers high education</th>\n",
-       "      <td>8.050875e+01</td>\n",
+       "      <th>1225417</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>978 rows × 1 columns</p>\n",
+       "<p>1225418 rows × 6 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                      Lower limit\n",
-       "Country Code Indicator Name                      \n",
-       "ARE          Alcohol per capita      2.190000e+00\n",
-       "             Education GExp          1.026766e+01\n",
-       "             Employment-agriculture -4.905000e+00\n",
-       "             Employment-industry     3.131501e+01\n",
-       "             Employment-services     5.269500e+01\n",
-       "...                                           ...\n",
-       "ZAF          Ninis                   2.684375e+01\n",
-       "             R&D GExp                5.828450e-01\n",
-       "             Renewable electricity  -2.623750e+08\n",
-       "             Suicide                 2.203750e+01\n",
-       "             Workers high education  8.050875e+01\n",
-       "\n",
-       "[978 rows x 1 columns]"
+       "        Country Code                                     Indicator Name  Date  \\\n",
+       "0                DZA  Access to clean fuels and technologies for coo...  2000   \n",
+       "1                DZA  Access to clean fuels and technologies for coo...  2001   \n",
+       "2                DZA  Access to clean fuels and technologies for coo...  2002   \n",
+       "3                DZA  Access to clean fuels and technologies for coo...  2003   \n",
+       "4                DZA  Access to clean fuels and technologies for coo...  2004   \n",
+       "...              ...                                                ...   ...   \n",
+       "1225413          YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1225414          YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1225415          YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1225416          YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1225417          YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "\n",
+       "         Value_x  Value_y  Value  \n",
+       "0           97.1     97.0  101.0  \n",
+       "1           97.3     97.0  101.0  \n",
+       "2           97.8     97.0  101.0  \n",
+       "3           98.0     97.0  101.0  \n",
+       "4           98.2     97.0  101.0  \n",
+       "...          ...      ...    ...  \n",
+       "1225413    200.0    -50.0  350.0  \n",
+       "1225414    200.0    -50.0  350.0  \n",
+       "1225415    200.0    -50.0  350.0  \n",
+       "1225416    200.0    -50.0  350.0  \n",
+       "1225417    200.0    -50.0  350.0  \n",
+       "\n",
+       "[1225418 rows x 6 columns]"
       ]
      },
      "execution_count": 15,
@@ -1788,9 +1645,9 @@
     }
    ],
    "source": [
-    "lower_limit=Q1 - 1.5 * IQR\n",
-    "lower=lower_limit.drop(['Date'],axis=1)\n",
-    "lower.rename(columns={\"Value\":\"Lower limit\"})"
+    "dfs = [BronzeDataFrame,lower,upper]\n",
+    "df_joined = ft.reduce(lambda left, right: pd.merge(left, right, on=['Country Code','Indicator Name']), dfs)\n",
+    "df_joined"
    ]
   },
   {
@@ -1800,103 +1657,8 @@
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Upper limit</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <td>4.750000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Education GExp</th>\n",
-       "      <td>1.026766e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <td>1.561500e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <td>3.711499e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-services</th>\n",
-       "      <td>6.801500e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <td>3.883375e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <td>9.872850e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <td>6.546250e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Suicide</th>\n",
-       "      <td>2.613750e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Workers high education</th>\n",
-       "      <td>8.735875e+01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>978 rows × 1 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                                      Upper limit\n",
-       "Country Code Indicator Name                      \n",
-       "ARE          Alcohol per capita      4.750000e+00\n",
-       "             Education GExp          1.026766e+01\n",
-       "             Employment-agriculture  1.561500e+01\n",
-       "             Employment-industry     3.711499e+01\n",
-       "             Employment-services     6.801500e+01\n",
-       "...                                           ...\n",
-       "ZAF          Ninis                   3.883375e+01\n",
-       "             R&D GExp                9.872850e-01\n",
-       "             Renewable electricity   6.546250e+08\n",
-       "             Suicide                 2.613750e+01\n",
-       "             Workers high education  8.735875e+01\n",
-       "\n",
-       "[978 rows x 1 columns]"
+       "['Country Code', 'Indicator Name', 'Date', 'Value_x', 'Value_y', 'Value']"
       ]
      },
      "execution_count": 16,
@@ -1905,16 +1667,14 @@
     }
    ],
    "source": [
-    "upper_limit=Q3 + 1.5 * IQR\n",
-    "upper=upper_limit.drop(['Date'],axis=1)\n",
-    "upper.rename(columns={\"Value\":\"Upper limit\"})"
+    "list(df_joined)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Thirdly, we join the three tables we have (main dataframe, upper limit and lower limit) by matching country code and indicator name.."
+    "We rename the columns of the new table, as the columns headers are not saved after the joining. "
    ]
   },
   {
@@ -1943,59 +1703,59 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Value_x</th>\n",
-       "      <th>Value_y</th>\n",
-       "      <th>Value</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Real value</th>\n",
+       "      <th>Lower value</th>\n",
+       "      <th>Upper value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>4.795977e+08</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>97.1</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>3.747657e+08</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>97.3</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>97.8</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>98.2</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2007,83 +1767,83 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20372</th>\n",
+       "      <th>1225413</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>7.900000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20373</th>\n",
+       "      <th>1225414</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20374</th>\n",
+       "      <th>1225415</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20375</th>\n",
+       "      <th>1225416</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20376</th>\n",
+       "      <th>1225417</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>20377 rows × 6 columns</p>\n",
+       "<p>1225418 rows × 6 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      Country Code               Indicator Name  Date       Value_x  \\\n",
-       "0              DZA  Exports-Commercial services  1990  4.795977e+08   \n",
-       "1              DZA  Exports-Commercial services  1991  3.747657e+08   \n",
-       "2              DZA  Exports-Commercial services  2005  2.466000e+09   \n",
-       "3              DZA  Exports-Commercial services  2006  2.512000e+09   \n",
-       "4              DZA  Exports-Commercial services  2007  2.786733e+09   \n",
-       "...            ...                          ...   ...           ...   \n",
-       "20372          YEM           Alcohol per capita  2000  7.900000e-01   \n",
-       "20373          YEM           Alcohol per capita  2005  3.400000e-01   \n",
-       "20374          YEM           Alcohol per capita  2010  1.800000e-01   \n",
-       "20375          YEM           Alcohol per capita  2015  5.500000e-02   \n",
-       "20376          YEM           Alcohol per capita  2018  5.100000e-02   \n",
-       "\n",
-       "            Value_y         Value  \n",
-       "0      1.736231e+09  4.453536e+09  \n",
-       "1      1.736231e+09  4.453536e+09  \n",
-       "2      1.736231e+09  4.453536e+09  \n",
-       "3      1.736231e+09  4.453536e+09  \n",
-       "4      1.736231e+09  4.453536e+09  \n",
-       "...             ...           ...  \n",
-       "20372 -3.725000e-01  7.675000e-01  \n",
-       "20373 -3.725000e-01  7.675000e-01  \n",
-       "20374 -3.725000e-01  7.675000e-01  \n",
-       "20375 -3.725000e-01  7.675000e-01  \n",
-       "20376 -3.725000e-01  7.675000e-01  \n",
-       "\n",
-       "[20377 rows x 6 columns]"
+       "        Country                                          Indicator  Year  \\\n",
+       "0           DZA  Access to clean fuels and technologies for coo...  2000   \n",
+       "1           DZA  Access to clean fuels and technologies for coo...  2001   \n",
+       "2           DZA  Access to clean fuels and technologies for coo...  2002   \n",
+       "3           DZA  Access to clean fuels and technologies for coo...  2003   \n",
+       "4           DZA  Access to clean fuels and technologies for coo...  2004   \n",
+       "...         ...                                                ...   ...   \n",
+       "1225413     YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1225414     YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1225415     YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1225416     YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1225417     YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "\n",
+       "         Real value  Lower value  Upper value  \n",
+       "0              97.1         97.0        101.0  \n",
+       "1              97.3         97.0        101.0  \n",
+       "2              97.8         97.0        101.0  \n",
+       "3              98.0         97.0        101.0  \n",
+       "4              98.2         97.0        101.0  \n",
+       "...             ...          ...          ...  \n",
+       "1225413       200.0        -50.0        350.0  \n",
+       "1225414       200.0        -50.0        350.0  \n",
+       "1225415       200.0        -50.0        350.0  \n",
+       "1225416       200.0        -50.0        350.0  \n",
+       "1225417       200.0        -50.0        350.0  \n",
+       "\n",
+       "[1225418 rows x 6 columns]"
       ]
      },
      "execution_count": 17,
@@ -2092,41 +1852,20 @@
     }
    ],
    "source": [
-    "dfs = [BronzeDataFrame,lower,upper]\n",
-    "df_joined = ft.reduce(lambda left, right: pd.merge(left, right, on=['Country Code','Indicator Name']), dfs)\n",
-    "df_joined"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Country Code', 'Indicator Name', 'Date', 'Value_x', 'Value_y', 'Value']"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(df_joined)"
+    "renamed=df_joined.set_axis(['Country','Indicator','Year', 'Real value', 'Lower value', 'Upper value'], axis=1, inplace=False)\n",
+    "renamed"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We rename the columns of the new table, as the columns headers are not saved after the joining. "
+    "Now that we have the table correctly defined, we remove from our dataframe the values that are outside our range, as it means that they are outliers."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2162,47 +1901,47 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>4.795977e+08</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>97.1</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>3.747657e+08</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>97.3</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>97.8</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>98.2</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2214,105 +1953,105 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20372</th>\n",
+       "      <th>1225413</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>7.900000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20373</th>\n",
+       "      <th>1225414</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20374</th>\n",
+       "      <th>1225415</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20375</th>\n",
+       "      <th>1225416</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20376</th>\n",
+       "      <th>1225417</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>20377 rows × 6 columns</p>\n",
+       "<p>1189068 rows × 6 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      Country                    Indicator  Year    Real value   Lower value  \\\n",
-       "0         DZA  Exports-Commercial services  1990  4.795977e+08  1.736231e+09   \n",
-       "1         DZA  Exports-Commercial services  1991  3.747657e+08  1.736231e+09   \n",
-       "2         DZA  Exports-Commercial services  2005  2.466000e+09  1.736231e+09   \n",
-       "3         DZA  Exports-Commercial services  2006  2.512000e+09  1.736231e+09   \n",
-       "4         DZA  Exports-Commercial services  2007  2.786733e+09  1.736231e+09   \n",
-       "...       ...                          ...   ...           ...           ...   \n",
-       "20372     YEM           Alcohol per capita  2000  7.900000e-01 -3.725000e-01   \n",
-       "20373     YEM           Alcohol per capita  2005  3.400000e-01 -3.725000e-01   \n",
-       "20374     YEM           Alcohol per capita  2010  1.800000e-01 -3.725000e-01   \n",
-       "20375     YEM           Alcohol per capita  2015  5.500000e-02 -3.725000e-01   \n",
-       "20376     YEM           Alcohol per capita  2018  5.100000e-02 -3.725000e-01   \n",
-       "\n",
-       "        Upper value  \n",
-       "0      4.453536e+09  \n",
-       "1      4.453536e+09  \n",
-       "2      4.453536e+09  \n",
-       "3      4.453536e+09  \n",
-       "4      4.453536e+09  \n",
-       "...             ...  \n",
-       "20372  7.675000e-01  \n",
-       "20373  7.675000e-01  \n",
-       "20374  7.675000e-01  \n",
-       "20375  7.675000e-01  \n",
-       "20376  7.675000e-01  \n",
+       "        Country                                          Indicator  Year  \\\n",
+       "0           DZA  Access to clean fuels and technologies for coo...  2000   \n",
+       "1           DZA  Access to clean fuels and technologies for coo...  2001   \n",
+       "2           DZA  Access to clean fuels and technologies for coo...  2002   \n",
+       "3           DZA  Access to clean fuels and technologies for coo...  2003   \n",
+       "4           DZA  Access to clean fuels and technologies for coo...  2004   \n",
+       "...         ...                                                ...   ...   \n",
+       "1225413     YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1225414     YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1225415     YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1225416     YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1225417     YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
        "\n",
-       "[20377 rows x 6 columns]"
+       "         Real value  Lower value  Upper value  \n",
+       "0              97.1         97.0        101.0  \n",
+       "1              97.3         97.0        101.0  \n",
+       "2              97.8         97.0        101.0  \n",
+       "3              98.0         97.0        101.0  \n",
+       "4              98.2         97.0        101.0  \n",
+       "...             ...          ...          ...  \n",
+       "1225413       200.0        -50.0        350.0  \n",
+       "1225414       200.0        -50.0        350.0  \n",
+       "1225415       200.0        -50.0        350.0  \n",
+       "1225416       200.0        -50.0        350.0  \n",
+       "1225417       200.0        -50.0        350.0  \n",
+       "\n",
+       "[1189068 rows x 6 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "renamed=df_joined.set_axis(['Country','Indicator','Year', 'Real value', 'Lower value', 'Upper value'], axis=1, inplace=False)\n",
-    "renamed"
+    "sin_outliers=renamed.loc[~((renamed['Real value']<renamed['Lower value']) | (renamed['Real value']>renamed['Upper value']))]\n",
+    "sin_outliers"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we have the table correctly defined, we remove from our dataframe the values that are outside our range, as it means that they are outliers."
+    "From the data above, we can perceive that our data comes down from 1225418  rows to 1189068, so 36.350  were outliers. The next steps are to order and display data better, removing those columns that we just do not need and pivoting the rows and columns. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2340,55 +2079,43 @@
        "      <th>Indicator</th>\n",
        "      <th>Year</th>\n",
        "      <th>Real value</th>\n",
-       "      <th>Lower value</th>\n",
-       "      <th>Upper value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>0</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>97.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>1</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>97.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>2</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>97.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>3</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>3.412421e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>98.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>4</th>\n",
        "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2009</td>\n",
-       "      <td>2.744716e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>98.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2396,251 +2123,99 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20371</th>\n",
+       "      <th>1225413</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Suicide</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>5.800000e+00</td>\n",
-       "      <td>5.400000e+00</td>\n",
-       "      <td>6.200000e+00</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20373</th>\n",
+       "      <th>1225414</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20374</th>\n",
+       "      <th>1225415</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20375</th>\n",
+       "      <th>1225416</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20376</th>\n",
+       "      <th>1225417</th>\n",
        "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>200.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>19847 rows × 6 columns</p>\n",
+       "<p>1189068 rows × 4 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      Country                    Indicator  Year    Real value   Lower value  \\\n",
-       "2         DZA  Exports-Commercial services  2005  2.466000e+09  1.736231e+09   \n",
-       "3         DZA  Exports-Commercial services  2006  2.512000e+09  1.736231e+09   \n",
-       "4         DZA  Exports-Commercial services  2007  2.786733e+09  1.736231e+09   \n",
-       "5         DZA  Exports-Commercial services  2008  3.412421e+09  1.736231e+09   \n",
-       "6         DZA  Exports-Commercial services  2009  2.744716e+09  1.736231e+09   \n",
-       "...       ...                          ...   ...           ...           ...   \n",
-       "20371     YEM                      Suicide  2019  5.800000e+00  5.400000e+00   \n",
-       "20373     YEM           Alcohol per capita  2005  3.400000e-01 -3.725000e-01   \n",
-       "20374     YEM           Alcohol per capita  2010  1.800000e-01 -3.725000e-01   \n",
-       "20375     YEM           Alcohol per capita  2015  5.500000e-02 -3.725000e-01   \n",
-       "20376     YEM           Alcohol per capita  2018  5.100000e-02 -3.725000e-01   \n",
-       "\n",
-       "        Upper value  \n",
-       "2      4.453536e+09  \n",
-       "3      4.453536e+09  \n",
-       "4      4.453536e+09  \n",
-       "5      4.453536e+09  \n",
-       "6      4.453536e+09  \n",
+       "        Country                                          Indicator  Year  \\\n",
+       "0           DZA  Access to clean fuels and technologies for coo...  2000   \n",
+       "1           DZA  Access to clean fuels and technologies for coo...  2001   \n",
+       "2           DZA  Access to clean fuels and technologies for coo...  2002   \n",
+       "3           DZA  Access to clean fuels and technologies for coo...  2003   \n",
+       "4           DZA  Access to clean fuels and technologies for coo...  2004   \n",
+       "...         ...                                                ...   ...   \n",
+       "1225413     YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1225414     YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1225415     YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1225416     YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1225417     YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "\n",
+       "         Real value  \n",
+       "0              97.1  \n",
+       "1              97.3  \n",
+       "2              97.8  \n",
+       "3              98.0  \n",
+       "4              98.2  \n",
        "...             ...  \n",
-       "20371  6.200000e+00  \n",
-       "20373  7.675000e-01  \n",
-       "20374  7.675000e-01  \n",
-       "20375  7.675000e-01  \n",
-       "20376  7.675000e-01  \n",
+       "1225413       200.0  \n",
+       "1225414       200.0  \n",
+       "1225415       200.0  \n",
+       "1225416       200.0  \n",
+       "1225417       200.0  \n",
        "\n",
-       "[19847 rows x 6 columns]"
+       "[1189068 rows x 4 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sin_outliers=renamed.loc[~((renamed['Real value']<renamed['Lower value']) | (renamed['Real value']>renamed['Upper value']))]\n",
-    "sin_outliers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From the data above, we can perceive that our data comes down from 19944 rows to 19424, so 500 were outliers. The next steps are to order and display data better, removing those columns that we just do not need and pivoting the rows and columns. "
+    "df_limpio=sin_outliers.drop(['Lower value','Upper value'],axis=1)\n",
+    "df_limpio"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Real value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>3.412421e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2009</td>\n",
-       "      <td>2.744716e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20371</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Suicide</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>5.800000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20373</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20374</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20375</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20376</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19847 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Country                    Indicator  Year    Real value\n",
-       "2         DZA  Exports-Commercial services  2005  2.466000e+09\n",
-       "3         DZA  Exports-Commercial services  2006  2.512000e+09\n",
-       "4         DZA  Exports-Commercial services  2007  2.786733e+09\n",
-       "5         DZA  Exports-Commercial services  2008  3.412421e+09\n",
-       "6         DZA  Exports-Commercial services  2009  2.744716e+09\n",
-       "...       ...                          ...   ...           ...\n",
-       "20371     YEM                      Suicide  2019  5.800000e+00\n",
-       "20373     YEM           Alcohol per capita  2005  3.400000e-01\n",
-       "20374     YEM           Alcohol per capita  2010  1.800000e-01\n",
-       "20375     YEM           Alcohol per capita  2015  5.500000e-02\n",
-       "20376     YEM           Alcohol per capita  2018  5.100000e-02\n",
-       "\n",
-       "[19847 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df_limpio=sin_outliers.drop(['Lower value','Upper value'],axis=1)\n",
-    "df_limpio"
+    "cols=df_limpio['Indicator'].unique().tolist()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2666,25 +2241,25 @@
        "      <th>Indicator</th>\n",
        "      <th>Country</th>\n",
        "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>Fertility rate</th>\n",
+       "      <th>ARI treatment (% of children under 5 taken to a health provider)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
        "      <th>...</th>\n",
-       "      <th>International taxes</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Mortality-pollution</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife (any of five reasons) (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she argues with him (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she burns the food (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she goes out without telling him (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she neglects the children (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2696,21 +2271,21 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.454</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>672.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>100.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2718,23 +2293,23 @@
        "      <td>1991</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>8.46</td>\n",
-       "      <td>33.330002</td>\n",
-       "      <td>58.200001</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>4.253</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>645.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>100.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2742,23 +2317,23 @@
        "      <td>1992</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>8.37</td>\n",
-       "      <td>33.360001</td>\n",
-       "      <td>58.279999</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>4.041</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>618.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>368126.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>100.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2766,23 +2341,23 @@
        "      <td>1993</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>8.24</td>\n",
-       "      <td>33.470001</td>\n",
-       "      <td>58.290001</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3.827</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>592.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>100.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2790,23 +2365,23 @@
        "      <td>1994</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>8.13</td>\n",
-       "      <td>33.490002</td>\n",
-       "      <td>58.380001</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3.618</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>568.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>100.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2833,107 +2408,107 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1504</th>\n",
+       "      <th>1531</th>\n",
        "      <td>ZAF</td>\n",
        "      <td>2017</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>18.719290</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>23.340000</td>\n",
-       "      <td>71.379997</td>\n",
-       "      <td>1.614806e+10</td>\n",
-       "      <td>1.042884e+11</td>\n",
-       "      <td>2.430</td>\n",
+       "      <td>85.2</td>\n",
+       "      <td>64.6</td>\n",
+       "      <td>94.20</td>\n",
+       "      <td>84.400002</td>\n",
+       "      <td>76.738983</td>\n",
+       "      <td>88.373024</td>\n",
+       "      <td>69.218491</td>\n",
        "      <td>...</td>\n",
-       "      <td>4.993941e+10</td>\n",
-       "      <td>87.046669</td>\n",
-       "      <td>32777.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>727026.0</td>\n",
-       "      <td>31.010000</td>\n",
-       "      <td>0.83215</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>25.2</td>\n",
-       "      <td>83.809998</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63.3</td>\n",
+       "      <td>100000.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1505</th>\n",
+       "      <th>1532</th>\n",
        "      <td>ZAF</td>\n",
        "      <td>2018</td>\n",
-       "      <td>9.52</td>\n",
-       "      <td>18.901590</td>\n",
-       "      <td>5.16</td>\n",
-       "      <td>23.129999</td>\n",
-       "      <td>71.709999</td>\n",
-       "      <td>1.670823e+10</td>\n",
-       "      <td>1.112854e+11</td>\n",
-       "      <td>2.405</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>85.7</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>94.65</td>\n",
+       "      <td>84.699997</td>\n",
+       "      <td>77.168495</td>\n",
+       "      <td>88.518814</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>5.572291e+10</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>31810.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>31.559999</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>24.1</td>\n",
-       "      <td>82.879997</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63.7</td>\n",
+       "      <td>92000.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1506</th>\n",
+       "      <th>1533</th>\n",
        "      <td>ZAF</td>\n",
        "      <td>2019</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>19.596230</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>22.309999</td>\n",
-       "      <td>72.410004</td>\n",
-       "      <td>1.554886e+10</td>\n",
-       "      <td>1.060698e+11</td>\n",
-       "      <td>2.381</td>\n",
+       "      <td>86.3</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>94.90</td>\n",
+       "      <td>85.000000</td>\n",
+       "      <td>77.611824</td>\n",
+       "      <td>88.662704</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>5.522342e+10</td>\n",
-       "      <td>95.022972</td>\n",
-       "      <td>30937.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>32.459999</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>23.5</td>\n",
-       "      <td>82.019997</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>85000.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1507</th>\n",
+       "      <th>1534</th>\n",
        "      <td>ZAF</td>\n",
        "      <td>2020</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>19.527281</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>86.8</td>\n",
+       "      <td>65.9</td>\n",
+       "      <td>95.20</td>\n",
+       "      <td>84.385536</td>\n",
+       "      <td>75.264854</td>\n",
+       "      <td>88.806267</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>8.404204e+09</td>\n",
-       "      <td>9.317915e+10</td>\n",
-       "      <td>2.358</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30153.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>32.400002</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>64.4</td>\n",
+       "      <td>79000.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1508</th>\n",
+       "      <th>1535</th>\n",
        "      <td>ZAF</td>\n",
        "      <td>2021</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>18.417240</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2954,92 +2529,274 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1509 rows × 24 columns</p>\n",
+       "<p>1536 rows × 1438 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "Indicator Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "0             ARE  1990                 NaN             NaN   \n",
-       "1             ARE  1991                 NaN             NaN   \n",
-       "2             ARE  1992                 NaN             NaN   \n",
-       "3             ARE  1993                 NaN             NaN   \n",
-       "4             ARE  1994                 NaN             NaN   \n",
-       "...           ...   ...                 ...             ...   \n",
-       "1504          ZAF  2017                 NaN       18.719290   \n",
-       "1505          ZAF  2018                9.52       18.901590   \n",
-       "1506          ZAF  2019                 NaN       19.596230   \n",
-       "1507          ZAF  2020                 NaN       19.527281   \n",
-       "1508          ZAF  2021                 NaN       18.417240   \n",
-       "\n",
-       "Indicator  Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "0                             NaN                  NaN                  NaN   \n",
-       "1                            8.46            33.330002            58.200001   \n",
-       "2                            8.37            33.360001            58.279999   \n",
-       "3                            8.24            33.470001            58.290001   \n",
-       "4                            8.13            33.490002            58.380001   \n",
-       "...                           ...                  ...                  ...   \n",
-       "1504                         5.28            23.340000            71.379997   \n",
-       "1505                         5.16            23.129999            71.709999   \n",
-       "1506                         5.28            22.309999            72.410004   \n",
-       "1507                          NaN                  NaN                  NaN   \n",
-       "1508                          NaN                  NaN                  NaN   \n",
-       "\n",
-       "Indicator  Exports-Commercial services   Exports-G&S  Fertility rate  ...  \\\n",
-       "0                                  NaN           NaN           4.454  ...   \n",
-       "1                                  NaN           NaN           4.253  ...   \n",
-       "2                                  NaN           NaN           4.041  ...   \n",
-       "3                                  NaN           NaN           3.827  ...   \n",
-       "4                                  NaN           NaN           3.618  ...   \n",
-       "...                                ...           ...             ...  ...   \n",
-       "1504                      1.614806e+10  1.042884e+11           2.430  ...   \n",
-       "1505                      1.670823e+10  1.112854e+11           2.405  ...   \n",
-       "1506                      1.554886e+10  1.060698e+11           2.381  ...   \n",
-       "1507                      8.404204e+09  9.317915e+10           2.358  ...   \n",
-       "1508                               NaN           NaN             NaN  ...   \n",
-       "\n",
-       "Indicator  International taxes  Literacy rate  Mortality-infants  \\\n",
-       "0                          NaN            NaN              672.0   \n",
-       "1                          NaN            NaN              645.0   \n",
-       "2                          NaN            NaN              618.0   \n",
-       "3                          NaN            NaN              592.0   \n",
-       "4                          NaN            NaN              568.0   \n",
-       "...                        ...            ...                ...   \n",
-       "1504              4.993941e+10      87.046669            32777.0   \n",
-       "1505              5.572291e+10            NaN            31810.0   \n",
-       "1506              5.522342e+10      95.022972            30937.0   \n",
-       "1507                       NaN            NaN            30153.0   \n",
-       "1508                       NaN            NaN                NaN   \n",
-       "\n",
-       "Indicator  Mortality-pollution  Net migration      Ninis  R&D GExp  \\\n",
-       "0                          NaN            NaN        NaN       NaN   \n",
-       "1                          NaN            NaN        NaN       NaN   \n",
-       "2                          NaN       368126.0        NaN       NaN   \n",
-       "3                          NaN            NaN        NaN       NaN   \n",
-       "4                          NaN            NaN        NaN       NaN   \n",
-       "...                        ...            ...        ...       ...   \n",
-       "1504                       NaN       727026.0  31.010000   0.83215   \n",
-       "1505                       NaN            NaN  31.559999       NaN   \n",
-       "1506                       NaN            NaN  32.459999       NaN   \n",
-       "1507                       NaN            NaN  32.400002       NaN   \n",
-       "1508                       NaN            NaN        NaN       NaN   \n",
-       "\n",
-       "Indicator  Renewable electricity  Suicide  Workers high education  \n",
-       "0                            0.0      NaN                     NaN  \n",
-       "1                            0.0      NaN                     NaN  \n",
-       "2                            0.0      NaN                     NaN  \n",
-       "3                            0.0      NaN                     NaN  \n",
-       "4                            0.0      NaN                     NaN  \n",
-       "...                          ...      ...                     ...  \n",
-       "1504                         NaN     25.2               83.809998  \n",
-       "1505                         NaN     24.1               82.879997  \n",
-       "1506                         NaN     23.5               82.019997  \n",
-       "1507                         NaN      NaN                     NaN  \n",
-       "1508                         NaN      NaN                     NaN  \n",
-       "\n",
-       "[1509 rows x 24 columns]"
+       "Indicator Country  Year  \\\n",
+       "0             ARE  1990   \n",
+       "1             ARE  1991   \n",
+       "2             ARE  1992   \n",
+       "3             ARE  1993   \n",
+       "4             ARE  1994   \n",
+       "...           ...   ...   \n",
+       "1531          ZAF  2017   \n",
+       "1532          ZAF  2018   \n",
+       "1533          ZAF  2019   \n",
+       "1534          ZAF  2020   \n",
+       "1535          ZAF  2021   \n",
+       "\n",
+       "Indicator  ARI treatment (% of children under 5 taken to a health provider)  \\\n",
+       "0                                                        NaN                  \n",
+       "1                                                        NaN                  \n",
+       "2                                                        NaN                  \n",
+       "3                                                        NaN                  \n",
+       "4                                                        NaN                  \n",
+       "...                                                      ...                  \n",
+       "1531                                                     NaN                  \n",
+       "1532                                                     NaN                  \n",
+       "1533                                                     NaN                  \n",
+       "1534                                                     NaN                  \n",
+       "1535                                                     NaN                  \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "0                                                        NaN                      \n",
+       "1                                                        NaN                      \n",
+       "2                                                        NaN                      \n",
+       "3                                                        NaN                      \n",
+       "4                                                        NaN                      \n",
+       "...                                                      ...                      \n",
+       "1531                                                    85.2                      \n",
+       "1532                                                    85.7                      \n",
+       "1533                                                    86.3                      \n",
+       "1534                                                    86.8                      \n",
+       "1535                                                     NaN                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "0                                                        NaN                                   \n",
+       "1                                                        NaN                                   \n",
+       "2                                                        NaN                                   \n",
+       "3                                                        NaN                                   \n",
+       "4                                                        NaN                                   \n",
+       "...                                                      ...                                   \n",
+       "1531                                                    64.6                                   \n",
+       "1532                                                    65.5                                   \n",
+       "1533                                                    65.5                                   \n",
+       "1534                                                    65.9                                   \n",
+       "1535                                                     NaN                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "0                                                        NaN                                   \n",
+       "1                                                        NaN                                   \n",
+       "2                                                        NaN                                   \n",
+       "3                                                        NaN                                   \n",
+       "4                                                        NaN                                   \n",
+       "...                                                      ...                                   \n",
+       "1531                                                   94.20                                   \n",
+       "1532                                                   94.65                                   \n",
+       "1533                                                   94.90                                   \n",
+       "1534                                                   95.20                                   \n",
+       "1535                                                     NaN                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "0                                       100.000000   \n",
+       "1                                       100.000000   \n",
+       "2                                       100.000000   \n",
+       "3                                       100.000000   \n",
+       "4                                       100.000000   \n",
+       "...                                            ...   \n",
+       "1531                                     84.400002   \n",
+       "1532                                     84.699997   \n",
+       "1533                                     85.000000   \n",
+       "1534                                     84.385536   \n",
+       "1535                                           NaN   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "0                                                 100.000000      \n",
+       "1                                                 100.000000      \n",
+       "2                                                 100.000000      \n",
+       "3                                                 100.000000      \n",
+       "4                                                 100.000000      \n",
+       "...                                                      ...      \n",
+       "1531                                               76.738983      \n",
+       "1532                                               77.168495      \n",
+       "1533                                               77.611824      \n",
+       "1534                                               75.264854      \n",
+       "1535                                                     NaN      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "0                                                 100.000000      \n",
+       "1                                                 100.000000      \n",
+       "2                                                 100.000000      \n",
+       "3                                                 100.000000      \n",
+       "4                                                 100.000000      \n",
+       "...                                                      ...      \n",
+       "1531                                               88.373024      \n",
+       "1532                                               88.518814      \n",
+       "1533                                               88.662704      \n",
+       "1534                                               88.806267      \n",
+       "1535                                                     NaN      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "0                                                        NaN                                                                 \n",
+       "1                                                        NaN                                                                 \n",
+       "2                                                        NaN                                                                 \n",
+       "3                                                        NaN                                                                 \n",
+       "4                                                        NaN                                                                 \n",
+       "...                                                      ...                                                                 \n",
+       "1531                                               69.218491                                                                 \n",
+       "1532                                                     NaN                                                                 \n",
+       "1533                                                     NaN                                                                 \n",
+       "1534                                                     NaN                                                                 \n",
+       "1535                                                     NaN                                                                 \n",
+       "\n",
+       "Indicator  ...  \\\n",
+       "0          ...   \n",
+       "1          ...   \n",
+       "2          ...   \n",
+       "3          ...   \n",
+       "4          ...   \n",
+       "...        ...   \n",
+       "1531       ...   \n",
+       "1532       ...   \n",
+       "1533       ...   \n",
+       "1534       ...   \n",
+       "1535       ...   \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife (any of five reasons) (%)  \\\n",
+       "0                                                        NaN                                        \n",
+       "1                                                        NaN                                        \n",
+       "2                                                        NaN                                        \n",
+       "3                                                        NaN                                        \n",
+       "4                                                        NaN                                        \n",
+       "...                                                      ...                                        \n",
+       "1531                                                     NaN                                        \n",
+       "1532                                                     NaN                                        \n",
+       "1533                                                     NaN                                        \n",
+       "1534                                                     NaN                                        \n",
+       "1535                                                     NaN                                        \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she argues with him (%)  \\\n",
+       "0                                                        NaN                                           \n",
+       "1                                                        NaN                                           \n",
+       "2                                                        NaN                                           \n",
+       "3                                                        NaN                                           \n",
+       "4                                                        NaN                                           \n",
+       "...                                                      ...                                           \n",
+       "1531                                                     NaN                                           \n",
+       "1532                                                     NaN                                           \n",
+       "1533                                                     NaN                                           \n",
+       "1534                                                     NaN                                           \n",
+       "1535                                                     NaN                                           \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she burns the food (%)  \\\n",
+       "0                                                        NaN                                          \n",
+       "1                                                        NaN                                          \n",
+       "2                                                        NaN                                          \n",
+       "3                                                        NaN                                          \n",
+       "4                                                        NaN                                          \n",
+       "...                                                      ...                                          \n",
+       "1531                                                     NaN                                          \n",
+       "1532                                                     NaN                                          \n",
+       "1533                                                     NaN                                          \n",
+       "1534                                                     NaN                                          \n",
+       "1535                                                     NaN                                          \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she goes out without telling him (%)  \\\n",
+       "0                                                        NaN                                                        \n",
+       "1                                                        NaN                                                        \n",
+       "2                                                        NaN                                                        \n",
+       "3                                                        NaN                                                        \n",
+       "4                                                        NaN                                                        \n",
+       "...                                                      ...                                                        \n",
+       "1531                                                     NaN                                                        \n",
+       "1532                                                     NaN                                                        \n",
+       "1533                                                     NaN                                                        \n",
+       "1534                                                     NaN                                                        \n",
+       "1535                                                     NaN                                                        \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she neglects the children (%)  \\\n",
+       "0                                                        NaN                                                 \n",
+       "1                                                        NaN                                                 \n",
+       "2                                                        NaN                                                 \n",
+       "3                                                        NaN                                                 \n",
+       "4                                                        NaN                                                 \n",
+       "...                                                      ...                                                 \n",
+       "1531                                                     NaN                                                 \n",
+       "1532                                                     NaN                                                 \n",
+       "1533                                                     NaN                                                 \n",
+       "1534                                                     NaN                                                 \n",
+       "1535                                                     NaN                                                 \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she refuses sex with him (%)  \\\n",
+       "0                                                        NaN                                                \n",
+       "1                                                        NaN                                                \n",
+       "2                                                        NaN                                                \n",
+       "3                                                        NaN                                                \n",
+       "4                                                        NaN                                                \n",
+       "...                                                      ...                                                \n",
+       "1531                                                     NaN                                                \n",
+       "1532                                                     NaN                                                \n",
+       "1533                                                     NaN                                                \n",
+       "1534                                                     NaN                                                \n",
+       "1535                                                     NaN                                                \n",
+       "\n",
+       "Indicator  Women who were first married by age 15 (% of women ages 20-24)  \\\n",
+       "0                                                        NaN                \n",
+       "1                                                        NaN                \n",
+       "2                                                        NaN                \n",
+       "3                                                        NaN                \n",
+       "4                                                        NaN                \n",
+       "...                                                      ...                \n",
+       "1531                                                     NaN                \n",
+       "1532                                                     NaN                \n",
+       "1533                                                     NaN                \n",
+       "1534                                                     NaN                \n",
+       "1535                                                     NaN                \n",
+       "\n",
+       "Indicator  Women who were first married by age 18 (% of women ages 20-24)  \\\n",
+       "0                                                        NaN                \n",
+       "1                                                        NaN                \n",
+       "2                                                        NaN                \n",
+       "3                                                        NaN                \n",
+       "4                                                        NaN                \n",
+       "...                                                      ...                \n",
+       "1531                                                     NaN                \n",
+       "1532                                                     NaN                \n",
+       "1533                                                     NaN                \n",
+       "1534                                                     NaN                \n",
+       "1535                                                     NaN                \n",
+       "\n",
+       "Indicator  Women's share of population ages 15+ living with HIV (%)  \\\n",
+       "0                                                       18.8          \n",
+       "1                                                       18.2          \n",
+       "2                                                       19.4          \n",
+       "3                                                       20.0          \n",
+       "4                                                       20.0          \n",
+       "...                                                      ...          \n",
+       "1531                                                    63.3          \n",
+       "1532                                                    63.7          \n",
+       "1533                                                    64.1          \n",
+       "1534                                                    64.4          \n",
+       "1535                                                     NaN          \n",
+       "\n",
+       "Indicator  Young people (ages 15-24) newly infected with HIV  \n",
+       "0                                                      100.0  \n",
+       "1                                                      100.0  \n",
+       "2                                                      100.0  \n",
+       "3                                                      100.0  \n",
+       "4                                                      100.0  \n",
+       "...                                                      ...  \n",
+       "1531                                                100000.0  \n",
+       "1532                                                 92000.0  \n",
+       "1533                                                 85000.0  \n",
+       "1534                                                 79000.0  \n",
+       "1535                                                     NaN  \n",
+       "\n",
+       "[1536 rows x 1438 columns]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3058,16 +2815,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "13351"
+       "1016628"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3092,20 +2849,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "df=SilverDataFrame\n",
     "europe_list=['DEU','FRA','SWE','GBR','ESP','HRV','POL','GRC','AUT','NLD']\n",
-    "persian_list=['IRQ','QAT','ARE','SAU','AZE','YEM','YDR','OMN']\n",
+    "persian_list=['IRQ','QAT','ARE','SAU','AZE','YEM','OMN']\n",
     "naf_list=['DZA','EGY','LBY','ISR','TUR','MAR']\n",
     "saf_list=['SEN','ZAF','LBR','MOZ','CMR','NGA','GHA']\n",
     "asia_list=['BGD','IND','VNM','THA','IDN','PHL','KOR']\n",
     "latam_list=['MEX','BRA','ARG','PER','VEN','COL','CHL','PAN','CRI']\n",
     "two_list=['USA','CHN']\n",
-    "country_list=europe_list+persian_list+naf_list+saf_list+asia_list+latam_list+two_list\n",
-    "col_to_scale=['Gender equality','Exports-Commercial services','Renewable electricity','Employment-agriculture','Employment-industry','Employment-services','Exports-G&S','Fertility rate','Foreign investment','GDP','Education GExp','Workers high education','Literacy rate','Mortality-pollution','Net migration','Mortality-infants','Health services use','R&D GExp','Ninis','Suicide','International taxes','Alcohol per capita']\n"
+    "country_list=europe_list+persian_list+naf_list+saf_list+asia_list+latam_list+two_list\n"
    ]
   },
   {
@@ -3117,16 +2873,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8519"
+       "685787"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3147,21 +2903,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we attempt the backward filling. (Filling the previous cell with future values)"
+    "Here we attempt the backward filling, filling the previous cell with future values."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4500"
+       "498648"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3182,21 +2938,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we will attempt the forward filling. (Filling the next cell with previous values)"
+    "Here we will attempt the forward filling, which concists of filling the next cell with previous values."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8519"
+       "685787"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3213,6 +2969,13 @@
     "data.isna().sum().sum()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The linear interpolation a form of interpolation, which involves the generation of new values based on an existing set of values. Linear interpolation is achieved by geometrically rendering a straight line between two adjacent points on a graph or plane. Whereas the backwards filling, will help us to arrive to those values which have not been fullfilled with the linear interpolation."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3222,16 +2985,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8519"
+       "685787"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3252,16 +3015,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2447"
+       "310048"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3289,16 +3052,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2447"
+       "310048"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3323,19 +3086,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Therefore, the preferred method for the Nan values´ treatment that we are going to develop is a mix, between the linear interpolation and backwards filling. The linear interpolation a form of interpolation, which involves the generation of new values based on an existing set of values. Linear interpolation is achieved by geometrically rendering a straight line between two adjacent points on a graph or plane. Whereas the backwards filling, will help us to arrive to those values which have not been fullfilled with the linear interpolation."
+    "##### Conclusion"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Moreover, we are also going to scale all the values between the max and min of each country for each variable."
+    "Therefore, the preferred method for the Nan values´ treatment that we are going to develop is a mix, between the linear interpolation and backwards filling."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -3361,147 +3124,147 @@
        "      <th>Indicator</th>\n",
        "      <th>Country</th>\n",
        "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>Fertility rate</th>\n",
+       "      <th>ARI treatment (% of children under 5 taken to a health provider)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
        "      <th>...</th>\n",
-       "      <th>International taxes</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Mortality-pollution</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife (any of five reasons) (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she argues with him (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she burns the food (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she goes out without telling him (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she neglects the children (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>347</th>\n",
+       "      <th>352</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1990</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002386</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.636364</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>348</th>\n",
+       "      <th>353</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1991</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.025535</td>\n",
-       "      <td>0.272727</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002525</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>349</th>\n",
+       "      <th>354</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1992</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.964758</td>\n",
-       "      <td>0.966793</td>\n",
-       "      <td>0.034321</td>\n",
-       "      <td>0.018415</td>\n",
-       "      <td>0.046400</td>\n",
-       "      <td>0.151515</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.875324</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.004025</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>350</th>\n",
+       "      <th>355</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1993</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.942731</td>\n",
-       "      <td>0.907021</td>\n",
-       "      <td>0.088143</td>\n",
-       "      <td>0.012182</td>\n",
-       "      <td>0.010957</td>\n",
-       "      <td>0.121212</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.765220</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.823187</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.005848</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>351</th>\n",
+       "      <th>356</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1994</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.380220</td>\n",
-       "      <td>0.903084</td>\n",
-       "      <td>0.876660</td>\n",
-       "      <td>0.119345</td>\n",
-       "      <td>0.018513</td>\n",
-       "      <td>0.041261</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.670984</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.680250</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011012</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -3528,213 +3291,395 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>247</th>\n",
+       "      <th>251</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>0.99002</td>\n",
-       "      <td>0.246406</td>\n",
-       "      <td>0.068956</td>\n",
-       "      <td>0.831461</td>\n",
-       "      <td>0.867725</td>\n",
-       "      <td>0.849888</td>\n",
-       "      <td>0.804316</td>\n",
-       "      <td>0.588235</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.2</td>\n",
+       "      <td>55.2</td>\n",
+       "      <td>86.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.790308</td>\n",
-       "      <td>0.977482</td>\n",
-       "      <td>0.042573</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.264203</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.974482</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.016129</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>248</th>\n",
+       "      <th>252</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>0.98004</td>\n",
-       "      <td>0.212024</td>\n",
-       "      <td>0.048007</td>\n",
-       "      <td>0.753933</td>\n",
-       "      <td>0.917460</td>\n",
-       "      <td>0.867490</td>\n",
-       "      <td>0.888169</td>\n",
-       "      <td>0.647059</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>75.6</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>87.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.988741</td>\n",
-       "      <td>0.030745</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.243061</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.984436</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>249</th>\n",
+       "      <th>253</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.021530</td>\n",
-       "      <td>0.777528</td>\n",
-       "      <td>0.942152</td>\n",
-       "      <td>0.953748</td>\n",
-       "      <td>0.974699</td>\n",
-       "      <td>0.698529</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>77.6</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.019650</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.243061</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>250</th>\n",
+       "      <th>254</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.964730</td>\n",
-       "      <td>0.742647</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79.4</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.009383</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.243061</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>251</th>\n",
+       "      <th>255</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.957514</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.772059</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79.4</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.243061</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1509 rows × 24 columns</p>\n",
+       "<p>1536 rows × 1438 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "Indicator Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "347           DEU  1990             1.00000        0.443529   \n",
-       "348           DEU  1991             1.00000        0.443529   \n",
-       "349           DEU  1992             1.00000        0.443529   \n",
-       "350           DEU  1993             1.00000        0.443529   \n",
-       "351           DEU  1994             1.00000        0.380220   \n",
-       "..            ...   ...                 ...             ...   \n",
-       "247           CHN  2016             0.99002        0.246406   \n",
-       "248           CHN  2017             0.98004        0.212024   \n",
-       "249           CHN  2018             0.97006        0.000000   \n",
-       "250           CHN  2019             0.97006        0.000000   \n",
-       "251           CHN  2020             0.97006        0.000000   \n",
-       "\n",
-       "Indicator  Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "347                      1.000000             1.000000             0.000000   \n",
-       "348                      1.000000             1.000000             0.000000   \n",
-       "349                      0.964758             0.966793             0.034321   \n",
-       "350                      0.942731             0.907021             0.088143   \n",
-       "351                      0.903084             0.876660             0.119345   \n",
-       "..                            ...                  ...                  ...   \n",
-       "247                      0.068956             0.831461             0.867725   \n",
-       "248                      0.048007             0.753933             0.917460   \n",
-       "249                      0.021530             0.777528             0.942152   \n",
-       "250                      0.000000             0.676405             1.000000   \n",
-       "251                      0.000000             0.676405             1.000000   \n",
-       "\n",
-       "Indicator  Exports-Commercial services  Exports-G&S  Fertility rate  ...  \\\n",
-       "347                           0.002386     0.000000        0.636364  ...   \n",
-       "348                           0.000000     0.025535        0.272727  ...   \n",
-       "349                           0.018415     0.046400        0.151515  ...   \n",
-       "350                           0.012182     0.010957        0.121212  ...   \n",
-       "351                           0.018513     0.041261        0.000000  ...   \n",
-       "..                                 ...          ...             ...  ...   \n",
-       "247                           0.849888     0.804316        0.588235  ...   \n",
-       "248                           0.867490     0.888169        0.647059  ...   \n",
-       "249                           0.953748     0.974699        0.698529  ...   \n",
-       "250                           1.000000     0.964730        0.742647  ...   \n",
-       "251                           0.957514     1.000000        0.772059  ...   \n",
-       "\n",
-       "Indicator  International taxes  Literacy rate  Mortality-infants  \\\n",
-       "347                        NaN            NaN           1.000000   \n",
-       "348                        NaN            NaN           1.000000   \n",
-       "349                        NaN            NaN           0.875324   \n",
-       "350                        NaN            NaN           0.765220   \n",
-       "351                        NaN            NaN           0.670984   \n",
-       "..                         ...            ...                ...   \n",
-       "247                   0.790308       0.977482           0.042573   \n",
-       "248                   1.000000       0.988741           0.030745   \n",
-       "249                   0.920304       1.000000           0.019650   \n",
-       "250                   0.920304       1.000000           0.009383   \n",
-       "251                   0.920304       1.000000           0.000000   \n",
-       "\n",
-       "Indicator  Mortality-pollution  Net migration    Ninis  R&D GExp  \\\n",
-       "347                        NaN       0.966124  0.77758  0.000000   \n",
-       "348                        NaN       0.966124  0.77758  0.000000   \n",
-       "349                        NaN       0.966124  0.77758  0.000000   \n",
-       "350                        NaN       0.823187  0.77758  0.000000   \n",
-       "351                        NaN       0.680250  0.77758  0.000000   \n",
-       "..                         ...            ...      ...       ...   \n",
-       "247                        NaN       0.264203      NaN  0.974482   \n",
-       "248                        NaN       0.243061      NaN  0.984436   \n",
-       "249                        NaN       0.243061      NaN  1.000000   \n",
-       "250                        NaN       0.243061      NaN  1.000000   \n",
-       "251                        NaN       0.243061      NaN  1.000000   \n",
-       "\n",
-       "Indicator  Renewable electricity   Suicide  Workers high education  \n",
-       "347                     0.000000  0.956522                     1.0  \n",
-       "348                     0.002525  0.956522                     1.0  \n",
-       "349                     0.004025  0.956522                     1.0  \n",
-       "350                     0.005848  0.956522                     1.0  \n",
-       "351                     0.011012  0.956522                     1.0  \n",
-       "..                           ...       ...                     ...  \n",
-       "247                     1.000000  0.016129                     NaN  \n",
-       "248                     1.000000  0.000000                     NaN  \n",
-       "249                     1.000000  0.000000                     NaN  \n",
-       "250                     1.000000  0.000000                     NaN  \n",
-       "251                     1.000000  0.000000                     NaN  \n",
-       "\n",
-       "[1509 rows x 24 columns]"
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  ARI treatment (% of children under 5 taken to a health provider)  \\\n",
+       "352                                                      NaN                  \n",
+       "353                                                      NaN                  \n",
+       "354                                                      NaN                  \n",
+       "355                                                      NaN                  \n",
+       "356                                                      NaN                  \n",
+       "..                                                       ...                  \n",
+       "251                                                      NaN                  \n",
+       "252                                                      NaN                  \n",
+       "253                                                      NaN                  \n",
+       "254                                                      NaN                  \n",
+       "255                                                      NaN                  \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                    100.0                      \n",
+       "353                                                    100.0                      \n",
+       "354                                                    100.0                      \n",
+       "355                                                    100.0                      \n",
+       "356                                                    100.0                      \n",
+       "..                                                       ...                      \n",
+       "251                                                     73.2                      \n",
+       "252                                                     75.6                      \n",
+       "253                                                     77.6                      \n",
+       "254                                                     79.4                      \n",
+       "255                                                     79.4                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                    100.0                                   \n",
+       "353                                                    100.0                                   \n",
+       "354                                                    100.0                                   \n",
+       "355                                                    100.0                                   \n",
+       "356                                                    100.0                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                     55.2                                   \n",
+       "252                                                     59.0                                   \n",
+       "253                                                     61.9                                   \n",
+       "254                                                     65.2                                   \n",
+       "255                                                     65.2                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                    100.0                                   \n",
+       "353                                                    100.0                                   \n",
+       "354                                                    100.0                                   \n",
+       "355                                                    100.0                                   \n",
+       "356                                                    100.0                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                     86.2                                   \n",
+       "252                                                     87.4                                   \n",
+       "253                                                     88.4                                   \n",
+       "254                                                     89.4                                   \n",
+       "255                                                     89.4                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                          100.0   \n",
+       "353                                          100.0   \n",
+       "354                                          100.0   \n",
+       "355                                          100.0   \n",
+       "356                                          100.0   \n",
+       "..                                             ...   \n",
+       "251                                          100.0   \n",
+       "252                                          100.0   \n",
+       "253                                          100.0   \n",
+       "254                                          100.0   \n",
+       "255                                          100.0   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                    100.0      \n",
+       "353                                                    100.0      \n",
+       "354                                                    100.0      \n",
+       "355                                                    100.0      \n",
+       "356                                                    100.0      \n",
+       "..                                                       ...      \n",
+       "251                                                    100.0      \n",
+       "252                                                    100.0      \n",
+       "253                                                    100.0      \n",
+       "254                                                    100.0      \n",
+       "255                                                    100.0      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                    100.0      \n",
+       "353                                                    100.0      \n",
+       "354                                                    100.0      \n",
+       "355                                                    100.0      \n",
+       "356                                                    100.0      \n",
+       "..                                                       ...      \n",
+       "251                                                    100.0      \n",
+       "252                                                    100.0      \n",
+       "253                                                    100.0      \n",
+       "254                                                    100.0      \n",
+       "255                                                    100.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                98.133621                                                                 \n",
+       "353                                                98.133621                                                                 \n",
+       "354                                                98.133621                                                                 \n",
+       "355                                                98.133621                                                                 \n",
+       "356                                                98.133621                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                80.229118                                                                 \n",
+       "252                                                80.229118                                                                 \n",
+       "253                                                80.229118                                                                 \n",
+       "254                                                80.229118                                                                 \n",
+       "255                                                80.229118                                                                 \n",
+       "\n",
+       "Indicator  ...  \\\n",
+       "352        ...   \n",
+       "353        ...   \n",
+       "354        ...   \n",
+       "355        ...   \n",
+       "356        ...   \n",
+       "..         ...   \n",
+       "251        ...   \n",
+       "252        ...   \n",
+       "253        ...   \n",
+       "254        ...   \n",
+       "255        ...   \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife (any of five reasons) (%)  \\\n",
+       "352                                                      NaN                                        \n",
+       "353                                                      NaN                                        \n",
+       "354                                                      NaN                                        \n",
+       "355                                                      NaN                                        \n",
+       "356                                                      NaN                                        \n",
+       "..                                                       ...                                        \n",
+       "251                                                      NaN                                        \n",
+       "252                                                      NaN                                        \n",
+       "253                                                      NaN                                        \n",
+       "254                                                      NaN                                        \n",
+       "255                                                      NaN                                        \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she argues with him (%)  \\\n",
+       "352                                                      NaN                                           \n",
+       "353                                                      NaN                                           \n",
+       "354                                                      NaN                                           \n",
+       "355                                                      NaN                                           \n",
+       "356                                                      NaN                                           \n",
+       "..                                                       ...                                           \n",
+       "251                                                      NaN                                           \n",
+       "252                                                      NaN                                           \n",
+       "253                                                      NaN                                           \n",
+       "254                                                      NaN                                           \n",
+       "255                                                      NaN                                           \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she burns the food (%)  \\\n",
+       "352                                                      NaN                                          \n",
+       "353                                                      NaN                                          \n",
+       "354                                                      NaN                                          \n",
+       "355                                                      NaN                                          \n",
+       "356                                                      NaN                                          \n",
+       "..                                                       ...                                          \n",
+       "251                                                      NaN                                          \n",
+       "252                                                      NaN                                          \n",
+       "253                                                      NaN                                          \n",
+       "254                                                      NaN                                          \n",
+       "255                                                      NaN                                          \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she goes out without telling him (%)  \\\n",
+       "352                                                      NaN                                                        \n",
+       "353                                                      NaN                                                        \n",
+       "354                                                      NaN                                                        \n",
+       "355                                                      NaN                                                        \n",
+       "356                                                      NaN                                                        \n",
+       "..                                                       ...                                                        \n",
+       "251                                                      NaN                                                        \n",
+       "252                                                      NaN                                                        \n",
+       "253                                                      NaN                                                        \n",
+       "254                                                      NaN                                                        \n",
+       "255                                                      NaN                                                        \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she neglects the children (%)  \\\n",
+       "352                                                      NaN                                                 \n",
+       "353                                                      NaN                                                 \n",
+       "354                                                      NaN                                                 \n",
+       "355                                                      NaN                                                 \n",
+       "356                                                      NaN                                                 \n",
+       "..                                                       ...                                                 \n",
+       "251                                                      NaN                                                 \n",
+       "252                                                      NaN                                                 \n",
+       "253                                                      NaN                                                 \n",
+       "254                                                      NaN                                                 \n",
+       "255                                                      NaN                                                 \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she refuses sex with him (%)  \\\n",
+       "352                                                      NaN                                                \n",
+       "353                                                      NaN                                                \n",
+       "354                                                      NaN                                                \n",
+       "355                                                      NaN                                                \n",
+       "356                                                      NaN                                                \n",
+       "..                                                       ...                                                \n",
+       "251                                                      NaN                                                \n",
+       "252                                                      NaN                                                \n",
+       "253                                                      NaN                                                \n",
+       "254                                                      NaN                                                \n",
+       "255                                                      NaN                                                \n",
+       "\n",
+       "Indicator  Women who were first married by age 15 (% of women ages 20-24)  \\\n",
+       "352                                                      NaN                \n",
+       "353                                                      NaN                \n",
+       "354                                                      NaN                \n",
+       "355                                                      NaN                \n",
+       "356                                                      NaN                \n",
+       "..                                                       ...                \n",
+       "251                                                      NaN                \n",
+       "252                                                      NaN                \n",
+       "253                                                      NaN                \n",
+       "254                                                      NaN                \n",
+       "255                                                      NaN                \n",
+       "\n",
+       "Indicator  Women who were first married by age 18 (% of women ages 20-24)  \\\n",
+       "352                                                      NaN                \n",
+       "353                                                      NaN                \n",
+       "354                                                      NaN                \n",
+       "355                                                      NaN                \n",
+       "356                                                      NaN                \n",
+       "..                                                       ...                \n",
+       "251                                                      NaN                \n",
+       "252                                                      NaN                \n",
+       "253                                                      NaN                \n",
+       "254                                                      NaN                \n",
+       "255                                                      NaN                \n",
+       "\n",
+       "Indicator  Women's share of population ages 15+ living with HIV (%)  \\\n",
+       "352                                                     19.1          \n",
+       "353                                                     19.1          \n",
+       "354                                                     19.1          \n",
+       "355                                                     19.1          \n",
+       "356                                                     19.1          \n",
+       "..                                                       ...          \n",
+       "251                                                      NaN          \n",
+       "252                                                      NaN          \n",
+       "253                                                      NaN          \n",
+       "254                                                      NaN          \n",
+       "255                                                      NaN          \n",
+       "\n",
+       "Indicator  Young people (ages 15-24) newly infected with HIV  \n",
+       "352                                                    500.0  \n",
+       "353                                                    500.0  \n",
+       "354                                                    500.0  \n",
+       "355                                                    500.0  \n",
+       "356                                                    500.0  \n",
+       "..                                                       ...  \n",
+       "251                                                      NaN  \n",
+       "252                                                      NaN  \n",
+       "253                                                      NaN  \n",
+       "254                                                      NaN  \n",
+       "255                                                      NaN  \n",
+       "\n",
+       "[1536 rows x 1438 columns]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3743,14 +3688,14 @@
     "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
     "datc=dat.interpolate(method=\"linear\")\n",
     "datf=datc.fillna(method='bfill')\n",
-    "datr[col_to_scale]=(datr[col_to_scale]-datr[col_to_scale].min())/(datr[col_to_scale].max()-datr[col_to_scale].min())\n",
+    "datr=datf.fillna(method='ffill')\n",
     "data=datr\n",
     "\n",
     "for i in range(1,len(country_list)):\n",
     "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
     "    datc=dat.interpolate(method=\"linear\")\n",
     "    datc=datc.fillna(method='bfill')\n",
-    "    datc[col_to_scale]=(datc[col_to_scale]-datc[col_to_scale].min())/(datc[col_to_scale].max()-datc[col_to_scale].min())\n",
+    "    datc=datc.fillna(method='ffill')\n",
     "    data=pd.concat((data, datc), axis = 0)\n",
     "data"
    ]
@@ -3759,7 +3704,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we will drop the columns which have over 1000 missing values, because the absence of data creates an unreliable source."
+    "Now, we will drop the columns which have over X% missing values because the absence of data creates an unreliable source. This % can be adjusted in the following slider. We have predetermined that 20% is a great starting point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9498d0b6c1d4484d991a48ffc80661e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.2, continuous_update=False, description='% that creates unreliable source:', max=1.0, read…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Slider1=widgets.FloatSlider(\n",
+    "    value=0.2,\n",
+    "    min=0,\n",
+    "    max=1.0,\n",
+    "    step=0.05,\n",
+    "    description='% that creates unreliable source:',\n",
+    "    disabled=False,\n",
+    "    continuous_update=False,\n",
+    "    orientation='horizontal',\n",
+    "    readout=True,\n",
+    "    readout_format='.1f',\n",
+    ")\n",
+    "Slider1"
    ]
   },
   {
@@ -3771,8 +3752,384 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Gender equality\n",
-      "Mortality-pollution\n"
+      "Adults (ages 15+) and children (ages 0-14) newly infected with HIV\n",
+      "Adults (ages 15-49) newly infected with HIV\n",
+      "Antiretroviral therapy coverage (% of people living with HIV)\n",
+      "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)\n",
+      "ARI treatment (% of children under 5 taken to a health provider)\n",
+      "Average transaction cost of sending remittances to a specific country (%)\n",
+      "Average working hours of children, study and work, ages 7-14 (hours per week)\n",
+      "Average working hours of children, study and work, female, ages 7-14 (hours per week)\n",
+      "Average working hours of children, study and work, male, ages 7-14 (hours per week)\n",
+      "Average working hours of children, working only, ages 7-14 (hours per week)\n",
+      "Average working hours of children, working only, female, ages 7-14 (hours per week)\n",
+      "Average working hours of children, working only, male, ages 7-14 (hours per week)\n",
+      "Bank capital to assets ratio (%)\n",
+      "Bank liquid reserves to bank assets ratio (%)\n",
+      "Bank nonperforming loans to total gross loans (%)\n",
+      "Battle-related deaths (number of people)\n",
+      "Borrowers from commercial banks (per 1,000 adults)\n",
+      "Bribery incidence (% of firms experiencing at least one bribe payment request)\n",
+      "Changes in inventories (constant LCU)\n",
+      "Children (0-14) living with HIV\n",
+      "Children (ages 0-14) newly infected with HIV\n",
+      "Children in employment, female (% of female children ages 7-14)\n",
+      "Children in employment, male (% of male children ages 7-14)\n",
+      "Children in employment, study and work (% of children in employment, ages 7-14)\n",
+      "Children in employment, study and work, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, study and work, male (% of male children in employment, ages 7-14)\n",
+      "Children in employment, total (% of children ages 7-14)\n",
+      "Children in employment, unpaid family workers (% of children in employment, ages 7-14)\n",
+      "Children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)\n",
+      "Children in employment, wage workers (% of children in employment, ages 7-14)\n",
+      "Children in employment, wage workers, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, wage workers, male (% of male children in employment, ages 7-14)\n",
+      "Children in employment, work only (% of children in employment, ages 7-14)\n",
+      "Children in employment, work only, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, work only, male (% of male children in employment, ages 7-14)\n",
+      "Claims on other sectors of the domestic economy (annual growth as % of broad money)\n",
+      "Commercial banks and other lending (PPG + PNG) (NFL, current US$)\n",
+      "Completeness of birth registration, rural (%)\n",
+      "Completeness of birth registration, urban (%)\n",
+      "Consumption of iodized salt (% of households)\n",
+      "Debt service (PPG and IMF only, % of exports of goods, services and primary income)\n",
+      "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)\n",
+      "Debt service on external debt, total (TDS, current US$)\n",
+      "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)\n",
+      "Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)\n",
+      "Diarrhea treatment (% of children under 5 who received ORS packet)\n",
+      "Disaster risk reduction progress score (1-5 scale; 5=best)\n",
+      "Exclusive breastfeeding (% of children under 6 months)\n",
+      "External debt stocks (% of GNI)\n",
+      "External debt stocks, long-term (DOD, current US$)\n",
+      "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)\n",
+      "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)\n",
+      "External debt stocks, short-term (DOD, current US$)\n",
+      "External debt stocks, total (DOD, current US$)\n",
+      "Financial intermediary services indirectly Measured (FISIM) (constant LCU)\n",
+      "Financial intermediary services indirectly Measured (FISIM) (current LCU)\n",
+      "Firms competing against unregistered firms (% of firms)\n",
+      "Firms formally registered when operations started (% of firms)\n",
+      "Firms that do not report all sales for tax purposes (% of firms)\n",
+      "Gross fixed capital formation, private sector (% of GDP)\n",
+      "Gross fixed capital formation, private sector (current LCU)\n",
+      "IBRD loans and IDA credits (DOD, current US$)\n",
+      "IFC, private nonguaranteed (NFL, current US$)\n",
+      "IMF repurchases and charges (TDS, current US$)\n",
+      "Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)\n",
+      "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)\n",
+      "Incidence of HIV, all (per 1,000 uninfected population)\n",
+      "Incidence of malaria (per 1,000 population at risk)\n",
+      "Informal payments to public officials (% of firms)\n",
+      "Intentional homicides, female (per 100,000 female)\n",
+      "Intentional homicides, male (per 100,000 male)\n",
+      "Interest rate spread (lending rate minus deposit rate, %)\n",
+      "Internally displaced persons, new displacement associated with conflict and violence (number of cases)\n",
+      "Internally displaced persons, total displaced by conflict and violence (number of people)\n",
+      "International tourism, number of departures\n",
+      "Investment in energy with private participation (current US$)\n",
+      "Investment in ICT with private participation (current US$)\n",
+      "Investment in transport with private participation (current US$)\n",
+      "Investment in water and sanitation with private participation (current US$)\n",
+      "Lending interest rate (%)\n",
+      "Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)\n",
+      "Merchandise imports from low- and middle-income economies within region (% of total merchandise imports)\n",
+      "Methodology assessment of statistical capacity (scale 0 - 100)\n",
+      "Multilateral debt service (% of public and publicly guaranteed debt service)\n",
+      "Multilateral debt service (TDS, current US$)\n",
+      "Net bilateral aid flows from DAC donors, Australia (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Belgium (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Canada (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Czech Republic (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Denmark (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Finland (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Greece (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Hungary (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Ireland (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Italy (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Korea, Rep. (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Luxembourg (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Netherlands (current US$)\n",
+      "Net bilateral aid flows from DAC donors, New Zealand (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Norway (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Poland (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Portugal (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Slovenia (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Spain (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Sweden (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Switzerland (current US$)\n",
+      "Net financial flows, bilateral (NFL, current US$)\n",
+      "Net financial flows, IBRD (NFL, current US$)\n",
+      "Net financial flows, IMF nonconcessional (NFL, current US$)\n",
+      "Net financial flows, multilateral (NFL, current US$)\n",
+      "Net financial flows, others (NFL, current US$)\n",
+      "Net financial flows, RDB concessional (NFL, current US$)\n",
+      "Net financial flows, RDB nonconcessional (NFL, current US$)\n",
+      "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)\n",
+      "Net intake rate in grade 1 (% of official school-age population)\n",
+      "Net intake rate in grade 1, female (% of official school-age population)\n",
+      "Net intake rate in grade 1, male (% of official school-age population)\n",
+      "Net ODA received (% of GNI)\n",
+      "Net ODA received (% of gross capital formation)\n",
+      "Net ODA received (% of imports of goods, services and primary income)\n",
+      "Net ODA received per capita (current US$)\n",
+      "Net official development assistance received (constant 2018 US$)\n",
+      "Net official development assistance received (current US$)\n",
+      "Net official flows from UN agencies, FAO (current US$)\n",
+      "Net official flows from UN agencies, IAEA (current US$)\n",
+      "Net official flows from UN agencies, IFAD (current US$)\n",
+      "Net official flows from UN agencies, ILO (current US$)\n",
+      "Net official flows from UN agencies, UNAIDS (current US$)\n",
+      "Net official flows from UN agencies, UNDP (current US$)\n",
+      "Net official flows from UN agencies, UNFPA (current US$)\n",
+      "Net official flows from UN agencies, UNHCR (current US$)\n",
+      "Net official flows from UN agencies, UNICEF (current US$)\n",
+      "Net official flows from UN agencies, WFP (current US$)\n",
+      "Net official flows from UN agencies, WHO (current US$)\n",
+      "Net secondary income (Net current transfers from abroad) (constant LCU)\n",
+      "Newborns protected against tetanus (%)\n",
+      "People using safely managed drinking water services (% of population)\n",
+      "People using safely managed drinking water services, rural (% of rural population)\n",
+      "People using safely managed drinking water services, urban (% of urban population)\n",
+      "People using safely managed sanitation services, rural (% of rural population)\n",
+      "People using safely managed sanitation services, urban (% of urban population)\n",
+      "People with basic handwashing facilities including soap and water (% of population)\n",
+      "People with basic handwashing facilities including soap and water, rural (% of rural population)\n",
+      "People with basic handwashing facilities including soap and water, urban (% of urban population)\n",
+      "Periodicity and timeliness assessment of statistical capacity (scale 0 - 100)\n",
+      "PNG, commercial banks and other creditors (NFL, current US$)\n",
+      "Portfolio investment, bonds (PPG + PNG) (NFL, current US$)\n",
+      "Power outages in firms in a typical month (number)\n",
+      "PPG, bonds (NFL, current US$)\n",
+      "PPG, commercial banks (NFL, current US$)\n",
+      "PPG, IBRD (DOD, current US$)\n",
+      "PPG, official creditors (NFL, current US$)\n",
+      "PPG, other private creditors (NFL, current US$)\n",
+      "PPG, private creditors (NFL, current US$)\n",
+      "Present value of external debt (% of exports of goods, services and primary income)\n",
+      "Present value of external debt (% of GNI)\n",
+      "Present value of external debt (current US$)\n",
+      "Prevalence of HIV, female (% ages 15-24)\n",
+      "Prevalence of HIV, male (% ages 15-24)\n",
+      "Prevalence of HIV, total (% of population ages 15-49)\n",
+      "Prevalence of moderate or severe food insecurity in the population (%)\n",
+      "Prevalence of overweight, weight for height, female (% of children under 5)\n",
+      "Prevalence of overweight, weight for height, male (% of children under 5)\n",
+      "Prevalence of severe food insecurity in the population (%)\n",
+      "Prevalence of severe wasting, weight for height (% of children under 5)\n",
+      "Prevalence of severe wasting, weight for height, female (% of children under 5)\n",
+      "Prevalence of severe wasting, weight for height, male (% of children under 5)\n",
+      "Prevalence of stunting, height for age, female (% of children under 5)\n",
+      "Prevalence of stunting, height for age, male (% of children under 5)\n",
+      "Prevalence of underweight, weight for age, female (% of children under 5)\n",
+      "Prevalence of underweight, weight for age, male (% of children under 5)\n",
+      "Prevalence of wasting, weight for height, female (% of children under 5)\n",
+      "Prevalence of wasting, weight for height, male (% of children under 5)\n",
+      "Primary government expenditures as a proportion of original approved budget (%)\n",
+      "Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)\n",
+      "Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)\n",
+      "Public and publicly guaranteed debt service (% of exports of goods, services and primary income)\n",
+      "Public and publicly guaranteed debt service (% of GNI)\n",
+      "Public private partnerships investment in energy (current US$)\n",
+      "Public private partnerships investment in transport (current US$)\n",
+      "Public private partnerships investment in water and sanitation (current US$)\n",
+      "Real effective exchange rate index (2010 = 100)\n",
+      "Real interest rate (%)\n",
+      "Risk premium on lending (lending rate minus treasury bill rate, %)\n",
+      "Short-term debt (% of exports of goods, services and primary income)\n",
+      "Short-term debt (% of total external debt)\n",
+      "Short-term debt (% of total reserves)\n",
+      "Source data assessment of statistical capacity (scale 0 - 100)\n",
+      "Statistical Capacity Score (Overall Average) (scale 0 - 100)\n",
+      "Technicians in R&D (per million people)\n",
+      "Time required to obtain an operating license (days)\n",
+      "Total debt service (% of exports of goods, services and primary income)\n",
+      "Total debt service (% of GNI)\n",
+      "Total reserves (% of total external debt)\n",
+      "Trained teachers in primary education (% of total teachers)\n",
+      "Trained teachers in primary education, female (% of female teachers)\n",
+      "Trained teachers in primary education, male (% of male teachers)\n",
+      "Unmet need for contraception (% of married women ages 15-49)\n",
+      "Use of IMF credit (DOD, current US$)\n",
+      "Women who believe a husband is justified in beating his wife (any of five reasons) (%)\n",
+      "Women who were first married by age 15 (% of women ages 20-24)\n",
+      "Women who were first married by age 18 (% of women ages 20-24)\n",
+      "Women's share of population ages 15+ living with HIV (%)\n",
+      "Young people (ages 15-24) newly infected with HIV\n",
+      "Adequacy of social insurance programs (% of total welfare of beneficiary households)\n",
+      "Adequacy of social protection and labor programs (% of total welfare of beneficiary households)\n",
+      "Adequacy of social safety net programs (% of total welfare of beneficiary households)\n",
+      "Adequacy of unemployment benefits and ALMP (% of total welfare of beneficiary households)\n",
+      "Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)\n",
+      "Annualized average growth rate in per capita real survey mean consumption or income, total population (%)\n",
+      "Benefit incidence of social insurance programs to poorest quintile (% of total social insurance benefits)\n",
+      "Benefit incidence of social protection and labor programs to poorest quintile (% of total SPL benefits)\n",
+      "Benefit incidence of social safety net programs to poorest quintile (% of total safety net benefits)\n",
+      "Benefit incidence of unemployment benefits and ALMP to poorest quintile (% of total U/ALMP benefits)\n",
+      "Children in employment, self-employed (% of children in employment, ages 7-14)\n",
+      "Children in employment, self-employed, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, self-employed, male (% of male children in employment, ages 7-14)\n",
+      "Compensation of employees (% of expense)\n",
+      "Compensation of employees (current LCU)\n",
+      "Completeness of death registration with cause-of-death information (%)\n",
+      "Coverage of social insurance programs (% of population)\n",
+      "Coverage of social insurance programs in 2nd quintile (% of population)\n",
+      "Coverage of social insurance programs in 3rd quintile (% of population)\n",
+      "Coverage of social insurance programs in 4th quintile (% of population)\n",
+      "Coverage of social insurance programs in poorest quintile (% of population)\n",
+      "Coverage of social insurance programs in richest quintile (% of population)\n",
+      "Coverage of social protection and labor programs (% of population)\n",
+      "Coverage of social safety net programs (% of population)\n",
+      "Coverage of social safety net programs in 2nd quintile (% of population)\n",
+      "Coverage of social safety net programs in 3rd quintile (% of population)\n",
+      "Coverage of social safety net programs in 4th quintile (% of population)\n",
+      "Coverage of social safety net programs in poorest quintile (% of population)\n",
+      "Coverage of social safety net programs in richest quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in 2nd quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in 3rd quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in 4th quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in poorest quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in richest quintile (% of population)\n",
+      "Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)\n",
+      "Depositors with commercial banks (per 1,000 adults)\n",
+      "Domestic credit provided by financial sector (% of GDP)\n",
+      "Expenditure on primary education (% of government expenditure on education)\n",
+      "Expenditure on secondary education (% of government expenditure on education)\n",
+      "Expense (% of GDP)\n",
+      "Expense (current LCU)\n",
+      "Female share of employment in senior and middle management (%)\n",
+      "Firms experiencing electrical outages (% of firms)\n",
+      "Firms experiencing losses due to theft and vandalism (% of firms)\n",
+      "Firms that spend on R&D (% of firms)\n",
+      "Firms visited or required meetings with tax officials (% of firms)\n",
+      "Firms with female top manager (% of firms)\n",
+      "Goods and services expense (% of expense)\n",
+      "Goods and services expense (current LCU)\n",
+      "Interest payments (% of expense)\n",
+      "Losses due to theft and vandalism (% of annual sales of affected firms)\n",
+      "Net acquisition of financial assets (% of GDP)\n",
+      "Net acquisition of financial assets (current LCU)\n",
+      "Net bilateral aid flows from DAC donors, Slovak Republic (current US$)\n",
+      "Net incurrence of liabilities, total (% of GDP)\n",
+      "Net incurrence of liabilities, total (current LCU)\n",
+      "Net investment in nonfinancial assets (% of GDP)\n",
+      "Net investment in nonfinancial assets (current LCU)\n",
+      "Net lending (+) / net borrowing (-) (% of GDP)\n",
+      "Net lending (+) / net borrowing (-) (current LCU)\n",
+      "Net ODA received (% of central government expense)\n",
+      "Number of visits or required meetings with tax officials (average for affected firms)\n",
+      "Other expense (% of expense)\n",
+      "Other expense (current LCU)\n",
+      "PNG, bonds (NFL, current US$)\n",
+      "Population living in slums (% of urban population)\n",
+      "Public private partnerships investment in ICT (current US$)\n",
+      "S&P Global Equity Indices (annual % change)\n",
+      "Subsidies and other transfers (% of expense)\n",
+      "Subsidies and other transfers (current LCU)\n",
+      "Survey mean consumption or income per capita, bottom 40% of population (2011 PPP $ per day)\n",
+      "Survey mean consumption or income per capita, total population (2011 PPP $ per day)\n",
+      "Value lost due to electrical outages (% of sales for affected firms)\n",
+      "Average transaction cost of sending remittances from a specific country (%)\n",
+      "Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)\n",
+      "Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)\n",
+      "Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)\n",
+      "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)\n",
+      "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)\n",
+      "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)\n",
+      "Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)\n",
+      "Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)\n",
+      "Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)\n",
+      "Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)\n",
+      "Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)\n",
+      "Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)\n",
+      "Multidimensional poverty headcount ratio (% of total population)\n",
+      "Multidimensional poverty headcount ratio, children (% of population ages 0-17)\n",
+      "Multidimensional poverty headcount ratio, female (% of female population)\n",
+      "Multidimensional poverty headcount ratio, male (% of male population)\n",
+      "Net ODA provided to the least developed countries (% of GNI)\n",
+      "Net ODA provided, to the least developed countries (current US$)\n",
+      "Net ODA provided, total (% of GNI)\n",
+      "Net ODA provided, total (constant 2015 US$)\n",
+      "Net ODA provided, total (current US$)\n",
+      "Net primary income (Net income from abroad) (constant LCU)\n",
+      "Number of surgical procedures (per 100,000 population)\n",
+      "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)\n",
+      "Wholesale price index (2010 = 100)\n",
+      "Central government debt, total (% of GDP)\n",
+      "Central government debt, total (current LCU)\n",
+      "Child employment in agriculture (% of economically active children ages 7-14)\n",
+      "Child employment in agriculture, female (% of female economically active children ages 7-14)\n",
+      "Child employment in agriculture, male (% of male economically active children ages 7-14)\n",
+      "Child employment in manufacturing (% of economically active children ages 7-14)\n",
+      "Child employment in manufacturing, female (% of female economically active children ages 7-14)\n",
+      "Child employment in manufacturing, male (% of male economically active children ages 7-14)\n",
+      "Child employment in services (% of economically active children ages 7-14)\n",
+      "Child employment in services, female (% of female economically active children ages 7-14)\n",
+      "Child employment in services, male (% of male economically active children ages 7-14)\n",
+      "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)\n",
+      "Claims on other sectors of the domestic economy (% of GDP)\n",
+      "Condom use, population ages 15-24, female (% of females ages 15-24)\n",
+      "Condom use, population ages 15-24, male (% of males ages 15-24)\n",
+      "CPIA building human resources rating (1=low to 6=high)\n",
+      "CPIA business regulatory environment rating (1=low to 6=high)\n",
+      "CPIA debt policy rating (1=low to 6=high)\n",
+      "CPIA economic management cluster average (1=low to 6=high)\n",
+      "CPIA efficiency of revenue mobilization rating (1=low to 6=high)\n",
+      "CPIA equity of public resource use rating (1=low to 6=high)\n",
+      "CPIA financial sector rating (1=low to 6=high)\n",
+      "CPIA fiscal policy rating (1=low to 6=high)\n",
+      "CPIA gender equality rating (1=low to 6=high)\n",
+      "CPIA macroeconomic management rating (1=low to 6=high)\n",
+      "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)\n",
+      "CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)\n",
+      "CPIA property rights and rule-based governance rating (1=low to 6=high)\n",
+      "CPIA public sector management and institutions cluster average (1=low to 6=high)\n",
+      "CPIA quality of budgetary and financial management rating (1=low to 6=high)\n",
+      "CPIA quality of public administration rating (1=low to 6=high)\n",
+      "CPIA social protection rating (1=low to 6=high)\n",
+      "CPIA structural policies cluster average (1=low to 6=high)\n",
+      "CPIA trade rating (1=low to 6=high)\n",
+      "CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)\n",
+      "Female headed households (% of households with a female head)\n",
+      "IDA resource allocation index (1=low to 6=high)\n",
+      "Net financial flows, IDA (NFL, current US$)\n",
+      "PPG, IDA (DOD, current US$)\n",
+      "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)\n",
+      "Trained teachers in lower secondary education (% of total teachers)\n",
+      "Trained teachers in lower secondary education, female (% of female teachers)\n",
+      "Trained teachers in lower secondary education, male (% of male teachers)\n",
+      "Trained teachers in preprimary education (% of total teachers)\n",
+      "Trained teachers in preprimary education, female (% of female teachers)\n",
+      "Trained teachers in preprimary education, male (% of male teachers)\n",
+      "Trained teachers in upper secondary education (% of total teachers)\n",
+      "Trained teachers in upper secondary education, female (% of female teachers)\n",
+      "Trained teachers in upper secondary education, male (% of male teachers)\n",
+      "Use of insecticide-treated bed nets (% of under-5 population)\n",
+      "Vitamin A supplementation coverage rate (% of children ages 6-59 months)\n",
+      "Wanted fertility rate (births per woman)\n",
+      "Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)\n",
+      "Women who believe a husband is justified in beating his wife when she argues with him (%)\n",
+      "Women who believe a husband is justified in beating his wife when she burns the food (%)\n",
+      "Women who believe a husband is justified in beating his wife when she goes out without telling him (%)\n",
+      "Women who believe a husband is justified in beating his wife when she neglects the children (%)\n",
+      "Women who believe a husband is justified in beating his wife when she refuses sex with him (%)\n",
+      "Community health workers (per 1,000 people)\n",
+      "Net bilateral aid flows from DAC donors, Iceland (current US$)\n",
+      "Trained teachers in secondary education (% of total teachers)\n",
+      "Trained teachers in secondary education, female (% of female teachers)\n",
+      "Trained teachers in secondary education, male (% of male teachers)\n",
+      "Women making their own informed decisions regarding sexual relations, contraceptive use and reproductive health care  (% of women age 15-49)\n",
+      "Female genital mutilation prevalence (%)\n",
+      "Net official flows from UN agencies, UNPBF (current US$)\n",
+      "Multidimensional poverty headcount ratio, household (% of total households)\n",
+      "Multidimensional poverty index (scale 0-1)\n",
+      "Multidimensional poverty intensity (average share of deprivations experienced by the poor)\n",
+      "Net financial flows, IMF concessional (NFL, current US$)\n",
+      "Net official aid received (constant 2018 US$)\n",
+      "Net official aid received (current US$)\n",
+      "Multidimensional poverty index, children (population ages 0-17) (scale 0-1)\n"
      ]
     },
     {
@@ -3798,147 +4155,147 @@
        "      <th>Indicator</th>\n",
        "      <th>Country</th>\n",
        "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>Fertility rate</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
        "      <th>...</th>\n",
-       "      <th>Health services use</th>\n",
-       "      <th>International taxes</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
+       "      <th>Urban population growth (annual %)</th>\n",
+       "      <th>Urban population living in areas where elevation is below 5 meters (% of total population)</th>\n",
+       "      <th>Vulnerable employment, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)</th>\n",
+       "      <th>Women Business and the Law Index Score (scale 1-100)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>347</th>\n",
+       "      <th>352</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1990</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002386</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.636364</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.056365</td>\n",
+       "      <td>3.031776</td>\n",
+       "      <td>5.700000</td>\n",
+       "      <td>4.800000</td>\n",
+       "      <td>5.170000</td>\n",
+       "      <td>92.050003</td>\n",
+       "      <td>89.110001</td>\n",
+       "      <td>90.330002</td>\n",
+       "      <td>54.519497</td>\n",
+       "      <td>71.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>348</th>\n",
+       "      <th>353</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1991</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.025535</td>\n",
-       "      <td>0.272727</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002525</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.934908</td>\n",
+       "      <td>3.029378</td>\n",
+       "      <td>5.700000</td>\n",
+       "      <td>4.800000</td>\n",
+       "      <td>5.170000</td>\n",
+       "      <td>92.050003</td>\n",
+       "      <td>89.110001</td>\n",
+       "      <td>90.330002</td>\n",
+       "      <td>54.519497</td>\n",
+       "      <td>71.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>349</th>\n",
+       "      <th>354</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1992</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.964758</td>\n",
-       "      <td>0.966793</td>\n",
-       "      <td>0.034321</td>\n",
-       "      <td>0.018415</td>\n",
-       "      <td>0.046400</td>\n",
-       "      <td>0.151515</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.875324</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.004025</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.884470</td>\n",
+       "      <td>3.026980</td>\n",
+       "      <td>5.740000</td>\n",
+       "      <td>4.930000</td>\n",
+       "      <td>5.270000</td>\n",
+       "      <td>91.910004</td>\n",
+       "      <td>88.589996</td>\n",
+       "      <td>89.970001</td>\n",
+       "      <td>54.519497</td>\n",
+       "      <td>71.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>350</th>\n",
+       "      <th>355</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1993</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.942731</td>\n",
-       "      <td>0.907021</td>\n",
-       "      <td>0.088143</td>\n",
-       "      <td>0.012182</td>\n",
-       "      <td>0.010957</td>\n",
-       "      <td>0.121212</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.765220</td>\n",
-       "      <td>0.823187</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.005848</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.843967</td>\n",
+       "      <td>3.024581</td>\n",
+       "      <td>5.850000</td>\n",
+       "      <td>5.040000</td>\n",
+       "      <td>5.380000</td>\n",
+       "      <td>91.669998</td>\n",
+       "      <td>88.250000</td>\n",
+       "      <td>89.669998</td>\n",
+       "      <td>56.039631</td>\n",
+       "      <td>71.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>351</th>\n",
+       "      <th>356</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1994</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.380220</td>\n",
-       "      <td>0.903084</td>\n",
-       "      <td>0.876660</td>\n",
-       "      <td>0.119345</td>\n",
-       "      <td>0.018513</td>\n",
-       "      <td>0.041261</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.670984</td>\n",
-       "      <td>0.680250</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011012</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.636245</td>\n",
+       "      <td>3.022183</td>\n",
+       "      <td>5.610000</td>\n",
+       "      <td>5.200000</td>\n",
+       "      <td>5.370000</td>\n",
+       "      <td>91.629997</td>\n",
+       "      <td>87.739998</td>\n",
+       "      <td>89.370003</td>\n",
+       "      <td>57.559764</td>\n",
+       "      <td>71.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -3965,281 +4322,427 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>247</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>0.99002</td>\n",
-       "      <td>0.246406</td>\n",
-       "      <td>0.068956</td>\n",
-       "      <td>0.831461</td>\n",
-       "      <td>0.867725</td>\n",
-       "      <td>0.849888</td>\n",
-       "      <td>0.804316</td>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.822241</td>\n",
-       "      <td>0.790308</td>\n",
-       "      <td>0.977482</td>\n",
-       "      <td>0.042573</td>\n",
-       "      <td>0.264203</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.974482</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.016129</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>248</th>\n",
+       "      <th>251</th>\n",
        "      <td>CHN</td>\n",
        "      <td>2017</td>\n",
-       "      <td>0.98004</td>\n",
-       "      <td>0.212024</td>\n",
-       "      <td>0.048007</td>\n",
-       "      <td>0.753933</td>\n",
-       "      <td>0.917460</td>\n",
-       "      <td>0.867490</td>\n",
-       "      <td>0.888169</td>\n",
-       "      <td>0.647059</td>\n",
+       "      <td>73.2</td>\n",
+       "      <td>55.2</td>\n",
+       "      <td>86.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.868064</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.988741</td>\n",
-       "      <td>0.030745</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.984436</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>2.739664</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>46.530001</td>\n",
+       "      <td>42.949999</td>\n",
+       "      <td>44.530002</td>\n",
+       "      <td>52.430000</td>\n",
+       "      <td>54.169998</td>\n",
+       "      <td>53.400002</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>249</th>\n",
+       "      <th>252</th>\n",
        "      <td>CHN</td>\n",
        "      <td>2018</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.021530</td>\n",
-       "      <td>0.777528</td>\n",
-       "      <td>0.942152</td>\n",
-       "      <td>0.953748</td>\n",
-       "      <td>0.974699</td>\n",
-       "      <td>0.698529</td>\n",
+       "      <td>75.6</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>87.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.912945</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.019650</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>2.503401</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>45.720001</td>\n",
+       "      <td>41.940001</td>\n",
+       "      <td>43.609999</td>\n",
+       "      <td>53.209999</td>\n",
+       "      <td>55.139999</td>\n",
+       "      <td>54.290001</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>250</th>\n",
+       "      <th>253</th>\n",
        "      <td>CHN</td>\n",
        "      <td>2019</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.964730</td>\n",
-       "      <td>0.742647</td>\n",
+       "      <td>77.6</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.956910</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.009383</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>2.290177</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>44.760000</td>\n",
+       "      <td>40.819999</td>\n",
+       "      <td>42.540000</td>\n",
+       "      <td>54.150002</td>\n",
+       "      <td>56.279999</td>\n",
+       "      <td>55.340000</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>251</th>\n",
+       "      <th>254</th>\n",
        "      <td>CHN</td>\n",
        "      <td>2020</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.957514</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.772059</td>\n",
+       "      <td>79.4</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>2.066047</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>44.760000</td>\n",
+       "      <td>40.819999</td>\n",
+       "      <td>42.540000</td>\n",
+       "      <td>54.150002</td>\n",
+       "      <td>56.279999</td>\n",
+       "      <td>55.340000</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>79.4</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.066047</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>44.760000</td>\n",
+       "      <td>40.819999</td>\n",
+       "      <td>42.540000</td>\n",
+       "      <td>54.150002</td>\n",
+       "      <td>56.279999</td>\n",
+       "      <td>55.340000</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1509 rows × 22 columns</p>\n",
+       "<p>1536 rows × 1060 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "Indicator Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "347           DEU  1990             1.00000        0.443529   \n",
-       "348           DEU  1991             1.00000        0.443529   \n",
-       "349           DEU  1992             1.00000        0.443529   \n",
-       "350           DEU  1993             1.00000        0.443529   \n",
-       "351           DEU  1994             1.00000        0.380220   \n",
-       "..            ...   ...                 ...             ...   \n",
-       "247           CHN  2016             0.99002        0.246406   \n",
-       "248           CHN  2017             0.98004        0.212024   \n",
-       "249           CHN  2018             0.97006        0.000000   \n",
-       "250           CHN  2019             0.97006        0.000000   \n",
-       "251           CHN  2020             0.97006        0.000000   \n",
-       "\n",
-       "Indicator  Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "347                      1.000000             1.000000             0.000000   \n",
-       "348                      1.000000             1.000000             0.000000   \n",
-       "349                      0.964758             0.966793             0.034321   \n",
-       "350                      0.942731             0.907021             0.088143   \n",
-       "351                      0.903084             0.876660             0.119345   \n",
-       "..                            ...                  ...                  ...   \n",
-       "247                      0.068956             0.831461             0.867725   \n",
-       "248                      0.048007             0.753933             0.917460   \n",
-       "249                      0.021530             0.777528             0.942152   \n",
-       "250                      0.000000             0.676405             1.000000   \n",
-       "251                      0.000000             0.676405             1.000000   \n",
-       "\n",
-       "Indicator  Exports-Commercial services  Exports-G&S  Fertility rate  ...  \\\n",
-       "347                           0.002386     0.000000        0.636364  ...   \n",
-       "348                           0.000000     0.025535        0.272727  ...   \n",
-       "349                           0.018415     0.046400        0.151515  ...   \n",
-       "350                           0.012182     0.010957        0.121212  ...   \n",
-       "351                           0.018513     0.041261        0.000000  ...   \n",
-       "..                                 ...          ...             ...  ...   \n",
-       "247                           0.849888     0.804316        0.588235  ...   \n",
-       "248                           0.867490     0.888169        0.647059  ...   \n",
-       "249                           0.953748     0.974699        0.698529  ...   \n",
-       "250                           1.000000     0.964730        0.742647  ...   \n",
-       "251                           0.957514     1.000000        0.772059  ...   \n",
-       "\n",
-       "Indicator  Health services use  International taxes  Literacy rate  \\\n",
-       "347                   0.000000                  NaN            NaN   \n",
-       "348                   0.000000                  NaN            NaN   \n",
-       "349                   0.000000                  NaN            NaN   \n",
-       "350                   0.000000                  NaN            NaN   \n",
-       "351                   0.000000                  NaN            NaN   \n",
-       "..                         ...                  ...            ...   \n",
-       "247                   0.822241             0.790308       0.977482   \n",
-       "248                   0.868064             1.000000       0.988741   \n",
-       "249                   0.912945             0.920304       1.000000   \n",
-       "250                   0.956910             0.920304       1.000000   \n",
-       "251                   1.000000             0.920304       1.000000   \n",
-       "\n",
-       "Indicator  Mortality-infants  Net migration    Ninis  R&D GExp  \\\n",
-       "347                 1.000000       0.966124  0.77758  0.000000   \n",
-       "348                 1.000000       0.966124  0.77758  0.000000   \n",
-       "349                 0.875324       0.966124  0.77758  0.000000   \n",
-       "350                 0.765220       0.823187  0.77758  0.000000   \n",
-       "351                 0.670984       0.680250  0.77758  0.000000   \n",
-       "..                       ...            ...      ...       ...   \n",
-       "247                 0.042573       0.264203      NaN  0.974482   \n",
-       "248                 0.030745       0.243061      NaN  0.984436   \n",
-       "249                 0.019650       0.243061      NaN  1.000000   \n",
-       "250                 0.009383       0.243061      NaN  1.000000   \n",
-       "251                 0.000000       0.243061      NaN  1.000000   \n",
-       "\n",
-       "Indicator  Renewable electricity   Suicide  Workers high education  \n",
-       "347                     0.000000  0.956522                     1.0  \n",
-       "348                     0.002525  0.956522                     1.0  \n",
-       "349                     0.004025  0.956522                     1.0  \n",
-       "350                     0.005848  0.956522                     1.0  \n",
-       "351                     0.011012  0.956522                     1.0  \n",
-       "..                           ...       ...                     ...  \n",
-       "247                     1.000000  0.016129                     NaN  \n",
-       "248                     1.000000  0.000000                     NaN  \n",
-       "249                     1.000000  0.000000                     NaN  \n",
-       "250                     1.000000  0.000000                     NaN  \n",
-       "251                     1.000000  0.000000                     NaN  \n",
-       "\n",
-       "[1509 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(0, len(col_to_scale)):\n",
-    "    if data[col_to_scale[i]].isna().sum()>1000:\n",
-    "        del(data[col_to_scale[i]])\n",
-    "        print(col_to_scale[i])\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a result we have dropped the *Gender equality* and *Mortality-pollution* variables."
-   ]
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                    100.0                      \n",
+       "353                                                    100.0                      \n",
+       "354                                                    100.0                      \n",
+       "355                                                    100.0                      \n",
+       "356                                                    100.0                      \n",
+       "..                                                       ...                      \n",
+       "251                                                     73.2                      \n",
+       "252                                                     75.6                      \n",
+       "253                                                     77.6                      \n",
+       "254                                                     79.4                      \n",
+       "255                                                     79.4                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                    100.0                                   \n",
+       "353                                                    100.0                                   \n",
+       "354                                                    100.0                                   \n",
+       "355                                                    100.0                                   \n",
+       "356                                                    100.0                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                     55.2                                   \n",
+       "252                                                     59.0                                   \n",
+       "253                                                     61.9                                   \n",
+       "254                                                     65.2                                   \n",
+       "255                                                     65.2                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                    100.0                                   \n",
+       "353                                                    100.0                                   \n",
+       "354                                                    100.0                                   \n",
+       "355                                                    100.0                                   \n",
+       "356                                                    100.0                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                     86.2                                   \n",
+       "252                                                     87.4                                   \n",
+       "253                                                     88.4                                   \n",
+       "254                                                     89.4                                   \n",
+       "255                                                     89.4                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                          100.0   \n",
+       "353                                          100.0   \n",
+       "354                                          100.0   \n",
+       "355                                          100.0   \n",
+       "356                                          100.0   \n",
+       "..                                             ...   \n",
+       "251                                          100.0   \n",
+       "252                                          100.0   \n",
+       "253                                          100.0   \n",
+       "254                                          100.0   \n",
+       "255                                          100.0   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                    100.0      \n",
+       "353                                                    100.0      \n",
+       "354                                                    100.0      \n",
+       "355                                                    100.0      \n",
+       "356                                                    100.0      \n",
+       "..                                                       ...      \n",
+       "251                                                    100.0      \n",
+       "252                                                    100.0      \n",
+       "253                                                    100.0      \n",
+       "254                                                    100.0      \n",
+       "255                                                    100.0      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                    100.0      \n",
+       "353                                                    100.0      \n",
+       "354                                                    100.0      \n",
+       "355                                                    100.0      \n",
+       "356                                                    100.0      \n",
+       "..                                                       ...      \n",
+       "251                                                    100.0      \n",
+       "252                                                    100.0      \n",
+       "253                                                    100.0      \n",
+       "254                                                    100.0      \n",
+       "255                                                    100.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                98.133621                                                                 \n",
+       "353                                                98.133621                                                                 \n",
+       "354                                                98.133621                                                                 \n",
+       "355                                                98.133621                                                                 \n",
+       "356                                                98.133621                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                80.229118                                                                 \n",
+       "252                                                80.229118                                                                 \n",
+       "253                                                80.229118                                                                 \n",
+       "254                                                80.229118                                                                 \n",
+       "255                                                80.229118                                                                 \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
+       "352                                                98.704536                                                                         \n",
+       "353                                                98.704536                                                                         \n",
+       "354                                                98.704536                                                                         \n",
+       "355                                                98.704536                                                                         \n",
+       "356                                                98.704536                                                                         \n",
+       "..                                                       ...                                                                         \n",
+       "251                                                76.364731                                                                         \n",
+       "252                                                76.364731                                                                         \n",
+       "253                                                76.364731                                                                         \n",
+       "254                                                76.364731                                                                         \n",
+       "255                                                76.364731                                                                         \n",
+       "\n",
+       "Indicator  ...  Urban population growth (annual %)  \\\n",
+       "352        ...                            1.056365   \n",
+       "353        ...                            0.934908   \n",
+       "354        ...                            0.884470   \n",
+       "355        ...                            0.843967   \n",
+       "356        ...                            0.636245   \n",
+       "..         ...                                 ...   \n",
+       "251        ...                            2.739664   \n",
+       "252        ...                            2.503401   \n",
+       "253        ...                            2.290177   \n",
+       "254        ...                            2.066047   \n",
+       "255        ...                            2.066047   \n",
+       "\n",
+       "Indicator  Urban population living in areas where elevation is below 5 meters (% of total population)  \\\n",
+       "352                                                 3.031776                                            \n",
+       "353                                                 3.029378                                            \n",
+       "354                                                 3.026980                                            \n",
+       "355                                                 3.024581                                            \n",
+       "356                                                 3.022183                                            \n",
+       "..                                                       ...                                            \n",
+       "251                                                 4.203002                                            \n",
+       "252                                                 4.203002                                            \n",
+       "253                                                 4.203002                                            \n",
+       "254                                                 4.203002                                            \n",
+       "255                                                 4.203002                                            \n",
+       "\n",
+       "Indicator  Vulnerable employment, female (% of female employment) (modeled ILO estimate)  \\\n",
+       "352                                                 5.700000                               \n",
+       "353                                                 5.700000                               \n",
+       "354                                                 5.740000                               \n",
+       "355                                                 5.850000                               \n",
+       "356                                                 5.610000                               \n",
+       "..                                                       ...                               \n",
+       "251                                                46.530001                               \n",
+       "252                                                45.720001                               \n",
+       "253                                                44.760000                               \n",
+       "254                                                44.760000                               \n",
+       "255                                                44.760000                               \n",
+       "\n",
+       "Indicator  Vulnerable employment, male (% of male employment) (modeled ILO estimate)  \\\n",
+       "352                                                 4.800000                           \n",
+       "353                                                 4.800000                           \n",
+       "354                                                 4.930000                           \n",
+       "355                                                 5.040000                           \n",
+       "356                                                 5.200000                           \n",
+       "..                                                       ...                           \n",
+       "251                                                42.949999                           \n",
+       "252                                                41.940001                           \n",
+       "253                                                40.819999                           \n",
+       "254                                                40.819999                           \n",
+       "255                                                40.819999                           \n",
+       "\n",
+       "Indicator  Vulnerable employment, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                 5.170000                             \n",
+       "353                                                 5.170000                             \n",
+       "354                                                 5.270000                             \n",
+       "355                                                 5.380000                             \n",
+       "356                                                 5.370000                             \n",
+       "..                                                       ...                             \n",
+       "251                                                44.530002                             \n",
+       "252                                                43.609999                             \n",
+       "253                                                42.540000                             \n",
+       "254                                                42.540000                             \n",
+       "255                                                42.540000                             \n",
+       "\n",
+       "Indicator  Wage and salaried workers, female (% of female employment) (modeled ILO estimate)  \\\n",
+       "352                                                92.050003                                   \n",
+       "353                                                92.050003                                   \n",
+       "354                                                91.910004                                   \n",
+       "355                                                91.669998                                   \n",
+       "356                                                91.629997                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                52.430000                                   \n",
+       "252                                                53.209999                                   \n",
+       "253                                                54.150002                                   \n",
+       "254                                                54.150002                                   \n",
+       "255                                                54.150002                                   \n",
+       "\n",
+       "Indicator  Wage and salaried workers, male (% of male employment) (modeled ILO estimate)  \\\n",
+       "352                                                89.110001                               \n",
+       "353                                                89.110001                               \n",
+       "354                                                88.589996                               \n",
+       "355                                                88.250000                               \n",
+       "356                                                87.739998                               \n",
+       "..                                                       ...                               \n",
+       "251                                                54.169998                               \n",
+       "252                                                55.139999                               \n",
+       "253                                                56.279999                               \n",
+       "254                                                56.279999                               \n",
+       "255                                                56.279999                               \n",
+       "\n",
+       "Indicator  Wage and salaried workers, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                90.330002                                 \n",
+       "353                                                90.330002                                 \n",
+       "354                                                89.970001                                 \n",
+       "355                                                89.669998                                 \n",
+       "356                                                89.370003                                 \n",
+       "..                                                       ...                                 \n",
+       "251                                                53.400002                                 \n",
+       "252                                                54.290001                                 \n",
+       "253                                                55.340000                                 \n",
+       "254                                                55.340000                                 \n",
+       "255                                                55.340000                                 \n",
+       "\n",
+       "Indicator  Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)  \\\n",
+       "352                                                54.519497                                                  \n",
+       "353                                                54.519497                                                  \n",
+       "354                                                54.519497                                                  \n",
+       "355                                                56.039631                                                  \n",
+       "356                                                57.559764                                                  \n",
+       "..                                                       ...                                                  \n",
+       "251                                                21.358958                                                  \n",
+       "252                                                21.358958                                                  \n",
+       "253                                                21.358958                                                  \n",
+       "254                                                21.358958                                                  \n",
+       "255                                                21.358958                                                  \n",
+       "\n",
+       "Indicator  Women Business and the Law Index Score (scale 1-100)  \n",
+       "352                                                   71.250     \n",
+       "353                                                   71.250     \n",
+       "354                                                   71.250     \n",
+       "355                                                   71.250     \n",
+       "356                                                   71.250     \n",
+       "..                                                       ...     \n",
+       "251                                                   75.625     \n",
+       "252                                                   75.625     \n",
+       "253                                                   75.625     \n",
+       "254                                                   75.625     \n",
+       "255                                                   75.625     \n",
+       "\n",
+       "[1536 rows x 1060 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "number_1=len(data.index)*Slider1.value\n",
+    "for i in range(0, len(cols)):\n",
+    "    if data[cols[i]].isna().sum()>number_1:\n",
+    "        del(data[cols[i]])\n",
+    "        print(cols[i])\n",
+    "data"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For the next part of analyzing this data, we think it is gonna be interesting to have it classify by the categories of the Country groups defined before, to which we call \"Continent\". This category is useful as it groups the nations with similar economies or geographical proximity, so we can extract common conclusions from them."
+    "Afterwards, we have scaled the values. The escalation process has been done dividing each value by the initial one of an indicator (value in 1990). Considering the start point as 1 (initial value divided by itself), each result will show the growth respect to the initial data."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 33,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "We create a dictionary with the regions and the countries included in each one. Where we will relate the countries and regions so then we can apply the .map function and arrive to the final dataframe."
+    "columns=data.columns.values.tolist()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'DEU': 'Europe', 'FRA': 'Europe', 'SWE': 'Europe', 'GBR': 'Europe', 'ESP': 'Europe', 'HRV': 'Europe', 'POL': 'Europe', 'GRC': 'Europe', 'AUT': 'Europe', 'NLD': 'Europe', 'IRQ': 'Persian Gulf', 'QAT': 'Persian Gulf', 'ARE': 'Persian Gulf', 'SAU': 'Persian Gulf', 'AZE': 'Persian Gulf', 'YEM': 'Persian Gulf', 'YDR': 'Persian Gulf', 'OMN': 'Persian Gulf', 'DZA': 'North Africa', 'EGY': 'North Africa', 'LBY': 'North Africa', 'ISR': 'North Africa', 'TUR': 'North Africa', 'MAR': 'North Africa', 'SEN': 'South Africa', 'ZAF': 'South Africa', 'LBR': 'South Africa', 'MOZ': 'South Africa', 'CMR': 'South Africa', 'NGA': 'South Africa', 'GHA': 'South Africa', 'BGD': 'Asia', 'IND': 'Asia', 'VNM': 'Asia', 'THA': 'Asia', 'IDN': 'Asia', 'PHL': 'Asia', 'KOR': 'Asia', 'MEX': 'Latam', 'BRA': 'Latam', 'ARG': 'Latam', 'PER': 'Latam', 'VEN': 'Latam', 'COL': 'Latam', 'CHL': 'Latam', 'PAN': 'Latam', 'CRI': 'Latam', 'USA': 'Pair', 'CHN': 'Pair'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "countries_by_region = {\n",
-    "    \"Europe\": ('DEU','FRA','SWE','GBR','ESP','HRV','POL','GRC','AUT','NLD'),\n",
-    "    'Persian Gulf': ('IRQ','QAT','ARE','SAU','AZE','YEM','YDR','OMN'),\n",
-    "    'North Africa':('DZA','EGY','LBY','ISR','TUR','MAR'),\n",
-    "    'South Africa':('SEN','ZAF','LBR','MOZ','CMR','NGA','GHA'),\n",
-    "    'Asia':('BGD','IND','VNM','THA','IDN','PHL','KOR'),\n",
-    "    'Latam':('MEX','BRA','ARG','PER','VEN','COL','CHL','PAN','CRI'),\n",
-    "    'Pair':('USA','CHN')\n",
-    "    }\n",
-    "\n",
-    "all_countries = {}\n",
-    "for region in countries_by_region.keys():\n",
-    "  for country in countries_by_region[region]:\n",
-    "    all_countries[country] = region\n",
-    "\n",
-    "print(all_countries)"
+    "datae=data.loc[data.loc[:, 'Country'] == country_list[0]]\n",
+    "for i in range(2,len(columns)):\n",
+    "    a=columns[i]\n",
+    "    datae[a]=datae[a]/datae.iloc[0,i]\n",
+    "datau=datae"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -4265,147 +4768,147 @@
        "      <th>Indicator</th>\n",
        "      <th>Country</th>\n",
        "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>Fertility rate</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
        "      <th>...</th>\n",
-       "      <th>International taxes</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
-       "      <th>Continent</th>\n",
+       "      <th>Urban population growth (annual %)</th>\n",
+       "      <th>Urban population living in areas where elevation is below 5 meters (% of total population)</th>\n",
+       "      <th>Vulnerable employment, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)</th>\n",
+       "      <th>Women Business and the Law Index Score (scale 1-100)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>347</th>\n",
+       "      <th>352</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1990</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002386</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.636364</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.956522</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>348</th>\n",
+       "      <th>353</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1991</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.025535</td>\n",
-       "      <td>0.272727</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002525</td>\n",
-       "      <td>0.956522</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.885023</td>\n",
+       "      <td>0.999209</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>349</th>\n",
+       "      <th>354</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1992</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.964758</td>\n",
-       "      <td>0.966793</td>\n",
-       "      <td>0.034321</td>\n",
-       "      <td>0.018415</td>\n",
-       "      <td>0.046400</td>\n",
-       "      <td>0.151515</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.875324</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.004025</td>\n",
-       "      <td>0.956522</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.837277</td>\n",
+       "      <td>0.998418</td>\n",
+       "      <td>1.007018</td>\n",
+       "      <td>1.027083</td>\n",
+       "      <td>1.019342</td>\n",
+       "      <td>0.998479</td>\n",
+       "      <td>0.994164</td>\n",
+       "      <td>0.996015</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>350</th>\n",
+       "      <th>355</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1993</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.942731</td>\n",
-       "      <td>0.907021</td>\n",
-       "      <td>0.088143</td>\n",
-       "      <td>0.012182</td>\n",
-       "      <td>0.010957</td>\n",
-       "      <td>0.121212</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.765220</td>\n",
-       "      <td>0.823187</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.005848</td>\n",
-       "      <td>0.956522</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.798935</td>\n",
+       "      <td>0.997627</td>\n",
+       "      <td>1.026316</td>\n",
+       "      <td>1.050000</td>\n",
+       "      <td>1.040619</td>\n",
+       "      <td>0.995872</td>\n",
+       "      <td>0.990349</td>\n",
+       "      <td>0.992693</td>\n",
+       "      <td>1.027882</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>351</th>\n",
+       "      <th>356</th>\n",
        "      <td>DEU</td>\n",
        "      <td>1994</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.380220</td>\n",
-       "      <td>0.903084</td>\n",
-       "      <td>0.876660</td>\n",
-       "      <td>0.119345</td>\n",
-       "      <td>0.018513</td>\n",
-       "      <td>0.041261</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.670984</td>\n",
-       "      <td>0.680250</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011012</td>\n",
-       "      <td>0.956522</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.602296</td>\n",
+       "      <td>0.996836</td>\n",
+       "      <td>0.984210</td>\n",
+       "      <td>1.083333</td>\n",
+       "      <td>1.038685</td>\n",
+       "      <td>0.995437</td>\n",
+       "      <td>0.984626</td>\n",
+       "      <td>0.989372</td>\n",
+       "      <td>1.055765</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -4432,450 +4935,411 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>247</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>0.99002</td>\n",
-       "      <td>0.246406</td>\n",
-       "      <td>0.068956</td>\n",
-       "      <td>0.831461</td>\n",
-       "      <td>0.867725</td>\n",
-       "      <td>0.849888</td>\n",
-       "      <td>0.804316</td>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.790308</td>\n",
-       "      <td>0.977482</td>\n",
-       "      <td>0.042573</td>\n",
-       "      <td>0.264203</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.974482</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.016129</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>248</th>\n",
+       "      <th>251</th>\n",
        "      <td>CHN</td>\n",
        "      <td>2017</td>\n",
-       "      <td>0.98004</td>\n",
-       "      <td>0.212024</td>\n",
-       "      <td>0.048007</td>\n",
-       "      <td>0.753933</td>\n",
-       "      <td>0.917460</td>\n",
-       "      <td>0.867490</td>\n",
-       "      <td>0.888169</td>\n",
-       "      <td>0.647059</td>\n",
+       "      <td>1.742857</td>\n",
+       "      <td>2.348936</td>\n",
+       "      <td>1.261156</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.988741</td>\n",
-       "      <td>0.030745</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.984436</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
+       "      <td>0.635700</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.707250</td>\n",
+       "      <td>0.617275</td>\n",
+       "      <td>0.656204</td>\n",
+       "      <td>1.555786</td>\n",
+       "      <td>1.984976</td>\n",
+       "      <td>1.768798</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>249</th>\n",
+       "      <th>252</th>\n",
        "      <td>CHN</td>\n",
        "      <td>2018</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.021530</td>\n",
-       "      <td>0.777528</td>\n",
-       "      <td>0.942152</td>\n",
-       "      <td>0.953748</td>\n",
-       "      <td>0.974699</td>\n",
-       "      <td>0.698529</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2.510638</td>\n",
+       "      <td>1.278713</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.019650</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
+       "      <td>0.580879</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.694938</td>\n",
+       "      <td>0.602759</td>\n",
+       "      <td>0.642647</td>\n",
+       "      <td>1.578932</td>\n",
+       "      <td>2.020520</td>\n",
+       "      <td>1.798278</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>250</th>\n",
+       "      <th>253</th>\n",
        "      <td>CHN</td>\n",
        "      <td>2019</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.964730</td>\n",
-       "      <td>0.742647</td>\n",
+       "      <td>1.847619</td>\n",
+       "      <td>2.634043</td>\n",
+       "      <td>1.293343</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.009383</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
+       "      <td>0.531403</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.680347</td>\n",
+       "      <td>0.586663</td>\n",
+       "      <td>0.626879</td>\n",
+       "      <td>1.606825</td>\n",
+       "      <td>2.062294</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>251</th>\n",
+       "      <th>254</th>\n",
        "      <td>CHN</td>\n",
        "      <td>2020</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.957514</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.772059</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
+       "      <td>0.479397</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.680347</td>\n",
+       "      <td>0.586663</td>\n",
+       "      <td>0.626879</td>\n",
+       "      <td>1.606825</td>\n",
+       "      <td>2.062294</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.479397</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.680347</td>\n",
+       "      <td>0.586663</td>\n",
+       "      <td>0.626879</td>\n",
+       "      <td>1.606825</td>\n",
+       "      <td>2.062294</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1509 rows × 23 columns</p>\n",
+       "<p>1536 rows × 1060 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "Indicator Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "347           DEU  1990             1.00000        0.443529   \n",
-       "348           DEU  1991             1.00000        0.443529   \n",
-       "349           DEU  1992             1.00000        0.443529   \n",
-       "350           DEU  1993             1.00000        0.443529   \n",
-       "351           DEU  1994             1.00000        0.380220   \n",
-       "..            ...   ...                 ...             ...   \n",
-       "247           CHN  2016             0.99002        0.246406   \n",
-       "248           CHN  2017             0.98004        0.212024   \n",
-       "249           CHN  2018             0.97006        0.000000   \n",
-       "250           CHN  2019             0.97006        0.000000   \n",
-       "251           CHN  2020             0.97006        0.000000   \n",
-       "\n",
-       "Indicator  Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "347                      1.000000             1.000000             0.000000   \n",
-       "348                      1.000000             1.000000             0.000000   \n",
-       "349                      0.964758             0.966793             0.034321   \n",
-       "350                      0.942731             0.907021             0.088143   \n",
-       "351                      0.903084             0.876660             0.119345   \n",
-       "..                            ...                  ...                  ...   \n",
-       "247                      0.068956             0.831461             0.867725   \n",
-       "248                      0.048007             0.753933             0.917460   \n",
-       "249                      0.021530             0.777528             0.942152   \n",
-       "250                      0.000000             0.676405             1.000000   \n",
-       "251                      0.000000             0.676405             1.000000   \n",
-       "\n",
-       "Indicator  Exports-Commercial services  Exports-G&S  Fertility rate  ...  \\\n",
-       "347                           0.002386     0.000000        0.636364  ...   \n",
-       "348                           0.000000     0.025535        0.272727  ...   \n",
-       "349                           0.018415     0.046400        0.151515  ...   \n",
-       "350                           0.012182     0.010957        0.121212  ...   \n",
-       "351                           0.018513     0.041261        0.000000  ...   \n",
-       "..                                 ...          ...             ...  ...   \n",
-       "247                           0.849888     0.804316        0.588235  ...   \n",
-       "248                           0.867490     0.888169        0.647059  ...   \n",
-       "249                           0.953748     0.974699        0.698529  ...   \n",
-       "250                           1.000000     0.964730        0.742647  ...   \n",
-       "251                           0.957514     1.000000        0.772059  ...   \n",
-       "\n",
-       "Indicator  International taxes  Literacy rate  Mortality-infants  \\\n",
-       "347                        NaN            NaN           1.000000   \n",
-       "348                        NaN            NaN           1.000000   \n",
-       "349                        NaN            NaN           0.875324   \n",
-       "350                        NaN            NaN           0.765220   \n",
-       "351                        NaN            NaN           0.670984   \n",
-       "..                         ...            ...                ...   \n",
-       "247                   0.790308       0.977482           0.042573   \n",
-       "248                   1.000000       0.988741           0.030745   \n",
-       "249                   0.920304       1.000000           0.019650   \n",
-       "250                   0.920304       1.000000           0.009383   \n",
-       "251                   0.920304       1.000000           0.000000   \n",
-       "\n",
-       "Indicator  Net migration    Ninis  R&D GExp  Renewable electricity   Suicide  \\\n",
-       "347             0.966124  0.77758  0.000000               0.000000  0.956522   \n",
-       "348             0.966124  0.77758  0.000000               0.002525  0.956522   \n",
-       "349             0.966124  0.77758  0.000000               0.004025  0.956522   \n",
-       "350             0.823187  0.77758  0.000000               0.005848  0.956522   \n",
-       "351             0.680250  0.77758  0.000000               0.011012  0.956522   \n",
-       "..                   ...      ...       ...                    ...       ...   \n",
-       "247             0.264203      NaN  0.974482               1.000000  0.016129   \n",
-       "248             0.243061      NaN  0.984436               1.000000  0.000000   \n",
-       "249             0.243061      NaN  1.000000               1.000000  0.000000   \n",
-       "250             0.243061      NaN  1.000000               1.000000  0.000000   \n",
-       "251             0.243061      NaN  1.000000               1.000000  0.000000   \n",
-       "\n",
-       "Indicator  Workers high education  Continent  \n",
-       "347                           1.0     Europe  \n",
-       "348                           1.0     Europe  \n",
-       "349                           1.0     Europe  \n",
-       "350                           1.0     Europe  \n",
-       "351                           1.0     Europe  \n",
-       "..                            ...        ...  \n",
-       "247                           NaN       Pair  \n",
-       "248                           NaN       Pair  \n",
-       "249                           NaN       Pair  \n",
-       "250                           NaN       Pair  \n",
-       "251                           NaN       Pair  \n",
-       "\n",
-       "[1509 rows x 23 columns]"
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                 1.000000                      \n",
+       "353                                                 1.000000                      \n",
+       "354                                                 1.000000                      \n",
+       "355                                                 1.000000                      \n",
+       "356                                                 1.000000                      \n",
+       "..                                                       ...                      \n",
+       "251                                                 1.742857                      \n",
+       "252                                                 1.800000                      \n",
+       "253                                                 1.847619                      \n",
+       "254                                                 1.890476                      \n",
+       "255                                                 1.890476                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 2.348936                                   \n",
+       "252                                                 2.510638                                   \n",
+       "253                                                 2.634043                                   \n",
+       "254                                                 2.774468                                   \n",
+       "255                                                 2.774468                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 1.261156                                   \n",
+       "252                                                 1.278713                                   \n",
+       "253                                                 1.293343                                   \n",
+       "254                                                 1.307974                                   \n",
+       "255                                                 1.307974                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                       1.000000   \n",
+       "353                                       1.000000   \n",
+       "354                                       1.000000   \n",
+       "355                                       1.000000   \n",
+       "356                                       1.000000   \n",
+       "..                                             ...   \n",
+       "251                                       1.030696   \n",
+       "252                                       1.030696   \n",
+       "253                                       1.030696   \n",
+       "254                                       1.030696   \n",
+       "255                                       1.030696   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.048707      \n",
+       "252                                                 1.048707      \n",
+       "253                                                 1.048707      \n",
+       "254                                                 1.048707      \n",
+       "255                                                 1.048707      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                      1.0      \n",
+       "353                                                      1.0      \n",
+       "354                                                      1.0      \n",
+       "355                                                      1.0      \n",
+       "356                                                      1.0      \n",
+       "..                                                       ...      \n",
+       "251                                                      1.0      \n",
+       "252                                                      1.0      \n",
+       "253                                                      1.0      \n",
+       "254                                                      1.0      \n",
+       "255                                                      1.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                 \n",
+       "353                                                 1.000000                                                                 \n",
+       "354                                                 1.000000                                                                 \n",
+       "355                                                 1.000000                                                                 \n",
+       "356                                                 1.000000                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                 1.257169                                                                 \n",
+       "252                                                 1.257169                                                                 \n",
+       "253                                                 1.257169                                                                 \n",
+       "254                                                 1.257169                                                                 \n",
+       "255                                                 1.257169                                                                 \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                         \n",
+       "353                                                 1.000000                                                                         \n",
+       "354                                                 1.000000                                                                         \n",
+       "355                                                 1.000000                                                                         \n",
+       "356                                                 1.000000                                                                         \n",
+       "..                                                       ...                                                                         \n",
+       "251                                                 1.272562                                                                         \n",
+       "252                                                 1.272562                                                                         \n",
+       "253                                                 1.272562                                                                         \n",
+       "254                                                 1.272562                                                                         \n",
+       "255                                                 1.272562                                                                         \n",
+       "\n",
+       "Indicator  ...  Urban population growth (annual %)  \\\n",
+       "352        ...                            1.000000   \n",
+       "353        ...                            0.885023   \n",
+       "354        ...                            0.837277   \n",
+       "355        ...                            0.798935   \n",
+       "356        ...                            0.602296   \n",
+       "..         ...                                 ...   \n",
+       "251        ...                            0.635700   \n",
+       "252        ...                            0.580879   \n",
+       "253        ...                            0.531403   \n",
+       "254        ...                            0.479397   \n",
+       "255        ...                            0.479397   \n",
+       "\n",
+       "Indicator  Urban population living in areas where elevation is below 5 meters (% of total population)  \\\n",
+       "352                                                 1.000000                                            \n",
+       "353                                                 0.999209                                            \n",
+       "354                                                 0.998418                                            \n",
+       "355                                                 0.997627                                            \n",
+       "356                                                 0.996836                                            \n",
+       "..                                                       ...                                            \n",
+       "251                                                 1.362819                                            \n",
+       "252                                                 1.362819                                            \n",
+       "253                                                 1.362819                                            \n",
+       "254                                                 1.362819                                            \n",
+       "255                                                 1.362819                                            \n",
+       "\n",
+       "Indicator  Vulnerable employment, female (% of female employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                               \n",
+       "353                                                 1.000000                               \n",
+       "354                                                 1.007018                               \n",
+       "355                                                 1.026316                               \n",
+       "356                                                 0.984210                               \n",
+       "..                                                       ...                               \n",
+       "251                                                 0.707250                               \n",
+       "252                                                 0.694938                               \n",
+       "253                                                 0.680347                               \n",
+       "254                                                 0.680347                               \n",
+       "255                                                 0.680347                               \n",
+       "\n",
+       "Indicator  Vulnerable employment, male (% of male employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                           \n",
+       "353                                                 1.000000                           \n",
+       "354                                                 1.027083                           \n",
+       "355                                                 1.050000                           \n",
+       "356                                                 1.083333                           \n",
+       "..                                                       ...                           \n",
+       "251                                                 0.617275                           \n",
+       "252                                                 0.602759                           \n",
+       "253                                                 0.586663                           \n",
+       "254                                                 0.586663                           \n",
+       "255                                                 0.586663                           \n",
+       "\n",
+       "Indicator  Vulnerable employment, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                             \n",
+       "353                                                 1.000000                             \n",
+       "354                                                 1.019342                             \n",
+       "355                                                 1.040619                             \n",
+       "356                                                 1.038685                             \n",
+       "..                                                       ...                             \n",
+       "251                                                 0.656204                             \n",
+       "252                                                 0.642647                             \n",
+       "253                                                 0.626879                             \n",
+       "254                                                 0.626879                             \n",
+       "255                                                 0.626879                             \n",
+       "\n",
+       "Indicator  Wage and salaried workers, female (% of female employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 0.998479                                   \n",
+       "355                                                 0.995872                                   \n",
+       "356                                                 0.995437                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 1.555786                                   \n",
+       "252                                                 1.578932                                   \n",
+       "253                                                 1.606825                                   \n",
+       "254                                                 1.606825                                   \n",
+       "255                                                 1.606825                                   \n",
+       "\n",
+       "Indicator  Wage and salaried workers, male (% of male employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                               \n",
+       "353                                                 1.000000                               \n",
+       "354                                                 0.994164                               \n",
+       "355                                                 0.990349                               \n",
+       "356                                                 0.984626                               \n",
+       "..                                                       ...                               \n",
+       "251                                                 1.984976                               \n",
+       "252                                                 2.020520                               \n",
+       "253                                                 2.062294                               \n",
+       "254                                                 2.062294                               \n",
+       "255                                                 2.062294                               \n",
+       "\n",
+       "Indicator  Wage and salaried workers, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                                 \n",
+       "353                                                 1.000000                                 \n",
+       "354                                                 0.996015                                 \n",
+       "355                                                 0.992693                                 \n",
+       "356                                                 0.989372                                 \n",
+       "..                                                       ...                                 \n",
+       "251                                                 1.768798                                 \n",
+       "252                                                 1.798278                                 \n",
+       "253                                                 1.833057                                 \n",
+       "254                                                 1.833057                                 \n",
+       "255                                                 1.833057                                 \n",
+       "\n",
+       "Indicator  Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)  \\\n",
+       "352                                                 1.000000                                                  \n",
+       "353                                                 1.000000                                                  \n",
+       "354                                                 1.000000                                                  \n",
+       "355                                                 1.027882                                                  \n",
+       "356                                                 1.055765                                                  \n",
+       "..                                                       ...                                                  \n",
+       "251                                                 8.610987                                                  \n",
+       "252                                                 8.610987                                                  \n",
+       "253                                                 8.610987                                                  \n",
+       "254                                                 8.610987                                                  \n",
+       "255                                                 8.610987                                                  \n",
+       "\n",
+       "Indicator  Women Business and the Law Index Score (scale 1-100)  \n",
+       "352                                                 1.000000     \n",
+       "353                                                 1.000000     \n",
+       "354                                                 1.000000     \n",
+       "355                                                 1.000000     \n",
+       "356                                                 1.000000     \n",
+       "..                                                       ...     \n",
+       "251                                                 1.273684     \n",
+       "252                                                 1.273684     \n",
+       "253                                                 1.273684     \n",
+       "254                                                 1.273684     \n",
+       "255                                                 1.273684     \n",
+       "\n",
+       "[1536 rows x 1060 columns]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data['Continent']=data['Country'].map(all_countries)\n",
-    "Goldendataframe=data\n",
-    "Goldendataframe"
+    "for u in range(1,len(country_list)):\n",
+    "    datae=data.loc[data.loc[:, 'Country'] == country_list[u]]   \n",
+    "    for i in range(2,len(columns)):\n",
+    "        a=columns[i]\n",
+    "        datae[a]=datae[a]/datae.iloc[0,i]\n",
+    "    datau=pd.concat((datau, datae), axis = 0)\n",
+    "datau"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With that all, we export our dataframe all-in-one and by the continent category."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Goldendataframe.to_csv(os.getcwd()+'/Data/GoldenDataFrame.csv')"
+    "As later on we want to study the correlations with time moved, we need to create new columns for it. The reason why is because, maybe the effect of a variable does not happen until a couple of years later. The time movements that have been considered are those of the Fibonacci serie within our time period. \n",
+    "\n",
+    "The following pictures helps to realize the behaviour that we were explaining. \n",
+    "\n",
+    "![](https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/Time%20moved.JPG)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "for region, data in Goldendataframe.groupby('Continent'):\n",
-    "   data.to_csv(os.getcwd()+'/Data/{}.csv'.format(region))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "columns=['Country','Year','Exports-Commercial services','Renewable electricity','Employment-agriculture','Employment-industry','Employment-services','Exports-G&S','Fertility rate','Foreign investment','GDP','Education GExp','Workers high education','Literacy rate','Net migration','Mortality-infants','Health services use','R&D GExp','Ninis','Suicide','International taxes','Alcohol per capita']\n",
-    "clist=Goldendataframe['Country'].unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following cell, we have defined a function that will allow us to calculate the different posibilities of relations: cuadratic, cubic and logaritmic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def multcolumn(frame):\n",
-    "    for u in range(3, len(columns)):\n",
-    "        name2=columns[u]+'.^2'\n",
-    "        name3=columns[u]+'.^3'\n",
-    "        namelog=columns[u]+'.log'\n",
-    "        frame.loc[:,name2] = frame[columns[u]]**2\n",
-    "        frame.loc[:,name3] = frame[columns[u]]**3\n",
-    "        frame.loc[:,namelog] = np.log(frame[columns[u]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Moreover, we want to know the correlation between all the variables, so to acomplish this, we have created the following loop, which will help us create a new dataframe where we will have: the *Indicator*, the *Type* of relation, the value of the *R^2*, its *Behaviour*, the *Country* and the *Continent*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= pd.read_csv (os.getcwd()+'/Data/'+'GoldenDataFrame.csv')\n",
-    "multcolumn(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Firstly we need to calculate the correlation table for each country, therefore we use the basic function `corr()` which provides the Pearson correlation table, as well as a filter for the countries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dat=df.loc[df.loc[:, 'Country'] == clist[0]]\n",
-    "cor=dat.corr()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we calculate the coefficient of determination which is the correlation squared."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cor.loc[:,'R^2 Pearson'] = cor['GDP']**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Moreover, we are going to create new columns to know which *Indicator* are we talking about, and the *Type* of correlation that is being analyzed (linear, cuadratic, cubic or logarithmic)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cor.loc[:,'Indicator']=cor.index\n",
-    "cor[['Indicator','Type']]=cor.Indicator.str.split('.',expand=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we can apply the filter we have consider that is enough, R^2>=0.75 to filter the correlations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corcolumn=cor[['Indicator','Type','R^2 Pearson','GDP']]\n",
-    "corcolumn=corcolumn.loc[corcolumn.loc[:, 'R^2 Pearson'] >= 0.75]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Furthermore, we add all the columns that we have created into a data frame, thanks to the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "id=corcolumn.groupby('Indicator')['R^2 Pearson'].transform(max)==corcolumn['R^2 Pearson']\n",
-    "corcolumn[id]\n",
-    "max_df=pd.DataFrame(corcolumn[id])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we conmute the values, by expressions. For example if the correlation is positive, we want in the new column called *Behaviour* the word Positive. Or for the *Type* column if the greatest correlation is cuadratic we want to put, Cuadratic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_df['Behaviour']=np.where(max_df['GDP']>0, 'Positive', 'Negative')\n",
-    "max_df['Type']=max_df['Type'].replace(['^2','^3','log'],['Cuadratic','Cubic','Logarithmic'])\n",
-    "max_df['Country']= clist[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition, we also drop the columns which do not add any value, as *GDP*, *Year*, and *Unnamed:0*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_df.drop(\"GDP\",axis=1,inplace=True)\n",
-    "max_df=max_df.reset_index(drop=True)\n",
-    "max_df = max_df.drop(max_df[max_df['Indicator']=='Year'].index)\n",
-    "max_df = max_df.drop(max_df[max_df['Indicator']=='GDP'].index)\n",
-    "max_df = max_df.drop(max_df[max_df['Indicator']=='Unnamed: 0'].index)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And finnally we sort the values in descending order by the column *R^2 Pearson*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_df=max_df.sort_values(by = 'R^2 Pearson',ascending = False)\n",
-    "demo2=max_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, we can do it with all the countries and create just one dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4897,60 +5361,150 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
        "      <th>Indicator</th>\n",
-       "      <th>Type</th>\n",
-       "      <th>R^2 Pearson</th>\n",
-       "      <th>Behaviour</th>\n",
        "      <th>Country</th>\n",
-       "      <th>Continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Wage and salaried workers, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)</th>\n",
+       "      <th>Women Business and the Law Index Score (scale 1-100)</th>\n",
+       "      <th>GDP (current US$)+1</th>\n",
+       "      <th>GDP (current US$)+2</th>\n",
+       "      <th>GDP (current US$)+3</th>\n",
+       "      <th>GDP (current US$)+5</th>\n",
+       "      <th>GDP (current US$)+8</th>\n",
+       "      <th>GDP (current US$)+13</th>\n",
+       "      <th>GDP (current US$)+21</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Exports-G&amp;S</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.954928</td>\n",
-       "      <td>Positive</td>\n",
+       "      <th>352</th>\n",
        "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Health services use</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.916594</td>\n",
-       "      <td>Positive</td>\n",
+       "      <th>353</th>\n",
        "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.911725</td>\n",
-       "      <td>Positive</td>\n",
+       "      <th>354</th>\n",
        "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.996015</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Employment-services</td>\n",
-       "      <td>Cuadratic</td>\n",
-       "      <td>0.883525</td>\n",
-       "      <td>Positive</td>\n",
+       "      <th>355</th>\n",
        "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.992693</td>\n",
+       "      <td>1.027882</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.203142</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>Cubic</td>\n",
-       "      <td>0.875820</td>\n",
-       "      <td>Negative</td>\n",
+       "      <th>356</th>\n",
        "      <td>DEU</td>\n",
-       "      <td>Europe</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.989372</td>\n",
+       "      <td>1.055765</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.169136</td>\n",
+       "      <td>1.203142</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -4960,456 +5514,22517 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Suicide</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.922759</td>\n",
-       "      <td>Negative</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Renewable electricity</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.912560</td>\n",
-       "      <td>Positive</td>\n",
+       "      <th>251</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1.742857</td>\n",
+       "      <td>2.348936</td>\n",
+       "      <td>1.261156</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.768798</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>29.029938</td>\n",
+       "      <td>23.644292</td>\n",
+       "      <td>14.137706</td>\n",
+       "      <td>5.418606</td>\n",
+       "      <td>2.393592</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.879177</td>\n",
-       "      <td>Positive</td>\n",
+       "      <th>252</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2.510638</td>\n",
+       "      <td>1.278713</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.798278</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>16.868589</td>\n",
+       "      <td>6.334809</td>\n",
+       "      <td>2.664772</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>Cuadratic</td>\n",
-       "      <td>0.867314</td>\n",
-       "      <td>Positive</td>\n",
+       "      <th>253</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1.847619</td>\n",
+       "      <td>2.634043</td>\n",
+       "      <td>1.293343</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>29.029938</td>\n",
+       "      <td>20.926519</td>\n",
+       "      <td>7.626636</td>\n",
+       "      <td>2.851657</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Literacy rate</td>\n",
-       "      <td>Cubic</td>\n",
-       "      <td>0.840397</td>\n",
-       "      <td>Positive</td>\n",
+       "      <th>254</th>\n",
        "      <td>CHN</td>\n",
-       "      <td>Pair</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>39.572189</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>23.644292</td>\n",
+       "      <td>9.838617</td>\n",
+       "      <td>3.031657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>40.799246</td>\n",
+       "      <td>39.572189</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>3.356853</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>444 rows × 6 columns</p>\n",
+       "<p>1536 rows × 1067 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                      Indicator       Type  R^2 Pearson Behaviour Country  \\\n",
-       "5                   Exports-G&S       None     0.954928  Positive     DEU   \n",
-       "7           Health services use       None     0.916594  Positive     DEU   \n",
-       "4   Exports-Commercial services       None     0.911725  Positive     DEU   \n",
-       "10          Employment-services  Cuadratic     0.883525  Positive     DEU   \n",
-       "12           Alcohol per capita      Cubic     0.875820  Negative     DEU   \n",
-       "..                          ...        ...          ...       ...     ...   \n",
-       "6                       Suicide       None     0.922759  Negative     CHN   \n",
-       "5         Renewable electricity       None     0.912560  Positive     CHN   \n",
-       "2   Exports-Commercial services       None     0.879177  Positive     CHN   \n",
-       "14           Alcohol per capita  Cuadratic     0.867314  Positive     CHN   \n",
-       "10                Literacy rate      Cubic     0.840397  Positive     CHN   \n",
-       "\n",
-       "   Continent  \n",
-       "5     Europe  \n",
-       "7     Europe  \n",
-       "4     Europe  \n",
-       "10    Europe  \n",
-       "12    Europe  \n",
-       "..       ...  \n",
-       "6       Pair  \n",
-       "5       Pair  \n",
-       "2       Pair  \n",
-       "14      Pair  \n",
-       "10      Pair  \n",
-       "\n",
-       "[444 rows x 6 columns]"
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                 1.000000                      \n",
+       "353                                                 1.000000                      \n",
+       "354                                                 1.000000                      \n",
+       "355                                                 1.000000                      \n",
+       "356                                                 1.000000                      \n",
+       "..                                                       ...                      \n",
+       "251                                                 1.742857                      \n",
+       "252                                                 1.800000                      \n",
+       "253                                                 1.847619                      \n",
+       "254                                                 1.890476                      \n",
+       "255                                                 1.890476                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 2.348936                                   \n",
+       "252                                                 2.510638                                   \n",
+       "253                                                 2.634043                                   \n",
+       "254                                                 2.774468                                   \n",
+       "255                                                 2.774468                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 1.261156                                   \n",
+       "252                                                 1.278713                                   \n",
+       "253                                                 1.293343                                   \n",
+       "254                                                 1.307974                                   \n",
+       "255                                                 1.307974                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                       1.000000   \n",
+       "353                                       1.000000   \n",
+       "354                                       1.000000   \n",
+       "355                                       1.000000   \n",
+       "356                                       1.000000   \n",
+       "..                                             ...   \n",
+       "251                                       1.030696   \n",
+       "252                                       1.030696   \n",
+       "253                                       1.030696   \n",
+       "254                                       1.030696   \n",
+       "255                                       1.030696   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.048707      \n",
+       "252                                                 1.048707      \n",
+       "253                                                 1.048707      \n",
+       "254                                                 1.048707      \n",
+       "255                                                 1.048707      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                      1.0      \n",
+       "353                                                      1.0      \n",
+       "354                                                      1.0      \n",
+       "355                                                      1.0      \n",
+       "356                                                      1.0      \n",
+       "..                                                       ...      \n",
+       "251                                                      1.0      \n",
+       "252                                                      1.0      \n",
+       "253                                                      1.0      \n",
+       "254                                                      1.0      \n",
+       "255                                                      1.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                 \n",
+       "353                                                 1.000000                                                                 \n",
+       "354                                                 1.000000                                                                 \n",
+       "355                                                 1.000000                                                                 \n",
+       "356                                                 1.000000                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                 1.257169                                                                 \n",
+       "252                                                 1.257169                                                                 \n",
+       "253                                                 1.257169                                                                 \n",
+       "254                                                 1.257169                                                                 \n",
+       "255                                                 1.257169                                                                 \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                         \n",
+       "353                                                 1.000000                                                                         \n",
+       "354                                                 1.000000                                                                         \n",
+       "355                                                 1.000000                                                                         \n",
+       "356                                                 1.000000                                                                         \n",
+       "..                                                       ...                                                                         \n",
+       "251                                                 1.272562                                                                         \n",
+       "252                                                 1.272562                                                                         \n",
+       "253                                                 1.272562                                                                         \n",
+       "254                                                 1.272562                                                                         \n",
+       "255                                                 1.272562                                                                         \n",
+       "\n",
+       "Indicator  ...  \\\n",
+       "352        ...   \n",
+       "353        ...   \n",
+       "354        ...   \n",
+       "355        ...   \n",
+       "356        ...   \n",
+       "..         ...   \n",
+       "251        ...   \n",
+       "252        ...   \n",
+       "253        ...   \n",
+       "254        ...   \n",
+       "255        ...   \n",
+       "\n",
+       "Indicator  Wage and salaried workers, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                                 \n",
+       "353                                                 1.000000                                 \n",
+       "354                                                 0.996015                                 \n",
+       "355                                                 0.992693                                 \n",
+       "356                                                 0.989372                                 \n",
+       "..                                                       ...                                 \n",
+       "251                                                 1.768798                                 \n",
+       "252                                                 1.798278                                 \n",
+       "253                                                 1.833057                                 \n",
+       "254                                                 1.833057                                 \n",
+       "255                                                 1.833057                                 \n",
+       "\n",
+       "Indicator  Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)  \\\n",
+       "352                                                 1.000000                                                  \n",
+       "353                                                 1.000000                                                  \n",
+       "354                                                 1.000000                                                  \n",
+       "355                                                 1.027882                                                  \n",
+       "356                                                 1.055765                                                  \n",
+       "..                                                       ...                                                  \n",
+       "251                                                 8.610987                                                  \n",
+       "252                                                 8.610987                                                  \n",
+       "253                                                 8.610987                                                  \n",
+       "254                                                 8.610987                                                  \n",
+       "255                                                 8.610987                                                  \n",
+       "\n",
+       "Indicator  Women Business and the Law Index Score (scale 1-100)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.273684      \n",
+       "252                                                 1.273684      \n",
+       "253                                                 1.273684      \n",
+       "254                                                 1.273684      \n",
+       "255                                                 1.273684      \n",
+       "\n",
+       "Indicator  GDP (current US$)+1  GDP (current US$)+2  GDP (current US$)+3  \\\n",
+       "352                        NaN                  NaN                  NaN   \n",
+       "353                   1.000000                  NaN                  NaN   \n",
+       "354                   1.054905             1.000000                  NaN   \n",
+       "355                   1.203142             1.054905             1.000000   \n",
+       "356                   1.169136             1.203142             1.054905   \n",
+       "..                         ...                  ...                  ...   \n",
+       "251                  31.129362            30.653486            29.029938   \n",
+       "252                  34.114284            31.129362            30.653486   \n",
+       "253                  38.504955            34.114284            31.129362   \n",
+       "254                  39.572189            38.504955            34.114284   \n",
+       "255                  40.799246            39.572189            38.504955   \n",
+       "\n",
+       "Indicator  GDP (current US$)+5  GDP (current US$)+8  GDP (current US$)+13  \\\n",
+       "352                        NaN                  NaN                   NaN   \n",
+       "353                        NaN                  NaN                   NaN   \n",
+       "354                        NaN                  NaN                   NaN   \n",
+       "355                        NaN                  NaN                   NaN   \n",
+       "356                        NaN                  NaN                   NaN   \n",
+       "..                         ...                  ...                   ...   \n",
+       "251                  23.644292            14.137706              5.418606   \n",
+       "252                  26.521259            16.868589              6.334809   \n",
+       "253                  29.029938            20.926519              7.626636   \n",
+       "254                  30.653486            23.644292              9.838617   \n",
+       "255                  31.129362            26.521259             12.731623   \n",
+       "\n",
+       "Indicator  GDP (current US$)+21  \n",
+       "352                         NaN  \n",
+       "353                         NaN  \n",
+       "354                         NaN  \n",
+       "355                         NaN  \n",
+       "356                         NaN  \n",
+       "..                          ...  \n",
+       "251                    2.393592  \n",
+       "252                    2.664772  \n",
+       "253                    2.851657  \n",
+       "254                    3.031657  \n",
+       "255                    3.356853  \n",
+       "\n",
+       "[1536 rows x 1067 columns]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "for i in range(1,len(clist)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == clist[i]]\n",
-    "    cor=dat.corr()    \n",
-    "    cor.loc[:,'R^2 Pearson'] = cor['GDP']**2\n",
-    "    cor.loc[:,'Indicator']=cor.index\n",
-    "    cor[['Indicator','Type']]=cor.Indicator.str.split('.',expand=True)  \n",
-    "    corcolumn=cor[['Indicator','Type','R^2 Pearson','GDP']]\n",
-    "    corcolumn=corcolumn.loc[corcolumn.loc[:, 'R^2 Pearson'] >= 0.75]\n",
-    "    id=corcolumn.groupby('Indicator')['R^2 Pearson'].transform(max)==corcolumn['R^2 Pearson']\n",
-    "    corcolumn[id]\n",
-    "    max_df=pd.DataFrame(corcolumn[id])\n",
-    "    max_df['Behaviour']=np.where(max_df['GDP']>0, 'Positive', 'Negative')\n",
-    "    max_df['Type']=max_df['Type'].replace(['^2','^3','log'],['Cuadratic','Cubic','Logarithmic'])\n",
-    "    max_df['Country']= clist[i]\n",
-    "    max_df.drop(\"GDP\",axis=1,inplace=True)\n",
-    "    max_df=max_df.reset_index(drop=True)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='Year'].index)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='GDP'].index)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='Unnamed: 0'].index)\n",
-    "    max_df=max_df.sort_values(by = 'R^2 Pearson',ascending = False)\n",
-    "    demo2=pd.concat((demo2, max_df), axis = 0)\n",
-    "demo2['Continent']=demo2['Country'].map(all_countries)\n",
-    "demo2"
+    "shifted=pd.DataFrame()\n",
+    "for i in range(0,len(country_list)):\n",
+    "    dat=datau.loc[datau.loc[:, 'Country'] == country_list[i]]\n",
+    "    dat['GDP (current US$)+1']=dat['GDP (current US$)'].shift(periods=1)\n",
+    "    dat['GDP (current US$)+2']=dat['GDP (current US$)'].shift(periods=2)\n",
+    "    dat['GDP (current US$)+3']=dat['GDP (current US$)'].shift(periods=3)\n",
+    "    dat['GDP (current US$)+5']=dat['GDP (current US$)'].shift(periods=5)\n",
+    "    dat['GDP (current US$)+8']=dat['GDP (current US$)'].shift(periods=8)\n",
+    "    dat['GDP (current US$)+13']=dat['GDP (current US$)'].shift(periods=13)\n",
+    "    dat['GDP (current US$)+21']=dat['GDP (current US$)'].shift(periods=21)\n",
+    "    shifted=pd.concat((shifted, dat), axis = 0)\n",
+    "shifted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=shifted"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the next part of analyzing this data, we think it is gonna be interesting to have it classify by the categories of the Country groups defined before, to which we call \"Continent\". This category is useful as it groups the nations with similar economies or geographical proximity, so we can extract common conclusions from them."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we’ve loaded the data, we can start right away to create widgets. These widgets are essentials to add interactivity to our visualizations. We’re going to use two widgets: both, multiple selection widgets. To create these widgets, we can use `ipywidgets` library that is available for Jupyter Notebook.\n",
+    "We create a dictionary with the regions and the countries included in each one. Where we will relate the countries and regions so then we can apply the .map function and arrive to the final dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'DEU': 'Europe', 'FRA': 'Europe', 'SWE': 'Europe', 'GBR': 'Europe', 'ESP': 'Europe', 'HRV': 'Europe', 'POL': 'Europe', 'GRC': 'Europe', 'AUT': 'Europe', 'NLD': 'Europe', 'IRQ': 'Persian Gulf', 'QAT': 'Persian Gulf', 'ARE': 'Persian Gulf', 'SAU': 'Persian Gulf', 'AZE': 'Persian Gulf', 'YEM': 'Persian Gulf', 'YDR': 'Persian Gulf', 'OMN': 'Persian Gulf', 'DZA': 'North Africa', 'EGY': 'North Africa', 'LBY': 'North Africa', 'ISR': 'North Africa', 'TUR': 'North Africa', 'MAR': 'North Africa', 'SEN': 'South Africa', 'ZAF': 'South Africa', 'LBR': 'South Africa', 'MOZ': 'South Africa', 'CMR': 'South Africa', 'NGA': 'South Africa', 'GHA': 'South Africa', 'BGD': 'Asia', 'IND': 'Asia', 'VNM': 'Asia', 'THA': 'Asia', 'IDN': 'Asia', 'PHL': 'Asia', 'KOR': 'Asia', 'MEX': 'Latam', 'BRA': 'Latam', 'ARG': 'Latam', 'PER': 'Latam', 'VEN': 'Latam', 'COL': 'Latam', 'CHL': 'Latam', 'PAN': 'Latam', 'CRI': 'Latam', 'USA': 'Pair', 'CHN': 'Pair'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "countries_by_region = {\n",
+    "    \"Europe\": ('DEU','FRA','SWE','GBR','ESP','HRV','POL','GRC','AUT','NLD'),\n",
+    "    'Persian Gulf': ('IRQ','QAT','ARE','SAU','AZE','YEM','YDR','OMN'),\n",
+    "    'North Africa':('DZA','EGY','LBY','ISR','TUR','MAR'),\n",
+    "    'South Africa':('SEN','ZAF','LBR','MOZ','CMR','NGA','GHA'),\n",
+    "    'Asia':('BGD','IND','VNM','THA','IDN','PHL','KOR'),\n",
+    "    'Latam':('MEX','BRA','ARG','PER','VEN','COL','CHL','PAN','CRI'),\n",
+    "    'Pair':('USA','CHN')\n",
+    "    }\n",
     "\n",
-    "The first widget that we are going to create is the multiple selection widget. We can do this by using `SelectMultiple()attribute` from `ipywidgets`. With this widget, we have the option to visualize the R^2 only in particular selection of indicators instead of all.\n",
+    "all_countries = {}\n",
+    "for region in countries_by_region.keys():\n",
+    "  for country in countries_by_region[region]:\n",
+    "    all_countries[country] = region\n",
     "\n",
-    "The first argument that we should specify is `options` , which should contain the list of available options of our variable (in our case different indicators). The next one is `value` , which should contain the variable values that we want to display as default, and then `description` is for the text field to describe the name of the widget.The rest of options are just visual details."
+    "print(all_countries)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "db323d6bd0264921b895b26f4aff87d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(SelectMultiple(description='Indicator', index=(0,), layout=Layout(height='80px', width='…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "coloraxis": "coloraxis",
-         "customdata": [
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           "Cuadratic",
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           "Cuadratic",
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           null,
-           "Positive"
-          ],
-          [
-           "Cuadratic",
-           "Positive"
-          ]
-         ],
-         "geo": "geo",
-         "hovertemplate": "<b>%{hovertext}</b><br><br>Country=%{location}<br>Type=%{customdata[0]}<br>Behaviour=%{customdata[1]}<br>R^2 Pearson=%{z}<extra></extra>",
-         "hovertext": [
-          "DEU",
-          "FRA",
-          "SWE",
-          "GBR",
-          "ESP",
-          "HRV",
-          "POL",
-          "AUT",
-          "NLD",
-          "IRQ",
-          "QAT",
-          "ARE",
-          "SAU",
-          "AZE",
-          "OMN",
-          "LBY",
-          "ISR",
-          "TUR",
-          "MAR",
-          "SEN",
-          "ZAF",
-          "MOZ",
-          "CMR",
-          "GHA",
-          "BGD",
-          "IND",
-          "VNM",
-          "THA",
-          "IDN",
-          "PHL",
-          "KOR",
-          "MEX",
-          "BRA",
-          "PER",
-          "VEN",
-          "COL",
-          "CHL",
-          "PAN",
-          "CRI",
-          "USA",
-          "CHN"
-         ],
-         "locationmode": "ISO-3",
-         "locations": [
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)</th>\n",
+       "      <th>Women Business and the Law Index Score (scale 1-100)</th>\n",
+       "      <th>GDP (current US$)+1</th>\n",
+       "      <th>GDP (current US$)+2</th>\n",
+       "      <th>GDP (current US$)+3</th>\n",
+       "      <th>GDP (current US$)+5</th>\n",
+       "      <th>GDP (current US$)+8</th>\n",
+       "      <th>GDP (current US$)+13</th>\n",
+       "      <th>GDP (current US$)+21</th>\n",
+       "      <th>Continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.027882</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.203142</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.055765</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.169136</td>\n",
+       "      <td>1.203142</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1.742857</td>\n",
+       "      <td>2.348936</td>\n",
+       "      <td>1.261156</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>29.029938</td>\n",
+       "      <td>23.644292</td>\n",
+       "      <td>14.137706</td>\n",
+       "      <td>5.418606</td>\n",
+       "      <td>2.393592</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2.510638</td>\n",
+       "      <td>1.278713</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>16.868589</td>\n",
+       "      <td>6.334809</td>\n",
+       "      <td>2.664772</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1.847619</td>\n",
+       "      <td>2.634043</td>\n",
+       "      <td>1.293343</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>29.029938</td>\n",
+       "      <td>20.926519</td>\n",
+       "      <td>7.626636</td>\n",
+       "      <td>2.851657</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>39.572189</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>23.644292</td>\n",
+       "      <td>9.838617</td>\n",
+       "      <td>3.031657</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>40.799246</td>\n",
+       "      <td>39.572189</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1536 rows × 1068 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                 1.000000                      \n",
+       "353                                                 1.000000                      \n",
+       "354                                                 1.000000                      \n",
+       "355                                                 1.000000                      \n",
+       "356                                                 1.000000                      \n",
+       "..                                                       ...                      \n",
+       "251                                                 1.742857                      \n",
+       "252                                                 1.800000                      \n",
+       "253                                                 1.847619                      \n",
+       "254                                                 1.890476                      \n",
+       "255                                                 1.890476                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 2.348936                                   \n",
+       "252                                                 2.510638                                   \n",
+       "253                                                 2.634043                                   \n",
+       "254                                                 2.774468                                   \n",
+       "255                                                 2.774468                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 1.261156                                   \n",
+       "252                                                 1.278713                                   \n",
+       "253                                                 1.293343                                   \n",
+       "254                                                 1.307974                                   \n",
+       "255                                                 1.307974                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                       1.000000   \n",
+       "353                                       1.000000   \n",
+       "354                                       1.000000   \n",
+       "355                                       1.000000   \n",
+       "356                                       1.000000   \n",
+       "..                                             ...   \n",
+       "251                                       1.030696   \n",
+       "252                                       1.030696   \n",
+       "253                                       1.030696   \n",
+       "254                                       1.030696   \n",
+       "255                                       1.030696   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.048707      \n",
+       "252                                                 1.048707      \n",
+       "253                                                 1.048707      \n",
+       "254                                                 1.048707      \n",
+       "255                                                 1.048707      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                      1.0      \n",
+       "353                                                      1.0      \n",
+       "354                                                      1.0      \n",
+       "355                                                      1.0      \n",
+       "356                                                      1.0      \n",
+       "..                                                       ...      \n",
+       "251                                                      1.0      \n",
+       "252                                                      1.0      \n",
+       "253                                                      1.0      \n",
+       "254                                                      1.0      \n",
+       "255                                                      1.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                 \n",
+       "353                                                 1.000000                                                                 \n",
+       "354                                                 1.000000                                                                 \n",
+       "355                                                 1.000000                                                                 \n",
+       "356                                                 1.000000                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                 1.257169                                                                 \n",
+       "252                                                 1.257169                                                                 \n",
+       "253                                                 1.257169                                                                 \n",
+       "254                                                 1.257169                                                                 \n",
+       "255                                                 1.257169                                                                 \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                         \n",
+       "353                                                 1.000000                                                                         \n",
+       "354                                                 1.000000                                                                         \n",
+       "355                                                 1.000000                                                                         \n",
+       "356                                                 1.000000                                                                         \n",
+       "..                                                       ...                                                                         \n",
+       "251                                                 1.272562                                                                         \n",
+       "252                                                 1.272562                                                                         \n",
+       "253                                                 1.272562                                                                         \n",
+       "254                                                 1.272562                                                                         \n",
+       "255                                                 1.272562                                                                         \n",
+       "\n",
+       "Indicator  ...  \\\n",
+       "352        ...   \n",
+       "353        ...   \n",
+       "354        ...   \n",
+       "355        ...   \n",
+       "356        ...   \n",
+       "..         ...   \n",
+       "251        ...   \n",
+       "252        ...   \n",
+       "253        ...   \n",
+       "254        ...   \n",
+       "255        ...   \n",
+       "\n",
+       "Indicator  Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)  \\\n",
+       "352                                                 1.000000                                                  \n",
+       "353                                                 1.000000                                                  \n",
+       "354                                                 1.000000                                                  \n",
+       "355                                                 1.027882                                                  \n",
+       "356                                                 1.055765                                                  \n",
+       "..                                                       ...                                                  \n",
+       "251                                                 8.610987                                                  \n",
+       "252                                                 8.610987                                                  \n",
+       "253                                                 8.610987                                                  \n",
+       "254                                                 8.610987                                                  \n",
+       "255                                                 8.610987                                                  \n",
+       "\n",
+       "Indicator  Women Business and the Law Index Score (scale 1-100)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.273684      \n",
+       "252                                                 1.273684      \n",
+       "253                                                 1.273684      \n",
+       "254                                                 1.273684      \n",
+       "255                                                 1.273684      \n",
+       "\n",
+       "Indicator  GDP (current US$)+1  GDP (current US$)+2  GDP (current US$)+3  \\\n",
+       "352                        NaN                  NaN                  NaN   \n",
+       "353                   1.000000                  NaN                  NaN   \n",
+       "354                   1.054905             1.000000                  NaN   \n",
+       "355                   1.203142             1.054905             1.000000   \n",
+       "356                   1.169136             1.203142             1.054905   \n",
+       "..                         ...                  ...                  ...   \n",
+       "251                  31.129362            30.653486            29.029938   \n",
+       "252                  34.114284            31.129362            30.653486   \n",
+       "253                  38.504955            34.114284            31.129362   \n",
+       "254                  39.572189            38.504955            34.114284   \n",
+       "255                  40.799246            39.572189            38.504955   \n",
+       "\n",
+       "Indicator  GDP (current US$)+5  GDP (current US$)+8  GDP (current US$)+13  \\\n",
+       "352                        NaN                  NaN                   NaN   \n",
+       "353                        NaN                  NaN                   NaN   \n",
+       "354                        NaN                  NaN                   NaN   \n",
+       "355                        NaN                  NaN                   NaN   \n",
+       "356                        NaN                  NaN                   NaN   \n",
+       "..                         ...                  ...                   ...   \n",
+       "251                  23.644292            14.137706              5.418606   \n",
+       "252                  26.521259            16.868589              6.334809   \n",
+       "253                  29.029938            20.926519              7.626636   \n",
+       "254                  30.653486            23.644292              9.838617   \n",
+       "255                  31.129362            26.521259             12.731623   \n",
+       "\n",
+       "Indicator  GDP (current US$)+21  Continent  \n",
+       "352                         NaN     Europe  \n",
+       "353                         NaN     Europe  \n",
+       "354                         NaN     Europe  \n",
+       "355                         NaN     Europe  \n",
+       "356                         NaN     Europe  \n",
+       "..                          ...        ...  \n",
+       "251                    2.393592       Pair  \n",
+       "252                    2.664772       Pair  \n",
+       "253                    2.851657       Pair  \n",
+       "254                    3.031657       Pair  \n",
+       "255                    3.356853       Pair  \n",
+       "\n",
+       "[1536 rows x 1068 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Continent']=data['Country'].map(all_countries)\n",
+    "Goldendataframe=data\n",
+    "Goldendataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With that all, we export our dataframe all-in-one and by the continent category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Goldendataframe.to_csv(os.getcwd()+'/Data/GoldenDataFrame.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for region, data in Goldendataframe.groupby('Continent'):\n",
+    "   data.to_csv(os.getcwd()+'/Data/{}.csv'.format(region))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Categorization of variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section, we are going to attempt a categorization of the whole of the variables, which most of them come the same sources and just differ in the units that are measured, or the total that they are refering, between others. For a simpler treatment of the data, the variables have been pivoted into the same column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_golden=list(Goldendataframe.columns)\n",
+    "del columns_golden[0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1540363 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \n",
+       "0        Access to clean fuels and technologies for coo...  \n",
+       "1        Access to clean fuels and technologies for coo...  \n",
+       "2        Access to clean fuels and technologies for coo...  \n",
+       "3                  Access to electricity (% of population)  \n",
+       "4        Access to electricity, rural (% of rural popul...  \n",
+       "...                                                    ...  \n",
+       "1540358                                GDP (current US$)+3  \n",
+       "1540359                                GDP (current US$)+5  \n",
+       "1540360                                GDP (current US$)+8  \n",
+       "1540361                               GDP (current US$)+13  \n",
+       "1540362                               GDP (current US$)+21  \n",
+       "\n",
+       "[1540363 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization=Goldendataframe.set_index(['Country','Year', 'Continent']).stack().reset_index()\n",
+    "Categorization['Short indicator']=Categorization['Indicator']\n",
+    "Categorization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " There are some indicators which represent exactly the same through different units, so, we are going to select only one type. For example, in monetary cases, indicators which are expressed with current US $ has been selected. Then, which are showed with the percentage and the total value, we have programmed to selct which ones which show a greater value.\n",
+    "\n",
+    "The links used to learn about these functions have been:\n",
+    "\n",
+    "https://www.geeksforgeeks.org/how-to-drop-rows-that-contain-a-specific-string-in-pandas/ \n",
+    "\n",
+    "https://www.statology.org/pandas-drop-rows-that-contain-string/ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \n",
+       "0        Access to clean fuels and technologies for coo...  \n",
+       "1        Access to clean fuels and technologies for coo...  \n",
+       "2        Access to clean fuels and technologies for coo...  \n",
+       "3                  Access to electricity (% of population)  \n",
+       "4        Access to electricity, rural (% of rural popul...  \n",
+       "...                                                    ...  \n",
+       "1540358                                GDP (current US$)+3  \n",
+       "1540359                                GDP (current US$)+5  \n",
+       "1540360                                GDP (current US$)+8  \n",
+       "1540361                               GDP (current US$)+13  \n",
+       "1540362                               GDP (current US$)+21  \n",
+       "\n",
+       "[1372098 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "discard=[\"annual % growth\",\"constant 2015 US[$]\",\"% of GNI\",\"constant LCU\",\"current LCU\"]\n",
+    "Categorization2=Categorization[~Categorization['Short indicator'].str.contains('|'.join(discard))]\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To check previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Categorization2.apply(lambda row: row.astype(str).str.contains('US').any(), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are going to structure the indicators in a same way to work better. The first step consist of making a new column that shows the units of each variable. Units are showed inside the parenthesis of the indicator name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>(% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                  Access to electricity (% of population)   \n",
+       "4        Access to electricity, rural (% of rural popul...   \n",
+       "...                                                    ...   \n",
+       "1540358                                GDP (current US$)+3   \n",
+       "1540359                                GDP (current US$)+5   \n",
+       "1540360                                GDP (current US$)+8   \n",
+       "1540361                               GDP (current US$)+13   \n",
+       "1540362                               GDP (current US$)+21   \n",
+       "\n",
+       "                           Units  \n",
+       "0              (% of population)  \n",
+       "1        (% of rural population)  \n",
+       "2        (% of urban population)  \n",
+       "3              (% of population)  \n",
+       "4        (% of rural population)  \n",
+       "...                          ...  \n",
+       "1540358            (current US$)  \n",
+       "1540359            (current US$)  \n",
+       "1540360            (current US$)  \n",
+       "1540361            (current US$)  \n",
+       "1540362            (current US$)  \n",
+       "\n",
+       "[1372098 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2['Units']=Categorization2['Short indicator'].str.extract(' (\\(.*\\))')\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, short indicator refers to the original indicator name without the units. The extracted information from the origin column has been deleted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                             Access to electricity, rural   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units  \n",
+       "0              (% of population)  \n",
+       "1        (% of rural population)  \n",
+       "2        (% of urban population)  \n",
+       "3              (% of population)  \n",
+       "4        (% of rural population)  \n",
+       "...                          ...  \n",
+       "1540358            (current US$)  \n",
+       "1540359            (current US$)  \n",
+       "1540360            (current US$)  \n",
+       "1540361            (current US$)  \n",
+       "1540362            (current US$)  \n",
+       "\n",
+       "[1372098 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2['Short indicator']=Categorization2['Short indicator'].str.replace(r\" (\\(.*\\))\",\"\")\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In some cases there are extra information in indicators name. The information of the second parenthesis is extracted as a new column too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, female (% of fema...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, female</td>\n",
+       "      <td>(% of female employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, male (% of male e...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, male</td>\n",
+       "      <td>(% of male employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, total (% of total...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, total</td>\n",
+       "      <td>(% of total employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1151</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, female (% of fema...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, female</td>\n",
+       "      <td>(% of female employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1152</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, male (% of male e...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, male</td>\n",
+       "      <td>(% of male employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538535</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, male (% of male e...</td>\n",
+       "      <td>0.252894</td>\n",
+       "      <td>Contributing family workers, male</td>\n",
+       "      <td>(% of male employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538536</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, total (% of total...</td>\n",
+       "      <td>0.329385</td>\n",
+       "      <td>Contributing family workers, total</td>\n",
+       "      <td>(% of total employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1539524</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, female (% of fema...</td>\n",
+       "      <td>0.386565</td>\n",
+       "      <td>Contributing family workers, female</td>\n",
+       "      <td>(% of female employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1539525</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, male (% of male e...</td>\n",
+       "      <td>0.252894</td>\n",
+       "      <td>Contributing family workers, male</td>\n",
+       "      <td>(% of male employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1539526</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, total (% of total...</td>\n",
+       "      <td>0.329385</td>\n",
+       "      <td>Contributing family workers, total</td>\n",
+       "      <td>(% of total employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4545 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "151         DEU  1990    Europe   \n",
+       "152         DEU  1990    Europe   \n",
+       "153         DEU  1990    Europe   \n",
+       "1151        DEU  1991    Europe   \n",
+       "1152        DEU  1991    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1538535     CHN  2020      Pair   \n",
+       "1538536     CHN  2020      Pair   \n",
+       "1539524     CHN  2021      Pair   \n",
+       "1539525     CHN  2021      Pair   \n",
+       "1539526     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator         0  \\\n",
+       "151      Contributing family workers, female (% of fema...  1.000000   \n",
+       "152      Contributing family workers, male (% of male e...  1.000000   \n",
+       "153      Contributing family workers, total (% of total...  1.000000   \n",
+       "1151     Contributing family workers, female (% of fema...  1.000000   \n",
+       "1152     Contributing family workers, male (% of male e...  1.000000   \n",
+       "...                                                    ...       ...   \n",
+       "1538535  Contributing family workers, male (% of male e...  0.252894   \n",
+       "1538536  Contributing family workers, total (% of total...  0.329385   \n",
+       "1539524  Contributing family workers, female (% of fema...  0.386565   \n",
+       "1539525  Contributing family workers, male (% of male e...  0.252894   \n",
+       "1539526  Contributing family workers, total (% of total...  0.329385   \n",
+       "\n",
+       "                             Short indicator  \\\n",
+       "151      Contributing family workers, female   \n",
+       "152        Contributing family workers, male   \n",
+       "153       Contributing family workers, total   \n",
+       "1151     Contributing family workers, female   \n",
+       "1152       Contributing family workers, male   \n",
+       "...                                      ...   \n",
+       "1538535    Contributing family workers, male   \n",
+       "1538536   Contributing family workers, total   \n",
+       "1539524  Contributing family workers, female   \n",
+       "1539525    Contributing family workers, male   \n",
+       "1539526   Contributing family workers, total   \n",
+       "\n",
+       "                                                   Units  \n",
+       "151      (% of female employment) (modeled ILO estimate)  \n",
+       "152        (% of male employment) (modeled ILO estimate)  \n",
+       "153       (% of total employment) (modeled ILO estimate)  \n",
+       "1151     (% of female employment) (modeled ILO estimate)  \n",
+       "1152       (% of male employment) (modeled ILO estimate)  \n",
+       "...                                                  ...  \n",
+       "1538535    (% of male employment) (modeled ILO estimate)  \n",
+       "1538536   (% of total employment) (modeled ILO estimate)  \n",
+       "1539524  (% of female employment) (modeled ILO estimate)  \n",
+       "1539525    (% of male employment) (modeled ILO estimate)  \n",
+       "1539526   (% of total employment) (modeled ILO estimate)  \n",
+       "\n",
+       "[4545 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_parent=Categorization2[Categorization2['Short indicator'].str.contains('Contributing family workers')]\n",
+    "two_parent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Moreover, there are some inidcators with an extra parenthesis adding some  more information. As this information isn't related with units, another column named as 'other specification' has been created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                             Access to electricity, rural   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units Other specification  \n",
+       "0              (% of population)                None  \n",
+       "1        (% of rural population)                None  \n",
+       "2        (% of urban population)                None  \n",
+       "3              (% of population)                None  \n",
+       "4        (% of rural population)                None  \n",
+       "...                          ...                 ...  \n",
+       "1540358            (current US$)                None  \n",
+       "1540359            (current US$)                None  \n",
+       "1540360            (current US$)                None  \n",
+       "1540361            (current US$)                None  \n",
+       "1540362            (current US$)                None  \n",
+       "\n",
+       "[1372098 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2[['Units','Other specification']]=Categorization2['Units'].str.split(\"\\) \", n=1,expand=True)\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the end of the variable name, separated by the last \",\" it is informing us about to which subgroup makes reference the variable. Thus, there are some indicators that have information divided for small groups. This information is shown as a new column named 'Subgroup'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "      <th>Subgroup</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>urban</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                             Access to electricity, rural   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units Other specification Subgroup  \n",
+       "0              (% of population)                None      NaN  \n",
+       "1        (% of rural population)                None    rural  \n",
+       "2        (% of urban population)                None    urban  \n",
+       "3              (% of population)                None      NaN  \n",
+       "4        (% of rural population)                None    rural  \n",
+       "...                          ...                 ...      ...  \n",
+       "1540358            (current US$)                None      NaN  \n",
+       "1540359            (current US$)                None      NaN  \n",
+       "1540360            (current US$)                None      NaN  \n",
+       "1540361            (current US$)                None      NaN  \n",
+       "1540362            (current US$)                None      NaN  \n",
+       "\n",
+       "[1372098 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2[['Subgroup']]=Categorization2['Short indicator'].str.extract(',(?P<field>[^,]*?)$')\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, information which is shown as a new column is deleted from the origin one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "      <th>Subgroup</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>urban</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                                    Access to electricity   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units Other specification Subgroup  \n",
+       "0              (% of population)                None      NaN  \n",
+       "1        (% of rural population)                None    rural  \n",
+       "2        (% of urban population)                None    urban  \n",
+       "3              (% of population)                None      NaN  \n",
+       "4        (% of rural population)                None    rural  \n",
+       "...                          ...                 ...      ...  \n",
+       "1540358            (current US$)                None      NaN  \n",
+       "1540359            (current US$)                None      NaN  \n",
+       "1540360            (current US$)                None      NaN  \n",
+       "1540361            (current US$)                None      NaN  \n",
+       "1540362            (current US$)                None      NaN  \n",
+       "\n",
+       "[1372098 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2['Short indicator']=Categorization2['Short indicator'].str.replace(',(?P<field>[^,]*?)$',\"\")\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the indicators don't have these elements. So, a checking point is needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Categorization2['Subgroup']=Categorization2['Subgroup'].replace(['None'],['total'])\n",
+    "Categorization2['Subgroup']=Categorization2['Subgroup'].fillna('total')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some duplicate variables which should be removed too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "      <th>Subgroup</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>urban</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1157276 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                                    Access to electricity   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units Other specification Subgroup  \n",
+       "0              (% of population)                None    total  \n",
+       "1        (% of rural population)                None    rural  \n",
+       "2        (% of urban population)                None    urban  \n",
+       "3              (% of population)                None    total  \n",
+       "4        (% of rural population)                None    rural  \n",
+       "...                          ...                 ...      ...  \n",
+       "1540358            (current US$)                None    total  \n",
+       "1540359            (current US$)                None    total  \n",
+       "1540360            (current US$)                None    total  \n",
+       "1540361            (current US$)                None    total  \n",
+       "1540362            (current US$)                None    total  \n",
+       "\n",
+       "[1157276 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2.drop_duplicates(subset=['Country','Year','Short indicator','Continent','Subgroup'], keep='first')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reordering columns, categorization3 is our df after all these division in categories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Subgroup</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>urban</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                  Access to electricity (% of population)   \n",
+       "4        Access to electricity, rural (% of rural popul...   \n",
+       "...                                                    ...   \n",
+       "1540358                                GDP (current US$)+3   \n",
+       "1540359                                GDP (current US$)+5   \n",
+       "1540360                                GDP (current US$)+8   \n",
+       "1540361                               GDP (current US$)+13   \n",
+       "1540362                               GDP (current US$)+21   \n",
+       "\n",
+       "                                           Short indicator      Value  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                                    Access to electricity   1.000000   \n",
+       "4                                    Access to electricity   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                              GDP+3  38.504955   \n",
+       "1540359                                              GDP+5  31.129362   \n",
+       "1540360                                              GDP+8  26.521259   \n",
+       "1540361                                             GDP+13  12.731623   \n",
+       "1540362                                             GDP+21   3.356853   \n",
+       "\n",
+       "        Subgroup                    Units Other specification  \n",
+       "0          total        (% of population)                None  \n",
+       "1          rural  (% of rural population)                None  \n",
+       "2          urban  (% of urban population)                None  \n",
+       "3          total        (% of population)                None  \n",
+       "4          rural  (% of rural population)                None  \n",
+       "...          ...                      ...                 ...  \n",
+       "1540358    total            (current US$)                None  \n",
+       "1540359    total            (current US$)                None  \n",
+       "1540360    total            (current US$)                None  \n",
+       "1540361    total            (current US$)                None  \n",
+       "1540362    total            (current US$)                None  \n",
+       "\n",
+       "[1372098 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2.rename(columns={Categorization2.columns[4]:'Value'},inplace=True)\n",
+    "Categorization3=Categorization2[['Country','Year','Continent','Indicator','Short indicator','Value','Subgroup','Units','Other specification']]\n",
+    "Categorization3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Subgroup</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Account ownership at a financial institution o...</td>\n",
+       "      <td>Account ownership at a financial institution o...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population ages 15+)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Adjusted net national income (current US$)</td>\n",
+       "      <td>Adjusted net national income</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Adjusted net national income per capita (curre...</td>\n",
+       "      <td>Adjusted net national income per capita</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>687830 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "6           DEU  1990    Europe   \n",
+       "20          DEU  1990    Europe   \n",
+       "23          DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "3                  Access to electricity (% of population)   \n",
+       "6        Account ownership at a financial institution o...   \n",
+       "20              Adjusted net national income (current US$)   \n",
+       "23       Adjusted net national income per capita (curre...   \n",
+       "...                                                    ...   \n",
+       "1540358                                GDP (current US$)+3   \n",
+       "1540359                                GDP (current US$)+5   \n",
+       "1540360                                GDP (current US$)+8   \n",
+       "1540361                               GDP (current US$)+13   \n",
+       "1540362                               GDP (current US$)+21   \n",
+       "\n",
+       "                                           Short indicator      Value  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                                    Access to electricity   1.000000   \n",
+       "6        Account ownership at a financial institution o...   1.000000   \n",
+       "20                            Adjusted net national income   1.000000   \n",
+       "23                 Adjusted net national income per capita   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                              GDP+3  38.504955   \n",
+       "1540359                                              GDP+5  31.129362   \n",
+       "1540360                                              GDP+8  26.521259   \n",
+       "1540361                                             GDP+13  12.731623   \n",
+       "1540362                                             GDP+21   3.356853   \n",
+       "\n",
+       "        Subgroup                       Units Other specification  \n",
+       "0          total           (% of population)                None  \n",
+       "3          total           (% of population)                None  \n",
+       "6          total  (% of population ages 15+)                None  \n",
+       "20         total               (current US$)                None  \n",
+       "23         total               (current US$)                None  \n",
+       "...          ...                         ...                 ...  \n",
+       "1540358    total               (current US$)                None  \n",
+       "1540359    total               (current US$)                None  \n",
+       "1540360    total               (current US$)                None  \n",
+       "1540361    total               (current US$)                None  \n",
+       "1540362    total               (current US$)                None  \n",
+       "\n",
+       "[687830 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization4=Categorization3.loc[Categorization3['Subgroup']=='total']\n",
+    "Categorization4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Categorization4.to_csv(os.getcwd()+'/Data/Categorization.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Correlation study"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the correlation study, we are going to check if the indicators are related in a relevantly way to GDP(current US$), through both Pearson and Spearman correlations and their respective p-values. Thus, our hypotheses are as follow:\n",
+    "\n",
+    "- H_0: the indicator and the GDP are uncorrelated.​\n",
+    "- H_1: the indicator and the GDP are correlated.​\n",
+    "\n",
+    "**If p-value < α then reject H_0 and accept H_1.​**\n",
+    "\n",
+    "P-value: is the probability of obtaining test results at least as extreme as the result actually observed. (Marging of the error)\n",
+    "\n",
+    "​Confidence level (α): probability that a population parameter will fall between a set of values for a certain proportion of times. α = 1 - p-value.\n",
+    "\n",
+    "​Significance level: probability of the study rejecting the null hypothesis when it is actually true.​ \n",
+    "\n",
+    "At the end we want to follow a process that checks if with Pearson and Spearman the correlation is relevant, as the image below ilustrates. \n",
+    "\n",
+    "![](https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/Spearman%20and%20pearson%20process.JPG)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Categorization4= pd.read_csv (os.getcwd()+'/Data/'+'Categorization.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "indicators_list=Categorization4['Indicator'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns=indicators_list+['Country','Year','Continent']\n",
+    "clist=Categorization4['Country'].unique()\n",
+    "common=['Unnamed: 0','Country','Year']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The correlation between two variables does not always need to be a linear one, since data of these variables can follow a different distribution that better adjusts. Therefore, we have computed four types (linear, quadratic, cubic and logarithmic) for each indicator in each country and we chose the one with the highest correlation. In the following cell, we have defined a function that will allow us to calculate the different posibilities of relations: cuadratic, cubic and logaritmic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def multcolumn(frame):\n",
+    "    for u in range(0, len(columns)-3):\n",
+    "        name=columns[u]+'¨l'\n",
+    "        name2=columns[u]+'¨^2'\n",
+    "        name3=columns[u]+'¨^3'\n",
+    "        namelog=columns[u]+'¨log'\n",
+    "        frame.loc[:,name2] = frame[columns[u]]**2\n",
+    "        frame.loc[:,name3] = frame[columns[u]]**3\n",
+    "        frame.loc[:,namelog] = np.log(frame[columns[u]])\n",
+    "        frame.rename(columns={columns[u]:name}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Moreover, we want to know the correlation between all the variables, so to acomplish this, we have created the following loop, which will help us create a new dataframe where we will have: the *Indicator*, the *Type* of relation, the value of the *R^2*, its *Behaviour*, the *Country* and the *Continent*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= pd.read_csv (os.getcwd()+'/Data/'+'GoldenDataFrame.csv')\n",
+    "df_study=df[[c for c in df.columns if c in columns]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "multcolumn(df_study)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Firstly we are going to create two lists for the variables, which their level of confidence for each correlation, so later on, we can calculate only the correlations of those variables. Which will be filtered by the values that we want from the following sliders. The predetermined values for each case are 0.05 for the values and 0.75 for them to be a relevant correlations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6434ddd4744e467a83205b4cae63765e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.05, continuous_update=False, description='Level of confidence (Pearson):', max=1.0, step=0…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6dee10d62ff4aa9b999eee272823db6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.75, continuous_update=False, description='Relevant correlation (Pearson):', max=1.0, step=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca03a412b61b4158971b64365a74db1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.05, continuous_update=False, description='Level of confidence (Spearman):', max=1.0, step=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa5db6230c794dc9804df49d39aa197b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.75, continuous_update=False, description='Relevant correlation (Spearman):', max=1.0, step…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "apv=widgets.FloatSlider(value=0.05,min=0,max=1.0,step=0.025,description='Level of confidence (Pearson):',disabled=False,continuous_update=False,orientation='horizontal',readout=True)\n",
+    "cpv=widgets.FloatSlider( value=0.05, min=0, max=1.0, step=0.05, description='Level of confidence (Spearman):', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "bcor=widgets.FloatSlider( value=0.75, min=0, max=1.0, step=0.05, description='Relevant correlation (Pearson):', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "dcor=widgets.FloatSlider( value=0.75, min=0, max=1.0, step=0.05, description='Relevant correlation (Spearman):', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "display(apv,bcor,cpv,dcor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following block, we check the hypotheses set before and extract only the variables that deny H_0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dat=df_study.loc[df_study.loc[:, 'Country'] == clist[0]]\n",
+    "listacorpe=[]\n",
+    "listacorsp=[]\n",
+    "clmns=dat.columns.values.tolist()\n",
+    "dat.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "for c in range(0, len(clmns)):\n",
+    "    if dat[clmns[c]].isna().sum()>=1:\n",
+    "        del(dat[clmns[c]])\n",
+    "pilares=dat.columns.values.tolist()\n",
+    "for u in range(0,len(pilares)):\n",
+    "    if is_numeric_dtype(dat[pilares[u]]):\n",
+    "        correlation, pvalue=pearsonr(dat[pilares[u]], dat['GDP (current US$)¨l'])\n",
+    "        if pvalue<=apv.value:\n",
+    "            listacorpe.append(pilares[u])\n",
+    "        else:\n",
+    "            pass\n",
+    "        correlation, pvalue=spearmanr(dat[pilares[u]], dat['GDP (current US$)¨l'])\n",
+    "        if pvalue<=cpv.value:\n",
+    "            listacorsp.append(pilares[u])\n",
+    "        else:\n",
+    "            pass\n",
+    "    else:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Secondly, we need to calculate the correlation table for each country, therefore we use the basic function `corr()` which provides either the Pearson correlation table or the Spearman correlation table, as well as a filter for the countries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dat=df_study.loc[df_study.loc[:, 'Country'] == clist[0]]\n",
+    "\n",
+    "datp=dat[dat.columns[dat.columns.isin(listacorpe)]]\n",
+    "corp=datp.corr('pearson')\n",
+    "\n",
+    "datsp=dat[dat.columns[dat.columns.isin(listacorsp)]]\n",
+    "cors=datsp.corr('spearman')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we calculate the coefficient of determination which is the correlation squared."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corp.loc[:,'R^2 Pearson'] = corp['GDP (current US$)¨l']**2\n",
+    "\n",
+    "cors.loc[:,'R^2 Spearman'] = cors['GDP (current US$)¨l']**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Moreover, we are going to create new columns to know which *Indicator* are we talking about, and the *Type* of correlation that is being analyzed (linear, cuadratic, cubic or logarithmic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corp.loc[:,'Indicator']=corp.index\n",
+    "corp[['Indicator','Type']]=corp.Indicator.str.split('¨',1, expand=True)\n",
+    "\n",
+    "cors.loc[:,'Indicator']=cors.index\n",
+    "cors[['Indicator','Type']]=cors.Indicator.str.split('¨',1, expand=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can apply the filter we have consider that is enough, selected on the slider. If is not varied it is R^2>=0.75 to filter the correlations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpcolumn=corp[['Indicator','R^2 Pearson','Type','GDP (current US$)¨l']]\n",
+    "corpcolumn=corpcolumn.loc[corpcolumn.loc[:, 'R^2 Pearson'] >=bcor.value]\n",
+    "\n",
+    "corscolumn=cors[['Indicator','R^2 Spearman','Type','GDP (current US$)¨l']]\n",
+    "corscolumn=corscolumn.loc[corscolumn.loc[:, 'R^2 Spearman'] >= dcor.value]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Furthermore, we add all the columns that we have created into a data frame, thanks to the following cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idp=corpcolumn.groupby('Indicator')['R^2 Pearson'].transform(max)==corpcolumn['R^2 Pearson']\n",
+    "corpcolumn[idp]\n",
+    "maxp_df=pd.DataFrame(corpcolumn[idp])\n",
+    "\n",
+    "ids=corscolumn.groupby('Indicator')['R^2 Spearman'].transform(max)==corscolumn['R^2 Spearman']\n",
+    "corscolumn[ids]\n",
+    "maxs_df=pd.DataFrame(corscolumn[ids])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we conmute the values, by expressions. For example if the correlation is positive, we want in the new column called *Behaviour* the word Positive. Or for the *Type* column if the greatest correlation is cuadratic we want to put, Cuadratic. We also add the country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maxp_df['Behaviour']=np.where(maxp_df['GDP (current US$)¨l']>0, 'Positive', 'Negative')\n",
+    "maxp_df['Type']=maxp_df['Type'].replace(['l','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
+    "maxp_df['Country']= clist[0]\n",
+    "\n",
+    "maxs_df['Behaviour']=np.where(maxs_df['GDP (current US$)¨l']>0, 'Positive', 'Negative')\n",
+    "maxs_df['Type']=maxs_df['Type'].replace(['l','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
+    "maxs_df['Country']= clist[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition, we also drop the columns which do not add any value, as *GDP*, *Year*, and *Unnamed:0*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maxp_df.drop(\"GDP (current US$)¨l\",axis=1,inplace=True)\n",
+    "maxp_df=maxp_df.reset_index(drop=True)\n",
+    "maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='Year'].index)\n",
+    "maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='GDP (current US$)'].index)\n",
+    "maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='Unnamed: 0'].index)\n",
+    "\n",
+    "maxs_df.drop(\"GDP (current US$)¨l\",axis=1,inplace=True)\n",
+    "maxs_df=maxs_df.reset_index(drop=True)\n",
+    "maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Year'].index)\n",
+    "maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='GDP (current US$)'].index)\n",
+    "maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Unnamed: 0'].index)\n",
+    "maxs_df=maxs_df.sort_values(by = 'R^2 Spearman',ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And finally we sort the values in descending order by the column *R^2 Pearson*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maxp_df_deu=maxp_df.sort_values(by = 'R^2 Pearson',ascending = False)\n",
+    "pearsondf= maxp_df_deu\n",
+    "spearmandf=maxs_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, we can do it with all the countries and create just one dataframe, where we match both data frames, the Pearson and the Spearman, only where there is a case in both sides. Meaning if there is a relevant correlation for Country x in Variable y for Pearson, it will only appear if there is also a relevant correlation for Country x in Variable y for Spearman."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>R^2 Pearson</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adjusted net national income (current US$)</td>\n",
+       "      <td>0.996519</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.999141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Gross value added at basic prices (GVA) (curre...</td>\n",
+       "      <td>0.996519</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.999851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>GNI (current US$)</td>\n",
+       "      <td>0.996337</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.999362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Gross national expenditure (current US$)</td>\n",
+       "      <td>0.990490</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.997181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Final consumption expenditure (current US$)</td>\n",
+       "      <td>0.988302</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.996540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5965</th>\n",
+       "      <td>Prevalence of anemia among women of reproducti...</td>\n",
+       "      <td>0.793298</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.752969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5966</th>\n",
+       "      <td>Logistics performance index: Ability to track ...</td>\n",
+       "      <td>0.788049</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.926947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5967</th>\n",
+       "      <td>Logistics performance index: Competence and qu...</td>\n",
+       "      <td>0.788049</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.890297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5968</th>\n",
+       "      <td>Out-of-pocket expenditure (% of current health...</td>\n",
+       "      <td>0.782573</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.929883</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5969</th>\n",
+       "      <td>Domestic general government health expenditure...</td>\n",
+       "      <td>0.758910</td>\n",
+       "      <td>Cubic</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.797637</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5970 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Indicator  R^2 Spearman  \\\n",
+       "0            Adjusted net national income (current US$)      0.996519   \n",
+       "1     Gross value added at basic prices (GVA) (curre...      0.996519   \n",
+       "2                                     GNI (current US$)      0.996337   \n",
+       "3              Gross national expenditure (current US$)      0.990490   \n",
+       "4           Final consumption expenditure (current US$)      0.988302   \n",
+       "...                                                 ...           ...   \n",
+       "5965  Prevalence of anemia among women of reproducti...      0.793298   \n",
+       "5966  Logistics performance index: Ability to track ...      0.788049   \n",
+       "5967  Logistics performance index: Competence and qu...      0.788049   \n",
+       "5968  Out-of-pocket expenditure (% of current health...      0.782573   \n",
+       "5969  Domestic general government health expenditure...      0.758910   \n",
+       "\n",
+       "             Type Behaviour Country  R^2 Pearson  \n",
+       "0          Linear  Positive     DEU     0.999141  \n",
+       "1          Linear  Positive     DEU     0.999851  \n",
+       "2          Linear  Positive     DEU     0.999362  \n",
+       "3          Linear  Positive     DEU     0.997181  \n",
+       "4          Linear  Positive     DEU     0.996540  \n",
+       "...           ...       ...     ...          ...  \n",
+       "5965  Logarithmic  Negative     CHN     0.752969  \n",
+       "5966  Logarithmic  Positive     CHN     0.926947  \n",
+       "5967  Logarithmic  Positive     CHN     0.890297  \n",
+       "5968  Logarithmic  Negative     CHN     0.929883  \n",
+       "5969        Cubic  Positive     CHN     0.797637  \n",
+       "\n",
+       "[5970 rows x 6 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pearsondf\n",
+    "spearmandf\n",
+    "for i in range(1,len(clist)):\n",
+    "    dat=df_study.loc[df_study.loc[:, 'Country'] == clist[i]]\n",
+    "    listacorpe=[]\n",
+    "    listacorsp=[]\n",
+    "    clmns=dat.columns.values.tolist()\n",
+    "    dat.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "    for c in range(0, len(clmns)):\n",
+    "        if dat[clmns[c]].isna().sum()>=1:\n",
+    "            del(dat[clmns[c]])\n",
+    "    pilares=dat.columns.values.tolist()\n",
+    "    for u in range(0,len(pilares)):\n",
+    "        if is_numeric_dtype(dat[pilares[u]]):\n",
+    "            correlation, pvalue=pearsonr(dat[pilares[u]], dat['GDP (current US$)¨l'])\n",
+    "            if pvalue<=apv.value:\n",
+    "                listacorpe.append(pilares[u])\n",
+    "            else:\n",
+    "                pass\n",
+    "            correlation, pvalue=spearmanr(dat[pilares[u]], dat['GDP (current US$)¨l'])\n",
+    "            if pvalue<=cpv.value:\n",
+    "                listacorsp.append(pilares[u])\n",
+    "            else:\n",
+    "                pass\n",
+    "        else:\n",
+    "            pass\n",
+    "    \n",
+    "    dat=df_study.loc[df_study.loc[:, 'Country'] == clist[i]]\n",
+    "\n",
+    "    datp=dat[dat.columns[dat.columns.isin(listacorpe)]]\n",
+    "    corp=datp.corr('pearson')\n",
+    "\n",
+    "    datsp=dat[dat.columns[dat.columns.isin(listacorsp)]]\n",
+    "    cors=datsp.corr('spearman')\n",
+    "\n",
+    "\n",
+    "    corp.loc[:,'R^2 Pearson'] = corp['GDP (current US$)¨l']**2\n",
+    "\n",
+    "    cors.loc[:,'R^2 Spearman'] = cors['GDP (current US$)¨l']**2\n",
+    "\n",
+    "\n",
+    "    corp.loc[:,'Indicator']=corp.index\n",
+    "    corp[['Indicator','Type']]=corp.Indicator.str.split('¨',1, expand=True)\n",
+    "\n",
+    "    cors.loc[:,'Indicator']=cors.index\n",
+    "    cors[['Indicator','Type']]=cors.Indicator.str.split('¨',1, expand=True)\n",
+    "\n",
+    "\n",
+    "    corpcolumn=corp[['Indicator','R^2 Pearson','Type','GDP (current US$)¨l']]\n",
+    "    corpcolumn=corpcolumn.loc[corpcolumn.loc[:, 'R^2 Pearson'] >= bcor.value]\n",
+    "    \n",
+    "    corscolumn=cors[['Indicator','R^2 Spearman','Type','GDP (current US$)¨l']]\n",
+    "    corscolumn=corscolumn.loc[corscolumn.loc[:, 'R^2 Spearman'] >= dcor.value]\n",
+    "\n",
+    "\n",
+    "    idp=corpcolumn.groupby('Indicator')['R^2 Pearson'].transform(max)==corpcolumn['R^2 Pearson']\n",
+    "    corpcolumn[idp]\n",
+    "    maxp_df=pd.DataFrame(corpcolumn[idp])\n",
+    "\n",
+    "    ids=corscolumn.groupby('Indicator')['R^2 Spearman'].transform(max)==corscolumn['R^2 Spearman']\n",
+    "    corscolumn[ids]\n",
+    "    maxs_df=pd.DataFrame(corscolumn[ids])\n",
+    "\n",
+    "\n",
+    "    maxp_df['Behaviour']=np.where(maxp_df['GDP (current US$)¨l']>0, 'Positive', 'Negative')\n",
+    "    maxp_df['Type']=maxp_df['Type'].replace(['l','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
+    "    maxp_df['Country']= clist[i]\n",
+    "\n",
+    "    maxs_df['Behaviour']=np.where(maxs_df['GDP (current US$)¨l']>0, 'Positive', 'Negative')\n",
+    "    maxs_df['Type']=maxs_df['Type'].replace(['l','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
+    "    maxs_df['Country']= clist[i]\n",
+    "\n",
+    "\n",
+    "    maxp_df.drop(\"GDP (current US$)¨l\",axis=1,inplace=True)\n",
+    "    maxp_df=maxp_df.reset_index(drop=True)\n",
+    "    maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='Year'].index)\n",
+    "    maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='GDP (current US$)'].index)\n",
+    "    maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='Unnamed: 0'].index)\n",
+    "\n",
+    "    maxs_df.drop(\"GDP (current US$)¨l\",axis=1,inplace=True)\n",
+    "    maxs_df=maxs_df.reset_index(drop=True)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Year'].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='GDP (current US$)'].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Unnamed: 0'].index)\n",
+    "    maxs_df=maxs_df.sort_values(by = 'R^2 Spearman',ascending = False)\n",
+    "\n",
+    "\n",
+    "    maxp_df=maxp_df.sort_values(by = 'R^2 Pearson',ascending = False)\n",
+    "    pearsondf=pd.concat((pearsondf, maxp_df), axis = 0)\n",
+    "    spearmandf=pd.concat((spearmandf, maxs_df), axis = 0)\n",
+    "\n",
+    "corrtable=spearmandf.merge(pearsondf, left_on=('Indicator', 'Country','Type','Behaviour'), right_on=('Indicator', 'Country','Type','Behaviour'))\n",
+    "display(corrtable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, a table has been created showing the number of times a variable has a high relationship in our 48 countries. These that appear many times will be interesting for us to draw conclusions. Then, we will checck if they are primary or seconday variable type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columnssf=corrtable.Indicator.to_list()\n",
+    "columnsf=np.unique(columnssf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Number of times repeated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>GDP per capita (current US$)</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Industry (including construction), value added...</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>GNI (current US$)</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>Households and NPISHs Final consumption expend...</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>Final consumption expenditure (current US$)</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>Net official development assistance and offici...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>Net capital account (BoP, current US$)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>Natural gas rents (% of GDP)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Arms exports (SIPRI trend indicator values)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>Gross fixed capital formation (% of GDP)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>405 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             Indicator  \\\n",
+       "127                       GDP per capita (current US$)   \n",
+       "178  Industry (including construction), value added...   \n",
+       "132                                  GNI (current US$)   \n",
+       "152  Households and NPISHs Final consumption expend...   \n",
+       "108        Final consumption expenditure (current US$)   \n",
+       "..                                                 ...   \n",
+       "250  Net official development assistance and offici...   \n",
+       "248             Net capital account (BoP, current US$)   \n",
+       "246                       Natural gas rents (% of GDP)   \n",
+       "29         Arms exports (SIPRI trend indicator values)   \n",
+       "142           Gross fixed capital formation (% of GDP)   \n",
+       "\n",
+       "     Number of times repeated  \n",
+       "127                        46  \n",
+       "178                        46  \n",
+       "132                        46  \n",
+       "152                        45  \n",
+       "108                        45  \n",
+       "..                        ...  \n",
+       "250                         1  \n",
+       "248                         1  \n",
+       "246                         1  \n",
+       "29                          1  \n",
+       "142                         1  \n",
+       "\n",
+       "[405 rows x 2 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "\n",
+    "df_indicators = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Indicator','Number of times repeated'])\n",
+    "df_indicators=df_indicators.sort_values(by = 'Number of times repeated',ascending = False)\n",
+    "df_indicators\n",
+    "display(df_indicators)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#To get list of all number of times repeated.\n",
+    "#from IPython.display import HTML\n",
+    "\n",
+    "#HTML(df_indicators.to_html(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While for the temporal diferences, we want to know which works better in each case, the process to be followed is described in the following picture.\n",
+    "\n",
+    "![](https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/Temporal%20process.JPG)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= pd.read_csv (os.getcwd()+'/Data/'+'GoldenDataFrame.csv')\n",
+    "df_study=df[[c for c in df.columns if c in columns]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moveddf=pd.DataFrame()\n",
+    "dat=df_study.loc[df_study.loc[:, 'Country'] == clist[0]]\n",
+    "clmns=dat.columns.values.tolist()\n",
+    "dat.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "tempdiffs=['GDP (current US$)+1','GDP (current US$)+2','GDP (current US$)+3','GDP (current US$)+5','GDP (current US$)+8','GDP (current US$)+13','GDP (current US$)+21']\n",
+    "cors=dat.corr('spearman')\n",
+    "for f in range(0, len(tempdiffs)):\n",
+    "    cors.loc[:,'R^2 Spearman'] = cors[tempdiffs[f]]**2\n",
+    "    cors.loc[:,'Indicator']=cors.index\n",
+    "    corscolumn=cors[['Indicator','R^2 Spearman',tempdiffs[f]]]\n",
+    "    corscolumn=corscolumn.loc[corscolumn.loc[:, 'R^2 Spearman'] >= dcor.value]\n",
+    "    ids=corscolumn.groupby('Indicator')['R^2 Spearman'].transform(max)==corscolumn['R^2 Spearman']\n",
+    "    corscolumn[ids]\n",
+    "    maxs_df=pd.DataFrame(corscolumn[ids])\n",
+    "    maxs_df['Behaviour']=np.where(maxs_df[tempdiffs[f]]>0, 'Positive', 'Negative')\n",
+    "    maxs_df['Country']= clist[0]\n",
+    "    maxs_df[['Variable','Moved']]=tempdiffs[f].split('+')\n",
+    "    maxs_df.drop(tempdiffs[f],axis=1,inplace=True)\n",
+    "    maxs_df.drop(columns='Variable',inplace=True)\n",
+    "    maxs_df=maxs_df.reset_index(drop=True)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Year'].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='GDP (current US$)'].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator'].isin(tempdiffs)].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Unnamed: 0'].index)\n",
+    "    maxs_df=maxs_df.sort_values(by = 'R^2 Spearman',ascending = False)\n",
+    "    moveddf=pd.concat((moveddf,maxs_df),axis=0)\n",
+    "ids=moveddf.groupby('Indicator')['R^2 Spearman'].transform(max)==moveddf['R^2 Spearman']\n",
+    "moveddf[ids]\n",
+    "moveddf=pd.DataFrame(moveddf[ids])\n",
+    "temporaldf=moveddf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adjusted savings: education expenditure (curre...</td>\n",
+       "      <td>0.824514</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>General government final consumption expenditu...</td>\n",
+       "      <td>0.822917</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Individuals using the Internet (% of population)</td>\n",
+       "      <td>0.822917</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>Surface area (sq. km)</td>\n",
+       "      <td>0.821642</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.817804</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>Pump price for diesel fuel (US$ per liter)</td>\n",
+       "      <td>0.787248</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>Pump price for gasoline (US$ per liter)</td>\n",
+       "      <td>0.787248</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Chemicals (% of value added in manufacturing)</td>\n",
+       "      <td>0.781385</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>Proportion of population pushed below the $1.9...</td>\n",
+       "      <td>0.769754</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Depth of credit information index (0=low to 8=...</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9700 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             Indicator  R^2 Spearman  \\\n",
+       "4    Adjusted savings: education expenditure (curre...      0.824514   \n",
+       "35   General government final consumption expenditu...      0.822917   \n",
+       "45    Individuals using the Internet (% of population)      0.822917   \n",
+       "78                               Surface area (sq. km)      0.821642   \n",
+       "5    Adolescent fertility rate (births per 1,000 wo...      0.817804   \n",
+       "..                                                 ...           ...   \n",
+       "167         Pump price for diesel fuel (US$ per liter)      0.787248   \n",
+       "168            Pump price for gasoline (US$ per liter)      0.787248   \n",
+       "30       Chemicals (% of value added in manufacturing)      0.781385   \n",
+       "162  Proportion of population pushed below the $1.9...      0.769754   \n",
+       "38   Depth of credit information index (0=low to 8=...      0.750000   \n",
+       "\n",
+       "    Behaviour Country Moved  \n",
+       "4    Positive     DEU     1  \n",
+       "35   Positive     DEU     1  \n",
+       "45   Positive     DEU     1  \n",
+       "78   Positive     DEU     1  \n",
+       "5    Negative     DEU     1  \n",
+       "..        ...     ...   ...  \n",
+       "167  Negative     CHN    21  \n",
+       "168  Negative     CHN    21  \n",
+       "30   Negative     CHN    21  \n",
+       "162  Positive     CHN    21  \n",
+       "38   Positive     CHN    21  \n",
+       "\n",
+       "[9700 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for i in range(1,len(clist)):\n",
+    "    dat=df_study.loc[df_study.loc[:, 'Country'] == clist[i]]\n",
+    "    clmns=dat.columns.values.tolist()\n",
+    "    dat.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "    tempdiffs=['GDP (current US$)+1','GDP (current US$)+2','GDP (current US$)+3','GDP (current US$)+5','GDP (current US$)+8','GDP (current US$)+13','GDP (current US$)+21']\n",
+    "    cors=dat.corr('spearman')\n",
+    "    moveddf=pd.DataFrame()\n",
+    "    for f in range(0, len(tempdiffs)):\n",
+    "        cors.loc[:,'R^2 Spearman'] = cors[tempdiffs[f]]**2\n",
+    "        cors.loc[:,'Indicator']=cors.index\n",
+    "        corscolumn=cors[['Indicator','R^2 Spearman',tempdiffs[f]]]\n",
+    "        corscolumn=corscolumn.loc[corscolumn.loc[:, 'R^2 Spearman'] >= dcor.value]\n",
+    "        ids=corscolumn.groupby('Indicator')['R^2 Spearman'].transform(max)==corscolumn['R^2 Spearman']\n",
+    "        corscolumn[ids]\n",
+    "        maxs_df=pd.DataFrame(corscolumn[ids])\n",
+    "        maxs_df['Behaviour']=np.where(maxs_df[tempdiffs[f]]>0, 'Positive', 'Negative')\n",
+    "        maxs_df['Country']= clist[i]\n",
+    "        maxs_df[['Variable','Moved']]=tempdiffs[f].split('+')\n",
+    "        maxs_df.drop(tempdiffs[f],axis=1,inplace=True)\n",
+    "        maxs_df.drop(columns='Variable',inplace=True)\n",
+    "        maxs_df=maxs_df.reset_index(drop=True)\n",
+    "        maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Year'].index)\n",
+    "        maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator'].isin(tempdiffs)].index)\n",
+    "        maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Unnamed: 0'].index)\n",
+    "        maxs_df=maxs_df.sort_values(by = 'R^2 Spearman',ascending = False)\n",
+    "        moveddf=pd.concat((moveddf,maxs_df),axis=0)\n",
+    "    ids=moveddf.groupby('Indicator')['R^2 Spearman'].transform(max)==moveddf['R^2 Spearman']\n",
+    "    moveddf[ids]\n",
+    "    moveddf=pd.DataFrame(moveddf[ids])\n",
+    "    temporaldf=pd.concat((temporaldf,moveddf),axis=0)\n",
+    "temporaldf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adjusted savings: education expenditure (curre...</td>\n",
+       "      <td>0.824514</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>General government final consumption expenditu...</td>\n",
+       "      <td>0.822917</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Individuals using the Internet (% of population)</td>\n",
+       "      <td>0.822917</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>Surface area (sq. km)</td>\n",
+       "      <td>0.821642</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.817804</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5965</th>\n",
+       "      <td>Prevalence of anemia among women of reproducti...</td>\n",
+       "      <td>0.793298</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5966</th>\n",
+       "      <td>Logistics performance index: Ability to track ...</td>\n",
+       "      <td>0.788049</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5967</th>\n",
+       "      <td>Logistics performance index: Competence and qu...</td>\n",
+       "      <td>0.788049</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5968</th>\n",
+       "      <td>Out-of-pocket expenditure (% of current health...</td>\n",
+       "      <td>0.782573</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5969</th>\n",
+       "      <td>Domestic general government health expenditure...</td>\n",
+       "      <td>0.758910</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Cubic</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15670 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Indicator  R^2 Spearman  \\\n",
+       "4     Adjusted savings: education expenditure (curre...      0.824514   \n",
+       "35    General government final consumption expenditu...      0.822917   \n",
+       "45     Individuals using the Internet (% of population)      0.822917   \n",
+       "78                                Surface area (sq. km)      0.821642   \n",
+       "5     Adolescent fertility rate (births per 1,000 wo...      0.817804   \n",
+       "...                                                 ...           ...   \n",
+       "5965  Prevalence of anemia among women of reproducti...      0.793298   \n",
+       "5966  Logistics performance index: Ability to track ...      0.788049   \n",
+       "5967  Logistics performance index: Competence and qu...      0.788049   \n",
+       "5968  Out-of-pocket expenditure (% of current health...      0.782573   \n",
+       "5969  Domestic general government health expenditure...      0.758910   \n",
+       "\n",
+       "     Behaviour Country           Moved            Type  \n",
+       "4     Positive     DEU               1  Does not apply  \n",
+       "35    Positive     DEU               1  Does not apply  \n",
+       "45    Positive     DEU               1  Does not apply  \n",
+       "78    Positive     DEU               1  Does not apply  \n",
+       "5     Negative     DEU               1  Does not apply  \n",
+       "...        ...     ...             ...             ...  \n",
+       "5965  Negative     CHN  Does not apply     Logarithmic  \n",
+       "5966  Positive     CHN  Does not apply     Logarithmic  \n",
+       "5967  Positive     CHN  Does not apply     Logarithmic  \n",
+       "5968  Negative     CHN  Does not apply     Logarithmic  \n",
+       "5969  Positive     CHN  Does not apply           Cubic  \n",
+       "\n",
+       "[15670 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alist=['Indicator','R^2 Spearman','Behaviour','Country','Type']\n",
+    "forcomparassion=corrtable[alist]\n",
+    "\n",
+    "quarterfinal=pd.concat((temporaldf,forcomparassion),axis=0)\n",
+    "quarterfinal.fillna('Does not apply',inplace=True)\n",
+    "quarterfinal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quarterfinal.to_csv(os.getcwd()+'/Data/Quarterfinal.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quarterfinal= pd.read_csv (os.getcwd()+'/Data/'+'Quarterfinal.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#To understand better the data, we categorize it (Area label and Primary/Secondary).\n",
+    "categories= pd.read_excel (os.getcwd()+'/Data/'+'dfindicators - Copy.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categories.rename(columns={'Type':'Group'}, inplace=True)\n",
+    "quarterfinal.drop(columns=('Unnamed: 0'), inplace=True)\n",
+    "clist=quarterfinal['Country'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To sum up, we compare both of the highest correlations and decide which is best, by comparing the R^2. So the process of searching for correlations is as follows.\n",
+    "\n",
+    "![](https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/Final%20table%20process.JPG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Number of times repeated_x</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Level</th>\n",
+       "      <th>Number of times repeated_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.817804</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.759333</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.936963</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>GBR</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.786708</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>HRV</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.924056</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>POL</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6640</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.839228</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>PER</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6641</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.792184</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>VEN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6642</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.840648</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>COL</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6643</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.963636</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>PAN</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6644</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.789474</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CRI</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6645 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Indicator  R^2 Spearman  \\\n",
+       "0     Adolescent fertility rate (births per 1,000 wo...      0.817804   \n",
+       "1     Adolescent fertility rate (births per 1,000 wo...      0.759333   \n",
+       "2     Adolescent fertility rate (births per 1,000 wo...      0.936963   \n",
+       "3     Adolescent fertility rate (births per 1,000 wo...      0.786708   \n",
+       "4     Adolescent fertility rate (births per 1,000 wo...      0.924056   \n",
+       "...                                                 ...           ...   \n",
+       "6640             Completeness of birth registration (%)      0.839228   \n",
+       "6641             Completeness of birth registration (%)      0.792184   \n",
+       "6642             Completeness of birth registration (%)      0.840648   \n",
+       "6643             Completeness of birth registration (%)      0.963636   \n",
+       "6644             Completeness of birth registration (%)      0.789474   \n",
+       "\n",
+       "     Behaviour Country Moved            Type Continent  \\\n",
+       "0     Negative     DEU     1  Does not apply    Europe   \n",
+       "1     Negative     SWE     3  Does not apply    Europe   \n",
+       "2     Negative     GBR    13  Does not apply    Europe   \n",
+       "3     Negative     HRV     8  Does not apply    Europe   \n",
+       "4     Negative     POL    21  Does not apply    Europe   \n",
+       "...        ...     ...   ...             ...       ...   \n",
+       "6640  Positive     PER     5  Does not apply     Latam   \n",
+       "6641  Negative     VEN     1  Does not apply     Latam   \n",
+       "6642  Positive     COL     2  Does not apply     Latam   \n",
+       "6643  Positive     PAN    21  Does not apply     Latam   \n",
+       "6644  Positive     CRI    13  Does not apply     Latam   \n",
+       "\n",
+       "      Number of times repeated_x       Group      Level  \\\n",
+       "0                             22  Demography  secondary   \n",
+       "1                             22  Demography  secondary   \n",
+       "2                             22  Demography  secondary   \n",
+       "3                             22  Demography  secondary   \n",
+       "4                             22  Demography  secondary   \n",
+       "...                          ...         ...        ...   \n",
+       "6640                          11   Demoraphy  secondary   \n",
+       "6641                          11   Demoraphy  secondary   \n",
+       "6642                          11   Demoraphy  secondary   \n",
+       "6643                          11   Demoraphy  secondary   \n",
+       "6644                          11   Demoraphy  secondary   \n",
+       "\n",
+       "      Number of times repeated_y  \n",
+       "0                             40  \n",
+       "1                             40  \n",
+       "2                             40  \n",
+       "3                             40  \n",
+       "4                             40  \n",
+       "...                          ...  \n",
+       "6640                          21  \n",
+       "6641                          21  \n",
+       "6642                          21  \n",
+       "6643                          21  \n",
+       "6644                          21  \n",
+       "\n",
+       "[6645 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final=pd.DataFrame()\n",
+    "for i in range(0,len(clist)):\n",
+    "    dat=quarterfinal.loc[quarterfinal.loc[:, 'Country'] == clist[i]]\n",
+    "    ids=dat.groupby('Indicator')['R^2 Spearman'].transform(max)==dat['R^2 Spearman']\n",
+    "    dat[ids]\n",
+    "    semifinal=pd.DataFrame(dat[ids])\n",
+    "    final=pd.concat((final,semifinal), axis=0)\n",
+    "final_indicators_list=categories.Indicator.unique()\n",
+    "final['Continent']=final['Country'].map(all_countries)\n",
+    "final=final.loc[final.loc[:, 'Indicator'].isin(np.array(final_indicators_list))]\n",
+    "final=pd.merge(final,categories, left_on='Indicator',right_on='Indicator')\n",
+    "\n",
+    "columnssf=final.Indicator.to_list()\n",
+    "columnsf=np.unique(columnssf)\n",
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "final_indicators = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Indicator','Number of times repeated'])\n",
+    "\n",
+    "final=pd.merge(final,final_indicators, left_on='Indicator',right_on='Indicator')\n",
+    "final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The former table final collects the indicators that have shown a high correlation with the GDP (according to the criteria established, see above). It provides detail of each case specifying the numerical value of the correlation (R^2 Spearman); the alignment with the GDP (Behaviour); the country and continent where it applies; if there is any temporal displacement; the group of interest it belongs to and the number of times repeated.\n",
+    "Moreover, if we focus over the column of \"Number of times repeated\". It reflects the number of times a high correlation of each indicator is repeated over the whole sample (aggregating all countries).\n",
+    "\n",
+    "In addition we have used the `itables` library to if wanted any one can search through the table as they wish."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"module\">\n",
+       "  window.__itables_render = async (table_id, dt_args) => {\n",
+       "    await initializeDataTables();\n",
+       "    $(table_id).DataTable(dt_args);\n",
+       "  };\n",
+       "\n",
+       "  async function initializeDataTables() {\n",
+       "    if ($.prototype.DataTable) {\n",
+       "      return;\n",
+       "    }\n",
+       "    const dt = (await import(\"data:text/javascript;base64,/*! DataTables 1.12.1
 * ©2008-2022 SpryMedia Ltd - datatables.net/license
 */

// ESM export of DataTables
export default function ($, root) {
	if (! root) {
		root = window;
	}

	if (! $) {
		$ = window.jQuery;
	}

	
	var DataTable = function ( selector, options )
	{
		// When creating with `new`, create a new DataTable, returning the API instance
		if (this instanceof DataTable) {
			return $(selector).DataTable(options);
		}
		else {
			// Argument switching
			options = selector;
		}
	
		/**
		 * Perform a jQuery selector action on the table's TR elements (from the tbody) and
		 * return the resulting jQuery object.
		 *  @param {string|node|jQuery} sSelector jQuery selector or node collection to act on
		 *  @param {object} [oOpts] Optional parameters for modifying the rows to be included
		 *  @param {string} [oOpts.filter=none] Select TR elements that meet the current filter
		 *    criterion ("applied") or all TR elements (i.e. no filter).
		 *  @param {string} [oOpts.order=current] Order of the TR elements in the processed array.
		 *    Can be either 'current', whereby the current sorting of the table is used, or
		 *    'original' whereby the original order the data was read into the table is used.
		 *  @param {string} [oOpts.page=all] Limit the selection to the currently displayed page
		 *    ("current") or not ("all"). If 'current' is given, then order is assumed to be
		 *    'current' and filter is 'applied', regardless of what they might be given as.
		 *  @returns {object} jQuery object, filtered by the given selector.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Highlight every second row
		 *      oTable.$('tr:odd').css('backgroundColor', 'blue');
		 *    } );
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Filter to rows with 'Webkit' in them, add a background colour and then
		 *      // remove the filter, thus highlighting the 'Webkit' rows only.
		 *      oTable.fnFilter('Webkit');
		 *      oTable.$('tr', {"search": "applied"}).css('backgroundColor', 'blue');
		 *      oTable.fnFilter('');
		 *    } );
		 */
		this.$ = function ( sSelector, oOpts )
		{
			return this.api(true).$( sSelector, oOpts );
		};
		
		
		/**
		 * Almost identical to $ in operation, but in this case returns the data for the matched
		 * rows - as such, the jQuery selector used should match TR row nodes or TD/TH cell nodes
		 * rather than any descendants, so the data can be obtained for the row/cell. If matching
		 * rows are found, the data returned is the original data array/object that was used to
		 * create the row (or a generated array if from a DOM source).
		 *
		 * This method is often useful in-combination with $ where both functions are given the
		 * same parameters and the array indexes will match identically.
		 *  @param {string|node|jQuery} sSelector jQuery selector or node collection to act on
		 *  @param {object} [oOpts] Optional parameters for modifying the rows to be included
		 *  @param {string} [oOpts.filter=none] Select elements that meet the current filter
		 *    criterion ("applied") or all elements (i.e. no filter).
		 *  @param {string} [oOpts.order=current] Order of the data in the processed array.
		 *    Can be either 'current', whereby the current sorting of the table is used, or
		 *    'original' whereby the original order the data was read into the table is used.
		 *  @param {string} [oOpts.page=all] Limit the selection to the currently displayed page
		 *    ("current") or not ("all"). If 'current' is given, then order is assumed to be
		 *    'current' and filter is 'applied', regardless of what they might be given as.
		 *  @returns {array} Data for the matched elements. If any elements, as a result of the
		 *    selector, were not TR, TD or TH elements in the DataTable, they will have a null
		 *    entry in the array.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Get the data from the first row in the table
		 *      var data = oTable._('tr:first');
		 *
		 *      // Do something useful with the data
		 *      alert( "First cell is: "+data[0] );
		 *    } );
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Filter to 'Webkit' and get all data for
		 *      oTable.fnFilter('Webkit');
		 *      var data = oTable._('tr', {"search": "applied"});
		 *
		 *      // Do something with the data
		 *      alert( data.length+" rows matched the search" );
		 *    } );
		 */
		this._ = function ( sSelector, oOpts )
		{
			return this.api(true).rows( sSelector, oOpts ).data();
		};
		
		
		/**
		 * Create a DataTables Api instance, with the currently selected tables for
		 * the Api's context.
		 * @param {boolean} [traditional=false] Set the API instance's context to be
		 *   only the table referred to by the `DataTable.ext.iApiIndex` option, as was
		 *   used in the API presented by DataTables 1.9- (i.e. the traditional mode),
		 *   or if all tables captured in the jQuery object should be used.
		 * @return {DataTables.Api}
		 */
		this.api = function ( traditional )
		{
			return traditional ?
				new _Api(
					_fnSettingsFromNode( this[ _ext.iApiIndex ] )
				) :
				new _Api( this );
		};
		
		
		/**
		 * Add a single new row or multiple rows of data to the table. Please note
		 * that this is suitable for client-side processing only - if you are using
		 * server-side processing (i.e. "bServerSide": true), then to add data, you
		 * must add it to the data source, i.e. the server-side, through an Ajax call.
		 *  @param {array|object} data The data to be added to the table. This can be:
		 *    <ul>
		 *      <li>1D array of data - add a single row with the data provided</li>
		 *      <li>2D array of arrays - add multiple rows in a single call</li>
		 *      <li>object - data object when using <i>mData</i></li>
		 *      <li>array of objects - multiple data objects when using <i>mData</i></li>
		 *    </ul>
		 *  @param {bool} [redraw=true] redraw the table or not
		 *  @returns {array} An array of integers, representing the list of indexes in
		 *    <i>aoData</i> ({@link DataTable.models.oSettings}) that have been added to
		 *    the table.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    // Global var for counter
		 *    var giCount = 2;
		 *
		 *    $(document).ready(function() {
		 *      $('#example').dataTable();
		 *    } );
		 *
		 *    function fnClickAddRow() {
		 *      $('#example').dataTable().fnAddData( [
		 *        giCount+".1",
		 *        giCount+".2",
		 *        giCount+".3",
		 *        giCount+".4" ]
		 *      );
		 *
		 *      giCount++;
		 *    }
		 */
		this.fnAddData = function( data, redraw )
		{
			var api = this.api( true );
		
			/* Check if we want to add multiple rows or not */
			var rows = Array.isArray(data) && ( Array.isArray(data[0]) || $.isPlainObject(data[0]) ) ?
				api.rows.add( data ) :
				api.row.add( data );
		
			if ( redraw === undefined || redraw ) {
				api.draw();
			}
		
			return rows.flatten().toArray();
		};
		
		
		/**
		 * This function will make DataTables recalculate the column sizes, based on the data
		 * contained in the table and the sizes applied to the columns (in the DOM, CSS or
		 * through the sWidth parameter). This can be useful when the width of the table's
		 * parent element changes (for example a window resize).
		 *  @param {boolean} [bRedraw=true] Redraw the table or not, you will typically want to
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable( {
		 *        "sScrollY": "200px",
		 *        "bPaginate": false
		 *      } );
		 *
		 *      $(window).on('resize', function () {
		 *        oTable.fnAdjustColumnSizing();
		 *      } );
		 *    } );
		 */
		this.fnAdjustColumnSizing = function ( bRedraw )
		{
			var api = this.api( true ).columns.adjust();
			var settings = api.settings()[0];
			var scroll = settings.oScroll;
		
			if ( bRedraw === undefined || bRedraw ) {
				api.draw( false );
			}
			else if ( scroll.sX !== "" || scroll.sY !== "" ) {
				/* If not redrawing, but scrolling, we want to apply the new column sizes anyway */
				_fnScrollDraw( settings );
			}
		};
		
		
		/**
		 * Quickly and simply clear a table
		 *  @param {bool} [bRedraw=true] redraw the table or not
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Immediately 'nuke' the current rows (perhaps waiting for an Ajax callback...)
		 *      oTable.fnClearTable();
		 *    } );
		 */
		this.fnClearTable = function( bRedraw )
		{
			var api = this.api( true ).clear();
		
			if ( bRedraw === undefined || bRedraw ) {
				api.draw();
			}
		};
		
		
		/**
		 * The exact opposite of 'opening' a row, this function will close any rows which
		 * are currently 'open'.
		 *  @param {node} nTr the table row to 'close'
		 *  @returns {int} 0 on success, or 1 if failed (can't find the row)
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable;
		 *
		 *      // 'open' an information row when a row is clicked on
		 *      $('#example tbody tr').click( function () {
		 *        if ( oTable.fnIsOpen(this) ) {
		 *          oTable.fnClose( this );
		 *        } else {
		 *          oTable.fnOpen( this, "Temporary row opened", "info_row" );
		 *        }
		 *      } );
		 *
		 *      oTable = $('#example').dataTable();
		 *    } );
		 */
		this.fnClose = function( nTr )
		{
			this.api( true ).row( nTr ).child.hide();
		};
		
		
		/**
		 * Remove a row for the table
		 *  @param {mixed} target The index of the row from aoData to be deleted, or
		 *    the TR element you want to delete
		 *  @param {function|null} [callBack] Callback function
		 *  @param {bool} [redraw=true] Redraw the table or not
		 *  @returns {array} The row that was deleted
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Immediately remove the first row
		 *      oTable.fnDeleteRow( 0 );
		 *    } );
		 */
		this.fnDeleteRow = function( target, callback, redraw )
		{
			var api = this.api( true );
			var rows = api.rows( target );
			var settings = rows.settings()[0];
			var data = settings.aoData[ rows[0][0] ];
		
			rows.remove();
		
			if ( callback ) {
				callback.call( this, settings, data );
			}
		
			if ( redraw === undefined || redraw ) {
				api.draw();
			}
		
			return data;
		};
		
		
		/**
		 * Restore the table to it's original state in the DOM by removing all of DataTables
		 * enhancements, alterations to the DOM structure of the table and event listeners.
		 *  @param {boolean} [remove=false] Completely remove the table from the DOM
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      // This example is fairly pointless in reality, but shows how fnDestroy can be used
		 *      var oTable = $('#example').dataTable();
		 *      oTable.fnDestroy();
		 *    } );
		 */
		this.fnDestroy = function ( remove )
		{
			this.api( true ).destroy( remove );
		};
		
		
		/**
		 * Redraw the table
		 *  @param {bool} [complete=true] Re-filter and resort (if enabled) the table before the draw.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Re-draw the table - you wouldn't want to do it here, but it's an example :-)
		 *      oTable.fnDraw();
		 *    } );
		 */
		this.fnDraw = function( complete )
		{
			// Note that this isn't an exact match to the old call to _fnDraw - it takes
			// into account the new data, but can hold position.
			this.api( true ).draw( complete );
		};
		
		
		/**
		 * Filter the input based on data
		 *  @param {string} sInput String to filter the table on
		 *  @param {int|null} [iColumn] Column to limit filtering to
		 *  @param {bool} [bRegex=false] Treat as regular expression or not
		 *  @param {bool} [bSmart=true] Perform smart filtering or not
		 *  @param {bool} [bShowGlobal=true] Show the input global filter in it's input box(es)
		 *  @param {bool} [bCaseInsensitive=true] Do case-insensitive matching (true) or not (false)
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Sometime later - filter...
		 *      oTable.fnFilter( 'test string' );
		 *    } );
		 */
		this.fnFilter = function( sInput, iColumn, bRegex, bSmart, bShowGlobal, bCaseInsensitive )
		{
			var api = this.api( true );
		
			if ( iColumn === null || iColumn === undefined ) {
				api.search( sInput, bRegex, bSmart, bCaseInsensitive );
			}
			else {
				api.column( iColumn ).search( sInput, bRegex, bSmart, bCaseInsensitive );
			}
		
			api.draw();
		};
		
		
		/**
		 * Get the data for the whole table, an individual row or an individual cell based on the
		 * provided parameters.
		 *  @param {int|node} [src] A TR row node, TD/TH cell node or an integer. If given as
		 *    a TR node then the data source for the whole row will be returned. If given as a
		 *    TD/TH cell node then iCol will be automatically calculated and the data for the
		 *    cell returned. If given as an integer, then this is treated as the aoData internal
		 *    data index for the row (see fnGetPosition) and the data for that row used.
		 *  @param {int} [col] Optional column index that you want the data of.
		 *  @returns {array|object|string} If mRow is undefined, then the data for all rows is
		 *    returned. If mRow is defined, just data for that row, and is iCol is
		 *    defined, only data for the designated cell is returned.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    // Row data
		 *    $(document).ready(function() {
		 *      oTable = $('#example').dataTable();
		 *
		 *      oTable.$('tr').click( function () {
		 *        var data = oTable.fnGetData( this );
		 *        // ... do something with the array / object of data for the row
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Individual cell data
		 *    $(document).ready(function() {
		 *      oTable = $('#example').dataTable();
		 *
		 *      oTable.$('td').click( function () {
		 *        var sData = oTable.fnGetData( this );
		 *        alert( 'The cell clicked on had the value of '+sData );
		 *      } );
		 *    } );
		 */
		this.fnGetData = function( src, col )
		{
			var api = this.api( true );
		
			if ( src !== undefined ) {
				var type = src.nodeName ? src.nodeName.toLowerCase() : '';
		
				return col !== undefined || type == 'td' || type == 'th' ?
					api.cell( src, col ).data() :
					api.row( src ).data() || null;
			}
		
			return api.data().toArray();
		};
		
		
		/**
		 * Get an array of the TR nodes that are used in the table's body. Note that you will
		 * typically want to use the '$' API method in preference to this as it is more
		 * flexible.
		 *  @param {int} [iRow] Optional row index for the TR element you want
		 *  @returns {array|node} If iRow is undefined, returns an array of all TR elements
		 *    in the table's body, or iRow is defined, just the TR element requested.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Get the nodes from the table
		 *      var nNodes = oTable.fnGetNodes( );
		 *    } );
		 */
		this.fnGetNodes = function( iRow )
		{
			var api = this.api( true );
		
			return iRow !== undefined ?
				api.row( iRow ).node() :
				api.rows().nodes().flatten().toArray();
		};
		
		
		/**
		 * Get the array indexes of a particular cell from it's DOM element
		 * and column index including hidden columns
		 *  @param {node} node this can either be a TR, TD or TH in the table's body
		 *  @returns {int} If nNode is given as a TR, then a single index is returned, or
		 *    if given as a cell, an array of [row index, column index (visible),
		 *    column index (all)] is given.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      $('#example tbody td').click( function () {
		 *        // Get the position of the current data from the node
		 *        var aPos = oTable.fnGetPosition( this );
		 *
		 *        // Get the data array for this row
		 *        var aData = oTable.fnGetData( aPos[0] );
		 *
		 *        // Update the data array and return the value
		 *        aData[ aPos[1] ] = 'clicked';
		 *        this.innerHTML = 'clicked';
		 *      } );
		 *
		 *      // Init DataTables
		 *      oTable = $('#example').dataTable();
		 *    } );
		 */
		this.fnGetPosition = function( node )
		{
			var api = this.api( true );
			var nodeName = node.nodeName.toUpperCase();
		
			if ( nodeName == 'TR' ) {
				return api.row( node ).index();
			}
			else if ( nodeName == 'TD' || nodeName == 'TH' ) {
				var cell = api.cell( node ).index();
		
				return [
					cell.row,
					cell.columnVisible,
					cell.column
				];
			}
			return null;
		};
		
		
		/**
		 * Check to see if a row is 'open' or not.
		 *  @param {node} nTr the table row to check
		 *  @returns {boolean} true if the row is currently open, false otherwise
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable;
		 *
		 *      // 'open' an information row when a row is clicked on
		 *      $('#example tbody tr').click( function () {
		 *        if ( oTable.fnIsOpen(this) ) {
		 *          oTable.fnClose( this );
		 *        } else {
		 *          oTable.fnOpen( this, "Temporary row opened", "info_row" );
		 *        }
		 *      } );
		 *
		 *      oTable = $('#example').dataTable();
		 *    } );
		 */
		this.fnIsOpen = function( nTr )
		{
			return this.api( true ).row( nTr ).child.isShown();
		};
		
		
		/**
		 * This function will place a new row directly after a row which is currently
		 * on display on the page, with the HTML contents that is passed into the
		 * function. This can be used, for example, to ask for confirmation that a
		 * particular record should be deleted.
		 *  @param {node} nTr The table row to 'open'
		 *  @param {string|node|jQuery} mHtml The HTML to put into the row
		 *  @param {string} sClass Class to give the new TD cell
		 *  @returns {node} The row opened. Note that if the table row passed in as the
		 *    first parameter, is not found in the table, this method will silently
		 *    return.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable;
		 *
		 *      // 'open' an information row when a row is clicked on
		 *      $('#example tbody tr').click( function () {
		 *        if ( oTable.fnIsOpen(this) ) {
		 *          oTable.fnClose( this );
		 *        } else {
		 *          oTable.fnOpen( this, "Temporary row opened", "info_row" );
		 *        }
		 *      } );
		 *
		 *      oTable = $('#example').dataTable();
		 *    } );
		 */
		this.fnOpen = function( nTr, mHtml, sClass )
		{
			return this.api( true )
				.row( nTr )
				.child( mHtml, sClass )
				.show()
				.child()[0];
		};
		
		
		/**
		 * Change the pagination - provides the internal logic for pagination in a simple API
		 * function. With this function you can have a DataTables table go to the next,
		 * previous, first or last pages.
		 *  @param {string|int} mAction Paging action to take: "first", "previous", "next" or "last"
		 *    or page number to jump to (integer), note that page 0 is the first page.
		 *  @param {bool} [bRedraw=true] Redraw the table or not
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *      oTable.fnPageChange( 'next' );
		 *    } );
		 */
		this.fnPageChange = function ( mAction, bRedraw )
		{
			var api = this.api( true ).page( mAction );
		
			if ( bRedraw === undefined || bRedraw ) {
				api.draw(false);
			}
		};
		
		
		/**
		 * Show a particular column
		 *  @param {int} iCol The column whose display should be changed
		 *  @param {bool} bShow Show (true) or hide (false) the column
		 *  @param {bool} [bRedraw=true] Redraw the table or not
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Hide the second column after initialisation
		 *      oTable.fnSetColumnVis( 1, false );
		 *    } );
		 */
		this.fnSetColumnVis = function ( iCol, bShow, bRedraw )
		{
			var api = this.api( true ).column( iCol ).visible( bShow );
		
			if ( bRedraw === undefined || bRedraw ) {
				api.columns.adjust().draw();
			}
		};
		
		
		/**
		 * Get the settings for a particular table for external manipulation
		 *  @returns {object} DataTables settings object. See
		 *    {@link DataTable.models.oSettings}
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *      var oSettings = oTable.fnSettings();
		 *
		 *      // Show an example parameter from the settings
		 *      alert( oSettings._iDisplayStart );
		 *    } );
		 */
		this.fnSettings = function()
		{
			return _fnSettingsFromNode( this[_ext.iApiIndex] );
		};
		
		
		/**
		 * Sort the table by a particular column
		 *  @param {int} iCol the data index to sort on. Note that this will not match the
		 *    'display index' if you have hidden data entries
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Sort immediately with columns 0 and 1
		 *      oTable.fnSort( [ [0,'asc'], [1,'asc'] ] );
		 *    } );
		 */
		this.fnSort = function( aaSort )
		{
			this.api( true ).order( aaSort ).draw();
		};
		
		
		/**
		 * Attach a sort listener to an element for a given column
		 *  @param {node} nNode the element to attach the sort listener to
		 *  @param {int} iColumn the column that a click on this node will sort on
		 *  @param {function} [fnCallback] callback function when sort is run
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Sort on column 1, when 'sorter' is clicked on
		 *      oTable.fnSortListener( document.getElementById('sorter'), 1 );
		 *    } );
		 */
		this.fnSortListener = function( nNode, iColumn, fnCallback )
		{
			this.api( true ).order.listener( nNode, iColumn, fnCallback );
		};
		
		
		/**
		 * Update a table cell or row - this method will accept either a single value to
		 * update the cell with, an array of values with one element for each column or
		 * an object in the same format as the original data source. The function is
		 * self-referencing in order to make the multi column updates easier.
		 *  @param {object|array|string} mData Data to update the cell/row with
		 *  @param {node|int} mRow TR element you want to update or the aoData index
		 *  @param {int} [iColumn] The column to update, give as null or undefined to
		 *    update a whole row.
		 *  @param {bool} [bRedraw=true] Redraw the table or not
		 *  @param {bool} [bAction=true] Perform pre-draw actions or not
		 *  @returns {int} 0 on success, 1 on error
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *      oTable.fnUpdate( 'Example update', 0, 0 ); // Single cell
		 *      oTable.fnUpdate( ['a', 'b', 'c', 'd', 'e'], $('tbody tr')[0] ); // Row
		 *    } );
		 */
		this.fnUpdate = function( mData, mRow, iColumn, bRedraw, bAction )
		{
			var api = this.api( true );
		
			if ( iColumn === undefined || iColumn === null ) {
				api.row( mRow ).data( mData );
			}
			else {
				api.cell( mRow, iColumn ).data( mData );
			}
		
			if ( bAction === undefined || bAction ) {
				api.columns.adjust();
			}
		
			if ( bRedraw === undefined || bRedraw ) {
				api.draw();
			}
			return 0;
		};
		
		
		/**
		 * Provide a common method for plug-ins to check the version of DataTables being used, in order
		 * to ensure compatibility.
		 *  @param {string} sVersion Version string to check for, in the format "X.Y.Z". Note that the
		 *    formats "X" and "X.Y" are also acceptable.
		 *  @returns {boolean} true if this version of DataTables is greater or equal to the required
		 *    version, or false if this version of DataTales is not suitable
		 *  @method
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *      alert( oTable.fnVersionCheck( '1.9.0' ) );
		 *    } );
		 */
		this.fnVersionCheck = _ext.fnVersionCheck;
		
	
		var _that = this;
		var emptyInit = options === undefined;
		var len = this.length;
	
		if ( emptyInit ) {
			options = {};
		}
	
		this.oApi = this.internal = _ext.internal;
	
		// Extend with old style plug-in API methods
		for ( var fn in DataTable.ext.internal ) {
			if ( fn ) {
				this[fn] = _fnExternApiFunc(fn);
			}
		}
	
		this.each(function() {
			// For each initialisation we want to give it a clean initialisation
			// object that can be bashed around
			var o = {};
			var oInit = len > 1 ? // optimisation for single table case
				_fnExtend( o, options, true ) :
				options;
	
			/*global oInit,_that,emptyInit*/
			var i=0, iLen, j, jLen, k, kLen;
			var sId = this.getAttribute( 'id' );
			var bInitHandedOff = false;
			var defaults = DataTable.defaults;
			var $this = $(this);
			
			
			/* Sanity check */
			if ( this.nodeName.toLowerCase() != 'table' )
			{
				_fnLog( null, 0, 'Non-table node initialisation ('+this.nodeName+')', 2 );
				return;
			}
			
			/* Backwards compatibility for the defaults */
			_fnCompatOpts( defaults );
			_fnCompatCols( defaults.column );
			
			/* Convert the camel-case defaults to Hungarian */
			_fnCamelToHungarian( defaults, defaults, true );
			_fnCamelToHungarian( defaults.column, defaults.column, true );
			
			/* Setting up the initialisation object */
			_fnCamelToHungarian( defaults, $.extend( oInit, $this.data() ), true );
			
			
			
			/* Check to see if we are re-initialising a table */
			var allSettings = DataTable.settings;
			for ( i=0, iLen=allSettings.length ; i<iLen ; i++ )
			{
				var s = allSettings[i];
			
				/* Base check on table node */
				if (
					s.nTable == this ||
					(s.nTHead && s.nTHead.parentNode == this) ||
					(s.nTFoot && s.nTFoot.parentNode == this)
				) {
					var bRetrieve = oInit.bRetrieve !== undefined ? oInit.bRetrieve : defaults.bRetrieve;
					var bDestroy = oInit.bDestroy !== undefined ? oInit.bDestroy : defaults.bDestroy;
			
					if ( emptyInit || bRetrieve )
					{
						return s.oInstance;
					}
					else if ( bDestroy )
					{
						s.oInstance.fnDestroy();
						break;
					}
					else
					{
						_fnLog( s, 0, 'Cannot reinitialise DataTable', 3 );
						return;
					}
				}
			
				/* If the element we are initialising has the same ID as a table which was previously
				 * initialised, but the table nodes don't match (from before) then we destroy the old
				 * instance by simply deleting it. This is under the assumption that the table has been
				 * destroyed by other methods. Anyone using non-id selectors will need to do this manually
				 */
				if ( s.sTableId == this.id )
				{
					allSettings.splice( i, 1 );
					break;
				}
			}
			
			/* Ensure the table has an ID - required for accessibility */
			if ( sId === null || sId === "" )
			{
				sId = "DataTables_Table_"+(DataTable.ext._unique++);
				this.id = sId;
			}
			
			/* Create the settings object for this table and set some of the default parameters */
			var oSettings = $.extend( true, {}, DataTable.models.oSettings, {
				"sDestroyWidth": $this[0].style.width,
				"sInstance":     sId,
				"sTableId":      sId
			} );
			oSettings.nTable = this;
			oSettings.oApi   = _that.internal;
			oSettings.oInit  = oInit;
			
			allSettings.push( oSettings );
			
			// Need to add the instance after the instance after the settings object has been added
			// to the settings array, so we can self reference the table instance if more than one
			oSettings.oInstance = (_that.length===1) ? _that : $this.dataTable();
			
			// Backwards compatibility, before we apply all the defaults
			_fnCompatOpts( oInit );
			_fnLanguageCompat( oInit.oLanguage );
			
			// If the length menu is given, but the init display length is not, use the length menu
			if ( oInit.aLengthMenu && ! oInit.iDisplayLength )
			{
				oInit.iDisplayLength = Array.isArray( oInit.aLengthMenu[0] ) ?
					oInit.aLengthMenu[0][0] : oInit.aLengthMenu[0];
			}
			
			// Apply the defaults and init options to make a single init object will all
			// options defined from defaults and instance options.
			oInit = _fnExtend( $.extend( true, {}, defaults ), oInit );
			
			
			// Map the initialisation options onto the settings object
			_fnMap( oSettings.oFeatures, oInit, [
				"bPaginate",
				"bLengthChange",
				"bFilter",
				"bSort",
				"bSortMulti",
				"bInfo",
				"bProcessing",
				"bAutoWidth",
				"bSortClasses",
				"bServerSide",
				"bDeferRender"
			] );
			_fnMap( oSettings, oInit, [
				"asStripeClasses",
				"ajax",
				"fnServerData",
				"fnFormatNumber",
				"sServerMethod",
				"aaSorting",
				"aaSortingFixed",
				"aLengthMenu",
				"sPaginationType",
				"sAjaxSource",
				"sAjaxDataProp",
				"iStateDuration",
				"sDom",
				"bSortCellsTop",
				"iTabIndex",
				"fnStateLoadCallback",
				"fnStateSaveCallback",
				"renderer",
				"searchDelay",
				"rowId",
				[ "iCookieDuration", "iStateDuration" ], // backwards compat
				[ "oSearch", "oPreviousSearch" ],
				[ "aoSearchCols", "aoPreSearchCols" ],
				[ "iDisplayLength", "_iDisplayLength" ]
			] );
			_fnMap( oSettings.oScroll, oInit, [
				[ "sScrollX", "sX" ],
				[ "sScrollXInner", "sXInner" ],
				[ "sScrollY", "sY" ],
				[ "bScrollCollapse", "bCollapse" ]
			] );
			_fnMap( oSettings.oLanguage, oInit, "fnInfoCallback" );
			
			/* Callback functions which are array driven */
			_fnCallbackReg( oSettings, 'aoDrawCallback',       oInit.fnDrawCallback,      'user' );
			_fnCallbackReg( oSettings, 'aoServerParams',       oInit.fnServerParams,      'user' );
			_fnCallbackReg( oSettings, 'aoStateSaveParams',    oInit.fnStateSaveParams,   'user' );
			_fnCallbackReg( oSettings, 'aoStateLoadParams',    oInit.fnStateLoadParams,   'user' );
			_fnCallbackReg( oSettings, 'aoStateLoaded',        oInit.fnStateLoaded,       'user' );
			_fnCallbackReg( oSettings, 'aoRowCallback',        oInit.fnRowCallback,       'user' );
			_fnCallbackReg( oSettings, 'aoRowCreatedCallback', oInit.fnCreatedRow,        'user' );
			_fnCallbackReg( oSettings, 'aoHeaderCallback',     oInit.fnHeaderCallback,    'user' );
			_fnCallbackReg( oSettings, 'aoFooterCallback',     oInit.fnFooterCallback,    'user' );
			_fnCallbackReg( oSettings, 'aoInitComplete',       oInit.fnInitComplete,      'user' );
			_fnCallbackReg( oSettings, 'aoPreDrawCallback',    oInit.fnPreDrawCallback,   'user' );
			
			oSettings.rowIdFn = _fnGetObjectDataFn( oInit.rowId );
			
			/* Browser support detection */
			_fnBrowserDetect( oSettings );
			
			var oClasses = oSettings.oClasses;
			
			$.extend( oClasses, DataTable.ext.classes, oInit.oClasses );
			$this.addClass( oClasses.sTable );
			
			
			if ( oSettings.iInitDisplayStart === undefined )
			{
				/* Display start point, taking into account the save saving */
				oSettings.iInitDisplayStart = oInit.iDisplayStart;
				oSettings._iDisplayStart = oInit.iDisplayStart;
			}
			
			if ( oInit.iDeferLoading !== null )
			{
				oSettings.bDeferLoading = true;
				var tmp = Array.isArray( oInit.iDeferLoading );
				oSettings._iRecordsDisplay = tmp ? oInit.iDeferLoading[0] : oInit.iDeferLoading;
				oSettings._iRecordsTotal = tmp ? oInit.iDeferLoading[1] : oInit.iDeferLoading;
			}
			
			/* Language definitions */
			var oLanguage = oSettings.oLanguage;
			$.extend( true, oLanguage, oInit.oLanguage );
			
			if ( oLanguage.sUrl )
			{
				/* Get the language definitions from a file - because this Ajax call makes the language
				 * get async to the remainder of this function we use bInitHandedOff to indicate that
				 * _fnInitialise will be fired by the returned Ajax handler, rather than the constructor
				 */
				$.ajax( {
					dataType: 'json',
					url: oLanguage.sUrl,
					success: function ( json ) {
						_fnCamelToHungarian( defaults.oLanguage, json );
						_fnLanguageCompat( json );
						$.extend( true, oLanguage, json, oSettings.oInit.oLanguage );
			
						_fnCallbackFire( oSettings, null, 'i18n', [oSettings]);
						_fnInitialise( oSettings );
					},
					error: function () {
						// Error occurred loading language file, continue on as best we can
						_fnInitialise( oSettings );
					}
				} );
				bInitHandedOff = true;
			}
			else {
				_fnCallbackFire( oSettings, null, 'i18n', [oSettings]);
			}
			
			/*
			 * Stripes
			 */
			if ( oInit.asStripeClasses === null )
			{
				oSettings.asStripeClasses =[
					oClasses.sStripeOdd,
					oClasses.sStripeEven
				];
			}
			
			/* Remove row stripe classes if they are already on the table row */
			var stripeClasses = oSettings.asStripeClasses;
			var rowOne = $this.children('tbody').find('tr').eq(0);
			if ( $.inArray( true, $.map( stripeClasses, function(el, i) {
				return rowOne.hasClass(el);
			} ) ) !== -1 ) {
				$('tbody tr', this).removeClass( stripeClasses.join(' ') );
				oSettings.asDestroyStripes = stripeClasses.slice();
			}
			
			/*
			 * Columns
			 * See if we should load columns automatically or use defined ones
			 */
			var anThs = [];
			var aoColumnsInit;
			var nThead = this.getElementsByTagName('thead');
			if ( nThead.length !== 0 )
			{
				_fnDetectHeader( oSettings.aoHeader, nThead[0] );
				anThs = _fnGetUniqueThs( oSettings );
			}
			
			/* If not given a column array, generate one with nulls */
			if ( oInit.aoColumns === null )
			{
				aoColumnsInit = [];
				for ( i=0, iLen=anThs.length ; i<iLen ; i++ )
				{
					aoColumnsInit.push( null );
				}
			}
			else
			{
				aoColumnsInit = oInit.aoColumns;
			}
			
			/* Add the columns */
			for ( i=0, iLen=aoColumnsInit.length ; i<iLen ; i++ )
			{
				_fnAddColumn( oSettings, anThs ? anThs[i] : null );
			}
			
			/* Apply the column definitions */
			_fnApplyColumnDefs( oSettings, oInit.aoColumnDefs, aoColumnsInit, function (iCol, oDef) {
				_fnColumnOptions( oSettings, iCol, oDef );
			} );
			
			/* HTML5 attribute detection - build an mData object automatically if the
			 * attributes are found
			 */
			if ( rowOne.length ) {
				var a = function ( cell, name ) {
					return cell.getAttribute( 'data-'+name ) !== null ? name : null;
				};
			
				$( rowOne[0] ).children('th, td').each( function (i, cell) {
					var col = oSettings.aoColumns[i];
			
					if ( col.mData === i ) {
						var sort = a( cell, 'sort' ) || a( cell, 'order' );
						var filter = a( cell, 'filter' ) || a( cell, 'search' );
			
						if ( sort !== null || filter !== null ) {
							col.mData = {
								_:      i+'.display',
								sort:   sort !== null   ? i+'.@data-'+sort   : undefined,
								type:   sort !== null   ? i+'.@data-'+sort   : undefined,
								filter: filter !== null ? i+'.@data-'+filter : undefined
							};
			
							_fnColumnOptions( oSettings, i );
						}
					}
				} );
			}
			
			var features = oSettings.oFeatures;
			var loadedInit = function () {
				/*
				 * Sorting
				 * @todo For modularisation (1.11) this needs to do into a sort start up handler
				 */
			
				// If aaSorting is not defined, then we use the first indicator in asSorting
				// in case that has been altered, so the default sort reflects that option
				if ( oInit.aaSorting === undefined ) {
					var sorting = oSettings.aaSorting;
					for ( i=0, iLen=sorting.length ; i<iLen ; i++ ) {
						sorting[i][1] = oSettings.aoColumns[ i ].asSorting[0];
					}
				}
			
				/* Do a first pass on the sorting classes (allows any size changes to be taken into
				 * account, and also will apply sorting disabled classes if disabled
				 */
				_fnSortingClasses( oSettings );
			
				if ( features.bSort ) {
					_fnCallbackReg( oSettings, 'aoDrawCallback', function () {
						if ( oSettings.bSorted ) {
							var aSort = _fnSortFlatten( oSettings );
							var sortedColumns = {};
			
							$.each( aSort, function (i, val) {
								sortedColumns[ val.src ] = val.dir;
							} );
			
							_fnCallbackFire( oSettings, null, 'order', [oSettings, aSort, sortedColumns] );
							_fnSortAria( oSettings );
						}
					} );
				}
			
				_fnCallbackReg( oSettings, 'aoDrawCallback', function () {
					if ( oSettings.bSorted || _fnDataSource( oSettings ) === 'ssp' || features.bDeferRender ) {
						_fnSortingClasses( oSettings );
					}
				}, 'sc' );
			
			
				/*
				 * Final init
				 * Cache the header, body and footer as required, creating them if needed
				 */
			
				// Work around for Webkit bug 83867 - store the caption-side before removing from doc
				var captions = $this.children('caption').each( function () {
					this._captionSide = $(this).css('caption-side');
				} );
			
				var thead = $this.children('thead');
				if ( thead.length === 0 ) {
					thead = $('<thead/>').appendTo($this);
				}
				oSettings.nTHead = thead[0];
			
				var tbody = $this.children('tbody');
				if ( tbody.length === 0 ) {
					tbody = $('<tbody/>').insertAfter(thead);
				}
				oSettings.nTBody = tbody[0];
			
				var tfoot = $this.children('tfoot');
				if ( tfoot.length === 0 && captions.length > 0 && (oSettings.oScroll.sX !== "" || oSettings.oScroll.sY !== "") ) {
					// If we are a scrolling table, and no footer has been given, then we need to create
					// a tfoot element for the caption element to be appended to
					tfoot = $('<tfoot/>').appendTo($this);
				}
			
				if ( tfoot.length === 0 || tfoot.children().length === 0 ) {
					$this.addClass( oClasses.sNoFooter );
				}
				else if ( tfoot.length > 0 ) {
					oSettings.nTFoot = tfoot[0];
					_fnDetectHeader( oSettings.aoFooter, oSettings.nTFoot );
				}
			
				/* Check if there is data passing into the constructor */
				if ( oInit.aaData ) {
					for ( i=0 ; i<oInit.aaData.length ; i++ ) {
						_fnAddData( oSettings, oInit.aaData[ i ] );
					}
				}
				else if ( oSettings.bDeferLoading || _fnDataSource( oSettings ) == 'dom' ) {
					/* Grab the data from the page - only do this when deferred loading or no Ajax
					 * source since there is no point in reading the DOM data if we are then going
					 * to replace it with Ajax data
					 */
					_fnAddTr( oSettings, $(oSettings.nTBody).children('tr') );
				}
			
				/* Copy the data index array */
				oSettings.aiDisplay = oSettings.aiDisplayMaster.slice();
			
				/* Initialisation complete - table can be drawn */
				oSettings.bInitialised = true;
			
				/* Check if we need to initialise the table (it might not have been handed off to the
				 * language processor)
				 */
				if ( bInitHandedOff === false ) {
					_fnInitialise( oSettings );
				}
			};
			
			/* Must be done after everything which can be overridden by the state saving! */
			_fnCallbackReg( oSettings, 'aoDrawCallback', _fnSaveState, 'state_save' );
			
			if ( oInit.bStateSave )
			{
				features.bStateSave = true;
				_fnLoadState( oSettings, oInit, loadedInit );
			}
			else {
				loadedInit();
			}
			
		} );
		_that = null;
		return this;
	};
	
	
	/*
	 * It is useful to have variables which are scoped locally so only the
	 * DataTables functions can access them and they don't leak into global space.
	 * At the same time these functions are often useful over multiple files in the
	 * core and API, so we list, or at least document, all variables which are used
	 * by DataTables as private variables here. This also ensures that there is no
	 * clashing of variable names and that they can easily referenced for reuse.
	 */
	
	
	// Defined else where
	//  _selector_run
	//  _selector_opts
	//  _selector_first
	//  _selector_row_indexes
	
	var _ext; // DataTable.ext
	var _Api; // DataTable.Api
	var _api_register; // DataTable.Api.register
	var _api_registerPlural; // DataTable.Api.registerPlural
	
	var _re_dic = {};
	var _re_new_lines = /[\r\n\u2028]/g;
	var _re_html = /<.*?>/g;
	
	// This is not strict ISO8601 - Date.parse() is quite lax, although
	// implementations differ between browsers.
	var _re_date = /^\d{2,4}[\.\/\-]\d{1,2}[\.\/\-]\d{1,2}([T ]{1}\d{1,2}[:\.]\d{2}([\.:]\d{2})?)?$/;
	
	// Escape regular expression special characters
	var _re_escape_regex = new RegExp( '(\\' + [ '/', '.', '*', '+', '?', '|', '(', ')', '[', ']', '{', '}', '\\', '$', '^', '-' ].join('|\\') + ')', 'g' );
	
	// http://en.wikipedia.org/wiki/Foreign_exchange_market
	// - \u20BD - Russian ruble.
	// - \u20a9 - South Korean Won
	// - \u20BA - Turkish Lira
	// - \u20B9 - Indian Rupee
	// - R - Brazil (R$) and South Africa
	// - fr - Swiss Franc
	// - kr - Swedish krona, Norwegian krone and Danish krone
	// - \u2009 is thin space and \u202F is narrow no-break space, both used in many
	// - Ƀ - Bitcoin
	// - Ξ - Ethereum
	//   standards as thousands separators.
	var _re_formatted_numeric = /['\u00A0,$£€¥%\u2009\u202F\u20BD\u20a9\u20BArfkɃΞ]/gi;
	
	
	var _empty = function ( d ) {
		return !d || d === true || d === '-' ? true : false;
	};
	
	
	var _intVal = function ( s ) {
		var integer = parseInt( s, 10 );
		return !isNaN(integer) && isFinite(s) ? integer : null;
	};
	
	// Convert from a formatted number with characters other than `.` as the
	// decimal place, to a Javascript number
	var _numToDecimal = function ( num, decimalPoint ) {
		// Cache created regular expressions for speed as this function is called often
		if ( ! _re_dic[ decimalPoint ] ) {
			_re_dic[ decimalPoint ] = new RegExp( _fnEscapeRegex( decimalPoint ), 'g' );
		}
		return typeof num === 'string' && decimalPoint !== '.' ?
			num.replace( /\./g, '' ).replace( _re_dic[ decimalPoint ], '.' ) :
			num;
	};
	
	
	var _isNumber = function ( d, decimalPoint, formatted ) {
		var strType = typeof d === 'string';
	
		// If empty return immediately so there must be a number if it is a
		// formatted string (this stops the string "k", or "kr", etc being detected
		// as a formatted number for currency
		if ( _empty( d ) ) {
			return true;
		}
	
		if ( decimalPoint && strType ) {
			d = _numToDecimal( d, decimalPoint );
		}
	
		if ( formatted && strType ) {
			d = d.replace( _re_formatted_numeric, '' );
		}
	
		return !isNaN( parseFloat(d) ) && isFinite( d );
	};
	
	
	// A string without HTML in it can be considered to be HTML still
	var _isHtml = function ( d ) {
		return _empty( d ) || typeof d === 'string';
	};
	
	
	var _htmlNumeric = function ( d, decimalPoint, formatted ) {
		if ( _empty( d ) ) {
			return true;
		}
	
		var html = _isHtml( d );
		return ! html ?
			null :
			_isNumber( _stripHtml( d ), decimalPoint, formatted ) ?
				true :
				null;
	};
	
	
	var _pluck = function ( a, prop, prop2 ) {
		var out = [];
		var i=0, ien=a.length;
	
		// Could have the test in the loop for slightly smaller code, but speed
		// is essential here
		if ( prop2 !== undefined ) {
			for ( ; i<ien ; i++ ) {
				if ( a[i] && a[i][ prop ] ) {
					out.push( a[i][ prop ][ prop2 ] );
				}
			}
		}
		else {
			for ( ; i<ien ; i++ ) {
				if ( a[i] ) {
					out.push( a[i][ prop ] );
				}
			}
		}
	
		return out;
	};
	
	
	// Basically the same as _pluck, but rather than looping over `a` we use `order`
	// as the indexes to pick from `a`
	var _pluck_order = function ( a, order, prop, prop2 )
	{
		var out = [];
		var i=0, ien=order.length;
	
		// Could have the test in the loop for slightly smaller code, but speed
		// is essential here
		if ( prop2 !== undefined ) {
			for ( ; i<ien ; i++ ) {
				if ( a[ order[i] ][ prop ] ) {
					out.push( a[ order[i] ][ prop ][ prop2 ] );
				}
			}
		}
		else {
			for ( ; i<ien ; i++ ) {
				out.push( a[ order[i] ][ prop ] );
			}
		}
	
		return out;
	};
	
	
	var _range = function ( len, start )
	{
		var out = [];
		var end;
	
		if ( start === undefined ) {
			start = 0;
			end = len;
		}
		else {
			end = start;
			start = len;
		}
	
		for ( var i=start ; i<end ; i++ ) {
			out.push( i );
		}
	
		return out;
	};
	
	
	var _removeEmpty = function ( a )
	{
		var out = [];
	
		for ( var i=0, ien=a.length ; i<ien ; i++ ) {
			if ( a[i] ) { // careful - will remove all falsy values!
				out.push( a[i] );
			}
		}
	
		return out;
	};
	
	
	var _stripHtml = function ( d ) {
		return d.replace( _re_html, '' );
	};
	
	
	/**
	 * Determine if all values in the array are unique. This means we can short
	 * cut the _unique method at the cost of a single loop. A sorted array is used
	 * to easily check the values.
	 *
	 * @param  {array} src Source array
	 * @return {boolean} true if all unique, false otherwise
	 * @ignore
	 */
	var _areAllUnique = function ( src ) {
		if ( src.length < 2 ) {
			return true;
		}
	
		var sorted = src.slice().sort();
		var last = sorted[0];
	
		for ( var i=1, ien=sorted.length ; i<ien ; i++ ) {
			if ( sorted[i] === last ) {
				return false;
			}
	
			last = sorted[i];
		}
	
		return true;
	};
	
	
	/**
	 * Find the unique elements in a source array.
	 *
	 * @param  {array} src Source array
	 * @return {array} Array of unique items
	 * @ignore
	 */
	var _unique = function ( src )
	{
		if ( _areAllUnique( src ) ) {
			return src.slice();
		}
	
		// A faster unique method is to use object keys to identify used values,
		// but this doesn't work with arrays or objects, which we must also
		// consider. See jsperf.com/compare-array-unique-versions/4 for more
		// information.
		var
			out = [],
			val,
			i, ien=src.length,
			j, k=0;
	
		again: for ( i=0 ; i<ien ; i++ ) {
			val = src[i];
	
			for ( j=0 ; j<k ; j++ ) {
				if ( out[j] === val ) {
					continue again;
				}
			}
	
			out.push( val );
			k++;
		}
	
		return out;
	};
	
	// Surprisingly this is faster than [].concat.apply
	// https://jsperf.com/flatten-an-array-loop-vs-reduce/2
	var _flatten = function (out, val) {
		if (Array.isArray(val)) {
			for (var i=0 ; i<val.length ; i++) {
				_flatten(out, val[i]);
			}
		}
		else {
			out.push(val);
		}
	  
		return out;
	}
	
	var _includes = function (search, start) {
		if (start === undefined) {
			start = 0;
		}
	
		return this.indexOf(search, start) !== -1;	
	};
	
	// Array.isArray polyfill.
	// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/isArray
	if (! Array.isArray) {
	    Array.isArray = function(arg) {
	        return Object.prototype.toString.call(arg) === '[object Array]';
	    };
	}
	
	if (! Array.prototype.includes) {
		Array.prototype.includes = _includes;
	}
	
	// .trim() polyfill
	// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/String/trim
	if (!String.prototype.trim) {
	  String.prototype.trim = function () {
	    return this.replace(/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, '');
	  };
	}
	
	if (! String.prototype.includes) {
		String.prototype.includes = _includes;
	}
	
	/**
	 * DataTables utility methods
	 * 
	 * This namespace provides helper methods that DataTables uses internally to
	 * create a DataTable, but which are not exclusively used only for DataTables.
	 * These methods can be used by extension authors to save the duplication of
	 * code.
	 *
	 *  @namespace
	 */
	DataTable.util = {
		/**
		 * Throttle the calls to a function. Arguments and context are maintained
		 * for the throttled function.
		 *
		 * @param {function} fn Function to be called
		 * @param {integer} freq Call frequency in mS
		 * @return {function} Wrapped function
		 */
		throttle: function ( fn, freq ) {
			var
				frequency = freq !== undefined ? freq : 200,
				last,
				timer;
	
			return function () {
				var
					that = this,
					now  = +new Date(),
					args = arguments;
	
				if ( last && now < last + frequency ) {
					clearTimeout( timer );
	
					timer = setTimeout( function () {
						last = undefined;
						fn.apply( that, args );
					}, frequency );
				}
				else {
					last = now;
					fn.apply( that, args );
				}
			};
		},
	
	
		/**
		 * Escape a string such that it can be used in a regular expression
		 *
		 *  @param {string} val string to escape
		 *  @returns {string} escaped string
		 */
		escapeRegex: function ( val ) {
			return val.replace( _re_escape_regex, '\\$1' );
		},
	
		/**
		 * Create a function that will write to a nested object or array
		 * @param {*} source JSON notation string
		 * @returns Write function
		 */
		set: function ( source ) {
			if ( $.isPlainObject( source ) ) {
				/* Unlike get, only the underscore (global) option is used for for
				 * setting data since we don't know the type here. This is why an object
				 * option is not documented for `mData` (which is read/write), but it is
				 * for `mRender` which is read only.
				 */
				return DataTable.util.set( source._ );
			}
			else if ( source === null ) {
				// Nothing to do when the data source is null
				return function () {};
			}
			else if ( typeof source === 'function' ) {
				return function (data, val, meta) {
					source( data, 'set', val, meta );
				};
			}
			else if ( typeof source === 'string' && (source.indexOf('.') !== -1 ||
					  source.indexOf('[') !== -1 || source.indexOf('(') !== -1) )
			{
				// Like the get, we need to get data from a nested object
				var setData = function (data, val, src) {
					var a = _fnSplitObjNotation( src ), b;
					var aLast = a[a.length-1];
					var arrayNotation, funcNotation, o, innerSrc;
		
					for ( var i=0, iLen=a.length-1 ; i<iLen ; i++ ) {
						// Protect against prototype pollution
						if (a[i] === '__proto__' || a[i] === 'constructor') {
							throw new Error('Cannot set prototype values');
						}
		
						// Check if we are dealing with an array notation request
						arrayNotation = a[i].match(__reArray);
						funcNotation = a[i].match(__reFn);
		
						if ( arrayNotation ) {
							a[i] = a[i].replace(__reArray, '');
							data[ a[i] ] = [];
		
							// Get the remainder of the nested object to set so we can recurse
							b = a.slice();
							b.splice( 0, i+1 );
							innerSrc = b.join('.');
		
							// Traverse each entry in the array setting the properties requested
							if ( Array.isArray( val ) ) {
								for ( var j=0, jLen=val.length ; j<jLen ; j++ ) {
									o = {};
									setData( o, val[j], innerSrc );
									data[ a[i] ].push( o );
								}
							}
							else {
								// We've been asked to save data to an array, but it
								// isn't array data to be saved. Best that can be done
								// is to just save the value.
								data[ a[i] ] = val;
							}
		
							// The inner call to setData has already traversed through the remainder
							// of the source and has set the data, thus we can exit here
							return;
						}
						else if ( funcNotation ) {
							// Function call
							a[i] = a[i].replace(__reFn, '');
							data = data[ a[i] ]( val );
						}
		
						// If the nested object doesn't currently exist - since we are
						// trying to set the value - create it
						if ( data[ a[i] ] === null || data[ a[i] ] === undefined ) {
							data[ a[i] ] = {};
						}
						data = data[ a[i] ];
					}
		
					// Last item in the input - i.e, the actual set
					if ( aLast.match(__reFn ) ) {
						// Function call
						data = data[ aLast.replace(__reFn, '') ]( val );
					}
					else {
						// If array notation is used, we just want to strip it and use the property name
						// and assign the value. If it isn't used, then we get the result we want anyway
						data[ aLast.replace(__reArray, '') ] = val;
					}
				};
		
				return function (data, val) { // meta is also passed in, but not used
					return setData( data, val, source );
				};
			}
			else {
				// Array or flat object mapping
				return function (data, val) { // meta is also passed in, but not used
					data[source] = val;
				};
			}
		},
	
		/**
		 * Create a function that will read nested objects from arrays, based on JSON notation
		 * @param {*} source JSON notation string
		 * @returns Value read
		 */
		get: function ( source ) {
			if ( $.isPlainObject( source ) ) {
				// Build an object of get functions, and wrap them in a single call
				var o = {};
				$.each( source, function (key, val) {
					if ( val ) {
						o[key] = DataTable.util.get( val );
					}
				} );
		
				return function (data, type, row, meta) {
					var t = o[type] || o._;
					return t !== undefined ?
						t(data, type, row, meta) :
						data;
				};
			}
			else if ( source === null ) {
				// Give an empty string for rendering / sorting etc
				return function (data) { // type, row and meta also passed, but not used
					return data;
				};
			}
			else if ( typeof source === 'function' ) {
				return function (data, type, row, meta) {
					return source( data, type, row, meta );
				};
			}
			else if ( typeof source === 'string' && (source.indexOf('.') !== -1 ||
					  source.indexOf('[') !== -1 || source.indexOf('(') !== -1) )
			{
				/* If there is a . in the source string then the data source is in a
				 * nested object so we loop over the data for each level to get the next
				 * level down. On each loop we test for undefined, and if found immediately
				 * return. This allows entire objects to be missing and sDefaultContent to
				 * be used if defined, rather than throwing an error
				 */
				var fetchData = function (data, type, src) {
					var arrayNotation, funcNotation, out, innerSrc;
		
					if ( src !== "" ) {
						var a = _fnSplitObjNotation( src );
		
						for ( var i=0, iLen=a.length ; i<iLen ; i++ ) {
							// Check if we are dealing with special notation
							arrayNotation = a[i].match(__reArray);
							funcNotation = a[i].match(__reFn);
		
							if ( arrayNotation ) {
								// Array notation
								a[i] = a[i].replace(__reArray, '');
		
								// Condition allows simply [] to be passed in
								if ( a[i] !== "" ) {
									data = data[ a[i] ];
								}
								out = [];
		
								// Get the remainder of the nested object to get
								a.splice( 0, i+1 );
								innerSrc = a.join('.');
		
								// Traverse each entry in the array getting the properties requested
								if ( Array.isArray( data ) ) {
									for ( var j=0, jLen=data.length ; j<jLen ; j++ ) {
										out.push( fetchData( data[j], type, innerSrc ) );
									}
								}
		
								// If a string is given in between the array notation indicators, that
								// is used to join the strings together, otherwise an array is returned
								var join = arrayNotation[0].substring(1, arrayNotation[0].length-1);
								data = (join==="") ? out : out.join(join);
		
								// The inner call to fetchData has already traversed through the remainder
								// of the source requested, so we exit from the loop
								break;
							}
							else if ( funcNotation ) {
								// Function call
								a[i] = a[i].replace(__reFn, '');
								data = data[ a[i] ]();
								continue;
							}
		
							if ( data === null || data[ a[i] ] === undefined ) {
								return undefined;
							}
	
							data = data[ a[i] ];
						}
					}
		
					return data;
				};
		
				return function (data, type) { // row and meta also passed, but not used
					return fetchData( data, type, source );
				};
			}
			else {
				// Array or flat object mapping
				return function (data, type) { // row and meta also passed, but not used
					return data[source];
				};
			}
		}
	};
	
	
	
	/**
	 * Create a mapping object that allows camel case parameters to be looked up
	 * for their Hungarian counterparts. The mapping is stored in a private
	 * parameter called `_hungarianMap` which can be accessed on the source object.
	 *  @param {object} o
	 *  @memberof DataTable#oApi
	 */
	function _fnHungarianMap ( o )
	{
		var
			hungarian = 'a aa ai ao as b fn i m o s ',
			match,
			newKey,
			map = {};
	
		$.each( o, function (key, val) {
			match = key.match(/^([^A-Z]+?)([A-Z])/);
	
			if ( match && hungarian.indexOf(match[1]+' ') !== -1 )
			{
				newKey = key.replace( match[0], match[2].toLowerCase() );
				map[ newKey ] = key;
	
				if ( match[1] === 'o' )
				{
					_fnHungarianMap( o[key] );
				}
			}
		} );
	
		o._hungarianMap = map;
	}
	
	
	/**
	 * Convert from camel case parameters to Hungarian, based on a Hungarian map
	 * created by _fnHungarianMap.
	 *  @param {object} src The model object which holds all parameters that can be
	 *    mapped.
	 *  @param {object} user The object to convert from camel case to Hungarian.
	 *  @param {boolean} force When set to `true`, properties which already have a
	 *    Hungarian value in the `user` object will be overwritten. Otherwise they
	 *    won't be.
	 *  @memberof DataTable#oApi
	 */
	function _fnCamelToHungarian ( src, user, force )
	{
		if ( ! src._hungarianMap ) {
			_fnHungarianMap( src );
		}
	
		var hungarianKey;
	
		$.each( user, function (key, val) {
			hungarianKey = src._hungarianMap[ key ];
	
			if ( hungarianKey !== undefined && (force || user[hungarianKey] === undefined) )
			{
				// For objects, we need to buzz down into the object to copy parameters
				if ( hungarianKey.charAt(0) === 'o' )
				{
					// Copy the camelCase options over to the hungarian
					if ( ! user[ hungarianKey ] ) {
						user[ hungarianKey ] = {};
					}
					$.extend( true, user[hungarianKey], user[key] );
	
					_fnCamelToHungarian( src[hungarianKey], user[hungarianKey], force );
				}
				else {
					user[hungarianKey] = user[ key ];
				}
			}
		} );
	}
	
	
	/**
	 * Language compatibility - when certain options are given, and others aren't, we
	 * need to duplicate the values over, in order to provide backwards compatibility
	 * with older language files.
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnLanguageCompat( lang )
	{
		// Note the use of the Hungarian notation for the parameters in this method as
		// this is called after the mapping of camelCase to Hungarian
		var defaults = DataTable.defaults.oLanguage;
	
		// Default mapping
		var defaultDecimal = defaults.sDecimal;
		if ( defaultDecimal ) {
			_addNumericSort( defaultDecimal );
		}
	
		if ( lang ) {
			var zeroRecords = lang.sZeroRecords;
	
			// Backwards compatibility - if there is no sEmptyTable given, then use the same as
			// sZeroRecords - assuming that is given.
			if ( ! lang.sEmptyTable && zeroRecords &&
				defaults.sEmptyTable === "No data available in table" )
			{
				_fnMap( lang, lang, 'sZeroRecords', 'sEmptyTable' );
			}
	
			// Likewise with loading records
			if ( ! lang.sLoadingRecords && zeroRecords &&
				defaults.sLoadingRecords === "Loading..." )
			{
				_fnMap( lang, lang, 'sZeroRecords', 'sLoadingRecords' );
			}
	
			// Old parameter name of the thousands separator mapped onto the new
			if ( lang.sInfoThousands ) {
				lang.sThousands = lang.sInfoThousands;
			}
	
			var decimal = lang.sDecimal;
			if ( decimal && defaultDecimal !== decimal ) {
				_addNumericSort( decimal );
			}
		}
	}
	
	
	/**
	 * Map one parameter onto another
	 *  @param {object} o Object to map
	 *  @param {*} knew The new parameter name
	 *  @param {*} old The old parameter name
	 */
	var _fnCompatMap = function ( o, knew, old ) {
		if ( o[ knew ] !== undefined ) {
			o[ old ] = o[ knew ];
		}
	};
	
	
	/**
	 * Provide backwards compatibility for the main DT options. Note that the new
	 * options are mapped onto the old parameters, so this is an external interface
	 * change only.
	 *  @param {object} init Object to map
	 */
	function _fnCompatOpts ( init )
	{
		_fnCompatMap( init, 'ordering',      'bSort' );
		_fnCompatMap( init, 'orderMulti',    'bSortMulti' );
		_fnCompatMap( init, 'orderClasses',  'bSortClasses' );
		_fnCompatMap( init, 'orderCellsTop', 'bSortCellsTop' );
		_fnCompatMap( init, 'order',         'aaSorting' );
		_fnCompatMap( init, 'orderFixed',    'aaSortingFixed' );
		_fnCompatMap( init, 'paging',        'bPaginate' );
		_fnCompatMap( init, 'pagingType',    'sPaginationType' );
		_fnCompatMap( init, 'pageLength',    'iDisplayLength' );
		_fnCompatMap( init, 'searching',     'bFilter' );
	
		// Boolean initialisation of x-scrolling
		if ( typeof init.sScrollX === 'boolean' ) {
			init.sScrollX = init.sScrollX ? '100%' : '';
		}
		if ( typeof init.scrollX === 'boolean' ) {
			init.scrollX = init.scrollX ? '100%' : '';
		}
	
		// Column search objects are in an array, so it needs to be converted
		// element by element
		var searchCols = init.aoSearchCols;
	
		if ( searchCols ) {
			for ( var i=0, ien=searchCols.length ; i<ien ; i++ ) {
				if ( searchCols[i] ) {
					_fnCamelToHungarian( DataTable.models.oSearch, searchCols[i] );
				}
			}
		}
	}
	
	
	/**
	 * Provide backwards compatibility for column options. Note that the new options
	 * are mapped onto the old parameters, so this is an external interface change
	 * only.
	 *  @param {object} init Object to map
	 */
	function _fnCompatCols ( init )
	{
		_fnCompatMap( init, 'orderable',     'bSortable' );
		_fnCompatMap( init, 'orderData',     'aDataSort' );
		_fnCompatMap( init, 'orderSequence', 'asSorting' );
		_fnCompatMap( init, 'orderDataType', 'sortDataType' );
	
		// orderData can be given as an integer
		var dataSort = init.aDataSort;
		if ( typeof dataSort === 'number' && ! Array.isArray( dataSort ) ) {
			init.aDataSort = [ dataSort ];
		}
	}
	
	
	/**
	 * Browser feature detection for capabilities, quirks
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnBrowserDetect( settings )
	{
		// We don't need to do this every time DataTables is constructed, the values
		// calculated are specific to the browser and OS configuration which we
		// don't expect to change between initialisations
		if ( ! DataTable.__browser ) {
			var browser = {};
			DataTable.__browser = browser;
	
			// Scrolling feature / quirks detection
			var n = $('<div/>')
				.css( {
					position: 'fixed',
					top: 0,
					left: $(window).scrollLeft()*-1, // allow for scrolling
					height: 1,
					width: 1,
					overflow: 'hidden'
				} )
				.append(
					$('<div/>')
						.css( {
							position: 'absolute',
							top: 1,
							left: 1,
							width: 100,
							overflow: 'scroll'
						} )
						.append(
							$('<div/>')
								.css( {
									width: '100%',
									height: 10
								} )
						)
				)
				.appendTo( 'body' );
	
			var outer = n.children();
			var inner = outer.children();
	
			// Numbers below, in order, are:
			// inner.offsetWidth, inner.clientWidth, outer.offsetWidth, outer.clientWidth
			//
			// IE6 XP:                           100 100 100  83
			// IE7 Vista:                        100 100 100  83
			// IE 8+ Windows:                     83  83 100  83
			// Evergreen Windows:                 83  83 100  83
			// Evergreen Mac with scrollbars:     85  85 100  85
			// Evergreen Mac without scrollbars: 100 100 100 100
	
			// Get scrollbar width
			browser.barWidth = outer[0].offsetWidth - outer[0].clientWidth;
	
			// IE6/7 will oversize a width 100% element inside a scrolling element, to
			// include the width of the scrollbar, while other browsers ensure the inner
			// element is contained without forcing scrolling
			browser.bScrollOversize = inner[0].offsetWidth === 100 && outer[0].clientWidth !== 100;
	
			// In rtl text layout, some browsers (most, but not all) will place the
			// scrollbar on the left, rather than the right.
			browser.bScrollbarLeft = Math.round( inner.offset().left ) !== 1;
	
			// IE8- don't provide height and width for getBoundingClientRect
			browser.bBounding = n[0].getBoundingClientRect().width ? true : false;
	
			n.remove();
		}
	
		$.extend( settings.oBrowser, DataTable.__browser );
		settings.oScroll.iBarWidth = DataTable.__browser.barWidth;
	}
	
	
	/**
	 * Array.prototype reduce[Right] method, used for browsers which don't support
	 * JS 1.6. Done this way to reduce code size, since we iterate either way
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnReduce ( that, fn, init, start, end, inc )
	{
		var
			i = start,
			value,
			isSet = false;
	
		if ( init !== undefined ) {
			value = init;
			isSet = true;
		}
	
		while ( i !== end ) {
			if ( ! that.hasOwnProperty(i) ) {
				continue;
			}
	
			value = isSet ?
				fn( value, that[i], i, that ) :
				that[i];
	
			isSet = true;
			i += inc;
		}
	
		return value;
	}
	
	/**
	 * Add a column to the list used for the table with default values
	 *  @param {object} oSettings dataTables settings object
	 *  @param {node} nTh The th element for this column
	 *  @memberof DataTable#oApi
	 */
	function _fnAddColumn( oSettings, nTh )
	{
		// Add column to aoColumns array
		var oDefaults = DataTable.defaults.column;
		var iCol = oSettings.aoColumns.length;
		var oCol = $.extend( {}, DataTable.models.oColumn, oDefaults, {
			"nTh": nTh ? nTh : document.createElement('th'),
			"sTitle":    oDefaults.sTitle    ? oDefaults.sTitle    : nTh ? nTh.innerHTML : '',
			"aDataSort": oDefaults.aDataSort ? oDefaults.aDataSort : [iCol],
			"mData": oDefaults.mData ? oDefaults.mData : iCol,
			idx: iCol
		} );
		oSettings.aoColumns.push( oCol );
	
		// Add search object for column specific search. Note that the `searchCols[ iCol ]`
		// passed into extend can be undefined. This allows the user to give a default
		// with only some of the parameters defined, and also not give a default
		var searchCols = oSettings.aoPreSearchCols;
		searchCols[ iCol ] = $.extend( {}, DataTable.models.oSearch, searchCols[ iCol ] );
	
		// Use the default column options function to initialise classes etc
		_fnColumnOptions( oSettings, iCol, $(nTh).data() );
	}
	
	
	/**
	 * Apply options for a column
	 *  @param {object} oSettings dataTables settings object
	 *  @param {int} iCol column index to consider
	 *  @param {object} oOptions object with sType, bVisible and bSearchable etc
	 *  @memberof DataTable#oApi
	 */
	function _fnColumnOptions( oSettings, iCol, oOptions )
	{
		var oCol = oSettings.aoColumns[ iCol ];
		var oClasses = oSettings.oClasses;
		var th = $(oCol.nTh);
	
		// Try to get width information from the DOM. We can't get it from CSS
		// as we'd need to parse the CSS stylesheet. `width` option can override
		if ( ! oCol.sWidthOrig ) {
			// Width attribute
			oCol.sWidthOrig = th.attr('width') || null;
	
			// Style attribute
			var t = (th.attr('style') || '').match(/width:\s*(\d+[pxem%]+)/);
			if ( t ) {
				oCol.sWidthOrig = t[1];
			}
		}
	
		/* User specified column options */
		if ( oOptions !== undefined && oOptions !== null )
		{
			// Backwards compatibility
			_fnCompatCols( oOptions );
	
			// Map camel case parameters to their Hungarian counterparts
			_fnCamelToHungarian( DataTable.defaults.column, oOptions, true );
	
			/* Backwards compatibility for mDataProp */
			if ( oOptions.mDataProp !== undefined && !oOptions.mData )
			{
				oOptions.mData = oOptions.mDataProp;
			}
	
			if ( oOptions.sType )
			{
				oCol._sManualType = oOptions.sType;
			}
	
			// `class` is a reserved word in Javascript, so we need to provide
			// the ability to use a valid name for the camel case input
			if ( oOptions.className && ! oOptions.sClass )
			{
				oOptions.sClass = oOptions.className;
			}
			if ( oOptions.sClass ) {
				th.addClass( oOptions.sClass );
			}
	
			var origClass = oCol.sClass;
	
			$.extend( oCol, oOptions );
			_fnMap( oCol, oOptions, "sWidth", "sWidthOrig" );
	
			// Merge class from previously defined classes with this one, rather than just
			// overwriting it in the extend above
			if (origClass !== oCol.sClass) {
				oCol.sClass = origClass + ' ' + oCol.sClass;
			}
	
			/* iDataSort to be applied (backwards compatibility), but aDataSort will take
			 * priority if defined
			 */
			if ( oOptions.iDataSort !== undefined )
			{
				oCol.aDataSort = [ oOptions.iDataSort ];
			}
			_fnMap( oCol, oOptions, "aDataSort" );
		}
	
		/* Cache the data get and set functions for speed */
		var mDataSrc = oCol.mData;
		var mData = _fnGetObjectDataFn( mDataSrc );
		var mRender = oCol.mRender ? _fnGetObjectDataFn( oCol.mRender ) : null;
	
		var attrTest = function( src ) {
			return typeof src === 'string' && src.indexOf('@') !== -1;
		};
		oCol._bAttrSrc = $.isPlainObject( mDataSrc ) && (
			attrTest(mDataSrc.sort) || attrTest(mDataSrc.type) || attrTest(mDataSrc.filter)
		);
		oCol._setter = null;
	
		oCol.fnGetData = function (rowData, type, meta) {
			var innerData = mData( rowData, type, undefined, meta );
	
			return mRender && type ?
				mRender( innerData, type, rowData, meta ) :
				innerData;
		};
		oCol.fnSetData = function ( rowData, val, meta ) {
			return _fnSetObjectDataFn( mDataSrc )( rowData, val, meta );
		};
	
		// Indicate if DataTables should read DOM data as an object or array
		// Used in _fnGetRowElements
		if ( typeof mDataSrc !== 'number' ) {
			oSettings._rowReadObject = true;
		}
	
		/* Feature sorting overrides column specific when off */
		if ( !oSettings.oFeatures.bSort )
		{
			oCol.bSortable = false;
			th.addClass( oClasses.sSortableNone ); // Have to add class here as order event isn't called
		}
	
		/* Check that the class assignment is correct for sorting */
		var bAsc = $.inArray('asc', oCol.asSorting) !== -1;
		var bDesc = $.inArray('desc', oCol.asSorting) !== -1;
		if ( !oCol.bSortable || (!bAsc && !bDesc) )
		{
			oCol.sSortingClass = oClasses.sSortableNone;
			oCol.sSortingClassJUI = "";
		}
		else if ( bAsc && !bDesc )
		{
			oCol.sSortingClass = oClasses.sSortableAsc;
			oCol.sSortingClassJUI = oClasses.sSortJUIAscAllowed;
		}
		else if ( !bAsc && bDesc )
		{
			oCol.sSortingClass = oClasses.sSortableDesc;
			oCol.sSortingClassJUI = oClasses.sSortJUIDescAllowed;
		}
		else
		{
			oCol.sSortingClass = oClasses.sSortable;
			oCol.sSortingClassJUI = oClasses.sSortJUI;
		}
	}
	
	
	/**
	 * Adjust the table column widths for new data. Note: you would probably want to
	 * do a redraw after calling this function!
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnAdjustColumnSizing ( settings )
	{
		/* Not interested in doing column width calculation if auto-width is disabled */
		if ( settings.oFeatures.bAutoWidth !== false )
		{
			var columns = settings.aoColumns;
	
			_fnCalculateColumnWidths( settings );
			for ( var i=0 , iLen=columns.length ; i<iLen ; i++ )
			{
				columns[i].nTh.style.width = columns[i].sWidth;
			}
		}
	
		var scroll = settings.oScroll;
		if ( scroll.sY !== '' || scroll.sX !== '')
		{
			_fnScrollDraw( settings );
		}
	
		_fnCallbackFire( settings, null, 'column-sizing', [settings] );
	}
	
	
	/**
	 * Convert the index of a visible column to the index in the data array (take account
	 * of hidden columns)
	 *  @param {object} oSettings dataTables settings object
	 *  @param {int} iMatch Visible column index to lookup
	 *  @returns {int} i the data index
	 *  @memberof DataTable#oApi
	 */
	function _fnVisibleToColumnIndex( oSettings, iMatch )
	{
		var aiVis = _fnGetColumns( oSettings, 'bVisible' );
	
		return typeof aiVis[iMatch] === 'number' ?
			aiVis[iMatch] :
			null;
	}
	
	
	/**
	 * Convert the index of an index in the data array and convert it to the visible
	 *   column index (take account of hidden columns)
	 *  @param {int} iMatch Column index to lookup
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {int} i the data index
	 *  @memberof DataTable#oApi
	 */
	function _fnColumnIndexToVisible( oSettings, iMatch )
	{
		var aiVis = _fnGetColumns( oSettings, 'bVisible' );
		var iPos = $.inArray( iMatch, aiVis );
	
		return iPos !== -1 ? iPos : null;
	}
	
	
	/**
	 * Get the number of visible columns
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {int} i the number of visible columns
	 *  @memberof DataTable#oApi
	 */
	function _fnVisbleColumns( oSettings )
	{
		var vis = 0;
	
		// No reduce in IE8, use a loop for now
		$.each( oSettings.aoColumns, function ( i, col ) {
			if ( col.bVisible && $(col.nTh).css('display') !== 'none' ) {
				vis++;
			}
		} );
	
		return vis;
	}
	
	
	/**
	 * Get an array of column indexes that match a given property
	 *  @param {object} oSettings dataTables settings object
	 *  @param {string} sParam Parameter in aoColumns to look for - typically
	 *    bVisible or bSearchable
	 *  @returns {array} Array of indexes with matched properties
	 *  @memberof DataTable#oApi
	 */
	function _fnGetColumns( oSettings, sParam )
	{
		var a = [];
	
		$.map( oSettings.aoColumns, function(val, i) {
			if ( val[sParam] ) {
				a.push( i );
			}
		} );
	
		return a;
	}
	
	
	/**
	 * Calculate the 'type' of a column
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnColumnTypes ( settings )
	{
		var columns = settings.aoColumns;
		var data = settings.aoData;
		var types = DataTable.ext.type.detect;
		var i, ien, j, jen, k, ken;
		var col, cell, detectedType, cache;
	
		// For each column, spin over the 
		for ( i=0, ien=columns.length ; i<ien ; i++ ) {
			col = columns[i];
			cache = [];
	
			if ( ! col.sType && col._sManualType ) {
				col.sType = col._sManualType;
			}
			else if ( ! col.sType ) {
				for ( j=0, jen=types.length ; j<jen ; j++ ) {
					for ( k=0, ken=data.length ; k<ken ; k++ ) {
						// Use a cache array so we only need to get the type data
						// from the formatter once (when using multiple detectors)
						if ( cache[k] === undefined ) {
							cache[k] = _fnGetCellData( settings, k, i, 'type' );
						}
	
						detectedType = types[j]( cache[k], settings );
	
						// If null, then this type can't apply to this column, so
						// rather than testing all cells, break out. There is an
						// exception for the last type which is `html`. We need to
						// scan all rows since it is possible to mix string and HTML
						// types
						if ( ! detectedType && j !== types.length-1 ) {
							break;
						}
	
						// Only a single match is needed for html type since it is
						// bottom of the pile and very similar to string - but it
						// must not be empty
						if ( detectedType === 'html' && ! _empty(cache[k]) ) {
							break;
						}
					}
	
					// Type is valid for all data points in the column - use this
					// type
					if ( detectedType ) {
						col.sType = detectedType;
						break;
					}
				}
	
				// Fall back - if no type was detected, always use string
				if ( ! col.sType ) {
					col.sType = 'string';
				}
			}
		}
	}
	
	
	/**
	 * Take the column definitions and static columns arrays and calculate how
	 * they relate to column indexes. The callback function will then apply the
	 * definition found for a column to a suitable configuration object.
	 *  @param {object} oSettings dataTables settings object
	 *  @param {array} aoColDefs The aoColumnDefs array that is to be applied
	 *  @param {array} aoCols The aoColumns array that defines columns individually
	 *  @param {function} fn Callback function - takes two parameters, the calculated
	 *    column index and the definition for that column.
	 *  @memberof DataTable#oApi
	 */
	function _fnApplyColumnDefs( oSettings, aoColDefs, aoCols, fn )
	{
		var i, iLen, j, jLen, k, kLen, def;
		var columns = oSettings.aoColumns;
	
		// Column definitions with aTargets
		if ( aoColDefs )
		{
			/* Loop over the definitions array - loop in reverse so first instance has priority */
			for ( i=aoColDefs.length-1 ; i>=0 ; i-- )
			{
				def = aoColDefs[i];
	
				/* Each definition can target multiple columns, as it is an array */
				var aTargets = def.target !== undefined
					? def.target
					: def.targets !== undefined
						? def.targets
						: def.aTargets;
	
				if ( ! Array.isArray( aTargets ) )
				{
					aTargets = [ aTargets ];
				}
	
				for ( j=0, jLen=aTargets.length ; j<jLen ; j++ )
				{
					if ( typeof aTargets[j] === 'number' && aTargets[j] >= 0 )
					{
						/* Add columns that we don't yet know about */
						while( columns.length <= aTargets[j] )
						{
							_fnAddColumn( oSettings );
						}
	
						/* Integer, basic index */
						fn( aTargets[j], def );
					}
					else if ( typeof aTargets[j] === 'number' && aTargets[j] < 0 )
					{
						/* Negative integer, right to left column counting */
						fn( columns.length+aTargets[j], def );
					}
					else if ( typeof aTargets[j] === 'string' )
					{
						/* Class name matching on TH element */
						for ( k=0, kLen=columns.length ; k<kLen ; k++ )
						{
							if ( aTargets[j] == "_all" ||
							     $(columns[k].nTh).hasClass( aTargets[j] ) )
							{
								fn( k, def );
							}
						}
					}
				}
			}
		}
	
		// Statically defined columns array
		if ( aoCols )
		{
			for ( i=0, iLen=aoCols.length ; i<iLen ; i++ )
			{
				fn( i, aoCols[i] );
			}
		}
	}
	
	/**
	 * Add a data array to the table, creating DOM node etc. This is the parallel to
	 * _fnGatherData, but for adding rows from a Javascript source, rather than a
	 * DOM source.
	 *  @param {object} oSettings dataTables settings object
	 *  @param {array} aData data array to be added
	 *  @param {node} [nTr] TR element to add to the table - optional. If not given,
	 *    DataTables will create a row automatically
	 *  @param {array} [anTds] Array of TD|TH elements for the row - must be given
	 *    if nTr is.
	 *  @returns {int} >=0 if successful (index of new aoData entry), -1 if failed
	 *  @memberof DataTable#oApi
	 */
	function _fnAddData ( oSettings, aDataIn, nTr, anTds )
	{
		/* Create the object for storing information about this new row */
		var iRow = oSettings.aoData.length;
		var oData = $.extend( true, {}, DataTable.models.oRow, {
			src: nTr ? 'dom' : 'data',
			idx: iRow
		} );
	
		oData._aData = aDataIn;
		oSettings.aoData.push( oData );
	
		/* Create the cells */
		var nTd, sThisType;
		var columns = oSettings.aoColumns;
	
		// Invalidate the column types as the new data needs to be revalidated
		for ( var i=0, iLen=columns.length ; i<iLen ; i++ )
		{
			columns[i].sType = null;
		}
	
		/* Add to the display array */
		oSettings.aiDisplayMaster.push( iRow );
	
		var id = oSettings.rowIdFn( aDataIn );
		if ( id !== undefined ) {
			oSettings.aIds[ id ] = oData;
		}
	
		/* Create the DOM information, or register it if already present */
		if ( nTr || ! oSettings.oFeatures.bDeferRender )
		{
			_fnCreateTr( oSettings, iRow, nTr, anTds );
		}
	
		return iRow;
	}
	
	
	/**
	 * Add one or more TR elements to the table. Generally we'd expect to
	 * use this for reading data from a DOM sourced table, but it could be
	 * used for an TR element. Note that if a TR is given, it is used (i.e.
	 * it is not cloned).
	 *  @param {object} settings dataTables settings object
	 *  @param {array|node|jQuery} trs The TR element(s) to add to the table
	 *  @returns {array} Array of indexes for the added rows
	 *  @memberof DataTable#oApi
	 */
	function _fnAddTr( settings, trs )
	{
		var row;
	
		// Allow an individual node to be passed in
		if ( ! (trs instanceof $) ) {
			trs = $(trs);
		}
	
		return trs.map( function (i, el) {
			row = _fnGetRowElements( settings, el );
			return _fnAddData( settings, row.data, el, row.cells );
		} );
	}
	
	
	/**
	 * Take a TR element and convert it to an index in aoData
	 *  @param {object} oSettings dataTables settings object
	 *  @param {node} n the TR element to find
	 *  @returns {int} index if the node is found, null if not
	 *  @memberof DataTable#oApi
	 */
	function _fnNodeToDataIndex( oSettings, n )
	{
		return (n._DT_RowIndex!==undefined) ? n._DT_RowIndex : null;
	}
	
	
	/**
	 * Take a TD element and convert it into a column data index (not the visible index)
	 *  @param {object} oSettings dataTables settings object
	 *  @param {int} iRow The row number the TD/TH can be found in
	 *  @param {node} n The TD/TH element to find
	 *  @returns {int} index if the node is found, -1 if not
	 *  @memberof DataTable#oApi
	 */
	function _fnNodeToColumnIndex( oSettings, iRow, n )
	{
		return $.inArray( n, oSettings.aoData[ iRow ].anCells );
	}
	
	
	/**
	 * Get the data for a given cell from the internal cache, taking into account data mapping
	 *  @param {object} settings dataTables settings object
	 *  @param {int} rowIdx aoData row id
	 *  @param {int} colIdx Column index
	 *  @param {string} type data get type ('display', 'type' 'filter|search' 'sort|order')
	 *  @returns {*} Cell data
	 *  @memberof DataTable#oApi
	 */
	function _fnGetCellData( settings, rowIdx, colIdx, type )
	{
		if (type === 'search') {
			type = 'filter';
		}
		else if (type === 'order') {
			type = 'sort';
		}
	
		var draw           = settings.iDraw;
		var col            = settings.aoColumns[colIdx];
		var rowData        = settings.aoData[rowIdx]._aData;
		var defaultContent = col.sDefaultContent;
		var cellData       = col.fnGetData( rowData, type, {
			settings: settings,
			row:      rowIdx,
			col:      colIdx
		} );
	
		if ( cellData === undefined ) {
			if ( settings.iDrawError != draw && defaultContent === null ) {
				_fnLog( settings, 0, "Requested unknown parameter "+
					(typeof col.mData=='function' ? '{function}' : "'"+col.mData+"'")+
					" for row "+rowIdx+", column "+colIdx, 4 );
				settings.iDrawError = draw;
			}
			return defaultContent;
		}
	
		// When the data source is null and a specific data type is requested (i.e.
		// not the original data), we can use default column data
		if ( (cellData === rowData || cellData === null) && defaultContent !== null && type !== undefined ) {
			cellData = defaultContent;
		}
		else if ( typeof cellData === 'function' ) {
			// If the data source is a function, then we run it and use the return,
			// executing in the scope of the data object (for instances)
			return cellData.call( rowData );
		}
	
		if ( cellData === null && type === 'display' ) {
			return '';
		}
	
		if ( type === 'filter' ) {
			var fomatters = DataTable.ext.type.search;
	
			if ( fomatters[ col.sType ] ) {
				cellData = fomatters[ col.sType ]( cellData );
			}
		}
	
		return cellData;
	}
	
	
	/**
	 * Set the value for a specific cell, into the internal data cache
	 *  @param {object} settings dataTables settings object
	 *  @param {int} rowIdx aoData row id
	 *  @param {int} colIdx Column index
	 *  @param {*} val Value to set
	 *  @memberof DataTable#oApi
	 */
	function _fnSetCellData( settings, rowIdx, colIdx, val )
	{
		var col     = settings.aoColumns[colIdx];
		var rowData = settings.aoData[rowIdx]._aData;
	
		col.fnSetData( rowData, val, {
			settings: settings,
			row:      rowIdx,
			col:      colIdx
		}  );
	}
	
	
	// Private variable that is used to match action syntax in the data property object
	var __reArray = /\[.*?\]$/;
	var __reFn = /\(\)$/;
	
	/**
	 * Split string on periods, taking into account escaped periods
	 * @param  {string} str String to split
	 * @return {array} Split string
	 */
	function _fnSplitObjNotation( str )
	{
		return $.map( str.match(/(\\.|[^\.])+/g) || [''], function ( s ) {
			return s.replace(/\\\./g, '.');
		} );
	}
	
	
	/**
	 * Return a function that can be used to get data from a source object, taking
	 * into account the ability to use nested objects as a source
	 *  @param {string|int|function} mSource The data source for the object
	 *  @returns {function} Data get function
	 *  @memberof DataTable#oApi
	 */
	var _fnGetObjectDataFn = DataTable.util.get;
	
	
	/**
	 * Return a function that can be used to set data from a source object, taking
	 * into account the ability to use nested objects as a source
	 *  @param {string|int|function} mSource The data source for the object
	 *  @returns {function} Data set function
	 *  @memberof DataTable#oApi
	 */
	var _fnSetObjectDataFn = DataTable.util.set;
	
	
	/**
	 * Return an array with the full table data
	 *  @param {object} oSettings dataTables settings object
	 *  @returns array {array} aData Master data array
	 *  @memberof DataTable#oApi
	 */
	function _fnGetDataMaster ( settings )
	{
		return _pluck( settings.aoData, '_aData' );
	}
	
	
	/**
	 * Nuke the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnClearTable( settings )
	{
		settings.aoData.length = 0;
		settings.aiDisplayMaster.length = 0;
		settings.aiDisplay.length = 0;
		settings.aIds = {};
	}
	
	
	 /**
	 * Take an array of integers (index array) and remove a target integer (value - not
	 * the key!)
	 *  @param {array} a Index array to target
	 *  @param {int} iTarget value to find
	 *  @memberof DataTable#oApi
	 */
	function _fnDeleteIndex( a, iTarget, splice )
	{
		var iTargetIndex = -1;
	
		for ( var i=0, iLen=a.length ; i<iLen ; i++ )
		{
			if ( a[i] == iTarget )
			{
				iTargetIndex = i;
			}
			else if ( a[i] > iTarget )
			{
				a[i]--;
			}
		}
	
		if ( iTargetIndex != -1 && splice === undefined )
		{
			a.splice( iTargetIndex, 1 );
		}
	}
	
	
	/**
	 * Mark cached data as invalid such that a re-read of the data will occur when
	 * the cached data is next requested. Also update from the data source object.
	 *
	 * @param {object} settings DataTables settings object
	 * @param {int}    rowIdx   Row index to invalidate
	 * @param {string} [src]    Source to invalidate from: undefined, 'auto', 'dom'
	 *     or 'data'
	 * @param {int}    [colIdx] Column index to invalidate. If undefined the whole
	 *     row will be invalidated
	 * @memberof DataTable#oApi
	 *
	 * @todo For the modularisation of v1.11 this will need to become a callback, so
	 *   the sort and filter methods can subscribe to it. That will required
	 *   initialisation options for sorting, which is why it is not already baked in
	 */
	function _fnInvalidate( settings, rowIdx, src, colIdx )
	{
		var row = settings.aoData[ rowIdx ];
		var i, ien;
		var cellWrite = function ( cell, col ) {
			// This is very frustrating, but in IE if you just write directly
			// to innerHTML, and elements that are overwritten are GC'ed,
			// even if there is a reference to them elsewhere
			while ( cell.childNodes.length ) {
				cell.removeChild( cell.firstChild );
			}
	
			cell.innerHTML = _fnGetCellData( settings, rowIdx, col, 'display' );
		};
	
		// Are we reading last data from DOM or the data object?
		if ( src === 'dom' || ((! src || src === 'auto') && row.src === 'dom') ) {
			// Read the data from the DOM
			row._aData = _fnGetRowElements(
					settings, row, colIdx, colIdx === undefined ? undefined : row._aData
				)
				.data;
		}
		else {
			// Reading from data object, update the DOM
			var cells = row.anCells;
	
			if ( cells ) {
				if ( colIdx !== undefined ) {
					cellWrite( cells[colIdx], colIdx );
				}
				else {
					for ( i=0, ien=cells.length ; i<ien ; i++ ) {
						cellWrite( cells[i], i );
					}
				}
			}
		}
	
		// For both row and cell invalidation, the cached data for sorting and
		// filtering is nulled out
		row._aSortData = null;
		row._aFilterData = null;
	
		// Invalidate the type for a specific column (if given) or all columns since
		// the data might have changed
		var cols = settings.aoColumns;
		if ( colIdx !== undefined ) {
			cols[ colIdx ].sType = null;
		}
		else {
			for ( i=0, ien=cols.length ; i<ien ; i++ ) {
				cols[i].sType = null;
			}
	
			// Update DataTables special `DT_*` attributes for the row
			_fnRowAttributes( settings, row );
		}
	}
	
	
	/**
	 * Build a data source object from an HTML row, reading the contents of the
	 * cells that are in the row.
	 *
	 * @param {object} settings DataTables settings object
	 * @param {node|object} TR element from which to read data or existing row
	 *   object from which to re-read the data from the cells
	 * @param {int} [colIdx] Optional column index
	 * @param {array|object} [d] Data source object. If `colIdx` is given then this
	 *   parameter should also be given and will be used to write the data into.
	 *   Only the column in question will be written
	 * @returns {object} Object with two parameters: `data` the data read, in
	 *   document order, and `cells` and array of nodes (they can be useful to the
	 *   caller, so rather than needing a second traversal to get them, just return
	 *   them from here).
	 * @memberof DataTable#oApi
	 */
	function _fnGetRowElements( settings, row, colIdx, d )
	{
		var
			tds = [],
			td = row.firstChild,
			name, col, o, i=0, contents,
			columns = settings.aoColumns,
			objectRead = settings._rowReadObject;
	
		// Allow the data object to be passed in, or construct
		d = d !== undefined ?
			d :
			objectRead ?
				{} :
				[];
	
		var attr = function ( str, td  ) {
			if ( typeof str === 'string' ) {
				var idx = str.indexOf('@');
	
				if ( idx !== -1 ) {
					var attr = str.substring( idx+1 );
					var setter = _fnSetObjectDataFn( str );
					setter( d, td.getAttribute( attr ) );
				}
			}
		};
	
		// Read data from a cell and store into the data object
		var cellProcess = function ( cell ) {
			if ( colIdx === undefined || colIdx === i ) {
				col = columns[i];
				contents = (cell.innerHTML).trim();
	
				if ( col && col._bAttrSrc ) {
					var setter = _fnSetObjectDataFn( col.mData._ );
					setter( d, contents );
	
					attr( col.mData.sort, cell );
					attr( col.mData.type, cell );
					attr( col.mData.filter, cell );
				}
				else {
					// Depending on the `data` option for the columns the data can
					// be read to either an object or an array.
					if ( objectRead ) {
						if ( ! col._setter ) {
							// Cache the setter function
							col._setter = _fnSetObjectDataFn( col.mData );
						}
						col._setter( d, contents );
					}
					else {
						d[i] = contents;
					}
				}
			}
	
			i++;
		};
	
		if ( td ) {
			// `tr` element was passed in
			while ( td ) {
				name = td.nodeName.toUpperCase();
	
				if ( name == "TD" || name == "TH" ) {
					cellProcess( td );
					tds.push( td );
				}
	
				td = td.nextSibling;
			}
		}
		else {
			// Existing row object passed in
			tds = row.anCells;
	
			for ( var j=0, jen=tds.length ; j<jen ; j++ ) {
				cellProcess( tds[j] );
			}
		}
	
		// Read the ID from the DOM if present
		var rowNode = row.firstChild ? row : row.nTr;
	
		if ( rowNode ) {
			var id = rowNode.getAttribute( 'id' );
	
			if ( id ) {
				_fnSetObjectDataFn( settings.rowId )( d, id );
			}
		}
	
		return {
			data: d,
			cells: tds
		};
	}
	/**
	 * Create a new TR element (and it's TD children) for a row
	 *  @param {object} oSettings dataTables settings object
	 *  @param {int} iRow Row to consider
	 *  @param {node} [nTrIn] TR element to add to the table - optional. If not given,
	 *    DataTables will create a row automatically
	 *  @param {array} [anTds] Array of TD|TH elements for the row - must be given
	 *    if nTr is.
	 *  @memberof DataTable#oApi
	 */
	function _fnCreateTr ( oSettings, iRow, nTrIn, anTds )
	{
		var
			row = oSettings.aoData[iRow],
			rowData = row._aData,
			cells = [],
			nTr, nTd, oCol,
			i, iLen, create;
	
		if ( row.nTr === null )
		{
			nTr = nTrIn || document.createElement('tr');
	
			row.nTr = nTr;
			row.anCells = cells;
	
			/* Use a private property on the node to allow reserve mapping from the node
			 * to the aoData array for fast look up
			 */
			nTr._DT_RowIndex = iRow;
	
			/* Special parameters can be given by the data source to be used on the row */
			_fnRowAttributes( oSettings, row );
	
			/* Process each column */
			for ( i=0, iLen=oSettings.aoColumns.length ; i<iLen ; i++ )
			{
				oCol = oSettings.aoColumns[i];
				create = nTrIn ? false : true;
	
				nTd = create ? document.createElement( oCol.sCellType ) : anTds[i];
				nTd._DT_CellIndex = {
					row: iRow,
					column: i
				};
				
				cells.push( nTd );
	
				// Need to create the HTML if new, or if a rendering function is defined
				if ( create || ((oCol.mRender || oCol.mData !== i) &&
					 (!$.isPlainObject(oCol.mData) || oCol.mData._ !== i+'.display')
				)) {
					nTd.innerHTML = _fnGetCellData( oSettings, iRow, i, 'display' );
				}
	
				/* Add user defined class */
				if ( oCol.sClass )
				{
					nTd.className += ' '+oCol.sClass;
				}
	
				// Visibility - add or remove as required
				if ( oCol.bVisible && ! nTrIn )
				{
					nTr.appendChild( nTd );
				}
				else if ( ! oCol.bVisible && nTrIn )
				{
					nTd.parentNode.removeChild( nTd );
				}
	
				if ( oCol.fnCreatedCell )
				{
					oCol.fnCreatedCell.call( oSettings.oInstance,
						nTd, _fnGetCellData( oSettings, iRow, i ), rowData, iRow, i
					);
				}
			}
	
			_fnCallbackFire( oSettings, 'aoRowCreatedCallback', null, [nTr, rowData, iRow, cells] );
		}
	}
	
	
	/**
	 * Add attributes to a row based on the special `DT_*` parameters in a data
	 * source object.
	 *  @param {object} settings DataTables settings object
	 *  @param {object} DataTables row object for the row to be modified
	 *  @memberof DataTable#oApi
	 */
	function _fnRowAttributes( settings, row )
	{
		var tr = row.nTr;
		var data = row._aData;
	
		if ( tr ) {
			var id = settings.rowIdFn( data );
	
			if ( id ) {
				tr.id = id;
			}
	
			if ( data.DT_RowClass ) {
				// Remove any classes added by DT_RowClass before
				var a = data.DT_RowClass.split(' ');
				row.__rowc = row.__rowc ?
					_unique( row.__rowc.concat( a ) ) :
					a;
	
				$(tr)
					.removeClass( row.__rowc.join(' ') )
					.addClass( data.DT_RowClass );
			}
	
			if ( data.DT_RowAttr ) {
				$(tr).attr( data.DT_RowAttr );
			}
	
			if ( data.DT_RowData ) {
				$(tr).data( data.DT_RowData );
			}
		}
	}
	
	
	/**
	 * Create the HTML header for the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnBuildHead( oSettings )
	{
		var i, ien, cell, row, column;
		var thead = oSettings.nTHead;
		var tfoot = oSettings.nTFoot;
		var createHeader = $('th, td', thead).length === 0;
		var classes = oSettings.oClasses;
		var columns = oSettings.aoColumns;
	
		if ( createHeader ) {
			row = $('<tr/>').appendTo( thead );
		}
	
		for ( i=0, ien=columns.length ; i<ien ; i++ ) {
			column = columns[i];
			cell = $( column.nTh ).addClass( column.sClass );
	
			if ( createHeader ) {
				cell.appendTo( row );
			}
	
			// 1.11 move into sorting
			if ( oSettings.oFeatures.bSort ) {
				cell.addClass( column.sSortingClass );
	
				if ( column.bSortable !== false ) {
					cell
						.attr( 'tabindex', oSettings.iTabIndex )
						.attr( 'aria-controls', oSettings.sTableId );
	
					_fnSortAttachListener( oSettings, column.nTh, i );
				}
			}
	
			if ( column.sTitle != cell[0].innerHTML ) {
				cell.html( column.sTitle );
			}
	
			_fnRenderer( oSettings, 'header' )(
				oSettings, cell, column, classes
			);
		}
	
		if ( createHeader ) {
			_fnDetectHeader( oSettings.aoHeader, thead );
		}
	
		/* Deal with the footer - add classes if required */
		$(thead).children('tr').children('th, td').addClass( classes.sHeaderTH );
		$(tfoot).children('tr').children('th, td').addClass( classes.sFooterTH );
	
		// Cache the footer cells. Note that we only take the cells from the first
		// row in the footer. If there is more than one row the user wants to
		// interact with, they need to use the table().foot() method. Note also this
		// allows cells to be used for multiple columns using colspan
		if ( tfoot !== null ) {
			var cells = oSettings.aoFooter[0];
	
			for ( i=0, ien=cells.length ; i<ien ; i++ ) {
				column = columns[i];
				column.nTf = cells[i].cell;
	
				if ( column.sClass ) {
					$(column.nTf).addClass( column.sClass );
				}
			}
		}
	}
	
	
	/**
	 * Draw the header (or footer) element based on the column visibility states. The
	 * methodology here is to use the layout array from _fnDetectHeader, modified for
	 * the instantaneous column visibility, to construct the new layout. The grid is
	 * traversed over cell at a time in a rows x columns grid fashion, although each
	 * cell insert can cover multiple elements in the grid - which is tracks using the
	 * aApplied array. Cell inserts in the grid will only occur where there isn't
	 * already a cell in that position.
	 *  @param {object} oSettings dataTables settings object
	 *  @param array {objects} aoSource Layout array from _fnDetectHeader
	 *  @param {boolean} [bIncludeHidden=false] If true then include the hidden columns in the calc,
	 *  @memberof DataTable#oApi
	 */
	function _fnDrawHead( oSettings, aoSource, bIncludeHidden )
	{
		var i, iLen, j, jLen, k, kLen, n, nLocalTr;
		var aoLocal = [];
		var aApplied = [];
		var iColumns = oSettings.aoColumns.length;
		var iRowspan, iColspan;
	
		if ( ! aoSource )
		{
			return;
		}
	
		if (  bIncludeHidden === undefined )
		{
			bIncludeHidden = false;
		}
	
		/* Make a copy of the master layout array, but without the visible columns in it */
		for ( i=0, iLen=aoSource.length ; i<iLen ; i++ )
		{
			aoLocal[i] = aoSource[i].slice();
			aoLocal[i].nTr = aoSource[i].nTr;
	
			/* Remove any columns which are currently hidden */
			for ( j=iColumns-1 ; j>=0 ; j-- )
			{
				if ( !oSettings.aoColumns[j].bVisible && !bIncludeHidden )
				{
					aoLocal[i].splice( j, 1 );
				}
			}
	
			/* Prep the applied array - it needs an element for each row */
			aApplied.push( [] );
		}
	
		for ( i=0, iLen=aoLocal.length ; i<iLen ; i++ )
		{
			nLocalTr = aoLocal[i].nTr;
	
			/* All cells are going to be replaced, so empty out the row */
			if ( nLocalTr )
			{
				while( (n = nLocalTr.firstChild) )
				{
					nLocalTr.removeChild( n );
				}
			}
	
			for ( j=0, jLen=aoLocal[i].length ; j<jLen ; j++ )
			{
				iRowspan = 1;
				iColspan = 1;
	
				/* Check to see if there is already a cell (row/colspan) covering our target
				 * insert point. If there is, then there is nothing to do.
				 */
				if ( aApplied[i][j] === undefined )
				{
					nLocalTr.appendChild( aoLocal[i][j].cell );
					aApplied[i][j] = 1;
	
					/* Expand the cell to cover as many rows as needed */
					while ( aoLocal[i+iRowspan] !== undefined &&
					        aoLocal[i][j].cell == aoLocal[i+iRowspan][j].cell )
					{
						aApplied[i+iRowspan][j] = 1;
						iRowspan++;
					}
	
					/* Expand the cell to cover as many columns as needed */
					while ( aoLocal[i][j+iColspan] !== undefined &&
					        aoLocal[i][j].cell == aoLocal[i][j+iColspan].cell )
					{
						/* Must update the applied array over the rows for the columns */
						for ( k=0 ; k<iRowspan ; k++ )
						{
							aApplied[i+k][j+iColspan] = 1;
						}
						iColspan++;
					}
	
					/* Do the actual expansion in the DOM */
					$(aoLocal[i][j].cell)
						.attr('rowspan', iRowspan)
						.attr('colspan', iColspan);
				}
			}
		}
	}
	
	
	/**
	 * Insert the required TR nodes into the table for display
	 *  @param {object} oSettings dataTables settings object
	 *  @param ajaxComplete true after ajax call to complete rendering
	 *  @memberof DataTable#oApi
	 */
	function _fnDraw( oSettings, ajaxComplete )
	{
		// Allow for state saving and a custom start position
		_fnStart( oSettings );
	
		/* Provide a pre-callback function which can be used to cancel the draw is false is returned */
		var aPreDraw = _fnCallbackFire( oSettings, 'aoPreDrawCallback', 'preDraw', [oSettings] );
		if ( $.inArray( false, aPreDraw ) !== -1 )
		{
			_fnProcessingDisplay( oSettings, false );
			return;
		}
	
		var anRows = [];
		var iRowCount = 0;
		var asStripeClasses = oSettings.asStripeClasses;
		var iStripes = asStripeClasses.length;
		var oLang = oSettings.oLanguage;
		var bServerSide = _fnDataSource( oSettings ) == 'ssp';
		var aiDisplay = oSettings.aiDisplay;
		var iDisplayStart = oSettings._iDisplayStart;
		var iDisplayEnd = oSettings.fnDisplayEnd();
	
		oSettings.bDrawing = true;
	
		/* Server-side processing draw intercept */
		if ( oSettings.bDeferLoading )
		{
			oSettings.bDeferLoading = false;
			oSettings.iDraw++;
			_fnProcessingDisplay( oSettings, false );
		}
		else if ( !bServerSide )
		{
			oSettings.iDraw++;
		}
		else if ( !oSettings.bDestroying && !ajaxComplete)
		{
			_fnAjaxUpdate( oSettings );
			return;
		}
	
		if ( aiDisplay.length !== 0 )
		{
			var iStart = bServerSide ? 0 : iDisplayStart;
			var iEnd = bServerSide ? oSettings.aoData.length : iDisplayEnd;
	
			for ( var j=iStart ; j<iEnd ; j++ )
			{
				var iDataIndex = aiDisplay[j];
				var aoData = oSettings.aoData[ iDataIndex ];
				if ( aoData.nTr === null )
				{
					_fnCreateTr( oSettings, iDataIndex );
				}
	
				var nRow = aoData.nTr;
	
				/* Remove the old striping classes and then add the new one */
				if ( iStripes !== 0 )
				{
					var sStripe = asStripeClasses[ iRowCount % iStripes ];
					if ( aoData._sRowStripe != sStripe )
					{
						$(nRow).removeClass( aoData._sRowStripe ).addClass( sStripe );
						aoData._sRowStripe = sStripe;
					}
				}
	
				// Row callback functions - might want to manipulate the row
				// iRowCount and j are not currently documented. Are they at all
				// useful?
				_fnCallbackFire( oSettings, 'aoRowCallback', null,
					[nRow, aoData._aData, iRowCount, j, iDataIndex] );
	
				anRows.push( nRow );
				iRowCount++;
			}
		}
		else
		{
			/* Table is empty - create a row with an empty message in it */
			var sZero = oLang.sZeroRecords;
			if ( oSettings.iDraw == 1 &&  _fnDataSource( oSettings ) == 'ajax' )
			{
				sZero = oLang.sLoadingRecords;
			}
			else if ( oLang.sEmptyTable && oSettings.fnRecordsTotal() === 0 )
			{
				sZero = oLang.sEmptyTable;
			}
	
			anRows[ 0 ] = $( '<tr/>', { 'class': iStripes ? asStripeClasses[0] : '' } )
				.append( $('<td />', {
					'valign':  'top',
					'colSpan': _fnVisbleColumns( oSettings ),
					'class':   oSettings.oClasses.sRowEmpty
				} ).html( sZero ) )[0];
		}
	
		/* Header and footer callbacks */
		_fnCallbackFire( oSettings, 'aoHeaderCallback', 'header', [ $(oSettings.nTHead).children('tr')[0],
			_fnGetDataMaster( oSettings ), iDisplayStart, iDisplayEnd, aiDisplay ] );
	
		_fnCallbackFire( oSettings, 'aoFooterCallback', 'footer', [ $(oSettings.nTFoot).children('tr')[0],
			_fnGetDataMaster( oSettings ), iDisplayStart, iDisplayEnd, aiDisplay ] );
	
		var body = $(oSettings.nTBody);
	
		body.children().detach();
		body.append( $(anRows) );
	
		/* Call all required callback functions for the end of a draw */
		_fnCallbackFire( oSettings, 'aoDrawCallback', 'draw', [oSettings] );
	
		/* Draw is complete, sorting and filtering must be as well */
		oSettings.bSorted = false;
		oSettings.bFiltered = false;
		oSettings.bDrawing = false;
	}
	
	
	/**
	 * Redraw the table - taking account of the various features which are enabled
	 *  @param {object} oSettings dataTables settings object
	 *  @param {boolean} [holdPosition] Keep the current paging position. By default
	 *    the paging is reset to the first page
	 *  @memberof DataTable#oApi
	 */
	function _fnReDraw( settings, holdPosition )
	{
		var
			features = settings.oFeatures,
			sort     = features.bSort,
			filter   = features.bFilter;
	
		if ( sort ) {
			_fnSort( settings );
		}
	
		if ( filter ) {
			_fnFilterComplete( settings, settings.oPreviousSearch );
		}
		else {
			// No filtering, so we want to just use the display master
			settings.aiDisplay = settings.aiDisplayMaster.slice();
		}
	
		if ( holdPosition !== true ) {
			settings._iDisplayStart = 0;
		}
	
		// Let any modules know about the draw hold position state (used by
		// scrolling internally)
		settings._drawHold = holdPosition;
	
		_fnDraw( settings );
	
		settings._drawHold = false;
	}
	
	
	/**
	 * Add the options to the page HTML for the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnAddOptionsHtml ( oSettings )
	{
		var classes = oSettings.oClasses;
		var table = $(oSettings.nTable);
		var holding = $('<div/>').insertBefore( table ); // Holding element for speed
		var features = oSettings.oFeatures;
	
		// All DataTables are wrapped in a div
		var insert = $('<div/>', {
			id:      oSettings.sTableId+'_wrapper',
			'class': classes.sWrapper + (oSettings.nTFoot ? '' : ' '+classes.sNoFooter)
		} );
	
		oSettings.nHolding = holding[0];
		oSettings.nTableWrapper = insert[0];
		oSettings.nTableReinsertBefore = oSettings.nTable.nextSibling;
	
		/* Loop over the user set positioning and place the elements as needed */
		var aDom = oSettings.sDom.split('');
		var featureNode, cOption, nNewNode, cNext, sAttr, j;
		for ( var i=0 ; i<aDom.length ; i++ )
		{
			featureNode = null;
			cOption = aDom[i];
	
			if ( cOption == '<' )
			{
				/* New container div */
				nNewNode = $('<div/>')[0];
	
				/* Check to see if we should append an id and/or a class name to the container */
				cNext = aDom[i+1];
				if ( cNext == "'" || cNext == '"' )
				{
					sAttr = "";
					j = 2;
					while ( aDom[i+j] != cNext )
					{
						sAttr += aDom[i+j];
						j++;
					}
	
					/* Replace jQuery UI constants @todo depreciated */
					if ( sAttr == "H" )
					{
						sAttr = classes.sJUIHeader;
					}
					else if ( sAttr == "F" )
					{
						sAttr = classes.sJUIFooter;
					}
	
					/* The attribute can be in the format of "#id.class", "#id" or "class" This logic
					 * breaks the string into parts and applies them as needed
					 */
					if ( sAttr.indexOf('.') != -1 )
					{
						var aSplit = sAttr.split('.');
						nNewNode.id = aSplit[0].substr(1, aSplit[0].length-1);
						nNewNode.className = aSplit[1];
					}
					else if ( sAttr.charAt(0) == "#" )
					{
						nNewNode.id = sAttr.substr(1, sAttr.length-1);
					}
					else
					{
						nNewNode.className = sAttr;
					}
	
					i += j; /* Move along the position array */
				}
	
				insert.append( nNewNode );
				insert = $(nNewNode);
			}
			else if ( cOption == '>' )
			{
				/* End container div */
				insert = insert.parent();
			}
			// @todo Move options into their own plugins?
			else if ( cOption == 'l' && features.bPaginate && features.bLengthChange )
			{
				/* Length */
				featureNode = _fnFeatureHtmlLength( oSettings );
			}
			else if ( cOption == 'f' && features.bFilter )
			{
				/* Filter */
				featureNode = _fnFeatureHtmlFilter( oSettings );
			}
			else if ( cOption == 'r' && features.bProcessing )
			{
				/* pRocessing */
				featureNode = _fnFeatureHtmlProcessing( oSettings );
			}
			else if ( cOption == 't' )
			{
				/* Table */
				featureNode = _fnFeatureHtmlTable( oSettings );
			}
			else if ( cOption ==  'i' && features.bInfo )
			{
				/* Info */
				featureNode = _fnFeatureHtmlInfo( oSettings );
			}
			else if ( cOption == 'p' && features.bPaginate )
			{
				/* Pagination */
				featureNode = _fnFeatureHtmlPaginate( oSettings );
			}
			else if ( DataTable.ext.feature.length !== 0 )
			{
				/* Plug-in features */
				var aoFeatures = DataTable.ext.feature;
				for ( var k=0, kLen=aoFeatures.length ; k<kLen ; k++ )
				{
					if ( cOption == aoFeatures[k].cFeature )
					{
						featureNode = aoFeatures[k].fnInit( oSettings );
						break;
					}
				}
			}
	
			/* Add to the 2D features array */
			if ( featureNode )
			{
				var aanFeatures = oSettings.aanFeatures;
	
				if ( ! aanFeatures[cOption] )
				{
					aanFeatures[cOption] = [];
				}
	
				aanFeatures[cOption].push( featureNode );
				insert.append( featureNode );
			}
		}
	
		/* Built our DOM structure - replace the holding div with what we want */
		holding.replaceWith( insert );
		oSettings.nHolding = null;
	}
	
	
	/**
	 * Use the DOM source to create up an array of header cells. The idea here is to
	 * create a layout grid (array) of rows x columns, which contains a reference
	 * to the cell that that point in the grid (regardless of col/rowspan), such that
	 * any column / row could be removed and the new grid constructed
	 *  @param array {object} aLayout Array to store the calculated layout in
	 *  @param {node} nThead The header/footer element for the table
	 *  @memberof DataTable#oApi
	 */
	function _fnDetectHeader ( aLayout, nThead )
	{
		var nTrs = $(nThead).children('tr');
		var nTr, nCell;
		var i, k, l, iLen, jLen, iColShifted, iColumn, iColspan, iRowspan;
		var bUnique;
		var fnShiftCol = function ( a, i, j ) {
			var k = a[i];
	                while ( k[j] ) {
				j++;
			}
			return j;
		};
	
		aLayout.splice( 0, aLayout.length );
	
		/* We know how many rows there are in the layout - so prep it */
		for ( i=0, iLen=nTrs.length ; i<iLen ; i++ )
		{
			aLayout.push( [] );
		}
	
		/* Calculate a layout array */
		for ( i=0, iLen=nTrs.length ; i<iLen ; i++ )
		{
			nTr = nTrs[i];
			iColumn = 0;
	
			/* For every cell in the row... */
			nCell = nTr.firstChild;
			while ( nCell ) {
				if ( nCell.nodeName.toUpperCase() == "TD" ||
				     nCell.nodeName.toUpperCase() == "TH" )
				{
					/* Get the col and rowspan attributes from the DOM and sanitise them */
					iColspan = nCell.getAttribute('colspan') * 1;
					iRowspan = nCell.getAttribute('rowspan') * 1;
					iColspan = (!iColspan || iColspan===0 || iColspan===1) ? 1 : iColspan;
					iRowspan = (!iRowspan || iRowspan===0 || iRowspan===1) ? 1 : iRowspan;
	
					/* There might be colspan cells already in this row, so shift our target
					 * accordingly
					 */
					iColShifted = fnShiftCol( aLayout, i, iColumn );
	
					/* Cache calculation for unique columns */
					bUnique = iColspan === 1 ? true : false;
	
					/* If there is col / rowspan, copy the information into the layout grid */
					for ( l=0 ; l<iColspan ; l++ )
					{
						for ( k=0 ; k<iRowspan ; k++ )
						{
							aLayout[i+k][iColShifted+l] = {
								"cell": nCell,
								"unique": bUnique
							};
							aLayout[i+k].nTr = nTr;
						}
					}
				}
				nCell = nCell.nextSibling;
			}
		}
	}
	
	
	/**
	 * Get an array of unique th elements, one for each column
	 *  @param {object} oSettings dataTables settings object
	 *  @param {node} nHeader automatically detect the layout from this node - optional
	 *  @param {array} aLayout thead/tfoot layout from _fnDetectHeader - optional
	 *  @returns array {node} aReturn list of unique th's
	 *  @memberof DataTable#oApi
	 */
	function _fnGetUniqueThs ( oSettings, nHeader, aLayout )
	{
		var aReturn = [];
		if ( !aLayout )
		{
			aLayout = oSettings.aoHeader;
			if ( nHeader )
			{
				aLayout = [];
				_fnDetectHeader( aLayout, nHeader );
			}
		}
	
		for ( var i=0, iLen=aLayout.length ; i<iLen ; i++ )
		{
			for ( var j=0, jLen=aLayout[i].length ; j<jLen ; j++ )
			{
				if ( aLayout[i][j].unique &&
					 (!aReturn[j] || !oSettings.bSortCellsTop) )
				{
					aReturn[j] = aLayout[i][j].cell;
				}
			}
		}
	
		return aReturn;
	}
	
	/**
	 * Set the start position for draw
	 *  @param {object} oSettings dataTables settings object
	 */
	function _fnStart( oSettings )
	{
		var bServerSide = _fnDataSource( oSettings ) == 'ssp';
		var iInitDisplayStart = oSettings.iInitDisplayStart;
	
		// Check and see if we have an initial draw position from state saving
		if ( iInitDisplayStart !== undefined && iInitDisplayStart !== -1 )
		{
			oSettings._iDisplayStart = bServerSide ?
				iInitDisplayStart :
				iInitDisplayStart >= oSettings.fnRecordsDisplay() ?
					0 :
					iInitDisplayStart;
	
			oSettings.iInitDisplayStart = -1;
		}
	}
	
	/**
	 * Create an Ajax call based on the table's settings, taking into account that
	 * parameters can have multiple forms, and backwards compatibility.
	 *
	 * @param {object} oSettings dataTables settings object
	 * @param {array} data Data to send to the server, required by
	 *     DataTables - may be augmented by developer callbacks
	 * @param {function} fn Callback function to run when data is obtained
	 */
	function _fnBuildAjax( oSettings, data, fn )
	{
		// Compatibility with 1.9-, allow fnServerData and event to manipulate
		_fnCallbackFire( oSettings, 'aoServerParams', 'serverParams', [data] );
	
		// Convert to object based for 1.10+ if using the old array scheme which can
		// come from server-side processing or serverParams
		if ( data && Array.isArray(data) ) {
			var tmp = {};
			var rbracket = /(.*?)\[\]$/;
	
			$.each( data, function (key, val) {
				var match = val.name.match(rbracket);
	
				if ( match ) {
					// Support for arrays
					var name = match[0];
	
					if ( ! tmp[ name ] ) {
						tmp[ name ] = [];
					}
					tmp[ name ].push( val.value );
				}
				else {
					tmp[val.name] = val.value;
				}
			} );
			data = tmp;
		}
	
		var ajaxData;
		var ajax = oSettings.ajax;
		var instance = oSettings.oInstance;
		var callback = function ( json ) {
			var status = oSettings.jqXHR
				? oSettings.jqXHR.status
				: null;
	
			if ( json === null || (typeof status === 'number' && status == 204 ) ) {
				json = {};
				_fnAjaxDataSrc( oSettings, json, [] );
			}
	
			var error = json.error || json.sError;
			if ( error ) {
				_fnLog( oSettings, 0, error );
			}
	
			oSettings.json = json;
	
			_fnCallbackFire( oSettings, null, 'xhr', [oSettings, json, oSettings.jqXHR] );
			fn( json );
		};
	
		if ( $.isPlainObject( ajax ) && ajax.data )
		{
			ajaxData = ajax.data;
	
			var newData = typeof ajaxData === 'function' ?
				ajaxData( data, oSettings ) :  // fn can manipulate data or return
				ajaxData;                      // an object object or array to merge
	
			// If the function returned something, use that alone
			data = typeof ajaxData === 'function' && newData ?
				newData :
				$.extend( true, data, newData );
	
			// Remove the data property as we've resolved it already and don't want
			// jQuery to do it again (it is restored at the end of the function)
			delete ajax.data;
		}
	
		var baseAjax = {
			"data": data,
			"success": callback,
			"dataType": "json",
			"cache": false,
			"type": oSettings.sServerMethod,
			"error": function (xhr, error, thrown) {
				var ret = _fnCallbackFire( oSettings, null, 'xhr', [oSettings, null, oSettings.jqXHR] );
	
				if ( $.inArray( true, ret ) === -1 ) {
					if ( error == "parsererror" ) {
						_fnLog( oSettings, 0, 'Invalid JSON response', 1 );
					}
					else if ( xhr.readyState === 4 ) {
						_fnLog( oSettings, 0, 'Ajax error', 7 );
					}
				}
	
				_fnProcessingDisplay( oSettings, false );
			}
		};
	
		// Store the data submitted for the API
		oSettings.oAjaxData = data;
	
		// Allow plug-ins and external processes to modify the data
		_fnCallbackFire( oSettings, null, 'preXhr', [oSettings, data] );
	
		if ( oSettings.fnServerData )
		{
			// DataTables 1.9- compatibility
			oSettings.fnServerData.call( instance,
				oSettings.sAjaxSource,
				$.map( data, function (val, key) { // Need to convert back to 1.9 trad format
					return { name: key, value: val };
				} ),
				callback,
				oSettings
			);
		}
		else if ( oSettings.sAjaxSource || typeof ajax === 'string' )
		{
			// DataTables 1.9- compatibility
			oSettings.jqXHR = $.ajax( $.extend( baseAjax, {
				url: ajax || oSettings.sAjaxSource
			} ) );
		}
		else if ( typeof ajax === 'function' )
		{
			// Is a function - let the caller define what needs to be done
			oSettings.jqXHR = ajax.call( instance, data, callback, oSettings );
		}
		else
		{
			// Object to extend the base settings
			oSettings.jqXHR = $.ajax( $.extend( baseAjax, ajax ) );
	
			// Restore for next time around
			ajax.data = ajaxData;
		}
	}
	
	
	/**
	 * Update the table using an Ajax call
	 *  @param {object} settings dataTables settings object
	 *  @returns {boolean} Block the table drawing or not
	 *  @memberof DataTable#oApi
	 */
	function _fnAjaxUpdate( settings )
	{
		settings.iDraw++;
		_fnProcessingDisplay( settings, true );
	
		_fnBuildAjax(
			settings,
			_fnAjaxParameters( settings ),
			function(json) {
				_fnAjaxUpdateDraw( settings, json );
			}
		);
	}
	
	
	/**
	 * Build up the parameters in an object needed for a server-side processing
	 * request. Note that this is basically done twice, is different ways - a modern
	 * method which is used by default in DataTables 1.10 which uses objects and
	 * arrays, or the 1.9- method with is name / value pairs. 1.9 method is used if
	 * the sAjaxSource option is used in the initialisation, or the legacyAjax
	 * option is set.
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {bool} block the table drawing or not
	 *  @memberof DataTable#oApi
	 */
	function _fnAjaxParameters( settings )
	{
		var
			columns = settings.aoColumns,
			columnCount = columns.length,
			features = settings.oFeatures,
			preSearch = settings.oPreviousSearch,
			preColSearch = settings.aoPreSearchCols,
			i, data = [], dataProp, column, columnSearch,
			sort = _fnSortFlatten( settings ),
			displayStart = settings._iDisplayStart,
			displayLength = features.bPaginate !== false ?
				settings._iDisplayLength :
				-1;
	
		var param = function ( name, value ) {
			data.push( { 'name': name, 'value': value } );
		};
	
		// DataTables 1.9- compatible method
		param( 'sEcho',          settings.iDraw );
		param( 'iColumns',       columnCount );
		param( 'sColumns',       _pluck( columns, 'sName' ).join(',') );
		param( 'iDisplayStart',  displayStart );
		param( 'iDisplayLength', displayLength );
	
		// DataTables 1.10+ method
		var d = {
			draw:    settings.iDraw,
			columns: [],
			order:   [],
			start:   displayStart,
			length:  displayLength,
			search:  {
				value: preSearch.sSearch,
				regex: preSearch.bRegex
			}
		};
	
		for ( i=0 ; i<columnCount ; i++ ) {
			column = columns[i];
			columnSearch = preColSearch[i];
			dataProp = typeof column.mData=="function" ? 'function' : column.mData ;
	
			d.columns.push( {
				data:       dataProp,
				name:       column.sName,
				searchable: column.bSearchable,
				orderable:  column.bSortable,
				search:     {
					value: columnSearch.sSearch,
					regex: columnSearch.bRegex
				}
			} );
	
			param( "mDataProp_"+i, dataProp );
	
			if ( features.bFilter ) {
				param( 'sSearch_'+i,     columnSearch.sSearch );
				param( 'bRegex_'+i,      columnSearch.bRegex );
				param( 'bSearchable_'+i, column.bSearchable );
			}
	
			if ( features.bSort ) {
				param( 'bSortable_'+i, column.bSortable );
			}
		}
	
		if ( features.bFilter ) {
			param( 'sSearch', preSearch.sSearch );
			param( 'bRegex', preSearch.bRegex );
		}
	
		if ( features.bSort ) {
			$.each( sort, function ( i, val ) {
				d.order.push( { column: val.col, dir: val.dir } );
	
				param( 'iSortCol_'+i, val.col );
				param( 'sSortDir_'+i, val.dir );
			} );
	
			param( 'iSortingCols', sort.length );
		}
	
		// If the legacy.ajax parameter is null, then we automatically decide which
		// form to use, based on sAjaxSource
		var legacy = DataTable.ext.legacy.ajax;
		if ( legacy === null ) {
			return settings.sAjaxSource ? data : d;
		}
	
		// Otherwise, if legacy has been specified then we use that to decide on the
		// form
		return legacy ? data : d;
	}
	
	
	/**
	 * Data the data from the server (nuking the old) and redraw the table
	 *  @param {object} oSettings dataTables settings object
	 *  @param {object} json json data return from the server.
	 *  @param {string} json.sEcho Tracking flag for DataTables to match requests
	 *  @param {int} json.iTotalRecords Number of records in the data set, not accounting for filtering
	 *  @param {int} json.iTotalDisplayRecords Number of records in the data set, accounting for filtering
	 *  @param {array} json.aaData The data to display on this page
	 *  @param {string} [json.sColumns] Column ordering (sName, comma separated)
	 *  @memberof DataTable#oApi
	 */
	function _fnAjaxUpdateDraw ( settings, json )
	{
		// v1.10 uses camelCase variables, while 1.9 uses Hungarian notation.
		// Support both
		var compat = function ( old, modern ) {
			return json[old] !== undefined ? json[old] : json[modern];
		};
	
		var data = _fnAjaxDataSrc( settings, json );
		var draw            = compat( 'sEcho',                'draw' );
		var recordsTotal    = compat( 'iTotalRecords',        'recordsTotal' );
		var recordsFiltered = compat( 'iTotalDisplayRecords', 'recordsFiltered' );
	
		if ( draw !== undefined ) {
			// Protect against out of sequence returns
			if ( draw*1 < settings.iDraw ) {
				return;
			}
			settings.iDraw = draw * 1;
		}
	
		// No data in returned object, so rather than an array, we show an empty table
		if ( ! data ) {
			data = [];
		}
	
		_fnClearTable( settings );
		settings._iRecordsTotal   = parseInt(recordsTotal, 10);
		settings._iRecordsDisplay = parseInt(recordsFiltered, 10);
	
		for ( var i=0, ien=data.length ; i<ien ; i++ ) {
			_fnAddData( settings, data[i] );
		}
		settings.aiDisplay = settings.aiDisplayMaster.slice();
	
		_fnDraw( settings, true );
	
		if ( ! settings._bInitComplete ) {
			_fnInitComplete( settings, json );
		}
	
		_fnProcessingDisplay( settings, false );
	}
	
	
	/**
	 * Get the data from the JSON data source to use for drawing a table. Using
	 * `_fnGetObjectDataFn` allows the data to be sourced from a property of the
	 * source object, or from a processing function.
	 *  @param {object} oSettings dataTables settings object
	 *  @param  {object} json Data source object / array from the server
	 *  @return {array} Array of data to use
	 */
	 function _fnAjaxDataSrc ( oSettings, json, write )
	 {
		var dataSrc = $.isPlainObject( oSettings.ajax ) && oSettings.ajax.dataSrc !== undefined ?
			oSettings.ajax.dataSrc :
			oSettings.sAjaxDataProp; // Compatibility with 1.9-.
	
		if ( ! write ) {
			if ( dataSrc === 'data' ) {
				// If the default, then we still want to support the old style, and safely ignore
				// it if possible
				return json.aaData || json[dataSrc];
			}
	
			return dataSrc !== "" ?
				_fnGetObjectDataFn( dataSrc )( json ) :
				json;
		}
	
		// set
		_fnSetObjectDataFn( dataSrc )( json, write );
	}
	
	/**
	 * Generate the node required for filtering text
	 *  @returns {node} Filter control element
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlFilter ( settings )
	{
		var classes = settings.oClasses;
		var tableId = settings.sTableId;
		var language = settings.oLanguage;
		var previousSearch = settings.oPreviousSearch;
		var features = settings.aanFeatures;
		var input = '<input type="search" class="'+classes.sFilterInput+'"/>';
	
		var str = language.sSearch;
		str = str.match(/_INPUT_/) ?
			str.replace('_INPUT_', input) :
			str+input;
	
		var filter = $('<div/>', {
				'id': ! features.f ? tableId+'_filter' : null,
				'class': classes.sFilter
			} )
			.append( $('<label/>' ).append( str ) );
	
		var searchFn = function(event) {
			/* Update all other filter input elements for the new display */
			var n = features.f;
			var val = !this.value ? "" : this.value; // mental IE8 fix :-(
			if(previousSearch.return && event.key !== "Enter") {
				return;
			}
			/* Now do the filter */
			if ( val != previousSearch.sSearch ) {
				_fnFilterComplete( settings, {
					"sSearch": val,
					"bRegex": previousSearch.bRegex,
					"bSmart": previousSearch.bSmart ,
					"bCaseInsensitive": previousSearch.bCaseInsensitive,
					"return": previousSearch.return
				} );
	
				// Need to redraw, without resorting
				settings._iDisplayStart = 0;
				_fnDraw( settings );
			}
		};
	
		var searchDelay = settings.searchDelay !== null ?
			settings.searchDelay :
			_fnDataSource( settings ) === 'ssp' ?
				400 :
				0;
	
		var jqFilter = $('input', filter)
			.val( previousSearch.sSearch )
			.attr( 'placeholder', language.sSearchPlaceholder )
			.on(
				'keyup.DT search.DT input.DT paste.DT cut.DT',
				searchDelay ?
					_fnThrottle( searchFn, searchDelay ) :
					searchFn
			)
			.on( 'mouseup', function(e) {
				// Edge fix! Edge 17 does not trigger anything other than mouse events when clicking
				// on the clear icon (Edge bug 17584515). This is safe in other browsers as `searchFn`
				// checks the value to see if it has changed. In other browsers it won't have.
				setTimeout( function () {
					searchFn.call(jqFilter[0], e);
				}, 10);
			} )
			.on( 'keypress.DT', function(e) {
				/* Prevent form submission */
				if ( e.keyCode == 13 ) {
					return false;
				}
			} )
			.attr('aria-controls', tableId);
	
		// Update the input elements whenever the table is filtered
		$(settings.nTable).on( 'search.dt.DT', function ( ev, s ) {
			if ( settings === s ) {
				// IE9 throws an 'unknown error' if document.activeElement is used
				// inside an iframe or frame...
				try {
					if ( jqFilter[0] !== document.activeElement ) {
						jqFilter.val( previousSearch.sSearch );
					}
				}
				catch ( e ) {}
			}
		} );
	
		return filter[0];
	}
	
	
	/**
	 * Filter the table using both the global filter and column based filtering
	 *  @param {object} oSettings dataTables settings object
	 *  @param {object} oSearch search information
	 *  @param {int} [iForce] force a research of the master array (1) or not (undefined or 0)
	 *  @memberof DataTable#oApi
	 */
	function _fnFilterComplete ( oSettings, oInput, iForce )
	{
		var oPrevSearch = oSettings.oPreviousSearch;
		var aoPrevSearch = oSettings.aoPreSearchCols;
		var fnSaveFilter = function ( oFilter ) {
			/* Save the filtering values */
			oPrevSearch.sSearch = oFilter.sSearch;
			oPrevSearch.bRegex = oFilter.bRegex;
			oPrevSearch.bSmart = oFilter.bSmart;
			oPrevSearch.bCaseInsensitive = oFilter.bCaseInsensitive;
			oPrevSearch.return = oFilter.return;
		};
		var fnRegex = function ( o ) {
			// Backwards compatibility with the bEscapeRegex option
			return o.bEscapeRegex !== undefined ? !o.bEscapeRegex : o.bRegex;
		};
	
		// Resolve any column types that are unknown due to addition or invalidation
		// @todo As per sort - can this be moved into an event handler?
		_fnColumnTypes( oSettings );
	
		/* In server-side processing all filtering is done by the server, so no point hanging around here */
		if ( _fnDataSource( oSettings ) != 'ssp' )
		{
			/* Global filter */
			_fnFilter( oSettings, oInput.sSearch, iForce, fnRegex(oInput), oInput.bSmart, oInput.bCaseInsensitive, oInput.return );
			fnSaveFilter( oInput );
	
			/* Now do the individual column filter */
			for ( var i=0 ; i<aoPrevSearch.length ; i++ )
			{
				_fnFilterColumn( oSettings, aoPrevSearch[i].sSearch, i, fnRegex(aoPrevSearch[i]),
					aoPrevSearch[i].bSmart, aoPrevSearch[i].bCaseInsensitive );
			}
	
			/* Custom filtering */
			_fnFilterCustom( oSettings );
		}
		else
		{
			fnSaveFilter( oInput );
		}
	
		/* Tell the draw function we have been filtering */
		oSettings.bFiltered = true;
		_fnCallbackFire( oSettings, null, 'search', [oSettings] );
	}
	
	
	/**
	 * Apply custom filtering functions
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnFilterCustom( settings )
	{
		var filters = DataTable.ext.search;
		var displayRows = settings.aiDisplay;
		var row, rowIdx;
	
		for ( var i=0, ien=filters.length ; i<ien ; i++ ) {
			var rows = [];
	
			// Loop over each row and see if it should be included
			for ( var j=0, jen=displayRows.length ; j<jen ; j++ ) {
				rowIdx = displayRows[ j ];
				row = settings.aoData[ rowIdx ];
	
				if ( filters[i]( settings, row._aFilterData, rowIdx, row._aData, j ) ) {
					rows.push( rowIdx );
				}
			}
	
			// So the array reference doesn't break set the results into the
			// existing array
			displayRows.length = 0;
			$.merge( displayRows, rows );
		}
	}
	
	
	/**
	 * Filter the table on a per-column basis
	 *  @param {object} oSettings dataTables settings object
	 *  @param {string} sInput string to filter on
	 *  @param {int} iColumn column to filter
	 *  @param {bool} bRegex treat search string as a regular expression or not
	 *  @param {bool} bSmart use smart filtering or not
	 *  @param {bool} bCaseInsensitive Do case insensitive matching or not
	 *  @memberof DataTable#oApi
	 */
	function _fnFilterColumn ( settings, searchStr, colIdx, regex, smart, caseInsensitive )
	{
		if ( searchStr === '' ) {
			return;
		}
	
		var data;
		var out = [];
		var display = settings.aiDisplay;
		var rpSearch = _fnFilterCreateSearch( searchStr, regex, smart, caseInsensitive );
	
		for ( var i=0 ; i<display.length ; i++ ) {
			data = settings.aoData[ display[i] ]._aFilterData[ colIdx ];
	
			if ( rpSearch.test( data ) ) {
				out.push( display[i] );
			}
		}
	
		settings.aiDisplay = out;
	}
	
	
	/**
	 * Filter the data table based on user input and draw the table
	 *  @param {object} settings dataTables settings object
	 *  @param {string} input string to filter on
	 *  @param {int} force optional - force a research of the master array (1) or not (undefined or 0)
	 *  @param {bool} regex treat as a regular expression or not
	 *  @param {bool} smart perform smart filtering or not
	 *  @param {bool} caseInsensitive Do case insensitive matching or not
	 *  @memberof DataTable#oApi
	 */
	function _fnFilter( settings, input, force, regex, smart, caseInsensitive )
	{
		var rpSearch = _fnFilterCreateSearch( input, regex, smart, caseInsensitive );
		var prevSearch = settings.oPreviousSearch.sSearch;
		var displayMaster = settings.aiDisplayMaster;
		var display, invalidated, i;
		var filtered = [];
	
		// Need to take account of custom filtering functions - always filter
		if ( DataTable.ext.search.length !== 0 ) {
			force = true;
		}
	
		// Check if any of the rows were invalidated
		invalidated = _fnFilterData( settings );
	
		// If the input is blank - we just want the full data set
		if ( input.length <= 0 ) {
			settings.aiDisplay = displayMaster.slice();
		}
		else {
			// New search - start from the master array
			if ( invalidated ||
				 force ||
				 regex ||
				 prevSearch.length > input.length ||
				 input.indexOf(prevSearch) !== 0 ||
				 settings.bSorted // On resort, the display master needs to be
				                  // re-filtered since indexes will have changed
			) {
				settings.aiDisplay = displayMaster.slice();
			}
	
			// Search the display array
			display = settings.aiDisplay;
	
			for ( i=0 ; i<display.length ; i++ ) {
				if ( rpSearch.test( settings.aoData[ display[i] ]._sFilterRow ) ) {
					filtered.push( display[i] );
				}
			}
	
			settings.aiDisplay = filtered;
		}
	}
	
	
	/**
	 * Build a regular expression object suitable for searching a table
	 *  @param {string} sSearch string to search for
	 *  @param {bool} bRegex treat as a regular expression or not
	 *  @param {bool} bSmart perform smart filtering or not
	 *  @param {bool} bCaseInsensitive Do case insensitive matching or not
	 *  @returns {RegExp} constructed object
	 *  @memberof DataTable#oApi
	 */
	function _fnFilterCreateSearch( search, regex, smart, caseInsensitive )
	{
		search = regex ?
			search :
			_fnEscapeRegex( search );
		
		if ( smart ) {
			/* For smart filtering we want to allow the search to work regardless of
			 * word order. We also want double quoted text to be preserved, so word
			 * order is important - a la google. So this is what we want to
			 * generate:
			 * 
			 * ^(?=.*?\bone\b)(?=.*?\btwo three\b)(?=.*?\bfour\b).*$
			 */
			var a = $.map( search.match( /"[^"]+"|[^ ]+/g ) || [''], function ( word ) {
				if ( word.charAt(0) === '"' ) {
					var m = word.match( /^"(.*)"$/ );
					word = m ? m[1] : word;
				}
	
				return word.replace('"', '');
			} );
	
			search = '^(?=.*?'+a.join( ')(?=.*?' )+').*$';
		}
	
		return new RegExp( search, caseInsensitive ? 'i' : '' );
	}
	
	
	/**
	 * Escape a string such that it can be used in a regular expression
	 *  @param {string} sVal string to escape
	 *  @returns {string} escaped string
	 *  @memberof DataTable#oApi
	 */
	var _fnEscapeRegex = DataTable.util.escapeRegex;
	
	var __filter_div = $('<div>')[0];
	var __filter_div_textContent = __filter_div.textContent !== undefined;
	
	// Update the filtering data for each row if needed (by invalidation or first run)
	function _fnFilterData ( settings )
	{
		var columns = settings.aoColumns;
		var column;
		var i, j, ien, jen, filterData, cellData, row;
		var wasInvalidated = false;
	
		for ( i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
			row = settings.aoData[i];
	
			if ( ! row._aFilterData ) {
				filterData = [];
	
				for ( j=0, jen=columns.length ; j<jen ; j++ ) {
					column = columns[j];
	
					if ( column.bSearchable ) {
						cellData = _fnGetCellData( settings, i, j, 'filter' );
	
						// Search in DataTables 1.10 is string based. In 1.11 this
						// should be altered to also allow strict type checking.
						if ( cellData === null ) {
							cellData = '';
						}
	
						if ( typeof cellData !== 'string' && cellData.toString ) {
							cellData = cellData.toString();
						}
					}
					else {
						cellData = '';
					}
	
					// If it looks like there is an HTML entity in the string,
					// attempt to decode it so sorting works as expected. Note that
					// we could use a single line of jQuery to do this, but the DOM
					// method used here is much faster http://jsperf.com/html-decode
					if ( cellData.indexOf && cellData.indexOf('&') !== -1 ) {
						__filter_div.innerHTML = cellData;
						cellData = __filter_div_textContent ?
							__filter_div.textContent :
							__filter_div.innerText;
					}
	
					if ( cellData.replace ) {
						cellData = cellData.replace(/[\r\n\u2028]/g, '');
					}
	
					filterData.push( cellData );
				}
	
				row._aFilterData = filterData;
				row._sFilterRow = filterData.join('  ');
				wasInvalidated = true;
			}
		}
	
		return wasInvalidated;
	}
	
	
	/**
	 * Convert from the internal Hungarian notation to camelCase for external
	 * interaction
	 *  @param {object} obj Object to convert
	 *  @returns {object} Inverted object
	 *  @memberof DataTable#oApi
	 */
	function _fnSearchToCamel ( obj )
	{
		return {
			search:          obj.sSearch,
			smart:           obj.bSmart,
			regex:           obj.bRegex,
			caseInsensitive: obj.bCaseInsensitive
		};
	}
	
	
	
	/**
	 * Convert from camelCase notation to the internal Hungarian. We could use the
	 * Hungarian convert function here, but this is cleaner
	 *  @param {object} obj Object to convert
	 *  @returns {object} Inverted object
	 *  @memberof DataTable#oApi
	 */
	function _fnSearchToHung ( obj )
	{
		return {
			sSearch:          obj.search,
			bSmart:           obj.smart,
			bRegex:           obj.regex,
			bCaseInsensitive: obj.caseInsensitive
		};
	}
	
	/**
	 * Generate the node required for the info display
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {node} Information element
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlInfo ( settings )
	{
		var
			tid = settings.sTableId,
			nodes = settings.aanFeatures.i,
			n = $('<div/>', {
				'class': settings.oClasses.sInfo,
				'id': ! nodes ? tid+'_info' : null
			} );
	
		if ( ! nodes ) {
			// Update display on each draw
			settings.aoDrawCallback.push( {
				"fn": _fnUpdateInfo,
				"sName": "information"
			} );
	
			n
				.attr( 'role', 'status' )
				.attr( 'aria-live', 'polite' );
	
			// Table is described by our info div
			$(settings.nTable).attr( 'aria-describedby', tid+'_info' );
		}
	
		return n[0];
	}
	
	
	/**
	 * Update the information elements in the display
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnUpdateInfo ( settings )
	{
		/* Show information about the table */
		var nodes = settings.aanFeatures.i;
		if ( nodes.length === 0 ) {
			return;
		}
	
		var
			lang  = settings.oLanguage,
			start = settings._iDisplayStart+1,
			end   = settings.fnDisplayEnd(),
			max   = settings.fnRecordsTotal(),
			total = settings.fnRecordsDisplay(),
			out   = total ?
				lang.sInfo :
				lang.sInfoEmpty;
	
		if ( total !== max ) {
			/* Record set after filtering */
			out += ' ' + lang.sInfoFiltered;
		}
	
		// Convert the macros
		out += lang.sInfoPostFix;
		out = _fnInfoMacros( settings, out );
	
		var callback = lang.fnInfoCallback;
		if ( callback !== null ) {
			out = callback.call( settings.oInstance,
				settings, start, end, max, total, out
			);
		}
	
		$(nodes).html( out );
	}
	
	
	function _fnInfoMacros ( settings, str )
	{
		// When infinite scrolling, we are always starting at 1. _iDisplayStart is used only
		// internally
		var
			formatter  = settings.fnFormatNumber,
			start      = settings._iDisplayStart+1,
			len        = settings._iDisplayLength,
			vis        = settings.fnRecordsDisplay(),
			all        = len === -1;
	
		return str.
			replace(/_START_/g, formatter.call( settings, start ) ).
			replace(/_END_/g,   formatter.call( settings, settings.fnDisplayEnd() ) ).
			replace(/_MAX_/g,   formatter.call( settings, settings.fnRecordsTotal() ) ).
			replace(/_TOTAL_/g, formatter.call( settings, vis ) ).
			replace(/_PAGE_/g,  formatter.call( settings, all ? 1 : Math.ceil( start / len ) ) ).
			replace(/_PAGES_/g, formatter.call( settings, all ? 1 : Math.ceil( vis / len ) ) );
	}
	
	
	
	/**
	 * Draw the table for the first time, adding all required features
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnInitialise ( settings )
	{
		var i, iLen, iAjaxStart=settings.iInitDisplayStart;
		var columns = settings.aoColumns, column;
		var features = settings.oFeatures;
		var deferLoading = settings.bDeferLoading; // value modified by the draw
	
		/* Ensure that the table data is fully initialised */
		if ( ! settings.bInitialised ) {
			setTimeout( function(){ _fnInitialise( settings ); }, 200 );
			return;
		}
	
		/* Show the display HTML options */
		_fnAddOptionsHtml( settings );
	
		/* Build and draw the header / footer for the table */
		_fnBuildHead( settings );
		_fnDrawHead( settings, settings.aoHeader );
		_fnDrawHead( settings, settings.aoFooter );
	
		/* Okay to show that something is going on now */
		_fnProcessingDisplay( settings, true );
	
		/* Calculate sizes for columns */
		if ( features.bAutoWidth ) {
			_fnCalculateColumnWidths( settings );
		}
	
		for ( i=0, iLen=columns.length ; i<iLen ; i++ ) {
			column = columns[i];
	
			if ( column.sWidth ) {
				column.nTh.style.width = _fnStringToCss( column.sWidth );
			}
		}
	
		_fnCallbackFire( settings, null, 'preInit', [settings] );
	
		// If there is default sorting required - let's do it. The sort function
		// will do the drawing for us. Otherwise we draw the table regardless of the
		// Ajax source - this allows the table to look initialised for Ajax sourcing
		// data (show 'loading' message possibly)
		_fnReDraw( settings );
	
		// Server-side processing init complete is done by _fnAjaxUpdateDraw
		var dataSrc = _fnDataSource( settings );
		if ( dataSrc != 'ssp' || deferLoading ) {
			// if there is an ajax source load the data
			if ( dataSrc == 'ajax' ) {
				_fnBuildAjax( settings, [], function(json) {
					var aData = _fnAjaxDataSrc( settings, json );
	
					// Got the data - add it to the table
					for ( i=0 ; i<aData.length ; i++ ) {
						_fnAddData( settings, aData[i] );
					}
	
					// Reset the init display for cookie saving. We've already done
					// a filter, and therefore cleared it before. So we need to make
					// it appear 'fresh'
					settings.iInitDisplayStart = iAjaxStart;
	
					_fnReDraw( settings );
	
					_fnProcessingDisplay( settings, false );
					_fnInitComplete( settings, json );
				}, settings );
			}
			else {
				_fnProcessingDisplay( settings, false );
				_fnInitComplete( settings );
			}
		}
	}
	
	
	/**
	 * Draw the table for the first time, adding all required features
	 *  @param {object} oSettings dataTables settings object
	 *  @param {object} [json] JSON from the server that completed the table, if using Ajax source
	 *    with client-side processing (optional)
	 *  @memberof DataTable#oApi
	 */
	function _fnInitComplete ( settings, json )
	{
		settings._bInitComplete = true;
	
		// When data was added after the initialisation (data or Ajax) we need to
		// calculate the column sizing
		if ( json || settings.oInit.aaData ) {
			_fnAdjustColumnSizing( settings );
		}
	
		_fnCallbackFire( settings, null, 'plugin-init', [settings, json] );
		_fnCallbackFire( settings, 'aoInitComplete', 'init', [settings, json] );
	}
	
	
	function _fnLengthChange ( settings, val )
	{
		var len = parseInt( val, 10 );
		settings._iDisplayLength = len;
	
		_fnLengthOverflow( settings );
	
		// Fire length change event
		_fnCallbackFire( settings, null, 'length', [settings, len] );
	}
	
	
	/**
	 * Generate the node required for user display length changing
	 *  @param {object} settings dataTables settings object
	 *  @returns {node} Display length feature node
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlLength ( settings )
	{
		var
			classes  = settings.oClasses,
			tableId  = settings.sTableId,
			menu     = settings.aLengthMenu,
			d2       = Array.isArray( menu[0] ),
			lengths  = d2 ? menu[0] : menu,
			language = d2 ? menu[1] : menu;
	
		var select = $('<select/>', {
			'name':          tableId+'_length',
			'aria-controls': tableId,
			'class':         classes.sLengthSelect
		} );
	
		for ( var i=0, ien=lengths.length ; i<ien ; i++ ) {
			select[0][ i ] = new Option(
				typeof language[i] === 'number' ?
					settings.fnFormatNumber( language[i] ) :
					language[i],
				lengths[i]
			);
		}
	
		var div = $('<div><label/></div>').addClass( classes.sLength );
		if ( ! settings.aanFeatures.l ) {
			div[0].id = tableId+'_length';
		}
	
		div.children().append(
			settings.oLanguage.sLengthMenu.replace( '_MENU_', select[0].outerHTML )
		);
	
		// Can't use `select` variable as user might provide their own and the
		// reference is broken by the use of outerHTML
		$('select', div)
			.val( settings._iDisplayLength )
			.on( 'change.DT', function(e) {
				_fnLengthChange( settings, $(this).val() );
				_fnDraw( settings );
			} );
	
		// Update node value whenever anything changes the table's length
		$(settings.nTable).on( 'length.dt.DT', function (e, s, len) {
			if ( settings === s ) {
				$('select', div).val( len );
			}
		} );
	
		return div[0];
	}
	
	
	
	/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
	 * Note that most of the paging logic is done in
	 * DataTable.ext.pager
	 */
	
	/**
	 * Generate the node required for default pagination
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {node} Pagination feature node
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlPaginate ( settings )
	{
		var
			type   = settings.sPaginationType,
			plugin = DataTable.ext.pager[ type ],
			modern = typeof plugin === 'function',
			redraw = function( settings ) {
				_fnDraw( settings );
			},
			node = $('<div/>').addClass( settings.oClasses.sPaging + type )[0],
			features = settings.aanFeatures;
	
		if ( ! modern ) {
			plugin.fnInit( settings, node, redraw );
		}
	
		/* Add a draw callback for the pagination on first instance, to update the paging display */
		if ( ! features.p )
		{
			node.id = settings.sTableId+'_paginate';
	
			settings.aoDrawCallback.push( {
				"fn": function( settings ) {
					if ( modern ) {
						var
							start      = settings._iDisplayStart,
							len        = settings._iDisplayLength,
							visRecords = settings.fnRecordsDisplay(),
							all        = len === -1,
							page = all ? 0 : Math.ceil( start / len ),
							pages = all ? 1 : Math.ceil( visRecords / len ),
							buttons = plugin(page, pages),
							i, ien;
	
						for ( i=0, ien=features.p.length ; i<ien ; i++ ) {
							_fnRenderer( settings, 'pageButton' )(
								settings, features.p[i], i, buttons, page, pages
							);
						}
					}
					else {
						plugin.fnUpdate( settings, redraw );
					}
				},
				"sName": "pagination"
			} );
		}
	
		return node;
	}
	
	
	/**
	 * Alter the display settings to change the page
	 *  @param {object} settings DataTables settings object
	 *  @param {string|int} action Paging action to take: "first", "previous",
	 *    "next" or "last" or page number to jump to (integer)
	 *  @param [bool] redraw Automatically draw the update or not
	 *  @returns {bool} true page has changed, false - no change
	 *  @memberof DataTable#oApi
	 */
	function _fnPageChange ( settings, action, redraw )
	{
		var
			start     = settings._iDisplayStart,
			len       = settings._iDisplayLength,
			records   = settings.fnRecordsDisplay();
	
		if ( records === 0 || len === -1 )
		{
			start = 0;
		}
		else if ( typeof action === "number" )
		{
			start = action * len;
	
			if ( start > records )
			{
				start = 0;
			}
		}
		else if ( action == "first" )
		{
			start = 0;
		}
		else if ( action == "previous" )
		{
			start = len >= 0 ?
				start - len :
				0;
	
			if ( start < 0 )
			{
			  start = 0;
			}
		}
		else if ( action == "next" )
		{
			if ( start + len < records )
			{
				start += len;
			}
		}
		else if ( action == "last" )
		{
			start = Math.floor( (records-1) / len) * len;
		}
		else
		{
			_fnLog( settings, 0, "Unknown paging action: "+action, 5 );
		}
	
		var changed = settings._iDisplayStart !== start;
		settings._iDisplayStart = start;
	
		if ( changed ) {
			_fnCallbackFire( settings, null, 'page', [settings] );
	
			if ( redraw ) {
				_fnDraw( settings );
			}
		}
	
		return changed;
	}
	
	
	
	/**
	 * Generate the node required for the processing node
	 *  @param {object} settings dataTables settings object
	 *  @returns {node} Processing element
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlProcessing ( settings )
	{
		return $('<div/>', {
				'id': ! settings.aanFeatures.r ? settings.sTableId+'_processing' : null,
				'class': settings.oClasses.sProcessing
			} )
			.html( settings.oLanguage.sProcessing )
			.append('<div><div></div><div></div><div></div><div></div></div>')
			.insertBefore( settings.nTable )[0];
	}
	
	
	/**
	 * Display or hide the processing indicator
	 *  @param {object} settings dataTables settings object
	 *  @param {bool} show Show the processing indicator (true) or not (false)
	 *  @memberof DataTable#oApi
	 */
	function _fnProcessingDisplay ( settings, show )
	{
		if ( settings.oFeatures.bProcessing ) {
			$(settings.aanFeatures.r).css( 'display', show ? 'block' : 'none' );
		}
	
		_fnCallbackFire( settings, null, 'processing', [settings, show] );
	}
	
	/**
	 * Add any control elements for the table - specifically scrolling
	 *  @param {object} settings dataTables settings object
	 *  @returns {node} Node to add to the DOM
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlTable ( settings )
	{
		var table = $(settings.nTable);
	
		// Scrolling from here on in
		var scroll = settings.oScroll;
	
		if ( scroll.sX === '' && scroll.sY === '' ) {
			return settings.nTable;
		}
	
		var scrollX = scroll.sX;
		var scrollY = scroll.sY;
		var classes = settings.oClasses;
		var caption = table.children('caption');
		var captionSide = caption.length ? caption[0]._captionSide : null;
		var headerClone = $( table[0].cloneNode(false) );
		var footerClone = $( table[0].cloneNode(false) );
		var footer = table.children('tfoot');
		var _div = '<div/>';
		var size = function ( s ) {
			return !s ? null : _fnStringToCss( s );
		};
	
		if ( ! footer.length ) {
			footer = null;
		}
	
		/*
		 * The HTML structure that we want to generate in this function is:
		 *  div - scroller
		 *    div - scroll head
		 *      div - scroll head inner
		 *        table - scroll head table
		 *          thead - thead
		 *    div - scroll body
		 *      table - table (master table)
		 *        thead - thead clone for sizing
		 *        tbody - tbody
		 *    div - scroll foot
		 *      div - scroll foot inner
		 *        table - scroll foot table
		 *          tfoot - tfoot
		 */
		var scroller = $( _div, { 'class': classes.sScrollWrapper } )
			.append(
				$(_div, { 'class': classes.sScrollHead } )
					.css( {
						overflow: 'hidden',
						position: 'relative',
						border: 0,
						width: scrollX ? size(scrollX) : '100%'
					} )
					.append(
						$(_div, { 'class': classes.sScrollHeadInner } )
							.css( {
								'box-sizing': 'content-box',
								width: scroll.sXInner || '100%'
							} )
							.append(
								headerClone
									.removeAttr('id')
									.css( 'margin-left', 0 )
									.append( captionSide === 'top' ? caption : null )
									.append(
										table.children('thead')
									)
							)
					)
			)
			.append(
				$(_div, { 'class': classes.sScrollBody } )
					.css( {
						position: 'relative',
						overflow: 'auto',
						width: size( scrollX )
					} )
					.append( table )
			);
	
		if ( footer ) {
			scroller.append(
				$(_div, { 'class': classes.sScrollFoot } )
					.css( {
						overflow: 'hidden',
						border: 0,
						width: scrollX ? size(scrollX) : '100%'
					} )
					.append(
						$(_div, { 'class': classes.sScrollFootInner } )
							.append(
								footerClone
									.removeAttr('id')
									.css( 'margin-left', 0 )
									.append( captionSide === 'bottom' ? caption : null )
									.append(
										table.children('tfoot')
									)
							)
					)
			);
		}
	
		var children = scroller.children();
		var scrollHead = children[0];
		var scrollBody = children[1];
		var scrollFoot = footer ? children[2] : null;
	
		// When the body is scrolled, then we also want to scroll the headers
		if ( scrollX ) {
			$(scrollBody).on( 'scroll.DT', function (e) {
				var scrollLeft = this.scrollLeft;
	
				scrollHead.scrollLeft = scrollLeft;
	
				if ( footer ) {
					scrollFoot.scrollLeft = scrollLeft;
				}
			} );
		}
	
		$(scrollBody).css('max-height', scrollY);
		if (! scroll.bCollapse) {
			$(scrollBody).css('height', scrollY);
		}
	
		settings.nScrollHead = scrollHead;
		settings.nScrollBody = scrollBody;
		settings.nScrollFoot = scrollFoot;
	
		// On redraw - align columns
		settings.aoDrawCallback.push( {
			"fn": _fnScrollDraw,
			"sName": "scrolling"
		} );
	
		return scroller[0];
	}
	
	
	
	/**
	 * Update the header, footer and body tables for resizing - i.e. column
	 * alignment.
	 *
	 * Welcome to the most horrible function DataTables. The process that this
	 * function follows is basically:
	 *   1. Re-create the table inside the scrolling div
	 *   2. Take live measurements from the DOM
	 *   3. Apply the measurements to align the columns
	 *   4. Clean up
	 *
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnScrollDraw ( settings )
	{
		// Given that this is such a monster function, a lot of variables are use
		// to try and keep the minimised size as small as possible
		var
			scroll         = settings.oScroll,
			scrollX        = scroll.sX,
			scrollXInner   = scroll.sXInner,
			scrollY        = scroll.sY,
			barWidth       = scroll.iBarWidth,
			divHeader      = $(settings.nScrollHead),
			divHeaderStyle = divHeader[0].style,
			divHeaderInner = divHeader.children('div'),
			divHeaderInnerStyle = divHeaderInner[0].style,
			divHeaderTable = divHeaderInner.children('table'),
			divBodyEl      = settings.nScrollBody,
			divBody        = $(divBodyEl),
			divBodyStyle   = divBodyEl.style,
			divFooter      = $(settings.nScrollFoot),
			divFooterInner = divFooter.children('div'),
			divFooterTable = divFooterInner.children('table'),
			header         = $(settings.nTHead),
			table          = $(settings.nTable),
			tableEl        = table[0],
			tableStyle     = tableEl.style,
			footer         = settings.nTFoot ? $(settings.nTFoot) : null,
			browser        = settings.oBrowser,
			ie67           = browser.bScrollOversize,
			dtHeaderCells  = _pluck( settings.aoColumns, 'nTh' ),
			headerTrgEls, footerTrgEls,
			headerSrcEls, footerSrcEls,
			headerCopy, footerCopy,
			headerWidths=[], footerWidths=[],
			headerContent=[], footerContent=[],
			idx, correction, sanityWidth,
			zeroOut = function(nSizer) {
				var style = nSizer.style;
				style.paddingTop = "0";
				style.paddingBottom = "0";
				style.borderTopWidth = "0";
				style.borderBottomWidth = "0";
				style.height = 0;
			};
	
		// If the scrollbar visibility has changed from the last draw, we need to
		// adjust the column sizes as the table width will have changed to account
		// for the scrollbar
		var scrollBarVis = divBodyEl.scrollHeight > divBodyEl.clientHeight;
		
		if ( settings.scrollBarVis !== scrollBarVis && settings.scrollBarVis !== undefined ) {
			settings.scrollBarVis = scrollBarVis;
			_fnAdjustColumnSizing( settings );
			return; // adjust column sizing will call this function again
		}
		else {
			settings.scrollBarVis = scrollBarVis;
		}
	
		/*
		 * 1. Re-create the table inside the scrolling div
		 */
	
		// Remove the old minimised thead and tfoot elements in the inner table
		table.children('thead, tfoot').remove();
	
		if ( footer ) {
			footerCopy = footer.clone().prependTo( table );
			footerTrgEls = footer.find('tr'); // the original tfoot is in its own table and must be sized
			footerSrcEls = footerCopy.find('tr');
			footerCopy.find('[id]').removeAttr('id');
		}
	
		// Clone the current header and footer elements and then place it into the inner table
		headerCopy = header.clone().prependTo( table );
		headerTrgEls = header.find('tr'); // original header is in its own table
		headerSrcEls = headerCopy.find('tr');
		headerCopy.find('th, td').removeAttr('tabindex');
		headerCopy.find('[id]').removeAttr('id');
	
	
		/*
		 * 2. Take live measurements from the DOM - do not alter the DOM itself!
		 */
	
		// Remove old sizing and apply the calculated column widths
		// Get the unique column headers in the newly created (cloned) header. We want to apply the
		// calculated sizes to this header
		if ( ! scrollX )
		{
			divBodyStyle.width = '100%';
			divHeader[0].style.width = '100%';
		}
	
		$.each( _fnGetUniqueThs( settings, headerCopy ), function ( i, el ) {
			idx = _fnVisibleToColumnIndex( settings, i );
			el.style.width = settings.aoColumns[idx].sWidth;
		} );
	
		if ( footer ) {
			_fnApplyToChildren( function(n) {
				n.style.width = "";
			}, footerSrcEls );
		}
	
		// Size the table as a whole
		sanityWidth = table.outerWidth();
		if ( scrollX === "" ) {
			// No x scrolling
			tableStyle.width = "100%";
	
			// IE7 will make the width of the table when 100% include the scrollbar
			// - which is shouldn't. When there is a scrollbar we need to take this
			// into account.
			if ( ie67 && (table.find('tbody').height() > divBodyEl.offsetHeight ||
				divBody.css('overflow-y') == "scroll")
			) {
				tableStyle.width = _fnStringToCss( table.outerWidth() - barWidth);
			}
	
			// Recalculate the sanity width
			sanityWidth = table.outerWidth();
		}
		else if ( scrollXInner !== "" ) {
			// legacy x scroll inner has been given - use it
			tableStyle.width = _fnStringToCss(scrollXInner);
	
			// Recalculate the sanity width
			sanityWidth = table.outerWidth();
		}
	
		// Hidden header should have zero height, so remove padding and borders. Then
		// set the width based on the real headers
	
		// Apply all styles in one pass
		_fnApplyToChildren( zeroOut, headerSrcEls );
	
		// Read all widths in next pass
		_fnApplyToChildren( function(nSizer) {
			var style = window.getComputedStyle ?
				window.getComputedStyle(nSizer).width :
				_fnStringToCss( $(nSizer).width() );
	
			headerContent.push( nSizer.innerHTML );
			headerWidths.push( style );
		}, headerSrcEls );
	
		// Apply all widths in final pass
		_fnApplyToChildren( function(nToSize, i) {
			nToSize.style.width = headerWidths[i];
		}, headerTrgEls );
	
		$(headerSrcEls).css('height', 0);
	
		/* Same again with the footer if we have one */
		if ( footer )
		{
			_fnApplyToChildren( zeroOut, footerSrcEls );
	
			_fnApplyToChildren( function(nSizer) {
				footerContent.push( nSizer.innerHTML );
				footerWidths.push( _fnStringToCss( $(nSizer).css('width') ) );
			}, footerSrcEls );
	
			_fnApplyToChildren( function(nToSize, i) {
				nToSize.style.width = footerWidths[i];
			}, footerTrgEls );
	
			$(footerSrcEls).height(0);
		}
	
	
		/*
		 * 3. Apply the measurements
		 */
	
		// "Hide" the header and footer that we used for the sizing. We need to keep
		// the content of the cell so that the width applied to the header and body
		// both match, but we want to hide it completely. We want to also fix their
		// width to what they currently are
		_fnApplyToChildren( function(nSizer, i) {
			nSizer.innerHTML = '<div class="dataTables_sizing">'+headerContent[i]+'</div>';
			nSizer.childNodes[0].style.height = "0";
			nSizer.childNodes[0].style.overflow = "hidden";
			nSizer.style.width = headerWidths[i];
		}, headerSrcEls );
	
		if ( footer )
		{
			_fnApplyToChildren( function(nSizer, i) {
				nSizer.innerHTML = '<div class="dataTables_sizing">'+footerContent[i]+'</div>';
				nSizer.childNodes[0].style.height = "0";
				nSizer.childNodes[0].style.overflow = "hidden";
				nSizer.style.width = footerWidths[i];
			}, footerSrcEls );
		}
	
		// Sanity check that the table is of a sensible width. If not then we are going to get
		// misalignment - try to prevent this by not allowing the table to shrink below its min width
		if ( Math.round(table.outerWidth()) < Math.round(sanityWidth) )
		{
			// The min width depends upon if we have a vertical scrollbar visible or not */
			correction = ((divBodyEl.scrollHeight > divBodyEl.offsetHeight ||
				divBody.css('overflow-y') == "scroll")) ?
					sanityWidth+barWidth :
					sanityWidth;
	
			// IE6/7 are a law unto themselves...
			if ( ie67 && (divBodyEl.scrollHeight >
				divBodyEl.offsetHeight || divBody.css('overflow-y') == "scroll")
			) {
				tableStyle.width = _fnStringToCss( correction-barWidth );
			}
	
			// And give the user a warning that we've stopped the table getting too small
			if ( scrollX === "" || scrollXInner !== "" ) {
				_fnLog( settings, 1, 'Possible column misalignment', 6 );
			}
		}
		else
		{
			correction = '100%';
		}
	
		// Apply to the container elements
		divBodyStyle.width = _fnStringToCss( correction );
		divHeaderStyle.width = _fnStringToCss( correction );
	
		if ( footer ) {
			settings.nScrollFoot.style.width = _fnStringToCss( correction );
		}
	
	
		/*
		 * 4. Clean up
		 */
		if ( ! scrollY ) {
			/* IE7< puts a vertical scrollbar in place (when it shouldn't be) due to subtracting
			 * the scrollbar height from the visible display, rather than adding it on. We need to
			 * set the height in order to sort this. Don't want to do it in any other browsers.
			 */
			if ( ie67 ) {
				divBodyStyle.height = _fnStringToCss( tableEl.offsetHeight+barWidth );
			}
		}
	
		/* Finally set the width's of the header and footer tables */
		var iOuterWidth = table.outerWidth();
		divHeaderTable[0].style.width = _fnStringToCss( iOuterWidth );
		divHeaderInnerStyle.width = _fnStringToCss( iOuterWidth );
	
		// Figure out if there are scrollbar present - if so then we need a the header and footer to
		// provide a bit more space to allow "overflow" scrolling (i.e. past the scrollbar)
		var bScrolling = table.height() > divBodyEl.clientHeight || divBody.css('overflow-y') == "scroll";
		var padding = 'padding' + (browser.bScrollbarLeft ? 'Left' : 'Right' );
		divHeaderInnerStyle[ padding ] = bScrolling ? barWidth+"px" : "0px";
	
		if ( footer ) {
			divFooterTable[0].style.width = _fnStringToCss( iOuterWidth );
			divFooterInner[0].style.width = _fnStringToCss( iOuterWidth );
			divFooterInner[0].style[padding] = bScrolling ? barWidth+"px" : "0px";
		}
	
		// Correct DOM ordering for colgroup - comes before the thead
		table.children('colgroup').insertBefore( table.children('thead') );
	
		/* Adjust the position of the header in case we loose the y-scrollbar */
		divBody.trigger('scroll');
	
		// If sorting or filtering has occurred, jump the scrolling back to the top
		// only if we aren't holding the position
		if ( (settings.bSorted || settings.bFiltered) && ! settings._drawHold ) {
			divBodyEl.scrollTop = 0;
		}
	}
	
	
	
	/**
	 * Apply a given function to the display child nodes of an element array (typically
	 * TD children of TR rows
	 *  @param {function} fn Method to apply to the objects
	 *  @param array {nodes} an1 List of elements to look through for display children
	 *  @param array {nodes} an2 Another list (identical structure to the first) - optional
	 *  @memberof DataTable#oApi
	 */
	function _fnApplyToChildren( fn, an1, an2 )
	{
		var index=0, i=0, iLen=an1.length;
		var nNode1, nNode2;
	
		while ( i < iLen ) {
			nNode1 = an1[i].firstChild;
			nNode2 = an2 ? an2[i].firstChild : null;
	
			while ( nNode1 ) {
				if ( nNode1.nodeType === 1 ) {
					if ( an2 ) {
						fn( nNode1, nNode2, index );
					}
					else {
						fn( nNode1, index );
					}
	
					index++;
				}
	
				nNode1 = nNode1.nextSibling;
				nNode2 = an2 ? nNode2.nextSibling : null;
			}
	
			i++;
		}
	}
	
	
	
	var __re_html_remove = /<.*?>/g;
	
	
	/**
	 * Calculate the width of columns for the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnCalculateColumnWidths ( oSettings )
	{
		var
			table = oSettings.nTable,
			columns = oSettings.aoColumns,
			scroll = oSettings.oScroll,
			scrollY = scroll.sY,
			scrollX = scroll.sX,
			scrollXInner = scroll.sXInner,
			columnCount = columns.length,
			visibleColumns = _fnGetColumns( oSettings, 'bVisible' ),
			headerCells = $('th', oSettings.nTHead),
			tableWidthAttr = table.getAttribute('width'), // from DOM element
			tableContainer = table.parentNode,
			userInputs = false,
			i, column, columnIdx, width, outerWidth,
			browser = oSettings.oBrowser,
			ie67 = browser.bScrollOversize;
	
		var styleWidth = table.style.width;
		if ( styleWidth && styleWidth.indexOf('%') !== -1 ) {
			tableWidthAttr = styleWidth;
		}
	
		/* Convert any user input sizes into pixel sizes */
		for ( i=0 ; i<visibleColumns.length ; i++ ) {
			column = columns[ visibleColumns[i] ];
	
			if ( column.sWidth !== null ) {
				column.sWidth = _fnConvertToWidth( column.sWidthOrig, tableContainer );
	
				userInputs = true;
			}
		}
	
		/* If the number of columns in the DOM equals the number that we have to
		 * process in DataTables, then we can use the offsets that are created by
		 * the web- browser. No custom sizes can be set in order for this to happen,
		 * nor scrolling used
		 */
		if ( ie67 || ! userInputs && ! scrollX && ! scrollY &&
		     columnCount == _fnVisbleColumns( oSettings ) &&
		     columnCount == headerCells.length
		) {
			for ( i=0 ; i<columnCount ; i++ ) {
				var colIdx = _fnVisibleToColumnIndex( oSettings, i );
	
				if ( colIdx !== null ) {
					columns[ colIdx ].sWidth = _fnStringToCss( headerCells.eq(i).width() );
				}
			}
		}
		else
		{
			// Otherwise construct a single row, worst case, table with the widest
			// node in the data, assign any user defined widths, then insert it into
			// the DOM and allow the browser to do all the hard work of calculating
			// table widths
			var tmpTable = $(table).clone() // don't use cloneNode - IE8 will remove events on the main table
				.css( 'visibility', 'hidden' )
				.removeAttr( 'id' );
	
			// Clean up the table body
			tmpTable.find('tbody tr').remove();
			var tr = $('<tr/>').appendTo( tmpTable.find('tbody') );
	
			// Clone the table header and footer - we can't use the header / footer
			// from the cloned table, since if scrolling is active, the table's
			// real header and footer are contained in different table tags
			tmpTable.find('thead, tfoot').remove();
			tmpTable
				.append( $(oSettings.nTHead).clone() )
				.append( $(oSettings.nTFoot).clone() );
	
			// Remove any assigned widths from the footer (from scrolling)
			tmpTable.find('tfoot th, tfoot td').css('width', '');
	
			// Apply custom sizing to the cloned header
			headerCells = _fnGetUniqueThs( oSettings, tmpTable.find('thead')[0] );
	
			for ( i=0 ; i<visibleColumns.length ; i++ ) {
				column = columns[ visibleColumns[i] ];
	
				headerCells[i].style.width = column.sWidthOrig !== null && column.sWidthOrig !== '' ?
					_fnStringToCss( column.sWidthOrig ) :
					'';
	
				// For scrollX we need to force the column width otherwise the
				// browser will collapse it. If this width is smaller than the
				// width the column requires, then it will have no effect
				if ( column.sWidthOrig && scrollX ) {
					$( headerCells[i] ).append( $('<div/>').css( {
						width: column.sWidthOrig,
						margin: 0,
						padding: 0,
						border: 0,
						height: 1
					} ) );
				}
			}
	
			// Find the widest cell for each column and put it into the table
			if ( oSettings.aoData.length ) {
				for ( i=0 ; i<visibleColumns.length ; i++ ) {
					columnIdx = visibleColumns[i];
					column = columns[ columnIdx ];
	
					$( _fnGetWidestNode( oSettings, columnIdx ) )
						.clone( false )
						.append( column.sContentPadding )
						.appendTo( tr );
				}
			}
	
			// Tidy the temporary table - remove name attributes so there aren't
			// duplicated in the dom (radio elements for example)
			$('[name]', tmpTable).removeAttr('name');
	
			// Table has been built, attach to the document so we can work with it.
			// A holding element is used, positioned at the top of the container
			// with minimal height, so it has no effect on if the container scrolls
			// or not. Otherwise it might trigger scrolling when it actually isn't
			// needed
			var holder = $('<div/>').css( scrollX || scrollY ?
					{
						position: 'absolute',
						top: 0,
						left: 0,
						height: 1,
						right: 0,
						overflow: 'hidden'
					} :
					{}
				)
				.append( tmpTable )
				.appendTo( tableContainer );
	
			// When scrolling (X or Y) we want to set the width of the table as 
			// appropriate. However, when not scrolling leave the table width as it
			// is. This results in slightly different, but I think correct behaviour
			if ( scrollX && scrollXInner ) {
				tmpTable.width( scrollXInner );
			}
			else if ( scrollX ) {
				tmpTable.css( 'width', 'auto' );
				tmpTable.removeAttr('width');
	
				// If there is no width attribute or style, then allow the table to
				// collapse
				if ( tmpTable.width() < tableContainer.clientWidth && tableWidthAttr ) {
					tmpTable.width( tableContainer.clientWidth );
				}
			}
			else if ( scrollY ) {
				tmpTable.width( tableContainer.clientWidth );
			}
			else if ( tableWidthAttr ) {
				tmpTable.width( tableWidthAttr );
			}
	
			// Get the width of each column in the constructed table - we need to
			// know the inner width (so it can be assigned to the other table's
			// cells) and the outer width so we can calculate the full width of the
			// table. This is safe since DataTables requires a unique cell for each
			// column, but if ever a header can span multiple columns, this will
			// need to be modified.
			var total = 0;
			for ( i=0 ; i<visibleColumns.length ; i++ ) {
				var cell = $(headerCells[i]);
				var border = cell.outerWidth() - cell.width();
	
				// Use getBounding... where possible (not IE8-) because it can give
				// sub-pixel accuracy, which we then want to round up!
				var bounding = browser.bBounding ?
					Math.ceil( headerCells[i].getBoundingClientRect().width ) :
					cell.outerWidth();
	
				// Total is tracked to remove any sub-pixel errors as the outerWidth
				// of the table might not equal the total given here (IE!).
				total += bounding;
	
				// Width for each column to use
				columns[ visibleColumns[i] ].sWidth = _fnStringToCss( bounding - border );
			}
	
			table.style.width = _fnStringToCss( total );
	
			// Finished with the table - ditch it
			holder.remove();
		}
	
		// If there is a width attr, we want to attach an event listener which
		// allows the table sizing to automatically adjust when the window is
		// resized. Use the width attr rather than CSS, since we can't know if the
		// CSS is a relative value or absolute - DOM read is always px.
		if ( tableWidthAttr ) {
			table.style.width = _fnStringToCss( tableWidthAttr );
		}
	
		if ( (tableWidthAttr || scrollX) && ! oSettings._reszEvt ) {
			var bindResize = function () {
				$(window).on('resize.DT-'+oSettings.sInstance, _fnThrottle( function () {
					_fnAdjustColumnSizing( oSettings );
				} ) );
			};
	
			// IE6/7 will crash if we bind a resize event handler on page load.
			// To be removed in 1.11 which drops IE6/7 support
			if ( ie67 ) {
				setTimeout( bindResize, 1000 );
			}
			else {
				bindResize();
			}
	
			oSettings._reszEvt = true;
		}
	}
	
	
	/**
	 * Throttle the calls to a function. Arguments and context are maintained for
	 * the throttled function
	 *  @param {function} fn Function to be called
	 *  @param {int} [freq=200] call frequency in mS
	 *  @returns {function} wrapped function
	 *  @memberof DataTable#oApi
	 */
	var _fnThrottle = DataTable.util.throttle;
	
	
	/**
	 * Convert a CSS unit width to pixels (e.g. 2em)
	 *  @param {string} width width to be converted
	 *  @param {node} parent parent to get the with for (required for relative widths) - optional
	 *  @returns {int} width in pixels
	 *  @memberof DataTable#oApi
	 */
	function _fnConvertToWidth ( width, parent )
	{
		if ( ! width ) {
			return 0;
		}
	
		var n = $('<div/>')
			.css( 'width', _fnStringToCss( width ) )
			.appendTo( parent || document.body );
	
		var val = n[0].offsetWidth;
		n.remove();
	
		return val;
	}
	
	
	/**
	 * Get the widest node
	 *  @param {object} settings dataTables settings object
	 *  @param {int} colIdx column of interest
	 *  @returns {node} widest table node
	 *  @memberof DataTable#oApi
	 */
	function _fnGetWidestNode( settings, colIdx )
	{
		var idx = _fnGetMaxLenString( settings, colIdx );
		if ( idx < 0 ) {
			return null;
		}
	
		var data = settings.aoData[ idx ];
		return ! data.nTr ? // Might not have been created when deferred rendering
			$('<td/>').html( _fnGetCellData( settings, idx, colIdx, 'display' ) )[0] :
			data.anCells[ colIdx ];
	}
	
	
	/**
	 * Get the maximum strlen for each data column
	 *  @param {object} settings dataTables settings object
	 *  @param {int} colIdx column of interest
	 *  @returns {string} max string length for each column
	 *  @memberof DataTable#oApi
	 */
	function _fnGetMaxLenString( settings, colIdx )
	{
		var s, max=-1, maxIdx = -1;
	
		for ( var i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
			s = _fnGetCellData( settings, i, colIdx, 'display' )+'';
			s = s.replace( __re_html_remove, '' );
			s = s.replace( /&nbsp;/g, ' ' );
	
			if ( s.length > max ) {
				max = s.length;
				maxIdx = i;
			}
		}
	
		return maxIdx;
	}
	
	
	/**
	 * Append a CSS unit (only if required) to a string
	 *  @param {string} value to css-ify
	 *  @returns {string} value with css unit
	 *  @memberof DataTable#oApi
	 */
	function _fnStringToCss( s )
	{
		if ( s === null ) {
			return '0px';
		}
	
		if ( typeof s == 'number' ) {
			return s < 0 ?
				'0px' :
				s+'px';
		}
	
		// Check it has a unit character already
		return s.match(/\d$/) ?
			s+'px' :
			s;
	}
	
	
	
	function _fnSortFlatten ( settings )
	{
		var
			i, iLen, k, kLen,
			aSort = [],
			aiOrig = [],
			aoColumns = settings.aoColumns,
			aDataSort, iCol, sType, srcCol,
			fixed = settings.aaSortingFixed,
			fixedObj = $.isPlainObject( fixed ),
			nestedSort = [],
			add = function ( a ) {
				if ( a.length && ! Array.isArray( a[0] ) ) {
					// 1D array
					nestedSort.push( a );
				}
				else {
					// 2D array
					$.merge( nestedSort, a );
				}
			};
	
		// Build the sort array, with pre-fix and post-fix options if they have been
		// specified
		if ( Array.isArray( fixed ) ) {
			add( fixed );
		}
	
		if ( fixedObj && fixed.pre ) {
			add( fixed.pre );
		}
	
		add( settings.aaSorting );
	
		if (fixedObj && fixed.post ) {
			add( fixed.post );
		}
	
		for ( i=0 ; i<nestedSort.length ; i++ )
		{
			srcCol = nestedSort[i][0];
			aDataSort = aoColumns[ srcCol ].aDataSort;
	
			for ( k=0, kLen=aDataSort.length ; k<kLen ; k++ )
			{
				iCol = aDataSort[k];
				sType = aoColumns[ iCol ].sType || 'string';
	
				if ( nestedSort[i]._idx === undefined ) {
					nestedSort[i]._idx = $.inArray( nestedSort[i][1], aoColumns[iCol].asSorting );
				}
	
				aSort.push( {
					src:       srcCol,
					col:       iCol,
					dir:       nestedSort[i][1],
					index:     nestedSort[i]._idx,
					type:      sType,
					formatter: DataTable.ext.type.order[ sType+"-pre" ]
				} );
			}
		}
	
		return aSort;
	}
	
	/**
	 * Change the order of the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 *  @todo This really needs split up!
	 */
	function _fnSort ( oSettings )
	{
		var
			i, ien, iLen, j, jLen, k, kLen,
			sDataType, nTh,
			aiOrig = [],
			oExtSort = DataTable.ext.type.order,
			aoData = oSettings.aoData,
			aoColumns = oSettings.aoColumns,
			aDataSort, data, iCol, sType, oSort,
			formatters = 0,
			sortCol,
			displayMaster = oSettings.aiDisplayMaster,
			aSort;
	
		// Resolve any column types that are unknown due to addition or invalidation
		// @todo Can this be moved into a 'data-ready' handler which is called when
		//   data is going to be used in the table?
		_fnColumnTypes( oSettings );
	
		aSort = _fnSortFlatten( oSettings );
	
		for ( i=0, ien=aSort.length ; i<ien ; i++ ) {
			sortCol = aSort[i];
	
			// Track if we can use the fast sort algorithm
			if ( sortCol.formatter ) {
				formatters++;
			}
	
			// Load the data needed for the sort, for each cell
			_fnSortData( oSettings, sortCol.col );
		}
	
		/* No sorting required if server-side or no sorting array */
		if ( _fnDataSource( oSettings ) != 'ssp' && aSort.length !== 0 )
		{
			// Create a value - key array of the current row positions such that we can use their
			// current position during the sort, if values match, in order to perform stable sorting
			for ( i=0, iLen=displayMaster.length ; i<iLen ; i++ ) {
				aiOrig[ displayMaster[i] ] = i;
			}
	
			/* Do the sort - here we want multi-column sorting based on a given data source (column)
			 * and sorting function (from oSort) in a certain direction. It's reasonably complex to
			 * follow on it's own, but this is what we want (example two column sorting):
			 *  fnLocalSorting = function(a,b){
			 *    var iTest;
			 *    iTest = oSort['string-asc']('data11', 'data12');
			 *      if (iTest !== 0)
			 *        return iTest;
			 *    iTest = oSort['numeric-desc']('data21', 'data22');
			 *    if (iTest !== 0)
			 *      return iTest;
			 *    return oSort['numeric-asc']( aiOrig[a], aiOrig[b] );
			 *  }
			 * Basically we have a test for each sorting column, if the data in that column is equal,
			 * test the next column. If all columns match, then we use a numeric sort on the row
			 * positions in the original data array to provide a stable sort.
			 *
			 * Note - I know it seems excessive to have two sorting methods, but the first is around
			 * 15% faster, so the second is only maintained for backwards compatibility with sorting
			 * methods which do not have a pre-sort formatting function.
			 */
			if ( formatters === aSort.length ) {
				// All sort types have formatting functions
				displayMaster.sort( function ( a, b ) {
					var
						x, y, k, test, sort,
						len=aSort.length,
						dataA = aoData[a]._aSortData,
						dataB = aoData[b]._aSortData;
	
					for ( k=0 ; k<len ; k++ ) {
						sort = aSort[k];
	
						x = dataA[ sort.col ];
						y = dataB[ sort.col ];
	
						test = x<y ? -1 : x>y ? 1 : 0;
						if ( test !== 0 ) {
							return sort.dir === 'asc' ? test : -test;
						}
					}
	
					x = aiOrig[a];
					y = aiOrig[b];
					return x<y ? -1 : x>y ? 1 : 0;
				} );
			}
			else {
				// Depreciated - remove in 1.11 (providing a plug-in option)
				// Not all sort types have formatting methods, so we have to call their sorting
				// methods.
				displayMaster.sort( function ( a, b ) {
					var
						x, y, k, l, test, sort, fn,
						len=aSort.length,
						dataA = aoData[a]._aSortData,
						dataB = aoData[b]._aSortData;
	
					for ( k=0 ; k<len ; k++ ) {
						sort = aSort[k];
	
						x = dataA[ sort.col ];
						y = dataB[ sort.col ];
	
						fn = oExtSort[ sort.type+"-"+sort.dir ] || oExtSort[ "string-"+sort.dir ];
						test = fn( x, y );
						if ( test !== 0 ) {
							return test;
						}
					}
	
					x = aiOrig[a];
					y = aiOrig[b];
					return x<y ? -1 : x>y ? 1 : 0;
				} );
			}
		}
	
		/* Tell the draw function that we have sorted the data */
		oSettings.bSorted = true;
	}
	
	
	function _fnSortAria ( settings )
	{
		var label;
		var nextSort;
		var columns = settings.aoColumns;
		var aSort = _fnSortFlatten( settings );
		var oAria = settings.oLanguage.oAria;
	
		// ARIA attributes - need to loop all columns, to update all (removing old
		// attributes as needed)
		for ( var i=0, iLen=columns.length ; i<iLen ; i++ )
		{
			var col = columns[i];
			var asSorting = col.asSorting;
			var sTitle = col.ariaTitle || col.sTitle.replace( /<.*?>/g, "" );
			var th = col.nTh;
	
			// IE7 is throwing an error when setting these properties with jQuery's
			// attr() and removeAttr() methods...
			th.removeAttribute('aria-sort');
	
			/* In ARIA only the first sorting column can be marked as sorting - no multi-sort option */
			if ( col.bSortable ) {
				if ( aSort.length > 0 && aSort[0].col == i ) {
					th.setAttribute('aria-sort', aSort[0].dir=="asc" ? "ascending" : "descending" );
					nextSort = asSorting[ aSort[0].index+1 ] || asSorting[0];
				}
				else {
					nextSort = asSorting[0];
				}
	
				label = sTitle + ( nextSort === "asc" ?
					oAria.sSortAscending :
					oAria.sSortDescending
				);
			}
			else {
				label = sTitle;
			}
	
			th.setAttribute('aria-label', label);
		}
	}
	
	
	/**
	 * Function to run on user sort request
	 *  @param {object} settings dataTables settings object
	 *  @param {node} attachTo node to attach the handler to
	 *  @param {int} colIdx column sorting index
	 *  @param {boolean} [append=false] Append the requested sort to the existing
	 *    sort if true (i.e. multi-column sort)
	 *  @param {function} [callback] callback function
	 *  @memberof DataTable#oApi
	 */
	function _fnSortListener ( settings, colIdx, append, callback )
	{
		var col = settings.aoColumns[ colIdx ];
		var sorting = settings.aaSorting;
		var asSorting = col.asSorting;
		var nextSortIdx;
		var next = function ( a, overflow ) {
			var idx = a._idx;
			if ( idx === undefined ) {
				idx = $.inArray( a[1], asSorting );
			}
	
			return idx+1 < asSorting.length ?
				idx+1 :
				overflow ?
					null :
					0;
		};
	
		// Convert to 2D array if needed
		if ( typeof sorting[0] === 'number' ) {
			sorting = settings.aaSorting = [ sorting ];
		}
	
		// If appending the sort then we are multi-column sorting
		if ( append && settings.oFeatures.bSortMulti ) {
			// Are we already doing some kind of sort on this column?
			var sortIdx = $.inArray( colIdx, _pluck(sorting, '0') );
	
			if ( sortIdx !== -1 ) {
				// Yes, modify the sort
				nextSortIdx = next( sorting[sortIdx], true );
	
				if ( nextSortIdx === null && sorting.length === 1 ) {
					nextSortIdx = 0; // can't remove sorting completely
				}
	
				if ( nextSortIdx === null ) {
					sorting.splice( sortIdx, 1 );
				}
				else {
					sorting[sortIdx][1] = asSorting[ nextSortIdx ];
					sorting[sortIdx]._idx = nextSortIdx;
				}
			}
			else {
				// No sort on this column yet
				sorting.push( [ colIdx, asSorting[0], 0 ] );
				sorting[sorting.length-1]._idx = 0;
			}
		}
		else if ( sorting.length && sorting[0][0] == colIdx ) {
			// Single column - already sorting on this column, modify the sort
			nextSortIdx = next( sorting[0] );
	
			sorting.length = 1;
			sorting[0][1] = asSorting[ nextSortIdx ];
			sorting[0]._idx = nextSortIdx;
		}
		else {
			// Single column - sort only on this column
			sorting.length = 0;
			sorting.push( [ colIdx, asSorting[0] ] );
			sorting[0]._idx = 0;
		}
	
		// Run the sort by calling a full redraw
		_fnReDraw( settings );
	
		// callback used for async user interaction
		if ( typeof callback == 'function' ) {
			callback( settings );
		}
	}
	
	
	/**
	 * Attach a sort handler (click) to a node
	 *  @param {object} settings dataTables settings object
	 *  @param {node} attachTo node to attach the handler to
	 *  @param {int} colIdx column sorting index
	 *  @param {function} [callback] callback function
	 *  @memberof DataTable#oApi
	 */
	function _fnSortAttachListener ( settings, attachTo, colIdx, callback )
	{
		var col = settings.aoColumns[ colIdx ];
	
		_fnBindAction( attachTo, {}, function (e) {
			/* If the column is not sortable - don't to anything */
			if ( col.bSortable === false ) {
				return;
			}
	
			// If processing is enabled use a timeout to allow the processing
			// display to be shown - otherwise to it synchronously
			if ( settings.oFeatures.bProcessing ) {
				_fnProcessingDisplay( settings, true );
	
				setTimeout( function() {
					_fnSortListener( settings, colIdx, e.shiftKey, callback );
	
					// In server-side processing, the draw callback will remove the
					// processing display
					if ( _fnDataSource( settings ) !== 'ssp' ) {
						_fnProcessingDisplay( settings, false );
					}
				}, 0 );
			}
			else {
				_fnSortListener( settings, colIdx, e.shiftKey, callback );
			}
		} );
	}
	
	
	/**
	 * Set the sorting classes on table's body, Note: it is safe to call this function
	 * when bSort and bSortClasses are false
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnSortingClasses( settings )
	{
		var oldSort = settings.aLastSort;
		var sortClass = settings.oClasses.sSortColumn;
		var sort = _fnSortFlatten( settings );
		var features = settings.oFeatures;
		var i, ien, colIdx;
	
		if ( features.bSort && features.bSortClasses ) {
			// Remove old sorting classes
			for ( i=0, ien=oldSort.length ; i<ien ; i++ ) {
				colIdx = oldSort[i].src;
	
				// Remove column sorting
				$( _pluck( settings.aoData, 'anCells', colIdx ) )
					.removeClass( sortClass + (i<2 ? i+1 : 3) );
			}
	
			// Add new column sorting
			for ( i=0, ien=sort.length ; i<ien ; i++ ) {
				colIdx = sort[i].src;
	
				$( _pluck( settings.aoData, 'anCells', colIdx ) )
					.addClass( sortClass + (i<2 ? i+1 : 3) );
			}
		}
	
		settings.aLastSort = sort;
	}
	
	
	// Get the data to sort a column, be it from cache, fresh (populating the
	// cache), or from a sort formatter
	function _fnSortData( settings, idx )
	{
		// Custom sorting function - provided by the sort data type
		var column = settings.aoColumns[ idx ];
		var customSort = DataTable.ext.order[ column.sSortDataType ];
		var customData;
	
		if ( customSort ) {
			customData = customSort.call( settings.oInstance, settings, idx,
				_fnColumnIndexToVisible( settings, idx )
			);
		}
	
		// Use / populate cache
		var row, cellData;
		var formatter = DataTable.ext.type.order[ column.sType+"-pre" ];
	
		for ( var i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
			row = settings.aoData[i];
	
			if ( ! row._aSortData ) {
				row._aSortData = [];
			}
	
			if ( ! row._aSortData[idx] || customSort ) {
				cellData = customSort ?
					customData[i] : // If there was a custom sort function, use data from there
					_fnGetCellData( settings, i, idx, 'sort' );
	
				row._aSortData[ idx ] = formatter ?
					formatter( cellData ) :
					cellData;
			}
		}
	}
	
	
	
	/**
	 * Save the state of a table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnSaveState ( settings )
	{
		if (settings._bLoadingState) {
			return;
		}
	
		/* Store the interesting variables */
		var state = {
			time:    +new Date(),
			start:   settings._iDisplayStart,
			length:  settings._iDisplayLength,
			order:   $.extend( true, [], settings.aaSorting ),
			search:  _fnSearchToCamel( settings.oPreviousSearch ),
			columns: $.map( settings.aoColumns, function ( col, i ) {
				return {
					visible: col.bVisible,
					search: _fnSearchToCamel( settings.aoPreSearchCols[i] )
				};
			} )
		};
	
		settings.oSavedState = state;
		_fnCallbackFire( settings, "aoStateSaveParams", 'stateSaveParams', [settings, state] );
		
		if ( settings.oFeatures.bStateSave && !settings.bDestroying )
		{
			settings.fnStateSaveCallback.call( settings.oInstance, settings, state );
		}	
	}
	
	
	/**
	 * Attempt to load a saved table state
	 *  @param {object} oSettings dataTables settings object
	 *  @param {object} oInit DataTables init object so we can override settings
	 *  @param {function} callback Callback to execute when the state has been loaded
	 *  @memberof DataTable#oApi
	 */
	function _fnLoadState ( settings, oInit, callback )
	{
		if ( ! settings.oFeatures.bStateSave ) {
			callback();
			return;
		}
	
		var loaded = function(state) {
			_fnImplementState(settings, state, callback);
		}
	
		var state = settings.fnStateLoadCallback.call( settings.oInstance, settings, loaded );
	
		if ( state !== undefined ) {
			_fnImplementState( settings, state, callback );
		}
		// otherwise, wait for the loaded callback to be executed
	
		return true;
	}
	
	function _fnImplementState ( settings, s, callback) {
		var i, ien;
		var columns = settings.aoColumns;
		settings._bLoadingState = true;
	
		// When StateRestore was introduced the state could now be implemented at any time
		// Not just initialisation. To do this an api instance is required in some places
		var api = settings._bInitComplete ? new DataTable.Api(settings) : null;
	
		if ( ! s || ! s.time ) {
			settings._bLoadingState = false;
			callback();
			return;
		}
	
		// Allow custom and plug-in manipulation functions to alter the saved data set and
		// cancelling of loading by returning false
		var abStateLoad = _fnCallbackFire( settings, 'aoStateLoadParams', 'stateLoadParams', [settings, s] );
		if ( $.inArray( false, abStateLoad ) !== -1 ) {
			settings._bLoadingState = false;
			callback();
			return;
		}
	
		// Reject old data
		var duration = settings.iStateDuration;
		if ( duration > 0 && s.time < +new Date() - (duration*1000) ) {
			settings._bLoadingState = false;
			callback();
			return;
		}
	
		// Number of columns have changed - all bets are off, no restore of settings
		if ( s.columns && columns.length !== s.columns.length ) {
			settings._bLoadingState = false;
			callback();
			return;
		}
	
		// Store the saved state so it might be accessed at any time
		settings.oLoadedState = $.extend( true, {}, s );
	
		// Page Length
		if ( s.length !== undefined ) {
			// If already initialised just set the value directly so that the select element is also updated
			if (api) {
				api.page.len(s.length)
			}
			else {
				settings._iDisplayLength   = s.length;
			}
		}
	
		// Restore key features - todo - for 1.11 this needs to be done by
		// subscribed events
		if ( s.start !== undefined ) {
			if(api === null) {
				settings._iDisplayStart    = s.start;
				settings.iInitDisplayStart = s.start;
			}
			else {
				_fnPageChange(settings, s.start/settings._iDisplayLength);
			}
		}
	
		// Order
		if ( s.order !== undefined ) {
			settings.aaSorting = [];
			$.each( s.order, function ( i, col ) {
				settings.aaSorting.push( col[0] >= columns.length ?
					[ 0, col[1] ] :
					col
				);
			} );
		}
	
		// Search
		if ( s.search !== undefined ) {
			$.extend( settings.oPreviousSearch, _fnSearchToHung( s.search ) );
		}
	
		// Columns
		if ( s.columns ) {
			for ( i=0, ien=s.columns.length ; i<ien ; i++ ) {
				var col = s.columns[i];
	
				// Visibility
				if ( col.visible !== undefined ) {
					// If the api is defined, the table has been initialised so we need to use it rather than internal settings
					if (api) {
						// Don't redraw the columns on every iteration of this loop, we will do this at the end instead
						api.column(i).visible(col.visible, false);
					}
					else {
						columns[i].bVisible = col.visible;
					}
				}
	
				// Search
				if ( col.search !== undefined ) {
					$.extend( settings.aoPreSearchCols[i], _fnSearchToHung( col.search ) );
				}
			}
			
			// If the api is defined then we need to adjust the columns once the visibility has been changed
			if (api) {
				api.columns.adjust();
			}
		}
	
		settings._bLoadingState = false;
		_fnCallbackFire( settings, 'aoStateLoaded', 'stateLoaded', [settings, s] );
		callback();
	};
	
	
	/**
	 * Return the settings object for a particular table
	 *  @param {node} table table we are using as a dataTable
	 *  @returns {object} Settings object - or null if not found
	 *  @memberof DataTable#oApi
	 */
	function _fnSettingsFromNode ( table )
	{
		var settings = DataTable.settings;
		var idx = $.inArray( table, _pluck( settings, 'nTable' ) );
	
		return idx !== -1 ?
			settings[ idx ] :
			null;
	}
	
	
	/**
	 * Log an error message
	 *  @param {object} settings dataTables settings object
	 *  @param {int} level log error messages, or display them to the user
	 *  @param {string} msg error message
	 *  @param {int} tn Technical note id to get more information about the error.
	 *  @memberof DataTable#oApi
	 */
	function _fnLog( settings, level, msg, tn )
	{
		msg = 'DataTables warning: '+
			(settings ? 'table id='+settings.sTableId+' - ' : '')+msg;
	
		if ( tn ) {
			msg += '. For more information about this error, please see '+
			'http://datatables.net/tn/'+tn;
		}
	
		if ( ! level  ) {
			// Backwards compatibility pre 1.10
			var ext = DataTable.ext;
			var type = ext.sErrMode || ext.errMode;
	
			if ( settings ) {
				_fnCallbackFire( settings, null, 'error', [ settings, tn, msg ] );
			}
	
			if ( type == 'alert' ) {
				alert( msg );
			}
			else if ( type == 'throw' ) {
				throw new Error(msg);
			}
			else if ( typeof type == 'function' ) {
				type( settings, tn, msg );
			}
		}
		else if ( window.console && console.log ) {
			console.log( msg );
		}
	}
	
	
	/**
	 * See if a property is defined on one object, if so assign it to the other object
	 *  @param {object} ret target object
	 *  @param {object} src source object
	 *  @param {string} name property
	 *  @param {string} [mappedName] name to map too - optional, name used if not given
	 *  @memberof DataTable#oApi
	 */
	function _fnMap( ret, src, name, mappedName )
	{
		if ( Array.isArray( name ) ) {
			$.each( name, function (i, val) {
				if ( Array.isArray( val ) ) {
					_fnMap( ret, src, val[0], val[1] );
				}
				else {
					_fnMap( ret, src, val );
				}
			} );
	
			return;
		}
	
		if ( mappedName === undefined ) {
			mappedName = name;
		}
	
		if ( src[name] !== undefined ) {
			ret[mappedName] = src[name];
		}
	}
	
	
	/**
	 * Extend objects - very similar to jQuery.extend, but deep copy objects, and
	 * shallow copy arrays. The reason we need to do this, is that we don't want to
	 * deep copy array init values (such as aaSorting) since the dev wouldn't be
	 * able to override them, but we do want to deep copy arrays.
	 *  @param {object} out Object to extend
	 *  @param {object} extender Object from which the properties will be applied to
	 *      out
	 *  @param {boolean} breakRefs If true, then arrays will be sliced to take an
	 *      independent copy with the exception of the `data` or `aaData` parameters
	 *      if they are present. This is so you can pass in a collection to
	 *      DataTables and have that used as your data source without breaking the
	 *      references
	 *  @returns {object} out Reference, just for convenience - out === the return.
	 *  @memberof DataTable#oApi
	 *  @todo This doesn't take account of arrays inside the deep copied objects.
	 */
	function _fnExtend( out, extender, breakRefs )
	{
		var val;
	
		for ( var prop in extender ) {
			if ( extender.hasOwnProperty(prop) ) {
				val = extender[prop];
	
				if ( $.isPlainObject( val ) ) {
					if ( ! $.isPlainObject( out[prop] ) ) {
						out[prop] = {};
					}
					$.extend( true, out[prop], val );
				}
				else if ( breakRefs && prop !== 'data' && prop !== 'aaData' && Array.isArray(val) ) {
					out[prop] = val.slice();
				}
				else {
					out[prop] = val;
				}
			}
		}
	
		return out;
	}
	
	
	/**
	 * Bind an event handers to allow a click or return key to activate the callback.
	 * This is good for accessibility since a return on the keyboard will have the
	 * same effect as a click, if the element has focus.
	 *  @param {element} n Element to bind the action to
	 *  @param {object} oData Data object to pass to the triggered function
	 *  @param {function} fn Callback function for when the event is triggered
	 *  @memberof DataTable#oApi
	 */
	function _fnBindAction( n, oData, fn )
	{
		$(n)
			.on( 'click.DT', oData, function (e) {
					$(n).trigger('blur'); // Remove focus outline for mouse users
					fn(e);
				} )
			.on( 'keypress.DT', oData, function (e){
					if ( e.which === 13 ) {
						e.preventDefault();
						fn(e);
					}
				} )
			.on( 'selectstart.DT', function () {
					/* Take the brutal approach to cancelling text selection */
					return false;
				} );
	}
	
	
	/**
	 * Register a callback function. Easily allows a callback function to be added to
	 * an array store of callback functions that can then all be called together.
	 *  @param {object} oSettings dataTables settings object
	 *  @param {string} sStore Name of the array storage for the callbacks in oSettings
	 *  @param {function} fn Function to be called back
	 *  @param {string} sName Identifying name for the callback (i.e. a label)
	 *  @memberof DataTable#oApi
	 */
	function _fnCallbackReg( oSettings, sStore, fn, sName )
	{
		if ( fn )
		{
			oSettings[sStore].push( {
				"fn": fn,
				"sName": sName
			} );
		}
	}
	
	
	/**
	 * Fire callback functions and trigger events. Note that the loop over the
	 * callback array store is done backwards! Further note that you do not want to
	 * fire off triggers in time sensitive applications (for example cell creation)
	 * as its slow.
	 *  @param {object} settings dataTables settings object
	 *  @param {string} callbackArr Name of the array storage for the callbacks in
	 *      oSettings
	 *  @param {string} eventName Name of the jQuery custom event to trigger. If
	 *      null no trigger is fired
	 *  @param {array} args Array of arguments to pass to the callback function /
	 *      trigger
	 *  @memberof DataTable#oApi
	 */
	function _fnCallbackFire( settings, callbackArr, eventName, args )
	{
		var ret = [];
	
		if ( callbackArr ) {
			ret = $.map( settings[callbackArr].slice().reverse(), function (val, i) {
				return val.fn.apply( settings.oInstance, args );
			} );
		}
	
		if ( eventName !== null ) {
			var e = $.Event( eventName+'.dt' );
	
			$(settings.nTable).trigger( e, args );
	
			ret.push( e.result );
		}
	
		return ret;
	}
	
	
	function _fnLengthOverflow ( settings )
	{
		var
			start = settings._iDisplayStart,
			end = settings.fnDisplayEnd(),
			len = settings._iDisplayLength;
	
		/* If we have space to show extra rows (backing up from the end point - then do so */
		if ( start >= end )
		{
			start = end - len;
		}
	
		// Keep the start record on the current page
		start -= (start % len);
	
		if ( len === -1 || start < 0 )
		{
			start = 0;
		}
	
		settings._iDisplayStart = start;
	}
	
	
	function _fnRenderer( settings, type )
	{
		var renderer = settings.renderer;
		var host = DataTable.ext.renderer[type];
	
		if ( $.isPlainObject( renderer ) && renderer[type] ) {
			// Specific renderer for this type. If available use it, otherwise use
			// the default.
			return host[renderer[type]] || host._;
		}
		else if ( typeof renderer === 'string' ) {
			// Common renderer - if there is one available for this type use it,
			// otherwise use the default
			return host[renderer] || host._;
		}
	
		// Use the default
		return host._;
	}
	
	
	/**
	 * Detect the data source being used for the table. Used to simplify the code
	 * a little (ajax) and to make it compress a little smaller.
	 *
	 *  @param {object} settings dataTables settings object
	 *  @returns {string} Data source
	 *  @memberof DataTable#oApi
	 */
	function _fnDataSource ( settings )
	{
		if ( settings.oFeatures.bServerSide ) {
			return 'ssp';
		}
		else if ( settings.ajax || settings.sAjaxSource ) {
			return 'ajax';
		}
		return 'dom';
	}
	
	
	
	
	/**
	 * Computed structure of the DataTables API, defined by the options passed to
	 * `DataTable.Api.register()` when building the API.
	 *
	 * The structure is built in order to speed creation and extension of the Api
	 * objects since the extensions are effectively pre-parsed.
	 *
	 * The array is an array of objects with the following structure, where this
	 * base array represents the Api prototype base:
	 *
	 *     [
	 *       {
	 *         name:      'data'                -- string   - Property name
	 *         val:       function () {},       -- function - Api method (or undefined if just an object
	 *         methodExt: [ ... ],              -- array    - Array of Api object definitions to extend the method result
	 *         propExt:   [ ... ]               -- array    - Array of Api object definitions to extend the property
	 *       },
	 *       {
	 *         name:     'row'
	 *         val:       {},
	 *         methodExt: [ ... ],
	 *         propExt:   [
	 *           {
	 *             name:      'data'
	 *             val:       function () {},
	 *             methodExt: [ ... ],
	 *             propExt:   [ ... ]
	 *           },
	 *           ...
	 *         ]
	 *       }
	 *     ]
	 *
	 * @type {Array}
	 * @ignore
	 */
	var __apiStruct = [];
	
	
	/**
	 * `Array.prototype` reference.
	 *
	 * @type object
	 * @ignore
	 */
	var __arrayProto = Array.prototype;
	
	
	/**
	 * Abstraction for `context` parameter of the `Api` constructor to allow it to
	 * take several different forms for ease of use.
	 *
	 * Each of the input parameter types will be converted to a DataTables settings
	 * object where possible.
	 *
	 * @param  {string|node|jQuery|object} mixed DataTable identifier. Can be one
	 *   of:
	 *
	 *   * `string` - jQuery selector. Any DataTables' matching the given selector
	 *     with be found and used.
	 *   * `node` - `TABLE` node which has already been formed into a DataTable.
	 *   * `jQuery` - A jQuery object of `TABLE` nodes.
	 *   * `object` - DataTables settings object
	 *   * `DataTables.Api` - API instance
	 * @return {array|null} Matching DataTables settings objects. `null` or
	 *   `undefined` is returned if no matching DataTable is found.
	 * @ignore
	 */
	var _toSettings = function ( mixed )
	{
		var idx, jq;
		var settings = DataTable.settings;
		var tables = $.map( settings, function (el, i) {
			return el.nTable;
		} );
	
		if ( ! mixed ) {
			return [];
		}
		else if ( mixed.nTable && mixed.oApi ) {
			// DataTables settings object
			return [ mixed ];
		}
		else if ( mixed.nodeName && mixed.nodeName.toLowerCase() === 'table' ) {
			// Table node
			idx = $.inArray( mixed, tables );
			return idx !== -1 ? [ settings[idx] ] : null;
		}
		else if ( mixed && typeof mixed.settings === 'function' ) {
			return mixed.settings().toArray();
		}
		else if ( typeof mixed === 'string' ) {
			// jQuery selector
			jq = $(mixed);
		}
		else if ( mixed instanceof $ ) {
			// jQuery object (also DataTables instance)
			jq = mixed;
		}
	
		if ( jq ) {
			return jq.map( function(i) {
				idx = $.inArray( this, tables );
				return idx !== -1 ? settings[idx] : null;
			} ).toArray();
		}
	};
	
	
	/**
	 * DataTables API class - used to control and interface with  one or more
	 * DataTables enhanced tables.
	 *
	 * The API class is heavily based on jQuery, presenting a chainable interface
	 * that you can use to interact with tables. Each instance of the API class has
	 * a "context" - i.e. the tables that it will operate on. This could be a single
	 * table, all tables on a page or a sub-set thereof.
	 *
	 * Additionally the API is designed to allow you to easily work with the data in
	 * the tables, retrieving and manipulating it as required. This is done by
	 * presenting the API class as an array like interface. The contents of the
	 * array depend upon the actions requested by each method (for example
	 * `rows().nodes()` will return an array of nodes, while `rows().data()` will
	 * return an array of objects or arrays depending upon your table's
	 * configuration). The API object has a number of array like methods (`push`,
	 * `pop`, `reverse` etc) as well as additional helper methods (`each`, `pluck`,
	 * `unique` etc) to assist your working with the data held in a table.
	 *
	 * Most methods (those which return an Api instance) are chainable, which means
	 * the return from a method call also has all of the methods available that the
	 * top level object had. For example, these two calls are equivalent:
	 *
	 *     // Not chained
	 *     api.row.add( {...} );
	 *     api.draw();
	 *
	 *     // Chained
	 *     api.row.add( {...} ).draw();
	 *
	 * @class DataTable.Api
	 * @param {array|object|string|jQuery} context DataTable identifier. This is
	 *   used to define which DataTables enhanced tables this API will operate on.
	 *   Can be one of:
	 *
	 *   * `string` - jQuery selector. Any DataTables' matching the given selector
	 *     with be found and used.
	 *   * `node` - `TABLE` node which has already been formed into a DataTable.
	 *   * `jQuery` - A jQuery object of `TABLE` nodes.
	 *   * `object` - DataTables settings object
	 * @param {array} [data] Data to initialise the Api instance with.
	 *
	 * @example
	 *   // Direct initialisation during DataTables construction
	 *   var api = $('#example').DataTable();
	 *
	 * @example
	 *   // Initialisation using a DataTables jQuery object
	 *   var api = $('#example').dataTable().api();
	 *
	 * @example
	 *   // Initialisation as a constructor
	 *   var api = new $.fn.DataTable.Api( 'table.dataTable' );
	 */
	_Api = function ( context, data )
	{
		if ( ! (this instanceof _Api) ) {
			return new _Api( context, data );
		}
	
		var settings = [];
		var ctxSettings = function ( o ) {
			var a = _toSettings( o );
			if ( a ) {
				settings.push.apply( settings, a );
			}
		};
	
		if ( Array.isArray( context ) ) {
			for ( var i=0, ien=context.length ; i<ien ; i++ ) {
				ctxSettings( context[i] );
			}
		}
		else {
			ctxSettings( context );
		}
	
		// Remove duplicates
		this.context = _unique( settings );
	
		// Initial data
		if ( data ) {
			$.merge( this, data );
		}
	
		// selector
		this.selector = {
			rows: null,
			cols: null,
			opts: null
		};
	
		_Api.extend( this, this, __apiStruct );
	};
	
	DataTable.Api = _Api;
	
	// Don't destroy the existing prototype, just extend it. Required for jQuery 2's
	// isPlainObject.
	$.extend( _Api.prototype, {
		any: function ()
		{
			return this.count() !== 0;
		},
	
	
		concat:  __arrayProto.concat,
	
	
		context: [], // array of table settings objects
	
	
		count: function ()
		{
			return this.flatten().length;
		},
	
	
		each: function ( fn )
		{
			for ( var i=0, ien=this.length ; i<ien; i++ ) {
				fn.call( this, this[i], i, this );
			}
	
			return this;
		},
	
	
		eq: function ( idx )
		{
			var ctx = this.context;
	
			return ctx.length > idx ?
				new _Api( ctx[idx], this[idx] ) :
				null;
		},
	
	
		filter: function ( fn )
		{
			var a = [];
	
			if ( __arrayProto.filter ) {
				a = __arrayProto.filter.call( this, fn, this );
			}
			else {
				// Compatibility for browsers without EMCA-252-5 (JS 1.6)
				for ( var i=0, ien=this.length ; i<ien ; i++ ) {
					if ( fn.call( this, this[i], i, this ) ) {
						a.push( this[i] );
					}
				}
			}
	
			return new _Api( this.context, a );
		},
	
	
		flatten: function ()
		{
			var a = [];
			return new _Api( this.context, a.concat.apply( a, this.toArray() ) );
		},
	
	
		join:    __arrayProto.join,
	
	
		indexOf: __arrayProto.indexOf || function (obj, start)
		{
			for ( var i=(start || 0), ien=this.length ; i<ien ; i++ ) {
				if ( this[i] === obj ) {
					return i;
				}
			}
			return -1;
		},
	
		iterator: function ( flatten, type, fn, alwaysNew ) {
			var
				a = [], ret,
				i, ien, j, jen,
				context = this.context,
				rows, items, item,
				selector = this.selector;
	
			// Argument shifting
			if ( typeof flatten === 'string' ) {
				alwaysNew = fn;
				fn = type;
				type = flatten;
				flatten = false;
			}
	
			for ( i=0, ien=context.length ; i<ien ; i++ ) {
				var apiInst = new _Api( context[i] );
	
				if ( type === 'table' ) {
					ret = fn.call( apiInst, context[i], i );
	
					if ( ret !== undefined ) {
						a.push( ret );
					}
				}
				else if ( type === 'columns' || type === 'rows' ) {
					// this has same length as context - one entry for each table
					ret = fn.call( apiInst, context[i], this[i], i );
	
					if ( ret !== undefined ) {
						a.push( ret );
					}
				}
				else if ( type === 'column' || type === 'column-rows' || type === 'row' || type === 'cell' ) {
					// columns and rows share the same structure.
					// 'this' is an array of column indexes for each context
					items = this[i];
	
					if ( type === 'column-rows' ) {
						rows = _selector_row_indexes( context[i], selector.opts );
					}
	
					for ( j=0, jen=items.length ; j<jen ; j++ ) {
						item = items[j];
	
						if ( type === 'cell' ) {
							ret = fn.call( apiInst, context[i], item.row, item.column, i, j );
						}
						else {
							ret = fn.call( apiInst, context[i], item, i, j, rows );
						}
	
						if ( ret !== undefined ) {
							a.push( ret );
						}
					}
				}
			}
	
			if ( a.length || alwaysNew ) {
				var api = new _Api( context, flatten ? a.concat.apply( [], a ) : a );
				var apiSelector = api.selector;
				apiSelector.rows = selector.rows;
				apiSelector.cols = selector.cols;
				apiSelector.opts = selector.opts;
				return api;
			}
			return this;
		},
	
	
		lastIndexOf: __arrayProto.lastIndexOf || function (obj, start)
		{
			// Bit cheeky...
			return this.indexOf.apply( this.toArray.reverse(), arguments );
		},
	
	
		length:  0,
	
	
		map: function ( fn )
		{
			var a = [];
	
			if ( __arrayProto.map ) {
				a = __arrayProto.map.call( this, fn, this );
			}
			else {
				// Compatibility for browsers without EMCA-252-5 (JS 1.6)
				for ( var i=0, ien=this.length ; i<ien ; i++ ) {
					a.push( fn.call( this, this[i], i ) );
				}
			}
	
			return new _Api( this.context, a );
		},
	
	
		pluck: function ( prop )
		{
			let fn = DataTable.util.get(prop);
	
			return this.map( function ( el ) {
				return fn(el);
			} );
		},
	
		pop:     __arrayProto.pop,
	
	
		push:    __arrayProto.push,
	
	
		// Does not return an API instance
		reduce: __arrayProto.reduce || function ( fn, init )
		{
			return _fnReduce( this, fn, init, 0, this.length, 1 );
		},
	
	
		reduceRight: __arrayProto.reduceRight || function ( fn, init )
		{
			return _fnReduce( this, fn, init, this.length-1, -1, -1 );
		},
	
	
		reverse: __arrayProto.reverse,
	
	
		// Object with rows, columns and opts
		selector: null,
	
	
		shift:   __arrayProto.shift,
	
	
		slice: function () {
			return new _Api( this.context, this );
		},
	
	
		sort:    __arrayProto.sort, // ? name - order?
	
	
		splice:  __arrayProto.splice,
	
	
		toArray: function ()
		{
			return __arrayProto.slice.call( this );
		},
	
	
		to$: function ()
		{
			return $( this );
		},
	
	
		toJQuery: function ()
		{
			return $( this );
		},
	
	
		unique: function ()
		{
			return new _Api( this.context, _unique(this) );
		},
	
	
		unshift: __arrayProto.unshift
	} );
	
	
	_Api.extend = function ( scope, obj, ext )
	{
		// Only extend API instances and static properties of the API
		if ( ! ext.length || ! obj || ( ! (obj instanceof _Api) && ! obj.__dt_wrapper ) ) {
			return;
		}
	
		var
			i, ien,
			struct,
			methodScoping = function ( scope, fn, struc ) {
				return function () {
					var ret = fn.apply( scope, arguments );
	
					// Method extension
					_Api.extend( ret, ret, struc.methodExt );
					return ret;
				};
			};
	
		for ( i=0, ien=ext.length ; i<ien ; i++ ) {
			struct = ext[i];
	
			// Value
			obj[ struct.name ] = struct.type === 'function' ?
				methodScoping( scope, struct.val, struct ) :
				struct.type === 'object' ?
					{} :
					struct.val;
	
			obj[ struct.name ].__dt_wrapper = true;
	
			// Property extension
			_Api.extend( scope, obj[ struct.name ], struct.propExt );
		}
	};
	
	
	// @todo - Is there need for an augment function?
	// _Api.augment = function ( inst, name )
	// {
	// 	// Find src object in the structure from the name
	// 	var parts = name.split('.');
	
	// 	_Api.extend( inst, obj );
	// };
	
	
	//     [
	//       {
	//         name:      'data'                -- string   - Property name
	//         val:       function () {},       -- function - Api method (or undefined if just an object
	//         methodExt: [ ... ],              -- array    - Array of Api object definitions to extend the method result
	//         propExt:   [ ... ]               -- array    - Array of Api object definitions to extend the property
	//       },
	//       {
	//         name:     'row'
	//         val:       {},
	//         methodExt: [ ... ],
	//         propExt:   [
	//           {
	//             name:      'data'
	//             val:       function () {},
	//             methodExt: [ ... ],
	//             propExt:   [ ... ]
	//           },
	//           ...
	//         ]
	//       }
	//     ]
	
	_Api.register = _api_register = function ( name, val )
	{
		if ( Array.isArray( name ) ) {
			for ( var j=0, jen=name.length ; j<jen ; j++ ) {
				_Api.register( name[j], val );
			}
			return;
		}
	
		var
			i, ien,
			heir = name.split('.'),
			struct = __apiStruct,
			key, method;
	
		var find = function ( src, name ) {
			for ( var i=0, ien=src.length ; i<ien ; i++ ) {
				if ( src[i].name === name ) {
					return src[i];
				}
			}
			return null;
		};
	
		for ( i=0, ien=heir.length ; i<ien ; i++ ) {
			method = heir[i].indexOf('()') !== -1;
			key = method ?
				heir[i].replace('()', '') :
				heir[i];
	
			var src = find( struct, key );
			if ( ! src ) {
				src = {
					name:      key,
					val:       {},
					methodExt: [],
					propExt:   [],
					type:      'object'
				};
				struct.push( src );
			}
	
			if ( i === ien-1 ) {
				src.val = val;
				src.type = typeof val === 'function' ?
					'function' :
					$.isPlainObject( val ) ?
						'object' :
						'other';
			}
			else {
				struct = method ?
					src.methodExt :
					src.propExt;
			}
		}
	};
	
	_Api.registerPlural = _api_registerPlural = function ( pluralName, singularName, val ) {
		_Api.register( pluralName, val );
	
		_Api.register( singularName, function () {
			var ret = val.apply( this, arguments );
	
			if ( ret === this ) {
				// Returned item is the API instance that was passed in, return it
				return this;
			}
			else if ( ret instanceof _Api ) {
				// New API instance returned, want the value from the first item
				// in the returned array for the singular result.
				return ret.length ?
					Array.isArray( ret[0] ) ?
						new _Api( ret.context, ret[0] ) : // Array results are 'enhanced'
						ret[0] :
					undefined;
			}
	
			// Non-API return - just fire it back
			return ret;
		} );
	};
	
	
	/**
	 * Selector for HTML tables. Apply the given selector to the give array of
	 * DataTables settings objects.
	 *
	 * @param {string|integer} [selector] jQuery selector string or integer
	 * @param  {array} Array of DataTables settings objects to be filtered
	 * @return {array}
	 * @ignore
	 */
	var __table_selector = function ( selector, a )
	{
		if ( Array.isArray(selector) ) {
			return $.map( selector, function (item) {
				return __table_selector(item, a);
			} );
		}
	
		// Integer is used to pick out a table by index
		if ( typeof selector === 'number' ) {
			return [ a[ selector ] ];
		}
	
		// Perform a jQuery selector on the table nodes
		var nodes = $.map( a, function (el, i) {
			return el.nTable;
		} );
	
		return $(nodes)
			.filter( selector )
			.map( function (i) {
				// Need to translate back from the table node to the settings
				var idx = $.inArray( this, nodes );
				return a[ idx ];
			} )
			.toArray();
	};
	
	
	
	/**
	 * Context selector for the API's context (i.e. the tables the API instance
	 * refers to.
	 *
	 * @name    DataTable.Api#tables
	 * @param {string|integer} [selector] Selector to pick which tables the iterator
	 *   should operate on. If not given, all tables in the current context are
	 *   used. This can be given as a jQuery selector (for example `':gt(0)'`) to
	 *   select multiple tables or as an integer to select a single table.
	 * @returns {DataTable.Api} Returns a new API instance if a selector is given.
	 */
	_api_register( 'tables()', function ( selector ) {
		// A new instance is created if there was a selector specified
		return selector !== undefined && selector !== null ?
			new _Api( __table_selector( selector, this.context ) ) :
			this;
	} );
	
	
	_api_register( 'table()', function ( selector ) {
		var tables = this.tables( selector );
		var ctx = tables.context;
	
		// Truncate to the first matched table
		return ctx.length ?
			new _Api( ctx[0] ) :
			tables;
	} );
	
	
	_api_registerPlural( 'tables().nodes()', 'table().node()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTable;
		}, 1 );
	} );
	
	
	_api_registerPlural( 'tables().body()', 'table().body()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTBody;
		}, 1 );
	} );
	
	
	_api_registerPlural( 'tables().header()', 'table().header()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTHead;
		}, 1 );
	} );
	
	
	_api_registerPlural( 'tables().footer()', 'table().footer()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTFoot;
		}, 1 );
	} );
	
	
	_api_registerPlural( 'tables().containers()', 'table().container()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTableWrapper;
		}, 1 );
	} );
	
	
	
	/**
	 * Redraw the tables in the current context.
	 */
	_api_register( 'draw()', function ( paging ) {
		return this.iterator( 'table', function ( settings ) {
			if ( paging === 'page' ) {
				_fnDraw( settings );
			}
			else {
				if ( typeof paging === 'string' ) {
					paging = paging === 'full-hold' ?
						false :
						true;
				}
	
				_fnReDraw( settings, paging===false );
			}
		} );
	} );
	
	
	
	/**
	 * Get the current page index.
	 *
	 * @return {integer} Current page index (zero based)
	 *//**
	 * Set the current page.
	 *
	 * Note that if you attempt to show a page which does not exist, DataTables will
	 * not throw an error, but rather reset the paging.
	 *
	 * @param {integer|string} action The paging action to take. This can be one of:
	 *  * `integer` - The page index to jump to
	 *  * `string` - An action to take:
	 *    * `first` - Jump to first page.
	 *    * `next` - Jump to the next page
	 *    * `previous` - Jump to previous page
	 *    * `last` - Jump to the last page.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'page()', function ( action ) {
		if ( action === undefined ) {
			return this.page.info().page; // not an expensive call
		}
	
		// else, have an action to take on all tables
		return this.iterator( 'table', function ( settings ) {
			_fnPageChange( settings, action );
		} );
	} );
	
	
	/**
	 * Paging information for the first table in the current context.
	 *
	 * If you require paging information for another table, use the `table()` method
	 * with a suitable selector.
	 *
	 * @return {object} Object with the following properties set:
	 *  * `page` - Current page index (zero based - i.e. the first page is `0`)
	 *  * `pages` - Total number of pages
	 *  * `start` - Display index for the first record shown on the current page
	 *  * `end` - Display index for the last record shown on the current page
	 *  * `length` - Display length (number of records). Note that generally `start
	 *    + length = end`, but this is not always true, for example if there are
	 *    only 2 records to show on the final page, with a length of 10.
	 *  * `recordsTotal` - Full data set length
	 *  * `recordsDisplay` - Data set length once the current filtering criterion
	 *    are applied.
	 */
	_api_register( 'page.info()', function ( action ) {
		if ( this.context.length === 0 ) {
			return undefined;
		}
	
		var
			settings   = this.context[0],
			start      = settings._iDisplayStart,
			len        = settings.oFeatures.bPaginate ? settings._iDisplayLength : -1,
			visRecords = settings.fnRecordsDisplay(),
			all        = len === -1;
	
		return {
			"page":           all ? 0 : Math.floor( start / len ),
			"pages":          all ? 1 : Math.ceil( visRecords / len ),
			"start":          start,
			"end":            settings.fnDisplayEnd(),
			"length":         len,
			"recordsTotal":   settings.fnRecordsTotal(),
			"recordsDisplay": visRecords,
			"serverSide":     _fnDataSource( settings ) === 'ssp'
		};
	} );
	
	
	/**
	 * Get the current page length.
	 *
	 * @return {integer} Current page length. Note `-1` indicates that all records
	 *   are to be shown.
	 *//**
	 * Set the current page length.
	 *
	 * @param {integer} Page length to set. Use `-1` to show all records.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'page.len()', function ( len ) {
		// Note that we can't call this function 'length()' because `length`
		// is a Javascript property of functions which defines how many arguments
		// the function expects.
		if ( len === undefined ) {
			return this.context.length !== 0 ?
				this.context[0]._iDisplayLength :
				undefined;
		}
	
		// else, set the page length
		return this.iterator( 'table', function ( settings ) {
			_fnLengthChange( settings, len );
		} );
	} );
	
	
	
	var __reload = function ( settings, holdPosition, callback ) {
		// Use the draw event to trigger a callback
		if ( callback ) {
			var api = new _Api( settings );
	
			api.one( 'draw', function () {
				callback( api.ajax.json() );
			} );
		}
	
		if ( _fnDataSource( settings ) == 'ssp' ) {
			_fnReDraw( settings, holdPosition );
		}
		else {
			_fnProcessingDisplay( settings, true );
	
			// Cancel an existing request
			var xhr = settings.jqXHR;
			if ( xhr && xhr.readyState !== 4 ) {
				xhr.abort();
			}
	
			// Trigger xhr
			_fnBuildAjax( settings, [], function( json ) {
				_fnClearTable( settings );
	
				var data = _fnAjaxDataSrc( settings, json );
				for ( var i=0, ien=data.length ; i<ien ; i++ ) {
					_fnAddData( settings, data[i] );
				}
	
				_fnReDraw( settings, holdPosition );
				_fnProcessingDisplay( settings, false );
			} );
		}
	};
	
	
	/**
	 * Get the JSON response from the last Ajax request that DataTables made to the
	 * server. Note that this returns the JSON from the first table in the current
	 * context.
	 *
	 * @return {object} JSON received from the server.
	 */
	_api_register( 'ajax.json()', function () {
		var ctx = this.context;
	
		if ( ctx.length > 0 ) {
			return ctx[0].json;
		}
	
		// else return undefined;
	} );
	
	
	/**
	 * Get the data submitted in the last Ajax request
	 */
	_api_register( 'ajax.params()', function () {
		var ctx = this.context;
	
		if ( ctx.length > 0 ) {
			return ctx[0].oAjaxData;
		}
	
		// else return undefined;
	} );
	
	
	/**
	 * Reload tables from the Ajax data source. Note that this function will
	 * automatically re-draw the table when the remote data has been loaded.
	 *
	 * @param {boolean} [reset=true] Reset (default) or hold the current paging
	 *   position. A full re-sort and re-filter is performed when this method is
	 *   called, which is why the pagination reset is the default action.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'ajax.reload()', function ( callback, resetPaging ) {
		return this.iterator( 'table', function (settings) {
			__reload( settings, resetPaging===false, callback );
		} );
	} );
	
	
	/**
	 * Get the current Ajax URL. Note that this returns the URL from the first
	 * table in the current context.
	 *
	 * @return {string} Current Ajax source URL
	 *//**
	 * Set the Ajax URL. Note that this will set the URL for all tables in the
	 * current context.
	 *
	 * @param {string} url URL to set.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'ajax.url()', function ( url ) {
		var ctx = this.context;
	
		if ( url === undefined ) {
			// get
			if ( ctx.length === 0 ) {
				return undefined;
			}
			ctx = ctx[0];
	
			return ctx.ajax ?
				$.isPlainObject( ctx.ajax ) ?
					ctx.ajax.url :
					ctx.ajax :
				ctx.sAjaxSource;
		}
	
		// set
		return this.iterator( 'table', function ( settings ) {
			if ( $.isPlainObject( settings.ajax ) ) {
				settings.ajax.url = url;
			}
			else {
				settings.ajax = url;
			}
			// No need to consider sAjaxSource here since DataTables gives priority
			// to `ajax` over `sAjaxSource`. So setting `ajax` here, renders any
			// value of `sAjaxSource` redundant.
		} );
	} );
	
	
	/**
	 * Load data from the newly set Ajax URL. Note that this method is only
	 * available when `ajax.url()` is used to set a URL. Additionally, this method
	 * has the same effect as calling `ajax.reload()` but is provided for
	 * convenience when setting a new URL. Like `ajax.reload()` it will
	 * automatically redraw the table once the remote data has been loaded.
	 *
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'ajax.url().load()', function ( callback, resetPaging ) {
		// Same as a reload, but makes sense to present it for easy access after a
		// url change
		return this.iterator( 'table', function ( ctx ) {
			__reload( ctx, resetPaging===false, callback );
		} );
	} );
	
	
	
	
	var _selector_run = function ( type, selector, selectFn, settings, opts )
	{
		var
			out = [], res,
			a, i, ien, j, jen,
			selectorType = typeof selector;
	
		// Can't just check for isArray here, as an API or jQuery instance might be
		// given with their array like look
		if ( ! selector || selectorType === 'string' || selectorType === 'function' || selector.length === undefined ) {
			selector = [ selector ];
		}
	
		for ( i=0, ien=selector.length ; i<ien ; i++ ) {
			// Only split on simple strings - complex expressions will be jQuery selectors
			a = selector[i] && selector[i].split && ! selector[i].match(/[\[\(:]/) ?
				selector[i].split(',') :
				[ selector[i] ];
	
			for ( j=0, jen=a.length ; j<jen ; j++ ) {
				res = selectFn( typeof a[j] === 'string' ? (a[j]).trim() : a[j] );
	
				if ( res && res.length ) {
					out = out.concat( res );
				}
			}
		}
	
		// selector extensions
		var ext = _ext.selector[ type ];
		if ( ext.length ) {
			for ( i=0, ien=ext.length ; i<ien ; i++ ) {
				out = ext[i]( settings, opts, out );
			}
		}
	
		return _unique( out );
	};
	
	
	var _selector_opts = function ( opts )
	{
		if ( ! opts ) {
			opts = {};
		}
	
		// Backwards compatibility for 1.9- which used the terminology filter rather
		// than search
		if ( opts.filter && opts.search === undefined ) {
			opts.search = opts.filter;
		}
	
		return $.extend( {
			search: 'none',
			order: 'current',
			page: 'all'
		}, opts );
	};
	
	
	var _selector_first = function ( inst )
	{
		// Reduce the API instance to the first item found
		for ( var i=0, ien=inst.length ; i<ien ; i++ ) {
			if ( inst[i].length > 0 ) {
				// Assign the first element to the first item in the instance
				// and truncate the instance and context
				inst[0] = inst[i];
				inst[0].length = 1;
				inst.length = 1;
				inst.context = [ inst.context[i] ];
	
				return inst;
			}
		}
	
		// Not found - return an empty instance
		inst.length = 0;
		return inst;
	};
	
	
	var _selector_row_indexes = function ( settings, opts )
	{
		var
			i, ien, tmp, a=[],
			displayFiltered = settings.aiDisplay,
			displayMaster = settings.aiDisplayMaster;
	
		var
			search = opts.search,  // none, applied, removed
			order  = opts.order,   // applied, current, index (original - compatibility with 1.9)
			page   = opts.page;    // all, current
	
		if ( _fnDataSource( settings ) == 'ssp' ) {
			// In server-side processing mode, most options are irrelevant since
			// rows not shown don't exist and the index order is the applied order
			// Removed is a special case - for consistency just return an empty
			// array
			return search === 'removed' ?
				[] :
				_range( 0, displayMaster.length );
		}
		else if ( page == 'current' ) {
			// Current page implies that order=current and filter=applied, since it is
			// fairly senseless otherwise, regardless of what order and search actually
			// are
			for ( i=settings._iDisplayStart, ien=settings.fnDisplayEnd() ; i<ien ; i++ ) {
				a.push( displayFiltered[i] );
			}
		}
		else if ( order == 'current' || order == 'applied' ) {
			if ( search == 'none') {
				a = displayMaster.slice();
			}
			else if ( search == 'applied' ) {
				a = displayFiltered.slice();
			}
			else if ( search == 'removed' ) {
				// O(n+m) solution by creating a hash map
				var displayFilteredMap = {};
	
				for ( var i=0, ien=displayFiltered.length ; i<ien ; i++ ) {
					displayFilteredMap[displayFiltered[i]] = null;
				}
	
				a = $.map( displayMaster, function (el) {
					return ! displayFilteredMap.hasOwnProperty(el) ?
						el :
						null;
				} );
			}
		}
		else if ( order == 'index' || order == 'original' ) {
			for ( i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
				if ( search == 'none' ) {
					a.push( i );
				}
				else { // applied | removed
					tmp = $.inArray( i, displayFiltered );
	
					if ((tmp === -1 && search == 'removed') ||
						(tmp >= 0   && search == 'applied') )
					{
						a.push( i );
					}
				}
			}
		}
	
		return a;
	};
	
	
	/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
	 * Rows
	 *
	 * {}          - no selector - use all available rows
	 * {integer}   - row aoData index
	 * {node}      - TR node
	 * {string}    - jQuery selector to apply to the TR elements
	 * {array}     - jQuery array of nodes, or simply an array of TR nodes
	 *
	 */
	var __row_selector = function ( settings, selector, opts )
	{
		var rows;
		var run = function ( sel ) {
			var selInt = _intVal( sel );
			var i, ien;
			var aoData = settings.aoData;
	
			// Short cut - selector is a number and no options provided (default is
			// all records, so no need to check if the index is in there, since it
			// must be - dev error if the index doesn't exist).
			if ( selInt !== null && ! opts ) {
				return [ selInt ];
			}
	
			if ( ! rows ) {
				rows = _selector_row_indexes( settings, opts );
			}
	
			if ( selInt !== null && $.inArray( selInt, rows ) !== -1 ) {
				// Selector - integer
				return [ selInt ];
			}
			else if ( sel === null || sel === undefined || sel === '' ) {
				// Selector - none
				return rows;
			}
	
			// Selector - function
			if ( typeof sel === 'function' ) {
				return $.map( rows, function (idx) {
					var row = aoData[ idx ];
					return sel( idx, row._aData, row.nTr ) ? idx : null;
				} );
			}
	
			// Selector - node
			if ( sel.nodeName ) {
				var rowIdx = sel._DT_RowIndex;  // Property added by DT for fast lookup
				var cellIdx = sel._DT_CellIndex;
	
				if ( rowIdx !== undefined ) {
					// Make sure that the row is actually still present in the table
					return aoData[ rowIdx ] && aoData[ rowIdx ].nTr === sel ?
						[ rowIdx ] :
						[];
				}
				else if ( cellIdx ) {
					return aoData[ cellIdx.row ] && aoData[ cellIdx.row ].nTr === sel.parentNode ?
						[ cellIdx.row ] :
						[];
				}
				else {
					var host = $(sel).closest('*[data-dt-row]');
					return host.length ?
						[ host.data('dt-row') ] :
						[];
				}
			}
	
			// ID selector. Want to always be able to select rows by id, regardless
			// of if the tr element has been created or not, so can't rely upon
			// jQuery here - hence a custom implementation. This does not match
			// Sizzle's fast selector or HTML4 - in HTML5 the ID can be anything,
			// but to select it using a CSS selector engine (like Sizzle or
			// querySelect) it would need to need to be escaped for some characters.
			// DataTables simplifies this for row selectors since you can select
			// only a row. A # indicates an id any anything that follows is the id -
			// unescaped.
			if ( typeof sel === 'string' && sel.charAt(0) === '#' ) {
				// get row index from id
				var rowObj = settings.aIds[ sel.replace( /^#/, '' ) ];
				if ( rowObj !== undefined ) {
					return [ rowObj.idx ];
				}
	
				// need to fall through to jQuery in case there is DOM id that
				// matches
			}
			
			// Get nodes in the order from the `rows` array with null values removed
			var nodes = _removeEmpty(
				_pluck_order( settings.aoData, rows, 'nTr' )
			);
	
			// Selector - jQuery selector string, array of nodes or jQuery object/
			// As jQuery's .filter() allows jQuery objects to be passed in filter,
			// it also allows arrays, so this will cope with all three options
			return $(nodes)
				.filter( sel )
				.map( function () {
					return this._DT_RowIndex;
				} )
				.toArray();
		};
	
		return _selector_run( 'row', selector, run, settings, opts );
	};
	
	
	_api_register( 'rows()', function ( selector, opts ) {
		// argument shifting
		if ( selector === undefined ) {
			selector = '';
		}
		else if ( $.isPlainObject( selector ) ) {
			opts = selector;
			selector = '';
		}
	
		opts = _selector_opts( opts );
	
		var inst = this.iterator( 'table', function ( settings ) {
			return __row_selector( settings, selector, opts );
		}, 1 );
	
		// Want argument shifting here and in __row_selector?
		inst.selector.rows = selector;
		inst.selector.opts = opts;
	
		return inst;
	} );
	
	_api_register( 'rows().nodes()', function () {
		return this.iterator( 'row', function ( settings, row ) {
			return settings.aoData[ row ].nTr || undefined;
		}, 1 );
	} );
	
	_api_register( 'rows().data()', function () {
		return this.iterator( true, 'rows', function ( settings, rows ) {
			return _pluck_order( settings.aoData, rows, '_aData' );
		}, 1 );
	} );
	
	_api_registerPlural( 'rows().cache()', 'row().cache()', function ( type ) {
		return this.iterator( 'row', function ( settings, row ) {
			var r = settings.aoData[ row ];
			return type === 'search' ? r._aFilterData : r._aSortData;
		}, 1 );
	} );
	
	_api_registerPlural( 'rows().invalidate()', 'row().invalidate()', function ( src ) {
		return this.iterator( 'row', function ( settings, row ) {
			_fnInvalidate( settings, row, src );
		} );
	} );
	
	_api_registerPlural( 'rows().indexes()', 'row().index()', function () {
		return this.iterator( 'row', function ( settings, row ) {
			return row;
		}, 1 );
	} );
	
	_api_registerPlural( 'rows().ids()', 'row().id()', function ( hash ) {
		var a = [];
		var context = this.context;
	
		// `iterator` will drop undefined values, but in this case we want them
		for ( var i=0, ien=context.length ; i<ien ; i++ ) {
			for ( var j=0, jen=this[i].length ; j<jen ; j++ ) {
				var id = context[i].rowIdFn( context[i].aoData[ this[i][j] ]._aData );
				a.push( (hash === true ? '#' : '' )+ id );
			}
		}
	
		return new _Api( context, a );
	} );
	
	_api_registerPlural( 'rows().remove()', 'row().remove()', function () {
		var that = this;
	
		this.iterator( 'row', function ( settings, row, thatIdx ) {
			var data = settings.aoData;
			var rowData = data[ row ];
			var i, ien, j, jen;
			var loopRow, loopCells;
	
			data.splice( row, 1 );
	
			// Update the cached indexes
			for ( i=0, ien=data.length ; i<ien ; i++ ) {
				loopRow = data[i];
				loopCells = loopRow.anCells;
	
				// Rows
				if ( loopRow.nTr !== null ) {
					loopRow.nTr._DT_RowIndex = i;
				}
	
				// Cells
				if ( loopCells !== null ) {
					for ( j=0, jen=loopCells.length ; j<jen ; j++ ) {
						loopCells[j]._DT_CellIndex.row = i;
					}
				}
			}
	
			// Delete from the display arrays
			_fnDeleteIndex( settings.aiDisplayMaster, row );
			_fnDeleteIndex( settings.aiDisplay, row );
			_fnDeleteIndex( that[ thatIdx ], row, false ); // maintain local indexes
	
			// For server-side processing tables - subtract the deleted row from the count
			if ( settings._iRecordsDisplay > 0 ) {
				settings._iRecordsDisplay--;
			}
	
			// Check for an 'overflow' they case for displaying the table
			_fnLengthOverflow( settings );
	
			// Remove the row's ID reference if there is one
			var id = settings.rowIdFn( rowData._aData );
			if ( id !== undefined ) {
				delete settings.aIds[ id ];
			}
		} );
	
		this.iterator( 'table', function ( settings ) {
			for ( var i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
				settings.aoData[i].idx = i;
			}
		} );
	
		return this;
	} );
	
	
	_api_register( 'rows.add()', function ( rows ) {
		var newRows = this.iterator( 'table', function ( settings ) {
				var row, i, ien;
				var out = [];
	
				for ( i=0, ien=rows.length ; i<ien ; i++ ) {
					row = rows[i];
	
					if ( row.nodeName && row.nodeName.toUpperCase() === 'TR' ) {
						out.push( _fnAddTr( settings, row )[0] );
					}
					else {
						out.push( _fnAddData( settings, row ) );
					}
				}
	
				return out;
			}, 1 );
	
		// Return an Api.rows() extended instance, so rows().nodes() etc can be used
		var modRows = this.rows( -1 );
		modRows.pop();
		$.merge( modRows, newRows );
	
		return modRows;
	} );
	
	
	
	
	
	/**
	 *
	 */
	_api_register( 'row()', function ( selector, opts ) {
		return _selector_first( this.rows( selector, opts ) );
	} );
	
	
	_api_register( 'row().data()', function ( data ) {
		var ctx = this.context;
	
		if ( data === undefined ) {
			// Get
			return ctx.length && this.length ?
				ctx[0].aoData[ this[0] ]._aData :
				undefined;
		}
	
		// Set
		var row = ctx[0].aoData[ this[0] ];
		row._aData = data;
	
		// If the DOM has an id, and the data source is an array
		if ( Array.isArray( data ) && row.nTr && row.nTr.id ) {
			_fnSetObjectDataFn( ctx[0].rowId )( data, row.nTr.id );
		}
	
		// Automatically invalidate
		_fnInvalidate( ctx[0], this[0], 'data' );
	
		return this;
	} );
	
	
	_api_register( 'row().node()', function () {
		var ctx = this.context;
	
		return ctx.length && this.length ?
			ctx[0].aoData[ this[0] ].nTr || null :
			null;
	} );
	
	
	_api_register( 'row.add()', function ( row ) {
		// Allow a jQuery object to be passed in - only a single row is added from
		// it though - the first element in the set
		if ( row instanceof $ && row.length ) {
			row = row[0];
		}
	
		var rows = this.iterator( 'table', function ( settings ) {
			if ( row.nodeName && row.nodeName.toUpperCase() === 'TR' ) {
				return _fnAddTr( settings, row )[0];
			}
			return _fnAddData( settings, row );
		} );
	
		// Return an Api.rows() extended instance, with the newly added row selected
		return this.row( rows[0] );
	} );
	
	
	$(document).on('plugin-init.dt', function (e, context) {
		var api = new _Api( context );
	
		api.on( 'stateSaveParams', function ( e, settings, d ) {
			// This could be more compact with the API, but it is a lot faster as a simple
			// internal loop
			var idFn = settings.rowIdFn;
			var data = settings.aoData;
			var ids = [];
	
			for (var i=0 ; i<data.length ; i++) {
				if (data[i]._detailsShow) {
					ids.push( '#' + idFn(data[i]._aData) );
				}
			}
	
			d.childRows = ids;
		})
	
		var loaded = api.state.loaded();
	
		if ( loaded && loaded.childRows ) {
			api
				.rows( $.map(loaded.childRows, function (id){
					return id.replace(/:/g, '\\:')
				}) )
				.every( function () {
					_fnCallbackFire( context, null, 'requestChild', [ this ] )
				});
		}
	});
	
	var __details_add = function ( ctx, row, data, klass )
	{
		// Convert to array of TR elements
		var rows = [];
		var addRow = function ( r, k ) {
			// Recursion to allow for arrays of jQuery objects
			if ( Array.isArray( r ) || r instanceof $ ) {
				for ( var i=0, ien=r.length ; i<ien ; i++ ) {
					addRow( r[i], k );
				}
				return;
			}
	
			// If we get a TR element, then just add it directly - up to the dev
			// to add the correct number of columns etc
			if ( r.nodeName && r.nodeName.toLowerCase() === 'tr' ) {
				rows.push( r );
			}
			else {
				// Otherwise create a row with a wrapper
				var created = $('<tr><td></td></tr>').addClass( k );
				$('td', created)
					.addClass( k )
					.html( r )
					[0].colSpan = _fnVisbleColumns( ctx );
	
				rows.push( created[0] );
			}
		};
	
		addRow( data, klass );
	
		if ( row._details ) {
			row._details.detach();
		}
	
		row._details = $(rows);
	
		// If the children were already shown, that state should be retained
		if ( row._detailsShow ) {
			row._details.insertAfter( row.nTr );
		}
	};
	
	
	// Make state saving of child row details async to allow them to be batch processed
	var __details_state = DataTable.util.throttle(
		function (ctx) {
			_fnSaveState( ctx[0] )
		},
		500
	);
	
	
	var __details_remove = function ( api, idx )
	{
		var ctx = api.context;
	
		if ( ctx.length ) {
			var row = ctx[0].aoData[ idx !== undefined ? idx : api[0] ];
	
			if ( row && row._details ) {
				row._details.remove();
	
				row._detailsShow = undefined;
				row._details = undefined;
				$( row.nTr ).removeClass( 'dt-hasChild' );
				__details_state( ctx );
			}
		}
	};
	
	
	var __details_display = function ( api, show ) {
		var ctx = api.context;
	
		if ( ctx.length && api.length ) {
			var row = ctx[0].aoData[ api[0] ];
	
			if ( row._details ) {
				row._detailsShow = show;
	
				if ( show ) {
					row._details.insertAfter( row.nTr );
					$( row.nTr ).addClass( 'dt-hasChild' );
				}
				else {
					row._details.detach();
					$( row.nTr ).removeClass( 'dt-hasChild' );
				}
	
				_fnCallbackFire( ctx[0], null, 'childRow', [ show, api.row( api[0] ) ] )
	
				__details_events( ctx[0] );
				__details_state( ctx );
			}
		}
	};
	
	
	var __details_events = function ( settings )
	{
		var api = new _Api( settings );
		var namespace = '.dt.DT_details';
		var drawEvent = 'draw'+namespace;
		var colvisEvent = 'column-sizing'+namespace;
		var destroyEvent = 'destroy'+namespace;
		var data = settings.aoData;
	
		api.off( drawEvent +' '+ colvisEvent +' '+ destroyEvent );
	
		if ( _pluck( data, '_details' ).length > 0 ) {
			// On each draw, insert the required elements into the document
			api.on( drawEvent, function ( e, ctx ) {
				if ( settings !== ctx ) {
					return;
				}
	
				api.rows( {page:'current'} ).eq(0).each( function (idx) {
					// Internal data grab
					var row = data[ idx ];
	
					if ( row._detailsShow ) {
						row._details.insertAfter( row.nTr );
					}
				} );
			} );
	
			// Column visibility change - update the colspan
			api.on( colvisEvent, function ( e, ctx, idx, vis ) {
				if ( settings !== ctx ) {
					return;
				}
	
				// Update the colspan for the details rows (note, only if it already has
				// a colspan)
				var row, visible = _fnVisbleColumns( ctx );
	
				for ( var i=0, ien=data.length ; i<ien ; i++ ) {
					row = data[i];
	
					if ( row._details ) {
						row._details.children('td[colspan]').attr('colspan', visible );
					}
				}
			} );
	
			// Table destroyed - nuke any child rows
			api.on( destroyEvent, function ( e, ctx ) {
				if ( settings !== ctx ) {
					return;
				}
	
				for ( var i=0, ien=data.length ; i<ien ; i++ ) {
					if ( data[i]._details ) {
						__details_remove( api, i );
					}
				}
			} );
		}
	};
	
	// Strings for the method names to help minification
	var _emp = '';
	var _child_obj = _emp+'row().child';
	var _child_mth = _child_obj+'()';
	
	// data can be:
	//  tr
	//  string
	//  jQuery or array of any of the above
	_api_register( _child_mth, function ( data, klass ) {
		var ctx = this.context;
	
		if ( data === undefined ) {
			// get
			return ctx.length && this.length ?
				ctx[0].aoData[ this[0] ]._details :
				undefined;
		}
		else if ( data === true ) {
			// show
			this.child.show();
		}
		else if ( data === false ) {
			// remove
			__details_remove( this );
		}
		else if ( ctx.length && this.length ) {
			// set
			__details_add( ctx[0], ctx[0].aoData[ this[0] ], data, klass );
		}
	
		return this;
	} );
	
	
	_api_register( [
		_child_obj+'.show()',
		_child_mth+'.show()' // only when `child()` was called with parameters (without
	], function ( show ) {   // it returns an object and this method is not executed)
		__details_display( this, true );
		return this;
	} );
	
	
	_api_register( [
		_child_obj+'.hide()',
		_child_mth+'.hide()' // only when `child()` was called with parameters (without
	], function () {         // it returns an object and this method is not executed)
		__details_display( this, false );
		return this;
	} );
	
	
	_api_register( [
		_child_obj+'.remove()',
		_child_mth+'.remove()' // only when `child()` was called with parameters (without
	], function () {           // it returns an object and this method is not executed)
		__details_remove( this );
		return this;
	} );
	
	
	_api_register( _child_obj+'.isShown()', function () {
		var ctx = this.context;
	
		if ( ctx.length && this.length ) {
			// _detailsShown as false or undefined will fall through to return false
			return ctx[0].aoData[ this[0] ]._detailsShow || false;
		}
		return false;
	} );
	
	
	
	/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
	 * Columns
	 *
	 * {integer}           - column index (>=0 count from left, <0 count from right)
	 * "{integer}:visIdx"  - visible column index (i.e. translate to column index)  (>=0 count from left, <0 count from right)
	 * "{integer}:visible" - alias for {integer}:visIdx  (>=0 count from left, <0 count from right)
	 * "{string}:name"     - column name
	 * "{string}"          - jQuery selector on column header nodes
	 *
	 */
	
	// can be an array of these items, comma separated list, or an array of comma
	// separated lists
	
	var __re_column_selector = /^([^:]+):(name|visIdx|visible)$/;
	
	
	// r1 and r2 are redundant - but it means that the parameters match for the
	// iterator callback in columns().data()
	var __columnData = function ( settings, column, r1, r2, rows ) {
		var a = [];
		for ( var row=0, ien=rows.length ; row<ien ; row++ ) {
			a.push( _fnGetCellData( settings, rows[row], column ) );
		}
		return a;
	};
	
	
	var __column_selector = function ( settings, selector, opts )
	{
		var
			columns = settings.aoColumns,
			names = _pluck( columns, 'sName' ),
			nodes = _pluck( columns, 'nTh' );
	
		var run = function ( s ) {
			var selInt = _intVal( s );
	
			// Selector - all
			if ( s === '' ) {
				return _range( columns.length );
			}
	
			// Selector - index
			if ( selInt !== null ) {
				return [ selInt >= 0 ?
					selInt : // Count from left
					columns.length + selInt // Count from right (+ because its a negative value)
				];
			}
	
			// Selector = function
			if ( typeof s === 'function' ) {
				var rows = _selector_row_indexes( settings, opts );
	
				return $.map( columns, function (col, idx) {
					return s(
							idx,
							__columnData( settings, idx, 0, 0, rows ),
							nodes[ idx ]
						) ? idx : null;
				} );
			}
	
			// jQuery or string selector
			var match = typeof s === 'string' ?
				s.match( __re_column_selector ) :
				'';
	
			if ( match ) {
				switch( match[2] ) {
					case 'visIdx':
					case 'visible':
						var idx = parseInt( match[1], 10 );
						// Visible index given, convert to column index
						if ( idx < 0 ) {
							// Counting from the right
							var visColumns = $.map( columns, function (col,i) {
								return col.bVisible ? i : null;
							} );
							return [ visColumns[ visColumns.length + idx ] ];
						}
						// Counting from the left
						return [ _fnVisibleToColumnIndex( settings, idx ) ];
	
					case 'name':
						// match by name. `names` is column index complete and in order
						return $.map( names, function (name, i) {
							return name === match[1] ? i : null;
						} );
	
					default:
						return [];
				}
			}
	
			// Cell in the table body
			if ( s.nodeName && s._DT_CellIndex ) {
				return [ s._DT_CellIndex.column ];
			}
	
			// jQuery selector on the TH elements for the columns
			var jqResult = $( nodes )
				.filter( s )
				.map( function () {
					return $.inArray( this, nodes ); // `nodes` is column index complete and in order
				} )
				.toArray();
	
			if ( jqResult.length || ! s.nodeName ) {
				return jqResult;
			}
	
			// Otherwise a node which might have a `dt-column` data attribute, or be
			// a child or such an element
			var host = $(s).closest('*[data-dt-column]');
			return host.length ?
				[ host.data('dt-column') ] :
				[];
		};
	
		return _selector_run( 'column', selector, run, settings, opts );
	};
	
	
	var __setColumnVis = function ( settings, column, vis ) {
		var
			cols = settings.aoColumns,
			col  = cols[ column ],
			data = settings.aoData,
			row, cells, i, ien, tr;
	
		// Get
		if ( vis === undefined ) {
			return col.bVisible;
		}
	
		// Set
		// No change
		if ( col.bVisible === vis ) {
			return;
		}
	
		if ( vis ) {
			// Insert column
			// Need to decide if we should use appendChild or insertBefore
			var insertBefore = $.inArray( true, _pluck(cols, 'bVisible'), column+1 );
	
			for ( i=0, ien=data.length ; i<ien ; i++ ) {
				tr = data[i].nTr;
				cells = data[i].anCells;
	
				if ( tr ) {
					// insertBefore can act like appendChild if 2nd arg is null
					tr.insertBefore( cells[ column ], cells[ insertBefore ] || null );
				}
			}
		}
		else {
			// Remove column
			$( _pluck( settings.aoData, 'anCells', column ) ).detach();
		}
	
		// Common actions
		col.bVisible = vis;
	};
	
	
	_api_register( 'columns()', function ( selector, opts ) {
		// argument shifting
		if ( selector === undefined ) {
			selector = '';
		}
		else if ( $.isPlainObject( selector ) ) {
			opts = selector;
			selector = '';
		}
	
		opts = _selector_opts( opts );
	
		var inst = this.iterator( 'table', function ( settings ) {
			return __column_selector( settings, selector, opts );
		}, 1 );
	
		// Want argument shifting here and in _row_selector?
		inst.selector.cols = selector;
		inst.selector.opts = opts;
	
		return inst;
	} );
	
	_api_registerPlural( 'columns().header()', 'column().header()', function ( selector, opts ) {
		return this.iterator( 'column', function ( settings, column ) {
			return settings.aoColumns[column].nTh;
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().footer()', 'column().footer()', function ( selector, opts ) {
		return this.iterator( 'column', function ( settings, column ) {
			return settings.aoColumns[column].nTf;
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().data()', 'column().data()', function () {
		return this.iterator( 'column-rows', __columnData, 1 );
	} );
	
	_api_registerPlural( 'columns().dataSrc()', 'column().dataSrc()', function () {
		return this.iterator( 'column', function ( settings, column ) {
			return settings.aoColumns[column].mData;
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().cache()', 'column().cache()', function ( type ) {
		return this.iterator( 'column-rows', function ( settings, column, i, j, rows ) {
			return _pluck_order( settings.aoData, rows,
				type === 'search' ? '_aFilterData' : '_aSortData', column
			);
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().nodes()', 'column().nodes()', function () {
		return this.iterator( 'column-rows', function ( settings, column, i, j, rows ) {
			return _pluck_order( settings.aoData, rows, 'anCells', column ) ;
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().visible()', 'column().visible()', function ( vis, calc ) {
		var that = this;
		var ret = this.iterator( 'column', function ( settings, column ) {
			if ( vis === undefined ) {
				return settings.aoColumns[ column ].bVisible;
			} // else
			__setColumnVis( settings, column, vis );
		} );
	
		// Group the column visibility changes
		if ( vis !== undefined ) {
			this.iterator( 'table', function ( settings ) {
				// Redraw the header after changes
				_fnDrawHead( settings, settings.aoHeader );
				_fnDrawHead( settings, settings.aoFooter );
		
				// Update colspan for no records display. Child rows and extensions will use their own
				// listeners to do this - only need to update the empty table item here
				if ( ! settings.aiDisplay.length ) {
					$(settings.nTBody).find('td[colspan]').attr('colspan', _fnVisbleColumns(settings));
				}
		
				_fnSaveState( settings );
	
				// Second loop once the first is done for events
				that.iterator( 'column', function ( settings, column ) {
					_fnCallbackFire( settings, null, 'column-visibility', [settings, column, vis, calc] );
				} );
	
				if ( calc === undefined || calc ) {
					that.columns.adjust();
				}
			});
		}
	
		return ret;
	} );
	
	_api_registerPlural( 'columns().indexes()', 'column().index()', function ( type ) {
		return this.iterator( 'column', function ( settings, column ) {
			return type === 'visible' ?
				_fnColumnIndexToVisible( settings, column ) :
				column;
		}, 1 );
	} );
	
	_api_register( 'columns.adjust()', function () {
		return this.iterator( 'table', function ( settings ) {
			_fnAdjustColumnSizing( settings );
		}, 1 );
	} );
	
	_api_register( 'column.index()', function ( type, idx ) {
		if ( this.context.length !== 0 ) {
			var ctx = this.context[0];
	
			if ( type === 'fromVisible' || type === 'toData' ) {
				return _fnVisibleToColumnIndex( ctx, idx );
			}
			else if ( type === 'fromData' || type === 'toVisible' ) {
				return _fnColumnIndexToVisible( ctx, idx );
			}
		}
	} );
	
	_api_register( 'column()', function ( selector, opts ) {
		return _selector_first( this.columns( selector, opts ) );
	} );
	
	var __cell_selector = function ( settings, selector, opts )
	{
		var data = settings.aoData;
		var rows = _selector_row_indexes( settings, opts );
		var cells = _removeEmpty( _pluck_order( data, rows, 'anCells' ) );
		var allCells = $(_flatten( [], cells ));
		var row;
		var columns = settings.aoColumns.length;
		var a, i, ien, j, o, host;
	
		var run = function ( s ) {
			var fnSelector = typeof s === 'function';
	
			if ( s === null || s === undefined || fnSelector ) {
				// All cells and function selectors
				a = [];
	
				for ( i=0, ien=rows.length ; i<ien ; i++ ) {
					row = rows[i];
	
					for ( j=0 ; j<columns ; j++ ) {
						o = {
							row: row,
							column: j
						};
	
						if ( fnSelector ) {
							// Selector - function
							host = data[ row ];
	
							if ( s( o, _fnGetCellData(settings, row, j), host.anCells ? host.anCells[j] : null ) ) {
								a.push( o );
							}
						}
						else {
							// Selector - all
							a.push( o );
						}
					}
				}
	
				return a;
			}
			
			// Selector - index
			if ( $.isPlainObject( s ) ) {
				// Valid cell index and its in the array of selectable rows
				return s.column !== undefined && s.row !== undefined && $.inArray( s.row, rows ) !== -1 ?
					[s] :
					[];
			}
	
			// Selector - jQuery filtered cells
			var jqResult = allCells
				.filter( s )
				.map( function (i, el) {
					return { // use a new object, in case someone changes the values
						row:    el._DT_CellIndex.row,
						column: el._DT_CellIndex.column
	 				};
				} )
				.toArray();
	
			if ( jqResult.length || ! s.nodeName ) {
				return jqResult;
			}
	
			// Otherwise the selector is a node, and there is one last option - the
			// element might be a child of an element which has dt-row and dt-column
			// data attributes
			host = $(s).closest('*[data-dt-row]');
			return host.length ?
				[ {
					row: host.data('dt-row'),
					column: host.data('dt-column')
				} ] :
				[];
		};
	
		return _selector_run( 'cell', selector, run, settings, opts );
	};
	
	
	
	
	_api_register( 'cells()', function ( rowSelector, columnSelector, opts ) {
		// Argument shifting
		if ( $.isPlainObject( rowSelector ) ) {
			// Indexes
			if ( rowSelector.row === undefined ) {
				// Selector options in first parameter
				opts = rowSelector;
				rowSelector = null;
			}
			else {
				// Cell index objects in first parameter
				opts = columnSelector;
				columnSelector = null;
			}
		}
		if ( $.isPlainObject( columnSelector ) ) {
			opts = columnSelector;
			columnSelector = null;
		}
	
		// Cell selector
		if ( columnSelector === null || columnSelector === undefined ) {
			return this.iterator( 'table', function ( settings ) {
				return __cell_selector( settings, rowSelector, _selector_opts( opts ) );
			} );
		}
	
		// The default built in options need to apply to row and columns
		var internalOpts = opts ? {
			page: opts.page,
			order: opts.order,
			search: opts.search
		} : {};
	
		// Row + column selector
		var columns = this.columns( columnSelector, internalOpts );
		var rows = this.rows( rowSelector, internalOpts );
		var i, ien, j, jen;
	
		var cellsNoOpts = this.iterator( 'table', function ( settings, idx ) {
			var a = [];
	
			for ( i=0, ien=rows[idx].length ; i<ien ; i++ ) {
				for ( j=0, jen=columns[idx].length ; j<jen ; j++ ) {
					a.push( {
						row:    rows[idx][i],
						column: columns[idx][j]
					} );
				}
			}
	
			return a;
		}, 1 );
	
		// There is currently only one extension which uses a cell selector extension
		// It is a _major_ performance drag to run this if it isn't needed, so this is
		// an extension specific check at the moment
		var cells = opts && opts.selected ?
			this.cells( cellsNoOpts, opts ) :
			cellsNoOpts;
	
		$.extend( cells.selector, {
			cols: columnSelector,
			rows: rowSelector,
			opts: opts
		} );
	
		return cells;
	} );
	
	
	_api_registerPlural( 'cells().nodes()', 'cell().node()', function () {
		return this.iterator( 'cell', function ( settings, row, column ) {
			var data = settings.aoData[ row ];
	
			return data && data.anCells ?
				data.anCells[ column ] :
				undefined;
		}, 1 );
	} );
	
	
	_api_register( 'cells().data()', function () {
		return this.iterator( 'cell', function ( settings, row, column ) {
			return _fnGetCellData( settings, row, column );
		}, 1 );
	} );
	
	
	_api_registerPlural( 'cells().cache()', 'cell().cache()', function ( type ) {
		type = type === 'search' ? '_aFilterData' : '_aSortData';
	
		return this.iterator( 'cell', function ( settings, row, column ) {
			return settings.aoData[ row ][ type ][ column ];
		}, 1 );
	} );
	
	
	_api_registerPlural( 'cells().render()', 'cell().render()', function ( type ) {
		return this.iterator( 'cell', function ( settings, row, column ) {
			return _fnGetCellData( settings, row, column, type );
		}, 1 );
	} );
	
	
	_api_registerPlural( 'cells().indexes()', 'cell().index()', function () {
		return this.iterator( 'cell', function ( settings, row, column ) {
			return {
				row: row,
				column: column,
				columnVisible: _fnColumnIndexToVisible( settings, column )
			};
		}, 1 );
	} );
	
	
	_api_registerPlural( 'cells().invalidate()', 'cell().invalidate()', function ( src ) {
		return this.iterator( 'cell', function ( settings, row, column ) {
			_fnInvalidate( settings, row, src, column );
		} );
	} );
	
	
	
	_api_register( 'cell()', function ( rowSelector, columnSelector, opts ) {
		return _selector_first( this.cells( rowSelector, columnSelector, opts ) );
	} );
	
	
	_api_register( 'cell().data()', function ( data ) {
		var ctx = this.context;
		var cell = this[0];
	
		if ( data === undefined ) {
			// Get
			return ctx.length && cell.length ?
				_fnGetCellData( ctx[0], cell[0].row, cell[0].column ) :
				undefined;
		}
	
		// Set
		_fnSetCellData( ctx[0], cell[0].row, cell[0].column, data );
		_fnInvalidate( ctx[0], cell[0].row, 'data', cell[0].column );
	
		return this;
	} );
	
	
	
	/**
	 * Get current ordering (sorting) that has been applied to the table.
	 *
	 * @returns {array} 2D array containing the sorting information for the first
	 *   table in the current context. Each element in the parent array represents
	 *   a column being sorted upon (i.e. multi-sorting with two columns would have
	 *   2 inner arrays). The inner arrays may have 2 or 3 elements. The first is
	 *   the column index that the sorting condition applies to, the second is the
	 *   direction of the sort (`desc` or `asc`) and, optionally, the third is the
	 *   index of the sorting order from the `column.sorting` initialisation array.
	 *//**
	 * Set the ordering for the table.
	 *
	 * @param {integer} order Column index to sort upon.
	 * @param {string} direction Direction of the sort to be applied (`asc` or `desc`)
	 * @returns {DataTables.Api} this
	 *//**
	 * Set the ordering for the table.
	 *
	 * @param {array} order 1D array of sorting information to be applied.
	 * @param {array} [...] Optional additional sorting conditions
	 * @returns {DataTables.Api} this
	 *//**
	 * Set the ordering for the table.
	 *
	 * @param {array} order 2D array of sorting information to be applied.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'order()', function ( order, dir ) {
		var ctx = this.context;
	
		if ( order === undefined ) {
			// get
			return ctx.length !== 0 ?
				ctx[0].aaSorting :
				undefined;
		}
	
		// set
		if ( typeof order === 'number' ) {
			// Simple column / direction passed in
			order = [ [ order, dir ] ];
		}
		else if ( order.length && ! Array.isArray( order[0] ) ) {
			// Arguments passed in (list of 1D arrays)
			order = Array.prototype.slice.call( arguments );
		}
		// otherwise a 2D array was passed in
	
		return this.iterator( 'table', function ( settings ) {
			settings.aaSorting = order.slice();
		} );
	} );
	
	
	/**
	 * Attach a sort listener to an element for a given column
	 *
	 * @param {node|jQuery|string} node Identifier for the element(s) to attach the
	 *   listener to. This can take the form of a single DOM node, a jQuery
	 *   collection of nodes or a jQuery selector which will identify the node(s).
	 * @param {integer} column the column that a click on this node will sort on
	 * @param {function} [callback] callback function when sort is run
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'order.listener()', function ( node, column, callback ) {
		return this.iterator( 'table', function ( settings ) {
			_fnSortAttachListener( settings, node, column, callback );
		} );
	} );
	
	
	_api_register( 'order.fixed()', function ( set ) {
		if ( ! set ) {
			var ctx = this.context;
			var fixed = ctx.length ?
				ctx[0].aaSortingFixed :
				undefined;
	
			return Array.isArray( fixed ) ?
				{ pre: fixed } :
				fixed;
		}
	
		return this.iterator( 'table', function ( settings ) {
			settings.aaSortingFixed = $.extend( true, {}, set );
		} );
	} );
	
	
	// Order by the selected column(s)
	_api_register( [
		'columns().order()',
		'column().order()'
	], function ( dir ) {
		var that = this;
	
		return this.iterator( 'table', function ( settings, i ) {
			var sort = [];
	
			$.each( that[i], function (j, col) {
				sort.push( [ col, dir ] );
			} );
	
			settings.aaSorting = sort;
		} );
	} );
	
	
	
	_api_register( 'search()', function ( input, regex, smart, caseInsen ) {
		var ctx = this.context;
	
		if ( input === undefined ) {
			// get
			return ctx.length !== 0 ?
				ctx[0].oPreviousSearch.sSearch :
				undefined;
		}
	
		// set
		return this.iterator( 'table', function ( settings ) {
			if ( ! settings.oFeatures.bFilter ) {
				return;
			}
	
			_fnFilterComplete( settings, $.extend( {}, settings.oPreviousSearch, {
				"sSearch": input+"",
				"bRegex":  regex === null ? false : regex,
				"bSmart":  smart === null ? true  : smart,
				"bCaseInsensitive": caseInsen === null ? true : caseInsen
			} ), 1 );
		} );
	} );
	
	
	_api_registerPlural(
		'columns().search()',
		'column().search()',
		function ( input, regex, smart, caseInsen ) {
			return this.iterator( 'column', function ( settings, column ) {
				var preSearch = settings.aoPreSearchCols;
	
				if ( input === undefined ) {
					// get
					return preSearch[ column ].sSearch;
				}
	
				// set
				if ( ! settings.oFeatures.bFilter ) {
					return;
				}
	
				$.extend( preSearch[ column ], {
					"sSearch": input+"",
					"bRegex":  regex === null ? false : regex,
					"bSmart":  smart === null ? true  : smart,
					"bCaseInsensitive": caseInsen === null ? true : caseInsen
				} );
	
				_fnFilterComplete( settings, settings.oPreviousSearch, 1 );
			} );
		}
	);
	
	/*
	 * State API methods
	 */
	
	_api_register( 'state()', function () {
		return this.context.length ?
			this.context[0].oSavedState :
			null;
	} );
	
	
	_api_register( 'state.clear()', function () {
		return this.iterator( 'table', function ( settings ) {
			// Save an empty object
			settings.fnStateSaveCallback.call( settings.oInstance, settings, {} );
		} );
	} );
	
	
	_api_register( 'state.loaded()', function () {
		return this.context.length ?
			this.context[0].oLoadedState :
			null;
	} );
	
	
	_api_register( 'state.save()', function () {
		return this.iterator( 'table', function ( settings ) {
			_fnSaveState( settings );
		} );
	} );
	
	
	
	/**
	 * Provide a common method for plug-ins to check the version of DataTables being
	 * used, in order to ensure compatibility.
	 *
	 *  @param {string} version Version string to check for, in the format "X.Y.Z".
	 *    Note that the formats "X" and "X.Y" are also acceptable.
	 *  @returns {boolean} true if this version of DataTables is greater or equal to
	 *    the required version, or false if this version of DataTales is not
	 *    suitable
	 *  @static
	 *  @dtopt API-Static
	 *
	 *  @example
	 *    alert( $.fn.dataTable.versionCheck( '1.9.0' ) );
	 */
	DataTable.versionCheck = DataTable.fnVersionCheck = function( version )
	{
		var aThis = DataTable.version.split('.');
		var aThat = version.split('.');
		var iThis, iThat;
	
		for ( var i=0, iLen=aThat.length ; i<iLen ; i++ ) {
			iThis = parseInt( aThis[i], 10 ) || 0;
			iThat = parseInt( aThat[i], 10 ) || 0;
	
			// Parts are the same, keep comparing
			if (iThis === iThat) {
				continue;
			}
	
			// Parts are different, return immediately
			return iThis > iThat;
		}
	
		return true;
	};
	
	
	/**
	 * Check if a `<table>` node is a DataTable table already or not.
	 *
	 *  @param {node|jquery|string} table Table node, jQuery object or jQuery
	 *      selector for the table to test. Note that if more than more than one
	 *      table is passed on, only the first will be checked
	 *  @returns {boolean} true the table given is a DataTable, or false otherwise
	 *  @static
	 *  @dtopt API-Static
	 *
	 *  @example
	 *    if ( ! $.fn.DataTable.isDataTable( '#example' ) ) {
	 *      $('#example').dataTable();
	 *    }
	 */
	DataTable.isDataTable = DataTable.fnIsDataTable = function ( table )
	{
		var t = $(table).get(0);
		var is = false;
	
		if ( table instanceof DataTable.Api ) {
			return true;
		}
	
		$.each( DataTable.settings, function (i, o) {
			var head = o.nScrollHead ? $('table', o.nScrollHead)[0] : null;
			var foot = o.nScrollFoot ? $('table', o.nScrollFoot)[0] : null;
	
			if ( o.nTable === t || head === t || foot === t ) {
				is = true;
			}
		} );
	
		return is;
	};
	
	
	/**
	 * Get all DataTable tables that have been initialised - optionally you can
	 * select to get only currently visible tables.
	 *
	 *  @param {boolean} [visible=false] Flag to indicate if you want all (default)
	 *    or visible tables only.
	 *  @returns {array} Array of `table` nodes (not DataTable instances) which are
	 *    DataTables
	 *  @static
	 *  @dtopt API-Static
	 *
	 *  @example
	 *    $.each( $.fn.dataTable.tables(true), function () {
	 *      $(table).DataTable().columns.adjust();
	 *    } );
	 */
	DataTable.tables = DataTable.fnTables = function ( visible )
	{
		var api = false;
	
		if ( $.isPlainObject( visible ) ) {
			api = visible.api;
			visible = visible.visible;
		}
	
		var a = $.map( DataTable.settings, function (o) {
			if ( !visible || (visible && $(o.nTable).is(':visible')) ) {
				return o.nTable;
			}
		} );
	
		return api ?
			new _Api( a ) :
			a;
	};
	
	
	/**
	 * Convert from camel case parameters to Hungarian notation. This is made public
	 * for the extensions to provide the same ability as DataTables core to accept
	 * either the 1.9 style Hungarian notation, or the 1.10+ style camelCase
	 * parameters.
	 *
	 *  @param {object} src The model object which holds all parameters that can be
	 *    mapped.
	 *  @param {object} user The object to convert from camel case to Hungarian.
	 *  @param {boolean} force When set to `true`, properties which already have a
	 *    Hungarian value in the `user` object will be overwritten. Otherwise they
	 *    won't be.
	 */
	DataTable.camelToHungarian = _fnCamelToHungarian;
	
	
	
	/**
	 *
	 */
	_api_register( '$()', function ( selector, opts ) {
		var
			rows   = this.rows( opts ).nodes(), // Get all rows
			jqRows = $(rows);
	
		return $( [].concat(
			jqRows.filter( selector ).toArray(),
			jqRows.find( selector ).toArray()
		) );
	} );
	
	
	// jQuery functions to operate on the tables
	$.each( [ 'on', 'one', 'off' ], function (i, key) {
		_api_register( key+'()', function ( /* event, handler */ ) {
			var args = Array.prototype.slice.call(arguments);
	
			// Add the `dt` namespace automatically if it isn't already present
			args[0] = $.map( args[0].split( /\s/ ), function ( e ) {
				return ! e.match(/\.dt\b/) ?
					e+'.dt' :
					e;
				} ).join( ' ' );
	
			var inst = $( this.tables().nodes() );
			inst[key].apply( inst, args );
			return this;
		} );
	} );
	
	
	_api_register( 'clear()', function () {
		return this.iterator( 'table', function ( settings ) {
			_fnClearTable( settings );
		} );
	} );
	
	
	_api_register( 'settings()', function () {
		return new _Api( this.context, this.context );
	} );
	
	
	_api_register( 'init()', function () {
		var ctx = this.context;
		return ctx.length ? ctx[0].oInit : null;
	} );
	
	
	_api_register( 'data()', function () {
		return this.iterator( 'table', function ( settings ) {
			return _pluck( settings.aoData, '_aData' );
		} ).flatten();
	} );
	
	
	_api_register( 'destroy()', function ( remove ) {
		remove = remove || false;
	
		return this.iterator( 'table', function ( settings ) {
			var classes   = settings.oClasses;
			var table     = settings.nTable;
			var tbody     = settings.nTBody;
			var thead     = settings.nTHead;
			var tfoot     = settings.nTFoot;
			var jqTable   = $(table);
			var jqTbody   = $(tbody);
			var jqWrapper = $(settings.nTableWrapper);
			var rows      = $.map( settings.aoData, function (r) { return r.nTr; } );
			var i, ien;
	
			// Flag to note that the table is currently being destroyed - no action
			// should be taken
			settings.bDestroying = true;
	
			// Fire off the destroy callbacks for plug-ins etc
			_fnCallbackFire( settings, "aoDestroyCallback", "destroy", [settings] );
	
			// If not being removed from the document, make all columns visible
			if ( ! remove ) {
				new _Api( settings ).columns().visible( true );
			}
	
			// Blitz all `DT` namespaced events (these are internal events, the
			// lowercase, `dt` events are user subscribed and they are responsible
			// for removing them
			jqWrapper.off('.DT').find(':not(tbody *)').off('.DT');
			$(window).off('.DT-'+settings.sInstance);
	
			// When scrolling we had to break the table up - restore it
			if ( table != thead.parentNode ) {
				jqTable.children('thead').detach();
				jqTable.append( thead );
			}
	
			if ( tfoot && table != tfoot.parentNode ) {
				jqTable.children('tfoot').detach();
				jqTable.append( tfoot );
			}
	
			settings.aaSorting = [];
			settings.aaSortingFixed = [];
			_fnSortingClasses( settings );
	
			$( rows ).removeClass( settings.asStripeClasses.join(' ') );
	
			$('th, td', thead).removeClass( classes.sSortable+' '+
				classes.sSortableAsc+' '+classes.sSortableDesc+' '+classes.sSortableNone
			);
	
			// Add the TR elements back into the table in their original order
			jqTbody.children().detach();
			jqTbody.append( rows );
	
			var orig = settings.nTableWrapper.parentNode;
	
			// Remove the DataTables generated nodes, events and classes
			var removedMethod = remove ? 'remove' : 'detach';
			jqTable[ removedMethod ]();
			jqWrapper[ removedMethod ]();
	
			// If we need to reattach the table to the document
			if ( ! remove && orig ) {
				// insertBefore acts like appendChild if !arg[1]
				orig.insertBefore( table, settings.nTableReinsertBefore );
	
				// Restore the width of the original table - was read from the style property,
				// so we can restore directly to that
				jqTable
					.css( 'width', settings.sDestroyWidth )
					.removeClass( classes.sTable );
	
				// If the were originally stripe classes - then we add them back here.
				// Note this is not fool proof (for example if not all rows had stripe
				// classes - but it's a good effort without getting carried away
				ien = settings.asDestroyStripes.length;
	
				if ( ien ) {
					jqTbody.children().each( function (i) {
						$(this).addClass( settings.asDestroyStripes[i % ien] );
					} );
				}
			}
	
			/* Remove the settings object from the settings array */
			var idx = $.inArray( settings, DataTable.settings );
			if ( idx !== -1 ) {
				DataTable.settings.splice( idx, 1 );
			}
		} );
	} );
	
	
	// Add the `every()` method for rows, columns and cells in a compact form
	$.each( [ 'column', 'row', 'cell' ], function ( i, type ) {
		_api_register( type+'s().every()', function ( fn ) {
			var opts = this.selector.opts;
			var api = this;
	
			return this.iterator( type, function ( settings, arg1, arg2, arg3, arg4 ) {
				// Rows and columns:
				//  arg1 - index
				//  arg2 - table counter
				//  arg3 - loop counter
				//  arg4 - undefined
				// Cells:
				//  arg1 - row index
				//  arg2 - column index
				//  arg3 - table counter
				//  arg4 - loop counter
				fn.call(
					api[ type ](
						arg1,
						type==='cell' ? arg2 : opts,
						type==='cell' ? opts : undefined
					),
					arg1, arg2, arg3, arg4
				);
			} );
		} );
	} );
	
	
	// i18n method for extensions to be able to use the language object from the
	// DataTable
	_api_register( 'i18n()', function ( token, def, plural ) {
		var ctx = this.context[0];
		var resolved = _fnGetObjectDataFn( token )( ctx.oLanguage );
	
		if ( resolved === undefined ) {
			resolved = def;
		}
	
		if ( plural !== undefined && $.isPlainObject( resolved ) ) {
			resolved = resolved[ plural ] !== undefined ?
				resolved[ plural ] :
				resolved._;
		}
	
		return resolved.replace( '%d', plural ); // nb: plural might be undefined,
	} );	
	/**
	 * Version string for plug-ins to check compatibility. Allowed format is
	 * `a.b.c-d` where: a:int, b:int, c:int, d:string(dev|beta|alpha). `d` is used
	 * only for non-release builds. See http://semver.org/ for more information.
	 *  @member
	 *  @type string
	 *  @default Version number
	 */
	DataTable.version = "1.12.1";
	
	/**
	 * Private data store, containing all of the settings objects that are
	 * created for the tables on a given page.
	 *
	 * Note that the `DataTable.settings` object is aliased to
	 * `jQuery.fn.dataTableExt` through which it may be accessed and
	 * manipulated, or `jQuery.fn.dataTable.settings`.
	 *  @member
	 *  @type array
	 *  @default []
	 *  @private
	 */
	DataTable.settings = [];
	
	/**
	 * Object models container, for the various models that DataTables has
	 * available to it. These models define the objects that are used to hold
	 * the active state and configuration of the table.
	 *  @namespace
	 */
	DataTable.models = {};
	
	
	
	/**
	 * Template object for the way in which DataTables holds information about
	 * search information for the global filter and individual column filters.
	 *  @namespace
	 */
	DataTable.models.oSearch = {
		/**
		 * Flag to indicate if the filtering should be case insensitive or not
		 *  @type boolean
		 *  @default true
		 */
		"bCaseInsensitive": true,
	
		/**
		 * Applied search term
		 *  @type string
		 *  @default <i>Empty string</i>
		 */
		"sSearch": "",
	
		/**
		 * Flag to indicate if the search term should be interpreted as a
		 * regular expression (true) or not (false) and therefore and special
		 * regex characters escaped.
		 *  @type boolean
		 *  @default false
		 */
		"bRegex": false,
	
		/**
		 * Flag to indicate if DataTables is to use its smart filtering or not.
		 *  @type boolean
		 *  @default true
		 */
		"bSmart": true,
	
		/**
		 * Flag to indicate if DataTables should only trigger a search when
		 * the return key is pressed.
		 *  @type boolean
		 *  @default false
		 */
		"return": false
	};
	
	
	
	
	/**
	 * Template object for the way in which DataTables holds information about
	 * each individual row. This is the object format used for the settings
	 * aoData array.
	 *  @namespace
	 */
	DataTable.models.oRow = {
		/**
		 * TR element for the row
		 *  @type node
		 *  @default null
		 */
		"nTr": null,
	
		/**
		 * Array of TD elements for each row. This is null until the row has been
		 * created.
		 *  @type array nodes
		 *  @default []
		 */
		"anCells": null,
	
		/**
		 * Data object from the original data source for the row. This is either
		 * an array if using the traditional form of DataTables, or an object if
		 * using mData options. The exact type will depend on the passed in
		 * data from the data source, or will be an array if using DOM a data
		 * source.
		 *  @type array|object
		 *  @default []
		 */
		"_aData": [],
	
		/**
		 * Sorting data cache - this array is ostensibly the same length as the
		 * number of columns (although each index is generated only as it is
		 * needed), and holds the data that is used for sorting each column in the
		 * row. We do this cache generation at the start of the sort in order that
		 * the formatting of the sort data need be done only once for each cell
		 * per sort. This array should not be read from or written to by anything
		 * other than the master sorting methods.
		 *  @type array
		 *  @default null
		 *  @private
		 */
		"_aSortData": null,
	
		/**
		 * Per cell filtering data cache. As per the sort data cache, used to
		 * increase the performance of the filtering in DataTables
		 *  @type array
		 *  @default null
		 *  @private
		 */
		"_aFilterData": null,
	
		/**
		 * Filtering data cache. This is the same as the cell filtering cache, but
		 * in this case a string rather than an array. This is easily computed with
		 * a join on `_aFilterData`, but is provided as a cache so the join isn't
		 * needed on every search (memory traded for performance)
		 *  @type array
		 *  @default null
		 *  @private
		 */
		"_sFilterRow": null,
	
		/**
		 * Cache of the class name that DataTables has applied to the row, so we
		 * can quickly look at this variable rather than needing to do a DOM check
		 * on className for the nTr property.
		 *  @type string
		 *  @default <i>Empty string</i>
		 *  @private
		 */
		"_sRowStripe": "",
	
		/**
		 * Denote if the original data source was from the DOM, or the data source
		 * object. This is used for invalidating data, so DataTables can
		 * automatically read data from the original source, unless uninstructed
		 * otherwise.
		 *  @type string
		 *  @default null
		 *  @private
		 */
		"src": null,
	
		/**
		 * Index in the aoData array. This saves an indexOf lookup when we have the
		 * object, but want to know the index
		 *  @type integer
		 *  @default -1
		 *  @private
		 */
		"idx": -1
	};
	
	
	/**
	 * Template object for the column information object in DataTables. This object
	 * is held in the settings aoColumns array and contains all the information that
	 * DataTables needs about each individual column.
	 *
	 * Note that this object is related to {@link DataTable.defaults.column}
	 * but this one is the internal data store for DataTables's cache of columns.
	 * It should NOT be manipulated outside of DataTables. Any configuration should
	 * be done through the initialisation options.
	 *  @namespace
	 */
	DataTable.models.oColumn = {
		/**
		 * Column index. This could be worked out on-the-fly with $.inArray, but it
		 * is faster to just hold it as a variable
		 *  @type integer
		 *  @default null
		 */
		"idx": null,
	
		/**
		 * A list of the columns that sorting should occur on when this column
		 * is sorted. That this property is an array allows multi-column sorting
		 * to be defined for a column (for example first name / last name columns
		 * would benefit from this). The values are integers pointing to the
		 * columns to be sorted on (typically it will be a single integer pointing
		 * at itself, but that doesn't need to be the case).
		 *  @type array
		 */
		"aDataSort": null,
	
		/**
		 * Define the sorting directions that are applied to the column, in sequence
		 * as the column is repeatedly sorted upon - i.e. the first value is used
		 * as the sorting direction when the column if first sorted (clicked on).
		 * Sort it again (click again) and it will move on to the next index.
		 * Repeat until loop.
		 *  @type array
		 */
		"asSorting": null,
	
		/**
		 * Flag to indicate if the column is searchable, and thus should be included
		 * in the filtering or not.
		 *  @type boolean
		 */
		"bSearchable": null,
	
		/**
		 * Flag to indicate if the column is sortable or not.
		 *  @type boolean
		 */
		"bSortable": null,
	
		/**
		 * Flag to indicate if the column is currently visible in the table or not
		 *  @type boolean
		 */
		"bVisible": null,
	
		/**
		 * Store for manual type assignment using the `column.type` option. This
		 * is held in store so we can manipulate the column's `sType` property.
		 *  @type string
		 *  @default null
		 *  @private
		 */
		"_sManualType": null,
	
		/**
		 * Flag to indicate if HTML5 data attributes should be used as the data
		 * source for filtering or sorting. True is either are.
		 *  @type boolean
		 *  @default false
		 *  @private
		 */
		"_bAttrSrc": false,
	
		/**
		 * Developer definable function that is called whenever a cell is created (Ajax source,
		 * etc) or processed for input (DOM source). This can be used as a compliment to mRender
		 * allowing you to modify the DOM element (add background colour for example) when the
		 * element is available.
		 *  @type function
		 *  @param {element} nTd The TD node that has been created
		 *  @param {*} sData The Data for the cell
		 *  @param {array|object} oData The data for the whole row
		 *  @param {int} iRow The row index for the aoData data store
		 *  @default null
		 */
		"fnCreatedCell": null,
	
		/**
		 * Function to get data from a cell in a column. You should <b>never</b>
		 * access data directly through _aData internally in DataTables - always use
		 * the method attached to this property. It allows mData to function as
		 * required. This function is automatically assigned by the column
		 * initialisation method
		 *  @type function
		 *  @param {array|object} oData The data array/object for the array
		 *    (i.e. aoData[]._aData)
		 *  @param {string} sSpecific The specific data type you want to get -
		 *    'display', 'type' 'filter' 'sort'
		 *  @returns {*} The data for the cell from the given row's data
		 *  @default null
		 */
		"fnGetData": null,
	
		/**
		 * Function to set data for a cell in the column. You should <b>never</b>
		 * set the data directly to _aData internally in DataTables - always use
		 * this method. It allows mData to function as required. This function
		 * is automatically assigned by the column initialisation method
		 *  @type function
		 *  @param {array|object} oData The data array/object for the array
		 *    (i.e. aoData[]._aData)
		 *  @param {*} sValue Value to set
		 *  @default null
		 */
		"fnSetData": null,
	
		/**
		 * Property to read the value for the cells in the column from the data
		 * source array / object. If null, then the default content is used, if a
		 * function is given then the return from the function is used.
		 *  @type function|int|string|null
		 *  @default null
		 */
		"mData": null,
	
		/**
		 * Partner property to mData which is used (only when defined) to get
		 * the data - i.e. it is basically the same as mData, but without the
		 * 'set' option, and also the data fed to it is the result from mData.
		 * This is the rendering method to match the data method of mData.
		 *  @type function|int|string|null
		 *  @default null
		 */
		"mRender": null,
	
		/**
		 * Unique header TH/TD element for this column - this is what the sorting
		 * listener is attached to (if sorting is enabled.)
		 *  @type node
		 *  @default null
		 */
		"nTh": null,
	
		/**
		 * Unique footer TH/TD element for this column (if there is one). Not used
		 * in DataTables as such, but can be used for plug-ins to reference the
		 * footer for each column.
		 *  @type node
		 *  @default null
		 */
		"nTf": null,
	
		/**
		 * The class to apply to all TD elements in the table's TBODY for the column
		 *  @type string
		 *  @default null
		 */
		"sClass": null,
	
		/**
		 * When DataTables calculates the column widths to assign to each column,
		 * it finds the longest string in each column and then constructs a
		 * temporary table and reads the widths from that. The problem with this
		 * is that "mmm" is much wider then "iiii", but the latter is a longer
		 * string - thus the calculation can go wrong (doing it properly and putting
		 * it into an DOM object and measuring that is horribly(!) slow). Thus as
		 * a "work around" we provide this option. It will append its value to the
		 * text that is found to be the longest string for the column - i.e. padding.
		 *  @type string
		 */
		"sContentPadding": null,
	
		/**
		 * Allows a default value to be given for a column's data, and will be used
		 * whenever a null data source is encountered (this can be because mData
		 * is set to null, or because the data source itself is null).
		 *  @type string
		 *  @default null
		 */
		"sDefaultContent": null,
	
		/**
		 * Name for the column, allowing reference to the column by name as well as
		 * by index (needs a lookup to work by name).
		 *  @type string
		 */
		"sName": null,
	
		/**
		 * Custom sorting data type - defines which of the available plug-ins in
		 * afnSortData the custom sorting will use - if any is defined.
		 *  @type string
		 *  @default std
		 */
		"sSortDataType": 'std',
	
		/**
		 * Class to be applied to the header element when sorting on this column
		 *  @type string
		 *  @default null
		 */
		"sSortingClass": null,
	
		/**
		 * Class to be applied to the header element when sorting on this column -
		 * when jQuery UI theming is used.
		 *  @type string
		 *  @default null
		 */
		"sSortingClassJUI": null,
	
		/**
		 * Title of the column - what is seen in the TH element (nTh).
		 *  @type string
		 */
		"sTitle": null,
	
		/**
		 * Column sorting and filtering type
		 *  @type string
		 *  @default null
		 */
		"sType": null,
	
		/**
		 * Width of the column
		 *  @type string
		 *  @default null
		 */
		"sWidth": null,
	
		/**
		 * Width of the column when it was first "encountered"
		 *  @type string
		 *  @default null
		 */
		"sWidthOrig": null
	};
	
	
	/*
	 * Developer note: The properties of the object below are given in Hungarian
	 * notation, that was used as the interface for DataTables prior to v1.10, however
	 * from v1.10 onwards the primary interface is camel case. In order to avoid
	 * breaking backwards compatibility utterly with this change, the Hungarian
	 * version is still, internally the primary interface, but is is not documented
	 * - hence the @name tags in each doc comment. This allows a Javascript function
	 * to create a map from Hungarian notation to camel case (going the other direction
	 * would require each property to be listed, which would add around 3K to the size
	 * of DataTables, while this method is about a 0.5K hit).
	 *
	 * Ultimately this does pave the way for Hungarian notation to be dropped
	 * completely, but that is a massive amount of work and will break current
	 * installs (therefore is on-hold until v2).
	 */
	
	/**
	 * Initialisation options that can be given to DataTables at initialisation
	 * time.
	 *  @namespace
	 */
	DataTable.defaults = {
		/**
		 * An array of data to use for the table, passed in at initialisation which
		 * will be used in preference to any data which is already in the DOM. This is
		 * particularly useful for constructing tables purely in Javascript, for
		 * example with a custom Ajax call.
		 *  @type array
		 *  @default null
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.data
		 *
		 *  @example
		 *    // Using a 2D array data source
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "data": [
		 *          ['Trident', 'Internet Explorer 4.0', 'Win 95+', 4, 'X'],
		 *          ['Trident', 'Internet Explorer 5.0', 'Win 95+', 5, 'C'],
		 *        ],
		 *        "columns": [
		 *          { "title": "Engine" },
		 *          { "title": "Browser" },
		 *          { "title": "Platform" },
		 *          { "title": "Version" },
		 *          { "title": "Grade" }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using an array of objects as a data source (`data`)
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "data": [
		 *          {
		 *            "engine":   "Trident",
		 *            "browser":  "Internet Explorer 4.0",
		 *            "platform": "Win 95+",
		 *            "version":  4,
		 *            "grade":    "X"
		 *          },
		 *          {
		 *            "engine":   "Trident",
		 *            "browser":  "Internet Explorer 5.0",
		 *            "platform": "Win 95+",
		 *            "version":  5,
		 *            "grade":    "C"
		 *          }
		 *        ],
		 *        "columns": [
		 *          { "title": "Engine",   "data": "engine" },
		 *          { "title": "Browser",  "data": "browser" },
		 *          { "title": "Platform", "data": "platform" },
		 *          { "title": "Version",  "data": "version" },
		 *          { "title": "Grade",    "data": "grade" }
		 *        ]
		 *      } );
		 *    } );
		 */
		"aaData": null,
	
	
		/**
		 * If ordering is enabled, then DataTables will perform a first pass sort on
		 * initialisation. You can define which column(s) the sort is performed
		 * upon, and the sorting direction, with this variable. The `sorting` array
		 * should contain an array for each column to be sorted initially containing
		 * the column's index and a direction string ('asc' or 'desc').
		 *  @type array
		 *  @default [[0,'asc']]
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.order
		 *
		 *  @example
		 *    // Sort by 3rd column first, and then 4th column
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "order": [[2,'asc'], [3,'desc']]
		 *      } );
		 *    } );
		 *
		 *    // No initial sorting
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "order": []
		 *      } );
		 *    } );
		 */
		"aaSorting": [[0,'asc']],
	
	
		/**
		 * This parameter is basically identical to the `sorting` parameter, but
		 * cannot be overridden by user interaction with the table. What this means
		 * is that you could have a column (visible or hidden) which the sorting
		 * will always be forced on first - any sorting after that (from the user)
		 * will then be performed as required. This can be useful for grouping rows
		 * together.
		 *  @type array
		 *  @default null
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.orderFixed
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "orderFixed": [[0,'asc']]
		 *      } );
		 *    } )
		 */
		"aaSortingFixed": [],
	
	
		/**
		 * DataTables can be instructed to load data to display in the table from a
		 * Ajax source. This option defines how that Ajax call is made and where to.
		 *
		 * The `ajax` property has three different modes of operation, depending on
		 * how it is defined. These are:
		 *
		 * * `string` - Set the URL from where the data should be loaded from.
		 * * `object` - Define properties for `jQuery.ajax`.
		 * * `function` - Custom data get function
		 *
		 * `string`
		 * --------
		 *
		 * As a string, the `ajax` property simply defines the URL from which
		 * DataTables will load data.
		 *
		 * `object`
		 * --------
		 *
		 * As an object, the parameters in the object are passed to
		 * [jQuery.ajax](http://api.jquery.com/jQuery.ajax/) allowing fine control
		 * of the Ajax request. DataTables has a number of default parameters which
		 * you can override using this option. Please refer to the jQuery
		 * documentation for a full description of the options available, although
		 * the following parameters provide additional options in DataTables or
		 * require special consideration:
		 *
		 * * `data` - As with jQuery, `data` can be provided as an object, but it
		 *   can also be used as a function to manipulate the data DataTables sends
		 *   to the server. The function takes a single parameter, an object of
		 *   parameters with the values that DataTables has readied for sending. An
		 *   object may be returned which will be merged into the DataTables
		 *   defaults, or you can add the items to the object that was passed in and
		 *   not return anything from the function. This supersedes `fnServerParams`
		 *   from DataTables 1.9-.
		 *
		 * * `dataSrc` - By default DataTables will look for the property `data` (or
		 *   `aaData` for compatibility with DataTables 1.9-) when obtaining data
		 *   from an Ajax source or for server-side processing - this parameter
		 *   allows that property to be changed. You can use Javascript dotted
		 *   object notation to get a data source for multiple levels of nesting, or
		 *   it my be used as a function. As a function it takes a single parameter,
		 *   the JSON returned from the server, which can be manipulated as
		 *   required, with the returned value being that used by DataTables as the
		 *   data source for the table. This supersedes `sAjaxDataProp` from
		 *   DataTables 1.9-.
		 *
		 * * `success` - Should not be overridden it is used internally in
		 *   DataTables. To manipulate / transform the data returned by the server
		 *   use `ajax.dataSrc`, or use `ajax` as a function (see below).
		 *
		 * `function`
		 * ----------
		 *
		 * As a function, making the Ajax call is left up to yourself allowing
		 * complete control of the Ajax request. Indeed, if desired, a method other
		 * than Ajax could be used to obtain the required data, such as Web storage
		 * or an AIR database.
		 *
		 * The function is given four parameters and no return is required. The
		 * parameters are:
		 *
		 * 1. _object_ - Data to send to the server
		 * 2. _function_ - Callback function that must be executed when the required
		 *    data has been obtained. That data should be passed into the callback
		 *    as the only parameter
		 * 3. _object_ - DataTables settings object for the table
		 *
		 * Note that this supersedes `fnServerData` from DataTables 1.9-.
		 *
		 *  @type string|object|function
		 *  @default null
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.ajax
		 *  @since 1.10.0
		 *
		 * @example
		 *   // Get JSON data from a file via Ajax.
		 *   // Note DataTables expects data in the form `{ data: [ ...data... ] }` by default).
		 *   $('#example').dataTable( {
		 *     "ajax": "data.json"
		 *   } );
		 *
		 * @example
		 *   // Get JSON data from a file via Ajax, using `dataSrc` to change
		 *   // `data` to `tableData` (i.e. `{ tableData: [ ...data... ] }`)
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "dataSrc": "tableData"
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Get JSON data from a file via Ajax, using `dataSrc` to read data
		 *   // from a plain array rather than an array in an object
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "dataSrc": ""
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Manipulate the data returned from the server - add a link to data
		 *   // (note this can, should, be done using `render` for the column - this
		 *   // is just a simple example of how the data can be manipulated).
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "dataSrc": function ( json ) {
		 *         for ( var i=0, ien=json.length ; i<ien ; i++ ) {
		 *           json[i][0] = '<a href="/message/'+json[i][0]+'>View message</a>';
		 *         }
		 *         return json;
		 *       }
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Add data to the request
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "data": function ( d ) {
		 *         return {
		 *           "extra_search": $('#extra').val()
		 *         };
		 *       }
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Send request as POST
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "type": "POST"
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Get the data from localStorage (could interface with a form for
		 *   // adding, editing and removing rows).
		 *   $('#example').dataTable( {
		 *     "ajax": function (data, callback, settings) {
		 *       callback(
		 *         JSON.parse( localStorage.getItem('dataTablesData') )
		 *       );
		 *     }
		 *   } );
		 */
		"ajax": null,
	
	
		/**
		 * This parameter allows you to readily specify the entries in the length drop
		 * down menu that DataTables shows when pagination is enabled. It can be
		 * either a 1D array of options which will be used for both the displayed
		 * option and the value, or a 2D array which will use the array in the first
		 * position as the value, and the array in the second position as the
		 * displayed options (useful for language strings such as 'All').
		 *
		 * Note that the `pageLength` property will be automatically set to the
		 * first value given in this array, unless `pageLength` is also provided.
		 *  @type array
		 *  @default [ 10, 25, 50, 100 ]
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.lengthMenu
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "lengthMenu": [[10, 25, 50, -1], [10, 25, 50, "All"]]
		 *      } );
		 *    } );
		 */
		"aLengthMenu": [ 10, 25, 50, 100 ],
	
	
		/**
		 * The `columns` option in the initialisation parameter allows you to define
		 * details about the way individual columns behave. For a full list of
		 * column options that can be set, please see
		 * {@link DataTable.defaults.column}. Note that if you use `columns` to
		 * define your columns, you must have an entry in the array for every single
		 * column that you have in your table (these can be null if you don't which
		 * to specify any options).
		 *  @member
		 *
		 *  @name DataTable.defaults.column
		 */
		"aoColumns": null,
	
		/**
		 * Very similar to `columns`, `columnDefs` allows you to target a specific
		 * column, multiple columns, or all columns, using the `targets` property of
		 * each object in the array. This allows great flexibility when creating
		 * tables, as the `columnDefs` arrays can be of any length, targeting the
		 * columns you specifically want. `columnDefs` may use any of the column
		 * options available: {@link DataTable.defaults.column}, but it _must_
		 * have `targets` defined in each object in the array. Values in the `targets`
		 * array may be:
		 *   <ul>
		 *     <li>a string - class name will be matched on the TH for the column</li>
		 *     <li>0 or a positive integer - column index counting from the left</li>
		 *     <li>a negative integer - column index counting from the right</li>
		 *     <li>the string "_all" - all columns (i.e. assign a default)</li>
		 *   </ul>
		 *  @member
		 *
		 *  @name DataTable.defaults.columnDefs
		 */
		"aoColumnDefs": null,
	
	
		/**
		 * Basically the same as `search`, this parameter defines the individual column
		 * filtering state at initialisation time. The array must be of the same size
		 * as the number of columns, and each element be an object with the parameters
		 * `search` and `escapeRegex` (the latter is optional). 'null' is also
		 * accepted and the default will be used.
		 *  @type array
		 *  @default []
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.searchCols
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "searchCols": [
		 *          null,
		 *          { "search": "My filter" },
		 *          null,
		 *          { "search": "^[0-9]", "escapeRegex": false }
		 *        ]
		 *      } );
		 *    } )
		 */
		"aoSearchCols": [],
	
	
		/**
		 * An array of CSS classes that should be applied to displayed rows. This
		 * array may be of any length, and DataTables will apply each class
		 * sequentially, looping when required.
		 *  @type array
		 *  @default null <i>Will take the values determined by the `oClasses.stripe*`
		 *    options</i>
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.stripeClasses
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stripeClasses": [ 'strip1', 'strip2', 'strip3' ]
		 *      } );
		 *    } )
		 */
		"asStripeClasses": null,
	
	
		/**
		 * Enable or disable automatic column width calculation. This can be disabled
		 * as an optimisation (it takes some time to calculate the widths) if the
		 * tables widths are passed in using `columns`.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.autoWidth
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "autoWidth": false
		 *      } );
		 *    } );
		 */
		"bAutoWidth": true,
	
	
		/**
		 * Deferred rendering can provide DataTables with a huge speed boost when you
		 * are using an Ajax or JS data source for the table. This option, when set to
		 * true, will cause DataTables to defer the creation of the table elements for
		 * each row until they are needed for a draw - saving a significant amount of
		 * time.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.deferRender
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "ajax": "sources/arrays.txt",
		 *        "deferRender": true
		 *      } );
		 *    } );
		 */
		"bDeferRender": false,
	
	
		/**
		 * Replace a DataTable which matches the given selector and replace it with
		 * one which has the properties of the new initialisation object passed. If no
		 * table matches the selector, then the new DataTable will be constructed as
		 * per normal.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.destroy
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "srollY": "200px",
		 *        "paginate": false
		 *      } );
		 *
		 *      // Some time later....
		 *      $('#example').dataTable( {
		 *        "filter": false,
		 *        "destroy": true
		 *      } );
		 *    } );
		 */
		"bDestroy": false,
	
	
		/**
		 * Enable or disable filtering of data. Filtering in DataTables is "smart" in
		 * that it allows the end user to input multiple words (space separated) and
		 * will match a row containing those words, even if not in the order that was
		 * specified (this allow matching across multiple columns). Note that if you
		 * wish to use filtering in DataTables this must remain 'true' - to remove the
		 * default filtering input box and retain filtering abilities, please use
		 * {@link DataTable.defaults.dom}.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.searching
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "searching": false
		 *      } );
		 *    } );
		 */
		"bFilter": true,
	
	
		/**
		 * Enable or disable the table information display. This shows information
		 * about the data that is currently visible on the page, including information
		 * about filtered data if that action is being performed.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.info
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "info": false
		 *      } );
		 *    } );
		 */
		"bInfo": true,
	
	
		/**
		 * Allows the end user to select the size of a formatted page from a select
		 * menu (sizes are 10, 25, 50 and 100). Requires pagination (`paginate`).
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.lengthChange
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "lengthChange": false
		 *      } );
		 *    } );
		 */
		"bLengthChange": true,
	
	
		/**
		 * Enable or disable pagination.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.paging
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "paging": false
		 *      } );
		 *    } );
		 */
		"bPaginate": true,
	
	
		/**
		 * Enable or disable the display of a 'processing' indicator when the table is
		 * being processed (e.g. a sort). This is particularly useful for tables with
		 * large amounts of data where it can take a noticeable amount of time to sort
		 * the entries.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.processing
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "processing": true
		 *      } );
		 *    } );
		 */
		"bProcessing": false,
	
	
		/**
		 * Retrieve the DataTables object for the given selector. Note that if the
		 * table has already been initialised, this parameter will cause DataTables
		 * to simply return the object that has already been set up - it will not take
		 * account of any changes you might have made to the initialisation object
		 * passed to DataTables (setting this parameter to true is an acknowledgement
		 * that you understand this). `destroy` can be used to reinitialise a table if
		 * you need.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.retrieve
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      initTable();
		 *      tableActions();
		 *    } );
		 *
		 *    function initTable ()
		 *    {
		 *      return $('#example').dataTable( {
		 *        "scrollY": "200px",
		 *        "paginate": false,
		 *        "retrieve": true
		 *      } );
		 *    }
		 *
		 *    function tableActions ()
		 *    {
		 *      var table = initTable();
		 *      // perform API operations with oTable
		 *    }
		 */
		"bRetrieve": false,
	
	
		/**
		 * When vertical (y) scrolling is enabled, DataTables will force the height of
		 * the table's viewport to the given height at all times (useful for layout).
		 * However, this can look odd when filtering data down to a small data set,
		 * and the footer is left "floating" further down. This parameter (when
		 * enabled) will cause DataTables to collapse the table's viewport down when
		 * the result set will fit within the given Y height.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.scrollCollapse
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "scrollY": "200",
		 *        "scrollCollapse": true
		 *      } );
		 *    } );
		 */
		"bScrollCollapse": false,
	
	
		/**
		 * Configure DataTables to use server-side processing. Note that the
		 * `ajax` parameter must also be given in order to give DataTables a
		 * source to obtain the required data for each draw.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Features
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.serverSide
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "serverSide": true,
		 *        "ajax": "xhr.php"
		 *      } );
		 *    } );
		 */
		"bServerSide": false,
	
	
		/**
		 * Enable or disable sorting of columns. Sorting of individual columns can be
		 * disabled by the `sortable` option for each column.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.ordering
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "ordering": false
		 *      } );
		 *    } );
		 */
		"bSort": true,
	
	
		/**
		 * Enable or display DataTables' ability to sort multiple columns at the
		 * same time (activated by shift-click by the user).
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.orderMulti
		 *
		 *  @example
		 *    // Disable multiple column sorting ability
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "orderMulti": false
		 *      } );
		 *    } );
		 */
		"bSortMulti": true,
	
	
		/**
		 * Allows control over whether DataTables should use the top (true) unique
		 * cell that is found for a single column, or the bottom (false - default).
		 * This is useful when using complex headers.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.orderCellsTop
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "orderCellsTop": true
		 *      } );
		 *    } );
		 */
		"bSortCellsTop": false,
	
	
		/**
		 * Enable or disable the addition of the classes `sorting\_1`, `sorting\_2` and
		 * `sorting\_3` to the columns which are currently being sorted on. This is
		 * presented as a feature switch as it can increase processing time (while
		 * classes are removed and added) so for large data sets you might want to
		 * turn this off.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.orderClasses
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "orderClasses": false
		 *      } );
		 *    } );
		 */
		"bSortClasses": true,
	
	
		/**
		 * Enable or disable state saving. When enabled HTML5 `localStorage` will be
		 * used to save table display information such as pagination information,
		 * display length, filtering and sorting. As such when the end user reloads
		 * the page the display display will match what thy had previously set up.
		 *
		 * Due to the use of `localStorage` the default state saving is not supported
		 * in IE6 or 7. If state saving is required in those browsers, use
		 * `stateSaveCallback` to provide a storage solution such as cookies.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.stateSave
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "stateSave": true
		 *      } );
		 *    } );
		 */
		"bStateSave": false,
	
	
		/**
		 * This function is called when a TR element is created (and all TD child
		 * elements have been inserted), or registered if using a DOM source, allowing
		 * manipulation of the TR element (adding classes etc).
		 *  @type function
		 *  @param {node} row "TR" element for the current row
		 *  @param {array} data Raw data array for this row
		 *  @param {int} dataIndex The index of this row in the internal aoData array
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.createdRow
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "createdRow": function( row, data, dataIndex ) {
		 *          // Bold the grade for all 'A' grade browsers
		 *          if ( data[4] == "A" )
		 *          {
		 *            $('td:eq(4)', row).html( '<b>A</b>' );
		 *          }
		 *        }
		 *      } );
		 *    } );
		 */
		"fnCreatedRow": null,
	
	
		/**
		 * This function is called on every 'draw' event, and allows you to
		 * dynamically modify any aspect you want about the created DOM.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.drawCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "drawCallback": function( settings ) {
		 *          alert( 'DataTables has redrawn the table' );
		 *        }
		 *      } );
		 *    } );
		 */
		"fnDrawCallback": null,
	
	
		/**
		 * Identical to fnHeaderCallback() but for the table footer this function
		 * allows you to modify the table footer on every 'draw' event.
		 *  @type function
		 *  @param {node} foot "TR" element for the footer
		 *  @param {array} data Full table data (as derived from the original HTML)
		 *  @param {int} start Index for the current display starting point in the
		 *    display array
		 *  @param {int} end Index for the current display ending point in the
		 *    display array
		 *  @param {array int} display Index array to translate the visual position
		 *    to the full data array
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.footerCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "footerCallback": function( tfoot, data, start, end, display ) {
		 *          tfoot.getElementsByTagName('th')[0].innerHTML = "Starting index is "+start;
		 *        }
		 *      } );
		 *    } )
		 */
		"fnFooterCallback": null,
	
	
		/**
		 * When rendering large numbers in the information element for the table
		 * (i.e. "Showing 1 to 10 of 57 entries") DataTables will render large numbers
		 * to have a comma separator for the 'thousands' units (e.g. 1 million is
		 * rendered as "1,000,000") to help readability for the end user. This
		 * function will override the default method DataTables uses.
		 *  @type function
		 *  @member
		 *  @param {int} toFormat number to be formatted
		 *  @returns {string} formatted string for DataTables to show the number
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.formatNumber
		 *
		 *  @example
		 *    // Format a number using a single quote for the separator (note that
		 *    // this can also be done with the language.thousands option)
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "formatNumber": function ( toFormat ) {
		 *          return toFormat.toString().replace(
		 *            /\B(?=(\d{3})+(?!\d))/g, "'"
		 *          );
		 *        };
		 *      } );
		 *    } );
		 */
		"fnFormatNumber": function ( toFormat ) {
			return toFormat.toString().replace(
				/\B(?=(\d{3})+(?!\d))/g,
				this.oLanguage.sThousands
			);
		},
	
	
		/**
		 * This function is called on every 'draw' event, and allows you to
		 * dynamically modify the header row. This can be used to calculate and
		 * display useful information about the table.
		 *  @type function
		 *  @param {node} head "TR" element for the header
		 *  @param {array} data Full table data (as derived from the original HTML)
		 *  @param {int} start Index for the current display starting point in the
		 *    display array
		 *  @param {int} end Index for the current display ending point in the
		 *    display array
		 *  @param {array int} display Index array to translate the visual position
		 *    to the full data array
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.headerCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "fheaderCallback": function( head, data, start, end, display ) {
		 *          head.getElementsByTagName('th')[0].innerHTML = "Displaying "+(end-start)+" records";
		 *        }
		 *      } );
		 *    } )
		 */
		"fnHeaderCallback": null,
	
	
		/**
		 * The information element can be used to convey information about the current
		 * state of the table. Although the internationalisation options presented by
		 * DataTables are quite capable of dealing with most customisations, there may
		 * be times where you wish to customise the string further. This callback
		 * allows you to do exactly that.
		 *  @type function
		 *  @param {object} oSettings DataTables settings object
		 *  @param {int} start Starting position in data for the draw
		 *  @param {int} end End position in data for the draw
		 *  @param {int} max Total number of rows in the table (regardless of
		 *    filtering)
		 *  @param {int} total Total number of rows in the data set, after filtering
		 *  @param {string} pre The string that DataTables has formatted using it's
		 *    own rules
		 *  @returns {string} The string to be displayed in the information element.
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.infoCallback
		 *
		 *  @example
		 *    $('#example').dataTable( {
		 *      "infoCallback": function( settings, start, end, max, total, pre ) {
		 *        return start +" to "+ end;
		 *      }
		 *    } );
		 */
		"fnInfoCallback": null,
	
	
		/**
		 * Called when the table has been initialised. Normally DataTables will
		 * initialise sequentially and there will be no need for this function,
		 * however, this does not hold true when using external language information
		 * since that is obtained using an async XHR call.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @param {object} json The JSON object request from the server - only
		 *    present if client-side Ajax sourced data is used
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.initComplete
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "initComplete": function(settings, json) {
		 *          alert( 'DataTables has finished its initialisation.' );
		 *        }
		 *      } );
		 *    } )
		 */
		"fnInitComplete": null,
	
	
		/**
		 * Called at the very start of each table draw and can be used to cancel the
		 * draw by returning false, any other return (including undefined) results in
		 * the full draw occurring).
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @returns {boolean} False will cancel the draw, anything else (including no
		 *    return) will allow it to complete.
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.preDrawCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "preDrawCallback": function( settings ) {
		 *          if ( $('#test').val() == 1 ) {
		 *            return false;
		 *          }
		 *        }
		 *      } );
		 *    } );
		 */
		"fnPreDrawCallback": null,
	
	
		/**
		 * This function allows you to 'post process' each row after it have been
		 * generated for each table draw, but before it is rendered on screen. This
		 * function might be used for setting the row class name etc.
		 *  @type function
		 *  @param {node} row "TR" element for the current row
		 *  @param {array} data Raw data array for this row
		 *  @param {int} displayIndex The display index for the current table draw
		 *  @param {int} displayIndexFull The index of the data in the full list of
		 *    rows (after filtering)
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.rowCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "rowCallback": function( row, data, displayIndex, displayIndexFull ) {
		 *          // Bold the grade for all 'A' grade browsers
		 *          if ( data[4] == "A" ) {
		 *            $('td:eq(4)', row).html( '<b>A</b>' );
		 *          }
		 *        }
		 *      } );
		 *    } );
		 */
		"fnRowCallback": null,
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 * This parameter allows you to override the default function which obtains
		 * the data from the server so something more suitable for your application.
		 * For example you could use POST data, or pull information from a Gears or
		 * AIR database.
		 *  @type function
		 *  @member
		 *  @param {string} source HTTP source to obtain the data from (`ajax`)
		 *  @param {array} data A key/value pair object containing the data to send
		 *    to the server
		 *  @param {function} callback to be called on completion of the data get
		 *    process that will draw the data on the page.
		 *  @param {object} settings DataTables settings object
		 *
		 *  @dtopt Callbacks
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.serverData
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"fnServerData": null,
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 *  It is often useful to send extra data to the server when making an Ajax
		 * request - for example custom filtering information, and this callback
		 * function makes it trivial to send extra information to the server. The
		 * passed in parameter is the data set that has been constructed by
		 * DataTables, and you can add to this or modify it as you require.
		 *  @type function
		 *  @param {array} data Data array (array of objects which are name/value
		 *    pairs) that has been constructed by DataTables and will be sent to the
		 *    server. In the case of Ajax sourced data with server-side processing
		 *    this will be an empty array, for server-side processing there will be a
		 *    significant number of parameters!
		 *  @returns {undefined} Ensure that you modify the data array passed in,
		 *    as this is passed by reference.
		 *
		 *  @dtopt Callbacks
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.serverParams
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"fnServerParams": null,
	
	
		/**
		 * Load the table state. With this function you can define from where, and how, the
		 * state of a table is loaded. By default DataTables will load from `localStorage`
		 * but you might wish to use a server-side database or cookies.
		 *  @type function
		 *  @member
		 *  @param {object} settings DataTables settings object
		 *  @param {object} callback Callback that can be executed when done. It
		 *    should be passed the loaded state object.
		 *  @return {object} The DataTables state object to be loaded
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateLoadCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateLoadCallback": function (settings, callback) {
		 *          $.ajax( {
		 *            "url": "/state_load",
		 *            "dataType": "json",
		 *            "success": function (json) {
		 *              callback( json );
		 *            }
		 *          } );
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateLoadCallback": function ( settings ) {
			try {
				return JSON.parse(
					(settings.iStateDuration === -1 ? sessionStorage : localStorage).getItem(
						'DataTables_'+settings.sInstance+'_'+location.pathname
					)
				);
			} catch (e) {
				return {};
			}
		},
	
	
		/**
		 * Callback which allows modification of the saved state prior to loading that state.
		 * This callback is called when the table is loading state from the stored data, but
		 * prior to the settings object being modified by the saved state. Note that for
		 * plug-in authors, you should use the `stateLoadParams` event to load parameters for
		 * a plug-in.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @param {object} data The state object that is to be loaded
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateLoadParams
		 *
		 *  @example
		 *    // Remove a saved filter, so filtering is never loaded
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateLoadParams": function (settings, data) {
		 *          data.oSearch.sSearch = "";
		 *        }
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Disallow state loading by returning false
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateLoadParams": function (settings, data) {
		 *          return false;
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateLoadParams": null,
	
	
		/**
		 * Callback that is called when the state has been loaded from the state saving method
		 * and the DataTables settings object has been modified as a result of the loaded state.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @param {object} data The state object that was loaded
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateLoaded
		 *
		 *  @example
		 *    // Show an alert with the filtering value that was saved
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateLoaded": function (settings, data) {
		 *          alert( 'Saved filter was: '+data.oSearch.sSearch );
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateLoaded": null,
	
	
		/**
		 * Save the table state. This function allows you to define where and how the state
		 * information for the table is stored By default DataTables will use `localStorage`
		 * but you might wish to use a server-side database or cookies.
		 *  @type function
		 *  @member
		 *  @param {object} settings DataTables settings object
		 *  @param {object} data The state object to be saved
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateSaveCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateSaveCallback": function (settings, data) {
		 *          // Send an Ajax request to the server with the state object
		 *          $.ajax( {
		 *            "url": "/state_save",
		 *            "data": data,
		 *            "dataType": "json",
		 *            "method": "POST"
		 *            "success": function () {}
		 *          } );
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateSaveCallback": function ( settings, data ) {
			try {
				(settings.iStateDuration === -1 ? sessionStorage : localStorage).setItem(
					'DataTables_'+settings.sInstance+'_'+location.pathname,
					JSON.stringify( data )
				);
			} catch (e) {}
		},
	
	
		/**
		 * Callback which allows modification of the state to be saved. Called when the table
		 * has changed state a new state save is required. This method allows modification of
		 * the state saving object prior to actually doing the save, including addition or
		 * other state properties or modification. Note that for plug-in authors, you should
		 * use the `stateSaveParams` event to save parameters for a plug-in.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @param {object} data The state object to be saved
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateSaveParams
		 *
		 *  @example
		 *    // Remove a saved filter, so filtering is never saved
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateSaveParams": function (settings, data) {
		 *          data.oSearch.sSearch = "";
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateSaveParams": null,
	
	
		/**
		 * Duration for which the saved state information is considered valid. After this period
		 * has elapsed the state will be returned to the default.
		 * Value is given in seconds.
		 *  @type int
		 *  @default 7200 <i>(2 hours)</i>
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.stateDuration
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateDuration": 60*60*24; // 1 day
		 *      } );
		 *    } )
		 */
		"iStateDuration": 7200,
	
	
		/**
		 * When enabled DataTables will not make a request to the server for the first
		 * page draw - rather it will use the data already on the page (no sorting etc
		 * will be applied to it), thus saving on an XHR at load time. `deferLoading`
		 * is used to indicate that deferred loading is required, but it is also used
		 * to tell DataTables how many records there are in the full table (allowing
		 * the information element and pagination to be displayed correctly). In the case
		 * where a filtering is applied to the table on initial load, this can be
		 * indicated by giving the parameter as an array, where the first element is
		 * the number of records available after filtering and the second element is the
		 * number of records without filtering (allowing the table information element
		 * to be shown correctly).
		 *  @type int | array
		 *  @default null
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.deferLoading
		 *
		 *  @example
		 *    // 57 records available in the table, no filtering applied
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "serverSide": true,
		 *        "ajax": "scripts/server_processing.php",
		 *        "deferLoading": 57
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // 57 records after filtering, 100 without filtering (an initial filter applied)
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "serverSide": true,
		 *        "ajax": "scripts/server_processing.php",
		 *        "deferLoading": [ 57, 100 ],
		 *        "search": {
		 *          "search": "my_filter"
		 *        }
		 *      } );
		 *    } );
		 */
		"iDeferLoading": null,
	
	
		/**
		 * Number of rows to display on a single page when using pagination. If
		 * feature enabled (`lengthChange`) then the end user will be able to override
		 * this to a custom setting using a pop-up menu.
		 *  @type int
		 *  @default 10
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.pageLength
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "pageLength": 50
		 *      } );
		 *    } )
		 */
		"iDisplayLength": 10,
	
	
		/**
		 * Define the starting point for data display when using DataTables with
		 * pagination. Note that this parameter is the number of records, rather than
		 * the page number, so if you have 10 records per page and want to start on
		 * the third page, it should be "20".
		 *  @type int
		 *  @default 0
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.displayStart
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "displayStart": 20
		 *      } );
		 *    } )
		 */
		"iDisplayStart": 0,
	
	
		/**
		 * By default DataTables allows keyboard navigation of the table (sorting, paging,
		 * and filtering) by adding a `tabindex` attribute to the required elements. This
		 * allows you to tab through the controls and press the enter key to activate them.
		 * The tabindex is default 0, meaning that the tab follows the flow of the document.
		 * You can overrule this using this parameter if you wish. Use a value of -1 to
		 * disable built-in keyboard navigation.
		 *  @type int
		 *  @default 0
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.tabIndex
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "tabIndex": 1
		 *      } );
		 *    } );
		 */
		"iTabIndex": 0,
	
	
		/**
		 * Classes that DataTables assigns to the various components and features
		 * that it adds to the HTML table. This allows classes to be configured
		 * during initialisation in addition to through the static
		 * {@link DataTable.ext.oStdClasses} object).
		 *  @namespace
		 *  @name DataTable.defaults.classes
		 */
		"oClasses": {},
	
	
		/**
		 * All strings that DataTables uses in the user interface that it creates
		 * are defined in this object, allowing you to modified them individually or
		 * completely replace them all as required.
		 *  @namespace
		 *  @name DataTable.defaults.language
		 */
		"oLanguage": {
			/**
			 * Strings that are used for WAI-ARIA labels and controls only (these are not
			 * actually visible on the page, but will be read by screenreaders, and thus
			 * must be internationalised as well).
			 *  @namespace
			 *  @name DataTable.defaults.language.aria
			 */
			"oAria": {
				/**
				 * ARIA label that is added to the table headers when the column may be
				 * sorted ascending by activing the column (click or return when focused).
				 * Note that the column header is prefixed to this string.
				 *  @type string
				 *  @default : activate to sort column ascending
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.aria.sortAscending
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "aria": {
				 *            "sortAscending": " - click/return to sort ascending"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sSortAscending": ": activate to sort column ascending",
	
				/**
				 * ARIA label that is added to the table headers when the column may be
				 * sorted descending by activing the column (click or return when focused).
				 * Note that the column header is prefixed to this string.
				 *  @type string
				 *  @default : activate to sort column ascending
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.aria.sortDescending
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "aria": {
				 *            "sortDescending": " - click/return to sort descending"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sSortDescending": ": activate to sort column descending"
			},
	
			/**
			 * Pagination string used by DataTables for the built-in pagination
			 * control types.
			 *  @namespace
			 *  @name DataTable.defaults.language.paginate
			 */
			"oPaginate": {
				/**
				 * Text to use when using the 'full_numbers' type of pagination for the
				 * button to take the user to the first page.
				 *  @type string
				 *  @default First
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.paginate.first
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "paginate": {
				 *            "first": "First page"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sFirst": "First",
	
	
				/**
				 * Text to use when using the 'full_numbers' type of pagination for the
				 * button to take the user to the last page.
				 *  @type string
				 *  @default Last
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.paginate.last
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "paginate": {
				 *            "last": "Last page"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sLast": "Last",
	
	
				/**
				 * Text to use for the 'next' pagination button (to take the user to the
				 * next page).
				 *  @type string
				 *  @default Next
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.paginate.next
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "paginate": {
				 *            "next": "Next page"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sNext": "Next",
	
	
				/**
				 * Text to use for the 'previous' pagination button (to take the user to
				 * the previous page).
				 *  @type string
				 *  @default Previous
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.paginate.previous
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "paginate": {
				 *            "previous": "Previous page"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sPrevious": "Previous"
			},
	
			/**
			 * This string is shown in preference to `zeroRecords` when the table is
			 * empty of data (regardless of filtering). Note that this is an optional
			 * parameter - if it is not given, the value of `zeroRecords` will be used
			 * instead (either the default or given value).
			 *  @type string
			 *  @default No data available in table
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.emptyTable
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "emptyTable": "No data available in table"
			 *        }
			 *      } );
			 *    } );
			 */
			"sEmptyTable": "No data available in table",
	
	
			/**
			 * This string gives information to the end user about the information
			 * that is current on display on the page. The following tokens can be
			 * used in the string and will be dynamically replaced as the table
			 * display updates. This tokens can be placed anywhere in the string, or
			 * removed as needed by the language requires:
			 *
			 * * `\_START\_` - Display index of the first record on the current page
			 * * `\_END\_` - Display index of the last record on the current page
			 * * `\_TOTAL\_` - Number of records in the table after filtering
			 * * `\_MAX\_` - Number of records in the table without filtering
			 * * `\_PAGE\_` - Current page number
			 * * `\_PAGES\_` - Total number of pages of data in the table
			 *
			 *  @type string
			 *  @default Showing _START_ to _END_ of _TOTAL_ entries
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.info
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "info": "Showing page _PAGE_ of _PAGES_"
			 *        }
			 *      } );
			 *    } );
			 */
			"sInfo": "Showing _START_ to _END_ of _TOTAL_ entries",
	
	
			/**
			 * Display information string for when the table is empty. Typically the
			 * format of this string should match `info`.
			 *  @type string
			 *  @default Showing 0 to 0 of 0 entries
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.infoEmpty
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "infoEmpty": "No entries to show"
			 *        }
			 *      } );
			 *    } );
			 */
			"sInfoEmpty": "Showing 0 to 0 of 0 entries",
	
	
			/**
			 * When a user filters the information in a table, this string is appended
			 * to the information (`info`) to give an idea of how strong the filtering
			 * is. The variable _MAX_ is dynamically updated.
			 *  @type string
			 *  @default (filtered from _MAX_ total entries)
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.infoFiltered
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "infoFiltered": " - filtering from _MAX_ records"
			 *        }
			 *      } );
			 *    } );
			 */
			"sInfoFiltered": "(filtered from _MAX_ total entries)",
	
	
			/**
			 * If can be useful to append extra information to the info string at times,
			 * and this variable does exactly that. This information will be appended to
			 * the `info` (`infoEmpty` and `infoFiltered` in whatever combination they are
			 * being used) at all times.
			 *  @type string
			 *  @default <i>Empty string</i>
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.infoPostFix
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "infoPostFix": "All records shown are derived from real information."
			 *        }
			 *      } );
			 *    } );
			 */
			"sInfoPostFix": "",
	
	
			/**
			 * This decimal place operator is a little different from the other
			 * language options since DataTables doesn't output floating point
			 * numbers, so it won't ever use this for display of a number. Rather,
			 * what this parameter does is modify the sort methods of the table so
			 * that numbers which are in a format which has a character other than
			 * a period (`.`) as a decimal place will be sorted numerically.
			 *
			 * Note that numbers with different decimal places cannot be shown in
			 * the same table and still be sortable, the table must be consistent.
			 * However, multiple different tables on the page can use different
			 * decimal place characters.
			 *  @type string
			 *  @default 
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.decimal
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "decimal": ","
			 *          "thousands": "."
			 *        }
			 *      } );
			 *    } );
			 */
			"sDecimal": "",
	
	
			/**
			 * DataTables has a build in number formatter (`formatNumber`) which is
			 * used to format large numbers that are used in the table information.
			 * By default a comma is used, but this can be trivially changed to any
			 * character you wish with this parameter.
			 *  @type string
			 *  @default ,
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.thousands
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "thousands": "'"
			 *        }
			 *      } );
			 *    } );
			 */
			"sThousands": ",",
	
	
			/**
			 * Detail the action that will be taken when the drop down menu for the
			 * pagination length option is changed. The '_MENU_' variable is replaced
			 * with a default select list of 10, 25, 50 and 100, and can be replaced
			 * with a custom select box if required.
			 *  @type string
			 *  @default Show _MENU_ entries
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.lengthMenu
			 *
			 *  @example
			 *    // Language change only
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "lengthMenu": "Display _MENU_ records"
			 *        }
			 *      } );
			 *    } );
			 *
			 *  @example
			 *    // Language and options change
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "lengthMenu": 'Display <select>'+
			 *            '<option value="10">10</option>'+
			 *            '<option value="20">20</option>'+
			 *            '<option value="30">30</option>'+
			 *            '<option value="40">40</option>'+
			 *            '<option value="50">50</option>'+
			 *            '<option value="-1">All</option>'+
			 *            '</select> records'
			 *        }
			 *      } );
			 *    } );
			 */
			"sLengthMenu": "Show _MENU_ entries",
	
	
			/**
			 * When using Ajax sourced data and during the first draw when DataTables is
			 * gathering the data, this message is shown in an empty row in the table to
			 * indicate to the end user the the data is being loaded. Note that this
			 * parameter is not used when loading data by server-side processing, just
			 * Ajax sourced data with client-side processing.
			 *  @type string
			 *  @default Loading...
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.loadingRecords
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "loadingRecords": "Please wait - loading..."
			 *        }
			 *      } );
			 *    } );
			 */
			"sLoadingRecords": "Loading...",
	
	
			/**
			 * Text which is displayed when the table is processing a user action
			 * (usually a sort command or similar).
			 *  @type string
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.processing
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "processing": "DataTables is currently busy"
			 *        }
			 *      } );
			 *    } );
			 */
			"sProcessing": "",
	
	
			/**
			 * Details the actions that will be taken when the user types into the
			 * filtering input text box. The variable "_INPUT_", if used in the string,
			 * is replaced with the HTML text box for the filtering input allowing
			 * control over where it appears in the string. If "_INPUT_" is not given
			 * then the input box is appended to the string automatically.
			 *  @type string
			 *  @default Search:
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.search
			 *
			 *  @example
			 *    // Input text box will be appended at the end automatically
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "search": "Filter records:"
			 *        }
			 *      } );
			 *    } );
			 *
			 *  @example
			 *    // Specify where the filter should appear
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "search": "Apply filter _INPUT_ to table"
			 *        }
			 *      } );
			 *    } );
			 */
			"sSearch": "Search:",
	
	
			/**
			 * Assign a `placeholder` attribute to the search `input` element
			 *  @type string
			 *  @default 
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.searchPlaceholder
			 */
			"sSearchPlaceholder": "",
	
	
			/**
			 * All of the language information can be stored in a file on the
			 * server-side, which DataTables will look up if this parameter is passed.
			 * It must store the URL of the language file, which is in a JSON format,
			 * and the object has the same properties as the oLanguage object in the
			 * initialiser object (i.e. the above parameters). Please refer to one of
			 * the example language files to see how this works in action.
			 *  @type string
			 *  @default <i>Empty string - i.e. disabled</i>
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.url
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "url": "http://www.sprymedia.co.uk/dataTables/lang.txt"
			 *        }
			 *      } );
			 *    } );
			 */
			"sUrl": "",
	
	
			/**
			 * Text shown inside the table records when the is no information to be
			 * displayed after filtering. `emptyTable` is shown when there is simply no
			 * information in the table at all (regardless of filtering).
			 *  @type string
			 *  @default No matching records found
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.zeroRecords
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "zeroRecords": "No records to display"
			 *        }
			 *      } );
			 *    } );
			 */
			"sZeroRecords": "No matching records found"
		},
	
	
		/**
		 * This parameter allows you to have define the global filtering state at
		 * initialisation time. As an object the `search` parameter must be
		 * defined, but all other parameters are optional. When `regex` is true,
		 * the search string will be treated as a regular expression, when false
		 * (default) it will be treated as a straight string. When `smart`
		 * DataTables will use it's smart filtering methods (to word match at
		 * any point in the data), when false this will not be done.
		 *  @namespace
		 *  @extends DataTable.models.oSearch
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.search
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "search": {"search": "Initial search"}
		 *      } );
		 *    } )
		 */
		"oSearch": $.extend( {}, DataTable.models.oSearch ),
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 * By default DataTables will look for the property `data` (or `aaData` for
		 * compatibility with DataTables 1.9-) when obtaining data from an Ajax
		 * source or for server-side processing - this parameter allows that
		 * property to be changed. You can use Javascript dotted object notation to
		 * get a data source for multiple levels of nesting.
		 *  @type string
		 *  @default data
		 *
		 *  @dtopt Options
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.ajaxDataProp
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"sAjaxDataProp": "data",
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 * You can instruct DataTables to load data from an external
		 * source using this parameter (use aData if you want to pass data in you
		 * already have). Simply provide a url a JSON object can be obtained from.
		 *  @type string
		 *  @default null
		 *
		 *  @dtopt Options
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.ajaxSource
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"sAjaxSource": null,
	
	
		/**
		 * This initialisation variable allows you to specify exactly where in the
		 * DOM you want DataTables to inject the various controls it adds to the page
		 * (for example you might want the pagination controls at the top of the
		 * table). DIV elements (with or without a custom class) can also be added to
		 * aid styling. The follow syntax is used:
		 *   <ul>
		 *     <li>The following options are allowed:
		 *       <ul>
		 *         <li>'l' - Length changing</li>
		 *         <li>'f' - Filtering input</li>
		 *         <li>'t' - The table!</li>
		 *         <li>'i' - Information</li>
		 *         <li>'p' - Pagination</li>
		 *         <li>'r' - pRocessing</li>
		 *       </ul>
		 *     </li>
		 *     <li>The following constants are allowed:
		 *       <ul>
		 *         <li>'H' - jQueryUI theme "header" classes ('fg-toolbar ui-widget-header ui-corner-tl ui-corner-tr ui-helper-clearfix')</li>
		 *         <li>'F' - jQueryUI theme "footer" classes ('fg-toolbar ui-widget-header ui-corner-bl ui-corner-br ui-helper-clearfix')</li>
		 *       </ul>
		 *     </li>
		 *     <li>The following syntax is expected:
		 *       <ul>
		 *         <li>'&lt;' and '&gt;' - div elements</li>
		 *         <li>'&lt;"class" and '&gt;' - div with a class</li>
		 *         <li>'&lt;"#id" and '&gt;' - div with an ID</li>
		 *       </ul>
		 *     </li>
		 *     <li>Examples:
		 *       <ul>
		 *         <li>'&lt;"wrapper"flipt&gt;'</li>
		 *         <li>'&lt;lf&lt;t&gt;ip&gt;'</li>
		 *       </ul>
		 *     </li>
		 *   </ul>
		 *  @type string
		 *  @default lfrtip <i>(when `jQueryUI` is false)</i> <b>or</b>
		 *    <"H"lfr>t<"F"ip> <i>(when `jQueryUI` is true)</i>
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.dom
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "dom": '&lt;"top"i&gt;rt&lt;"bottom"flp&gt;&lt;"clear"&gt;'
		 *      } );
		 *    } );
		 */
		"sDom": "lfrtip",
	
	
		/**
		 * Search delay option. This will throttle full table searches that use the
		 * DataTables provided search input element (it does not effect calls to
		 * `dt-api search()`, providing a delay before the search is made.
		 *  @type integer
		 *  @default 0
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.searchDelay
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "searchDelay": 200
		 *      } );
		 *    } )
		 */
		"searchDelay": null,
	
	
		/**
		 * DataTables features six different built-in options for the buttons to
		 * display for pagination control:
		 *
		 * * `numbers` - Page number buttons only
		 * * `simple` - 'Previous' and 'Next' buttons only
		 * * 'simple_numbers` - 'Previous' and 'Next' buttons, plus page numbers
		 * * `full` - 'First', 'Previous', 'Next' and 'Last' buttons
		 * * `full_numbers` - 'First', 'Previous', 'Next' and 'Last' buttons, plus page numbers
		 * * `first_last_numbers` - 'First' and 'Last' buttons, plus page numbers
		 *  
		 * Further methods can be added using {@link DataTable.ext.oPagination}.
		 *  @type string
		 *  @default simple_numbers
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.pagingType
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "pagingType": "full_numbers"
		 *      } );
		 *    } )
		 */
		"sPaginationType": "simple_numbers",
	
	
		/**
		 * Enable horizontal scrolling. When a table is too wide to fit into a
		 * certain layout, or you have a large number of columns in the table, you
		 * can enable x-scrolling to show the table in a viewport, which can be
		 * scrolled. This property can be `true` which will allow the table to
		 * scroll horizontally when needed, or any CSS unit, or a number (in which
		 * case it will be treated as a pixel measurement). Setting as simply `true`
		 * is recommended.
		 *  @type boolean|string
		 *  @default <i>blank string - i.e. disabled</i>
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.scrollX
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "scrollX": true,
		 *        "scrollCollapse": true
		 *      } );
		 *    } );
		 */
		"sScrollX": "",
	
	
		/**
		 * This property can be used to force a DataTable to use more width than it
		 * might otherwise do when x-scrolling is enabled. For example if you have a
		 * table which requires to be well spaced, this parameter is useful for
		 * "over-sizing" the table, and thus forcing scrolling. This property can by
		 * any CSS unit, or a number (in which case it will be treated as a pixel
		 * measurement).
		 *  @type string
		 *  @default <i>blank string - i.e. disabled</i>
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.scrollXInner
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "scrollX": "100%",
		 *        "scrollXInner": "110%"
		 *      } );
		 *    } );
		 */
		"sScrollXInner": "",
	
	
		/**
		 * Enable vertical scrolling. Vertical scrolling will constrain the DataTable
		 * to the given height, and enable scrolling for any data which overflows the
		 * current viewport. This can be used as an alternative to paging to display
		 * a lot of data in a small area (although paging and scrolling can both be
		 * enabled at the same time). This property can be any CSS unit, or a number
		 * (in which case it will be treated as a pixel measurement).
		 *  @type string
		 *  @default <i>blank string - i.e. disabled</i>
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.scrollY
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "scrollY": "200px",
		 *        "paginate": false
		 *      } );
		 *    } );
		 */
		"sScrollY": "",
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 * Set the HTTP method that is used to make the Ajax call for server-side
		 * processing or Ajax sourced data.
		 *  @type string
		 *  @default GET
		 *
		 *  @dtopt Options
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.serverMethod
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"sServerMethod": "GET",
	
	
		/**
		 * DataTables makes use of renderers when displaying HTML elements for
		 * a table. These renderers can be added or modified by plug-ins to
		 * generate suitable mark-up for a site. For example the Bootstrap
		 * integration plug-in for DataTables uses a paging button renderer to
		 * display pagination buttons in the mark-up required by Bootstrap.
		 *
		 * For further information about the renderers available see
		 * DataTable.ext.renderer
		 *  @type string|object
		 *  @default null
		 *
		 *  @name DataTable.defaults.renderer
		 *
		 */
		"renderer": null,
	
	
		/**
		 * Set the data property name that DataTables should use to get a row's id
		 * to set as the `id` property in the node.
		 *  @type string
		 *  @default DT_RowId
		 *
		 *  @name DataTable.defaults.rowId
		 */
		"rowId": "DT_RowId"
	};
	
	_fnHungarianMap( DataTable.defaults );
	
	
	
	/*
	 * Developer note - See note in model.defaults.js about the use of Hungarian
	 * notation and camel case.
	 */
	
	/**
	 * Column options that can be given to DataTables at initialisation time.
	 *  @namespace
	 */
	DataTable.defaults.column = {
		/**
		 * Define which column(s) an order will occur on for this column. This
		 * allows a column's ordering to take multiple columns into account when
		 * doing a sort or use the data from a different column. For example first
		 * name / last name columns make sense to do a multi-column sort over the
		 * two columns.
		 *  @type array|int
		 *  @default null <i>Takes the value of the column index automatically</i>
		 *
		 *  @name DataTable.defaults.column.orderData
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "orderData": [ 0, 1 ], "targets": [ 0 ] },
		 *          { "orderData": [ 1, 0 ], "targets": [ 1 ] },
		 *          { "orderData": 2, "targets": [ 2 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "orderData": [ 0, 1 ] },
		 *          { "orderData": [ 1, 0 ] },
		 *          { "orderData": 2 },
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"aDataSort": null,
		"iDataSort": -1,
	
	
		/**
		 * You can control the default ordering direction, and even alter the
		 * behaviour of the sort handler (i.e. only allow ascending ordering etc)
		 * using this parameter.
		 *  @type array
		 *  @default [ 'asc', 'desc' ]
		 *
		 *  @name DataTable.defaults.column.orderSequence
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "orderSequence": [ "asc" ], "targets": [ 1 ] },
		 *          { "orderSequence": [ "desc", "asc", "asc" ], "targets": [ 2 ] },
		 *          { "orderSequence": [ "desc" ], "targets": [ 3 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          null,
		 *          { "orderSequence": [ "asc" ] },
		 *          { "orderSequence": [ "desc", "asc", "asc" ] },
		 *          { "orderSequence": [ "desc" ] },
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"asSorting": [ 'asc', 'desc' ],
	
	
		/**
		 * Enable or disable filtering on the data in this column.
		 *  @type boolean
		 *  @default true
		 *
		 *  @name DataTable.defaults.column.searchable
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "searchable": false, "targets": [ 0 ] }
		 *        ] } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "searchable": false },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ] } );
		 *    } );
		 */
		"bSearchable": true,
	
	
		/**
		 * Enable or disable ordering on this column.
		 *  @type boolean
		 *  @default true
		 *
		 *  @name DataTable.defaults.column.orderable
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "orderable": false, "targets": [ 0 ] }
		 *        ] } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "orderable": false },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ] } );
		 *    } );
		 */
		"bSortable": true,
	
	
		/**
		 * Enable or disable the display of this column.
		 *  @type boolean
		 *  @default true
		 *
		 *  @name DataTable.defaults.column.visible
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "visible": false, "targets": [ 0 ] }
		 *        ] } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "visible": false },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ] } );
		 *    } );
		 */
		"bVisible": true,
	
	
		/**
		 * Developer definable function that is called whenever a cell is created (Ajax source,
		 * etc) or processed for input (DOM source). This can be used as a compliment to mRender
		 * allowing you to modify the DOM element (add background colour for example) when the
		 * element is available.
		 *  @type function
		 *  @param {element} td The TD node that has been created
		 *  @param {*} cellData The Data for the cell
		 *  @param {array|object} rowData The data for the whole row
		 *  @param {int} row The row index for the aoData data store
		 *  @param {int} col The column index for aoColumns
		 *
		 *  @name DataTable.defaults.column.createdCell
		 *  @dtopt Columns
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [3],
		 *          "createdCell": function (td, cellData, rowData, row, col) {
		 *            if ( cellData == "1.7" ) {
		 *              $(td).css('color', 'blue')
		 *            }
		 *          }
		 *        } ]
		 *      });
		 *    } );
		 */
		"fnCreatedCell": null,
	
	
		/**
		 * This parameter has been replaced by `data` in DataTables to ensure naming
		 * consistency. `dataProp` can still be used, as there is backwards
		 * compatibility in DataTables for this option, but it is strongly
		 * recommended that you use `data` in preference to `dataProp`.
		 *  @name DataTable.defaults.column.dataProp
		 */
	
	
		/**
		 * This property can be used to read data from any data source property,
		 * including deeply nested objects / properties. `data` can be given in a
		 * number of different ways which effect its behaviour:
		 *
		 * * `integer` - treated as an array index for the data source. This is the
		 *   default that DataTables uses (incrementally increased for each column).
		 * * `string` - read an object property from the data source. There are
		 *   three 'special' options that can be used in the string to alter how
		 *   DataTables reads the data from the source object:
		 *    * `.` - Dotted Javascript notation. Just as you use a `.` in
		 *      Javascript to read from nested objects, so to can the options
		 *      specified in `data`. For example: `browser.version` or
		 *      `browser.name`. If your object parameter name contains a period, use
		 *      `\\` to escape it - i.e. `first\\.name`.
		 *    * `[]` - Array notation. DataTables can automatically combine data
		 *      from and array source, joining the data with the characters provided
		 *      between the two brackets. For example: `name[, ]` would provide a
		 *      comma-space separated list from the source array. If no characters
		 *      are provided between the brackets, the original array source is
		 *      returned.
		 *    * `()` - Function notation. Adding `()` to the end of a parameter will
		 *      execute a function of the name given. For example: `browser()` for a
		 *      simple function on the data source, `browser.version()` for a
		 *      function in a nested property or even `browser().version` to get an
		 *      object property if the function called returns an object. Note that
		 *      function notation is recommended for use in `render` rather than
		 *      `data` as it is much simpler to use as a renderer.
		 * * `null` - use the original data source for the row rather than plucking
		 *   data directly from it. This action has effects on two other
		 *   initialisation options:
		 *    * `defaultContent` - When null is given as the `data` option and
		 *      `defaultContent` is specified for the column, the value defined by
		 *      `defaultContent` will be used for the cell.
		 *    * `render` - When null is used for the `data` option and the `render`
		 *      option is specified for the column, the whole data source for the
		 *      row is used for the renderer.
		 * * `function` - the function given will be executed whenever DataTables
		 *   needs to set or get the data for a cell in the column. The function
		 *   takes three parameters:
		 *    * Parameters:
		 *      * `{array|object}` The data source for the row
		 *      * `{string}` The type call data requested - this will be 'set' when
		 *        setting data or 'filter', 'display', 'type', 'sort' or undefined
		 *        when gathering data. Note that when `undefined` is given for the
		 *        type DataTables expects to get the raw data for the object back<
		 *      * `{*}` Data to set when the second parameter is 'set'.
		 *    * Return:
		 *      * The return value from the function is not required when 'set' is
		 *        the type of call, but otherwise the return is what will be used
		 *        for the data requested.
		 *
		 * Note that `data` is a getter and setter option. If you just require
		 * formatting of data for output, you will likely want to use `render` which
		 * is simply a getter and thus simpler to use.
		 *
		 * Note that prior to DataTables 1.9.2 `data` was called `mDataProp`. The
		 * name change reflects the flexibility of this property and is consistent
		 * with the naming of mRender. If 'mDataProp' is given, then it will still
		 * be used by DataTables, as it automatically maps the old name to the new
		 * if required.
		 *
		 *  @type string|int|function|null
		 *  @default null <i>Use automatically calculated column index</i>
		 *
		 *  @name DataTable.defaults.column.data
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Read table data from objects
		 *    // JSON structure for each row:
		 *    //   {
		 *    //      "engine": {value},
		 *    //      "browser": {value},
		 *    //      "platform": {value},
		 *    //      "version": {value},
		 *    //      "grade": {value}
		 *    //   }
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "ajaxSource": "sources/objects.txt",
		 *        "columns": [
		 *          { "data": "engine" },
		 *          { "data": "browser" },
		 *          { "data": "platform" },
		 *          { "data": "version" },
		 *          { "data": "grade" }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Read information from deeply nested objects
		 *    // JSON structure for each row:
		 *    //   {
		 *    //      "engine": {value},
		 *    //      "browser": {value},
		 *    //      "platform": {
		 *    //         "inner": {value}
		 *    //      },
		 *    //      "details": [
		 *    //         {value}, {value}
		 *    //      ]
		 *    //   }
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "ajaxSource": "sources/deep.txt",
		 *        "columns": [
		 *          { "data": "engine" },
		 *          { "data": "browser" },
		 *          { "data": "platform.inner" },
		 *          { "data": "details.0" },
		 *          { "data": "details.1" }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `data` as a function to provide different information for
		 *    // sorting, filtering and display. In this case, currency (price)
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": function ( source, type, val ) {
		 *            if (type === 'set') {
		 *              source.price = val;
		 *              // Store the computed display and filter values for efficiency
		 *              source.price_display = val=="" ? "" : "$"+numberFormat(val);
		 *              source.price_filter  = val=="" ? "" : "$"+numberFormat(val)+" "+val;
		 *              return;
		 *            }
		 *            else if (type === 'display') {
		 *              return source.price_display;
		 *            }
		 *            else if (type === 'filter') {
		 *              return source.price_filter;
		 *            }
		 *            // 'sort', 'type' and undefined all just use the integer
		 *            return source.price;
		 *          }
		 *        } ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using default content
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": null,
		 *          "defaultContent": "Click to edit"
		 *        } ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using array notation - outputting a list from an array
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": "name[, ]"
		 *        } ]
		 *      } );
		 *    } );
		 *
		 */
		"mData": null,
	
	
		/**
		 * This property is the rendering partner to `data` and it is suggested that
		 * when you want to manipulate data for display (including filtering,
		 * sorting etc) without altering the underlying data for the table, use this
		 * property. `render` can be considered to be the the read only companion to
		 * `data` which is read / write (then as such more complex). Like `data`
		 * this option can be given in a number of different ways to effect its
		 * behaviour:
		 *
		 * * `integer` - treated as an array index for the data source. This is the
		 *   default that DataTables uses (incrementally increased for each column).
		 * * `string` - read an object property from the data source. There are
		 *   three 'special' options that can be used in the string to alter how
		 *   DataTables reads the data from the source object:
		 *    * `.` - Dotted Javascript notation. Just as you use a `.` in
		 *      Javascript to read from nested objects, so to can the options
		 *      specified in `data`. For example: `browser.version` or
		 *      `browser.name`. If your object parameter name contains a period, use
		 *      `\\` to escape it - i.e. `first\\.name`.
		 *    * `[]` - Array notation. DataTables can automatically combine data
		 *      from and array source, joining the data with the characters provided
		 *      between the two brackets. For example: `name[, ]` would provide a
		 *      comma-space separated list from the source array. If no characters
		 *      are provided between the brackets, the original array source is
		 *      returned.
		 *    * `()` - Function notation. Adding `()` to the end of a parameter will
		 *      execute a function of the name given. For example: `browser()` for a
		 *      simple function on the data source, `browser.version()` for a
		 *      function in a nested property or even `browser().version` to get an
		 *      object property if the function called returns an object.
		 * * `object` - use different data for the different data types requested by
		 *   DataTables ('filter', 'display', 'type' or 'sort'). The property names
		 *   of the object is the data type the property refers to and the value can
		 *   defined using an integer, string or function using the same rules as
		 *   `render` normally does. Note that an `_` option _must_ be specified.
		 *   This is the default value to use if you haven't specified a value for
		 *   the data type requested by DataTables.
		 * * `function` - the function given will be executed whenever DataTables
		 *   needs to set or get the data for a cell in the column. The function
		 *   takes three parameters:
		 *    * Parameters:
		 *      * {array|object} The data source for the row (based on `data`)
		 *      * {string} The type call data requested - this will be 'filter',
		 *        'display', 'type' or 'sort'.
		 *      * {array|object} The full data source for the row (not based on
		 *        `data`)
		 *    * Return:
		 *      * The return value from the function is what will be used for the
		 *        data requested.
		 *
		 *  @type string|int|function|object|null
		 *  @default null Use the data source value.
		 *
		 *  @name DataTable.defaults.column.render
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Create a comma separated list from an array of objects
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "ajaxSource": "sources/deep.txt",
		 *        "columns": [
		 *          { "data": "engine" },
		 *          { "data": "browser" },
		 *          {
		 *            "data": "platform",
		 *            "render": "[, ].name"
		 *          }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Execute a function to obtain data
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": null, // Use the full data source object for the renderer's source
		 *          "render": "browserName()"
		 *        } ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // As an object, extracting different data for the different types
		 *    // This would be used with a data source such as:
		 *    //   { "phone": 5552368, "phone_filter": "5552368 555-2368", "phone_display": "555-2368" }
		 *    // Here the `phone` integer is used for sorting and type detection, while `phone_filter`
		 *    // (which has both forms) is used for filtering for if a user inputs either format, while
		 *    // the formatted phone number is the one that is shown in the table.
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": null, // Use the full data source object for the renderer's source
		 *          "render": {
		 *            "_": "phone",
		 *            "filter": "phone_filter",
		 *            "display": "phone_display"
		 *          }
		 *        } ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Use as a function to create a link from the data source
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": "download_link",
		 *          "render": function ( data, type, full ) {
		 *            return '<a href="'+data+'">Download</a>';
		 *          }
		 *        } ]
		 *      } );
		 *    } );
		 */
		"mRender": null,
	
	
		/**
		 * Change the cell type created for the column - either TD cells or TH cells. This
		 * can be useful as TH cells have semantic meaning in the table body, allowing them
		 * to act as a header for a row (you may wish to add scope='row' to the TH elements).
		 *  @type string
		 *  @default td
		 *
		 *  @name DataTable.defaults.column.cellType
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Make the first column use TH cells
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "cellType": "th"
		 *        } ]
		 *      } );
		 *    } );
		 */
		"sCellType": "td",
	
	
		/**
		 * Class to give to each cell in this column.
		 *  @type string
		 *  @default <i>Empty string</i>
		 *
		 *  @name DataTable.defaults.column.class
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "class": "my_class", "targets": [ 0 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "class": "my_class" },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"sClass": "",
	
		/**
		 * When DataTables calculates the column widths to assign to each column,
		 * it finds the longest string in each column and then constructs a
		 * temporary table and reads the widths from that. The problem with this
		 * is that "mmm" is much wider then "iiii", but the latter is a longer
		 * string - thus the calculation can go wrong (doing it properly and putting
		 * it into an DOM object and measuring that is horribly(!) slow). Thus as
		 * a "work around" we provide this option. It will append its value to the
		 * text that is found to be the longest string for the column - i.e. padding.
		 * Generally you shouldn't need this!
		 *  @type string
		 *  @default <i>Empty string<i>
		 *
		 *  @name DataTable.defaults.column.contentPadding
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          null,
		 *          null,
		 *          null,
		 *          {
		 *            "contentPadding": "mmm"
		 *          }
		 *        ]
		 *      } );
		 *    } );
		 */
		"sContentPadding": "",
	
	
		/**
		 * Allows a default value to be given for a column's data, and will be used
		 * whenever a null data source is encountered (this can be because `data`
		 * is set to null, or because the data source itself is null).
		 *  @type string
		 *  @default null
		 *
		 *  @name DataTable.defaults.column.defaultContent
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          {
		 *            "data": null,
		 *            "defaultContent": "Edit",
		 *            "targets": [ -1 ]
		 *          }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          null,
		 *          null,
		 *          null,
		 *          {
		 *            "data": null,
		 *            "defaultContent": "Edit"
		 *          }
		 *        ]
		 *      } );
		 *    } );
		 */
		"sDefaultContent": null,
	
	
		/**
		 * This parameter is only used in DataTables' server-side processing. It can
		 * be exceptionally useful to know what columns are being displayed on the
		 * client side, and to map these to database fields. When defined, the names
		 * also allow DataTables to reorder information from the server if it comes
		 * back in an unexpected order (i.e. if you switch your columns around on the
		 * client-side, your server-side code does not also need updating).
		 *  @type string
		 *  @default <i>Empty string</i>
		 *
		 *  @name DataTable.defaults.column.name
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "name": "engine", "targets": [ 0 ] },
		 *          { "name": "browser", "targets": [ 1 ] },
		 *          { "name": "platform", "targets": [ 2 ] },
		 *          { "name": "version", "targets": [ 3 ] },
		 *          { "name": "grade", "targets": [ 4 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "name": "engine" },
		 *          { "name": "browser" },
		 *          { "name": "platform" },
		 *          { "name": "version" },
		 *          { "name": "grade" }
		 *        ]
		 *      } );
		 *    } );
		 */
		"sName": "",
	
	
		/**
		 * Defines a data source type for the ordering which can be used to read
		 * real-time information from the table (updating the internally cached
		 * version) prior to ordering. This allows ordering to occur on user
		 * editable elements such as form inputs.
		 *  @type string
		 *  @default std
		 *
		 *  @name DataTable.defaults.column.orderDataType
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "orderDataType": "dom-text", "targets": [ 2, 3 ] },
		 *          { "type": "numeric", "targets": [ 3 ] },
		 *          { "orderDataType": "dom-select", "targets": [ 4 ] },
		 *          { "orderDataType": "dom-checkbox", "targets": [ 5 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          null,
		 *          null,
		 *          { "orderDataType": "dom-text" },
		 *          { "orderDataType": "dom-text", "type": "numeric" },
		 *          { "orderDataType": "dom-select" },
		 *          { "orderDataType": "dom-checkbox" }
		 *        ]
		 *      } );
		 *    } );
		 */
		"sSortDataType": "std",
	
	
		/**
		 * The title of this column.
		 *  @type string
		 *  @default null <i>Derived from the 'TH' value for this column in the
		 *    original HTML table.</i>
		 *
		 *  @name DataTable.defaults.column.title
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "title": "My column title", "targets": [ 0 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "title": "My column title" },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"sTitle": null,
	
	
		/**
		 * The type allows you to specify how the data for this column will be
		 * ordered. Four types (string, numeric, date and html (which will strip
		 * HTML tags before ordering)) are currently available. Note that only date
		 * formats understood by Javascript's Date() object will be accepted as type
		 * date. For example: "Mar 26, 2008 5:03 PM". May take the values: 'string',
		 * 'numeric', 'date' or 'html' (by default). Further types can be adding
		 * through plug-ins.
		 *  @type string
		 *  @default null <i>Auto-detected from raw data</i>
		 *
		 *  @name DataTable.defaults.column.type
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "type": "html", "targets": [ 0 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "type": "html" },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"sType": null,
	
	
		/**
		 * Defining the width of the column, this parameter may take any CSS value
		 * (3em, 20px etc). DataTables applies 'smart' widths to columns which have not
		 * been given a specific width through this interface ensuring that the table
		 * remains readable.
		 *  @type string
		 *  @default null <i>Automatic</i>
		 *
		 *  @name DataTable.defaults.column.width
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "width": "20%", "targets": [ 0 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "width": "20%" },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"sWidth": null
	};
	
	_fnHungarianMap( DataTable.defaults.column );
	
	
	
	/**
	 * DataTables settings object - this holds all the information needed for a
	 * given table, including configuration, data and current application of the
	 * table options. DataTables does not have a single instance for each DataTable
	 * with the settings attached to that instance, but rather instances of the
	 * DataTable "class" are created on-the-fly as needed (typically by a
	 * $().dataTable() call) and the settings object is then applied to that
	 * instance.
	 *
	 * Note that this object is related to {@link DataTable.defaults} but this
	 * one is the internal data store for DataTables's cache of columns. It should
	 * NOT be manipulated outside of DataTables. Any configuration should be done
	 * through the initialisation options.
	 *  @namespace
	 *  @todo Really should attach the settings object to individual instances so we
	 *    don't need to create new instances on each $().dataTable() call (if the
	 *    table already exists). It would also save passing oSettings around and
	 *    into every single function. However, this is a very significant
	 *    architecture change for DataTables and will almost certainly break
	 *    backwards compatibility with older installations. This is something that
	 *    will be done in 2.0.
	 */
	DataTable.models.oSettings = {
		/**
		 * Primary features of DataTables and their enablement state.
		 *  @namespace
		 */
		"oFeatures": {
	
			/**
			 * Flag to say if DataTables should automatically try to calculate the
			 * optimum table and columns widths (true) or not (false).
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bAutoWidth": null,
	
			/**
			 * Delay the creation of TR and TD elements until they are actually
			 * needed by a driven page draw. This can give a significant speed
			 * increase for Ajax source and Javascript source data, but makes no
			 * difference at all for DOM and server-side processing tables.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bDeferRender": null,
	
			/**
			 * Enable filtering on the table or not. Note that if this is disabled
			 * then there is no filtering at all on the table, including fnFilter.
			 * To just remove the filtering input use sDom and remove the 'f' option.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bFilter": null,
	
			/**
			 * Table information element (the 'Showing x of y records' div) enable
			 * flag.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bInfo": null,
	
			/**
			 * Present a user control allowing the end user to change the page size
			 * when pagination is enabled.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bLengthChange": null,
	
			/**
			 * Pagination enabled or not. Note that if this is disabled then length
			 * changing must also be disabled.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bPaginate": null,
	
			/**
			 * Processing indicator enable flag whenever DataTables is enacting a
			 * user request - typically an Ajax request for server-side processing.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bProcessing": null,
	
			/**
			 * Server-side processing enabled flag - when enabled DataTables will
			 * get all data from the server for every draw - there is no filtering,
			 * sorting or paging done on the client-side.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bServerSide": null,
	
			/**
			 * Sorting enablement flag.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bSort": null,
	
			/**
			 * Multi-column sorting
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bSortMulti": null,
	
			/**
			 * Apply a class to the columns which are being sorted to provide a
			 * visual highlight or not. This can slow things down when enabled since
			 * there is a lot of DOM interaction.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bSortClasses": null,
	
			/**
			 * State saving enablement flag.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bStateSave": null
		},
	
	
		/**
		 * Scrolling settings for a table.
		 *  @namespace
		 */
		"oScroll": {
			/**
			 * When the table is shorter in height than sScrollY, collapse the
			 * table container down to the height of the table (when true).
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bCollapse": null,
	
			/**
			 * Width of the scrollbar for the web-browser's platform. Calculated
			 * during table initialisation.
			 *  @type int
			 *  @default 0
			 */
			"iBarWidth": 0,
	
			/**
			 * Viewport width for horizontal scrolling. Horizontal scrolling is
			 * disabled if an empty string.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type string
			 */
			"sX": null,
	
			/**
			 * Width to expand the table to when using x-scrolling. Typically you
			 * should not need to use this.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type string
			 *  @deprecated
			 */
			"sXInner": null,
	
			/**
			 * Viewport height for vertical scrolling. Vertical scrolling is disabled
			 * if an empty string.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type string
			 */
			"sY": null
		},
	
		/**
		 * Language information for the table.
		 *  @namespace
		 *  @extends DataTable.defaults.oLanguage
		 */
		"oLanguage": {
			/**
			 * Information callback function. See
			 * {@link DataTable.defaults.fnInfoCallback}
			 *  @type function
			 *  @default null
			 */
			"fnInfoCallback": null
		},
	
		/**
		 * Browser support parameters
		 *  @namespace
		 */
		"oBrowser": {
			/**
			 * Indicate if the browser incorrectly calculates width:100% inside a
			 * scrolling element (IE6/7)
			 *  @type boolean
			 *  @default false
			 */
			"bScrollOversize": false,
	
			/**
			 * Determine if the vertical scrollbar is on the right or left of the
			 * scrolling container - needed for rtl language layout, although not
			 * all browsers move the scrollbar (Safari).
			 *  @type boolean
			 *  @default false
			 */
			"bScrollbarLeft": false,
	
			/**
			 * Flag for if `getBoundingClientRect` is fully supported or not
			 *  @type boolean
			 *  @default false
			 */
			"bBounding": false,
	
			/**
			 * Browser scrollbar width
			 *  @type integer
			 *  @default 0
			 */
			"barWidth": 0
		},
	
	
		"ajax": null,
	
	
		/**
		 * Array referencing the nodes which are used for the features. The
		 * parameters of this object match what is allowed by sDom - i.e.
		 *   <ul>
		 *     <li>'l' - Length changing</li>
		 *     <li>'f' - Filtering input</li>
		 *     <li>'t' - The table!</li>
		 *     <li>'i' - Information</li>
		 *     <li>'p' - Pagination</li>
		 *     <li>'r' - pRocessing</li>
		 *   </ul>
		 *  @type array
		 *  @default []
		 */
		"aanFeatures": [],
	
		/**
		 * Store data information - see {@link DataTable.models.oRow} for detailed
		 * information.
		 *  @type array
		 *  @default []
		 */
		"aoData": [],
	
		/**
		 * Array of indexes which are in the current display (after filtering etc)
		 *  @type array
		 *  @default []
		 */
		"aiDisplay": [],
	
		/**
		 * Array of indexes for display - no filtering
		 *  @type array
		 *  @default []
		 */
		"aiDisplayMaster": [],
	
		/**
		 * Map of row ids to data indexes
		 *  @type object
		 *  @default {}
		 */
		"aIds": {},
	
		/**
		 * Store information about each column that is in use
		 *  @type array
		 *  @default []
		 */
		"aoColumns": [],
	
		/**
		 * Store information about the table's header
		 *  @type array
		 *  @default []
		 */
		"aoHeader": [],
	
		/**
		 * Store information about the table's footer
		 *  @type array
		 *  @default []
		 */
		"aoFooter": [],
	
		/**
		 * Store the applied global search information in case we want to force a
		 * research or compare the old search to a new one.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @namespace
		 *  @extends DataTable.models.oSearch
		 */
		"oPreviousSearch": {},
	
		/**
		 * Store the applied search for each column - see
		 * {@link DataTable.models.oSearch} for the format that is used for the
		 * filtering information for each column.
		 *  @type array
		 *  @default []
		 */
		"aoPreSearchCols": [],
	
		/**
		 * Sorting that is applied to the table. Note that the inner arrays are
		 * used in the following manner:
		 * <ul>
		 *   <li>Index 0 - column number</li>
		 *   <li>Index 1 - current sorting direction</li>
		 * </ul>
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type array
		 *  @todo These inner arrays should really be objects
		 */
		"aaSorting": null,
	
		/**
		 * Sorting that is always applied to the table (i.e. prefixed in front of
		 * aaSorting).
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type array
		 *  @default []
		 */
		"aaSortingFixed": [],
	
		/**
		 * Classes to use for the striping of a table.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type array
		 *  @default []
		 */
		"asStripeClasses": null,
	
		/**
		 * If restoring a table - we should restore its striping classes as well
		 *  @type array
		 *  @default []
		 */
		"asDestroyStripes": [],
	
		/**
		 * If restoring a table - we should restore its width
		 *  @type int
		 *  @default 0
		 */
		"sDestroyWidth": 0,
	
		/**
		 * Callback functions array for every time a row is inserted (i.e. on a draw).
		 *  @type array
		 *  @default []
		 */
		"aoRowCallback": [],
	
		/**
		 * Callback functions for the header on each draw.
		 *  @type array
		 *  @default []
		 */
		"aoHeaderCallback": [],
	
		/**
		 * Callback function for the footer on each draw.
		 *  @type array
		 *  @default []
		 */
		"aoFooterCallback": [],
	
		/**
		 * Array of callback functions for draw callback functions
		 *  @type array
		 *  @default []
		 */
		"aoDrawCallback": [],
	
		/**
		 * Array of callback functions for row created function
		 *  @type array
		 *  @default []
		 */
		"aoRowCreatedCallback": [],
	
		/**
		 * Callback functions for just before the table is redrawn. A return of
		 * false will be used to cancel the draw.
		 *  @type array
		 *  @default []
		 */
		"aoPreDrawCallback": [],
	
		/**
		 * Callback functions for when the table has been initialised.
		 *  @type array
		 *  @default []
		 */
		"aoInitComplete": [],
	
	
		/**
		 * Callbacks for modifying the settings to be stored for state saving, prior to
		 * saving state.
		 *  @type array
		 *  @default []
		 */
		"aoStateSaveParams": [],
	
		/**
		 * Callbacks for modifying the settings that have been stored for state saving
		 * prior to using the stored values to restore the state.
		 *  @type array
		 *  @default []
		 */
		"aoStateLoadParams": [],
	
		/**
		 * Callbacks for operating on the settings object once the saved state has been
		 * loaded
		 *  @type array
		 *  @default []
		 */
		"aoStateLoaded": [],
	
		/**
		 * Cache the table ID for quick access
		 *  @type string
		 *  @default <i>Empty string</i>
		 */
		"sTableId": "",
	
		/**
		 * The TABLE node for the main table
		 *  @type node
		 *  @default null
		 */
		"nTable": null,
	
		/**
		 * Permanent ref to the thead element
		 *  @type node
		 *  @default null
		 */
		"nTHead": null,
	
		/**
		 * Permanent ref to the tfoot element - if it exists
		 *  @type node
		 *  @default null
		 */
		"nTFoot": null,
	
		/**
		 * Permanent ref to the tbody element
		 *  @type node
		 *  @default null
		 */
		"nTBody": null,
	
		/**
		 * Cache the wrapper node (contains all DataTables controlled elements)
		 *  @type node
		 *  @default null
		 */
		"nTableWrapper": null,
	
		/**
		 * Indicate if when using server-side processing the loading of data
		 * should be deferred until the second draw.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type boolean
		 *  @default false
		 */
		"bDeferLoading": false,
	
		/**
		 * Indicate if all required information has been read in
		 *  @type boolean
		 *  @default false
		 */
		"bInitialised": false,
	
		/**
		 * Information about open rows. Each object in the array has the parameters
		 * 'nTr' and 'nParent'
		 *  @type array
		 *  @default []
		 */
		"aoOpenRows": [],
	
		/**
		 * Dictate the positioning of DataTables' control elements - see
		 * {@link DataTable.model.oInit.sDom}.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 *  @default null
		 */
		"sDom": null,
	
		/**
		 * Search delay (in mS)
		 *  @type integer
		 *  @default null
		 */
		"searchDelay": null,
	
		/**
		 * Which type of pagination should be used.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 *  @default two_button
		 */
		"sPaginationType": "two_button",
	
		/**
		 * The state duration (for `stateSave`) in seconds.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type int
		 *  @default 0
		 */
		"iStateDuration": 0,
	
		/**
		 * Array of callback functions for state saving. Each array element is an
		 * object with the following parameters:
		 *   <ul>
		 *     <li>function:fn - function to call. Takes two parameters, oSettings
		 *       and the JSON string to save that has been thus far created. Returns
		 *       a JSON string to be inserted into a json object
		 *       (i.e. '"param": [ 0, 1, 2]')</li>
		 *     <li>string:sName - name of callback</li>
		 *   </ul>
		 *  @type array
		 *  @default []
		 */
		"aoStateSave": [],
	
		/**
		 * Array of callback functions for state loading. Each array element is an
		 * object with the following parameters:
		 *   <ul>
		 *     <li>function:fn - function to call. Takes two parameters, oSettings
		 *       and the object stored. May return false to cancel state loading</li>
		 *     <li>string:sName - name of callback</li>
		 *   </ul>
		 *  @type array
		 *  @default []
		 */
		"aoStateLoad": [],
	
		/**
		 * State that was saved. Useful for back reference
		 *  @type object
		 *  @default null
		 */
		"oSavedState": null,
	
		/**
		 * State that was loaded. Useful for back reference
		 *  @type object
		 *  @default null
		 */
		"oLoadedState": null,
	
		/**
		 * Source url for AJAX data for the table.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 *  @default null
		 */
		"sAjaxSource": null,
	
		/**
		 * Property from a given object from which to read the table data from. This
		 * can be an empty string (when not server-side processing), in which case
		 * it is  assumed an an array is given directly.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 */
		"sAjaxDataProp": null,
	
		/**
		 * The last jQuery XHR object that was used for server-side data gathering.
		 * This can be used for working with the XHR information in one of the
		 * callbacks
		 *  @type object
		 *  @default null
		 */
		"jqXHR": null,
	
		/**
		 * JSON returned from the server in the last Ajax request
		 *  @type object
		 *  @default undefined
		 */
		"json": undefined,
	
		/**
		 * Data submitted as part of the last Ajax request
		 *  @type object
		 *  @default undefined
		 */
		"oAjaxData": undefined,
	
		/**
		 * Function to get the server-side data.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type function
		 */
		"fnServerData": null,
	
		/**
		 * Functions which are called prior to sending an Ajax request so extra
		 * parameters can easily be sent to the server
		 *  @type array
		 *  @default []
		 */
		"aoServerParams": [],
	
		/**
		 * Send the XHR HTTP method - GET or POST (could be PUT or DELETE if
		 * required).
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 */
		"sServerMethod": null,
	
		/**
		 * Format numbers for display.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type function
		 */
		"fnFormatNumber": null,
	
		/**
		 * List of options that can be used for the user selectable length menu.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type array
		 *  @default []
		 */
		"aLengthMenu": null,
	
		/**
		 * Counter for the draws that the table does. Also used as a tracker for
		 * server-side processing
		 *  @type int
		 *  @default 0
		 */
		"iDraw": 0,
	
		/**
		 * Indicate if a redraw is being done - useful for Ajax
		 *  @type boolean
		 *  @default false
		 */
		"bDrawing": false,
	
		/**
		 * Draw index (iDraw) of the last error when parsing the returned data
		 *  @type int
		 *  @default -1
		 */
		"iDrawError": -1,
	
		/**
		 * Paging display length
		 *  @type int
		 *  @default 10
		 */
		"_iDisplayLength": 10,
	
		/**
		 * Paging start point - aiDisplay index
		 *  @type int
		 *  @default 0
		 */
		"_iDisplayStart": 0,
	
		/**
		 * Server-side processing - number of records in the result set
		 * (i.e. before filtering), Use fnRecordsTotal rather than
		 * this property to get the value of the number of records, regardless of
		 * the server-side processing setting.
		 *  @type int
		 *  @default 0
		 *  @private
		 */
		"_iRecordsTotal": 0,
	
		/**
		 * Server-side processing - number of records in the current display set
		 * (i.e. after filtering). Use fnRecordsDisplay rather than
		 * this property to get the value of the number of records, regardless of
		 * the server-side processing setting.
		 *  @type boolean
		 *  @default 0
		 *  @private
		 */
		"_iRecordsDisplay": 0,
	
		/**
		 * The classes to use for the table
		 *  @type object
		 *  @default {}
		 */
		"oClasses": {},
	
		/**
		 * Flag attached to the settings object so you can check in the draw
		 * callback if filtering has been done in the draw. Deprecated in favour of
		 * events.
		 *  @type boolean
		 *  @default false
		 *  @deprecated
		 */
		"bFiltered": false,
	
		/**
		 * Flag attached to the settings object so you can check in the draw
		 * callback if sorting has been done in the draw. Deprecated in favour of
		 * events.
		 *  @type boolean
		 *  @default false
		 *  @deprecated
		 */
		"bSorted": false,
	
		/**
		 * Indicate that if multiple rows are in the header and there is more than
		 * one unique cell per column, if the top one (true) or bottom one (false)
		 * should be used for sorting / title by DataTables.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type boolean
		 */
		"bSortCellsTop": null,
	
		/**
		 * Initialisation object that is used for the table
		 *  @type object
		 *  @default null
		 */
		"oInit": null,
	
		/**
		 * Destroy callback functions - for plug-ins to attach themselves to the
		 * destroy so they can clean up markup and events.
		 *  @type array
		 *  @default []
		 */
		"aoDestroyCallback": [],
	
	
		/**
		 * Get the number of records in the current record set, before filtering
		 *  @type function
		 */
		"fnRecordsTotal": function ()
		{
			return _fnDataSource( this ) == 'ssp' ?
				this._iRecordsTotal * 1 :
				this.aiDisplayMaster.length;
		},
	
		/**
		 * Get the number of records in the current record set, after filtering
		 *  @type function
		 */
		"fnRecordsDisplay": function ()
		{
			return _fnDataSource( this ) == 'ssp' ?
				this._iRecordsDisplay * 1 :
				this.aiDisplay.length;
		},
	
		/**
		 * Get the display end point - aiDisplay index
		 *  @type function
		 */
		"fnDisplayEnd": function ()
		{
			var
				len      = this._iDisplayLength,
				start    = this._iDisplayStart,
				calc     = start + len,
				records  = this.aiDisplay.length,
				features = this.oFeatures,
				paginate = features.bPaginate;
	
			if ( features.bServerSide ) {
				return paginate === false || len === -1 ?
					start + records :
					Math.min( start+len, this._iRecordsDisplay );
			}
			else {
				return ! paginate || calc>records || len===-1 ?
					records :
					calc;
			}
		},
	
		/**
		 * The DataTables object for this table
		 *  @type object
		 *  @default null
		 */
		"oInstance": null,
	
		/**
		 * Unique identifier for each instance of the DataTables object. If there
		 * is an ID on the table node, then it takes that value, otherwise an
		 * incrementing internal counter is used.
		 *  @type string
		 *  @default null
		 */
		"sInstance": null,
	
		/**
		 * tabindex attribute value that is added to DataTables control elements, allowing
		 * keyboard navigation of the table and its controls.
		 */
		"iTabIndex": 0,
	
		/**
		 * DIV container for the footer scrolling table if scrolling
		 */
		"nScrollHead": null,
	
		/**
		 * DIV container for the footer scrolling table if scrolling
		 */
		"nScrollFoot": null,
	
		/**
		 * Last applied sort
		 *  @type array
		 *  @default []
		 */
		"aLastSort": [],
	
		/**
		 * Stored plug-in instances
		 *  @type object
		 *  @default {}
		 */
		"oPlugins": {},
	
		/**
		 * Function used to get a row's id from the row's data
		 *  @type function
		 *  @default null
		 */
		"rowIdFn": null,
	
		/**
		 * Data location where to store a row's id
		 *  @type string
		 *  @default null
		 */
		"rowId": null
	};
	
	/**
	 * Extension object for DataTables that is used to provide all extension
	 * options.
	 *
	 * Note that the `DataTable.ext` object is available through
	 * `jQuery.fn.dataTable.ext` where it may be accessed and manipulated. It is
	 * also aliased to `jQuery.fn.dataTableExt` for historic reasons.
	 *  @namespace
	 *  @extends DataTable.models.ext
	 */
	
	
	/**
	 * DataTables extensions
	 * 
	 * This namespace acts as a collection area for plug-ins that can be used to
	 * extend DataTables capabilities. Indeed many of the build in methods
	 * use this method to provide their own capabilities (sorting methods for
	 * example).
	 *
	 * Note that this namespace is aliased to `jQuery.fn.dataTableExt` for legacy
	 * reasons
	 *
	 *  @namespace
	 */
	DataTable.ext = _ext = {
		/**
		 * Buttons. For use with the Buttons extension for DataTables. This is
		 * defined here so other extensions can define buttons regardless of load
		 * order. It is _not_ used by DataTables core.
		 *
		 *  @type object
		 *  @default {}
		 */
		buttons: {},
	
	
		/**
		 * Element class names
		 *
		 *  @type object
		 *  @default {}
		 */
		classes: {},
	
	
		/**
		 * DataTables build type (expanded by the download builder)
		 *
		 *  @type string
		 */
		builder: "-source-",
	
	
		/**
		 * Error reporting.
		 * 
		 * How should DataTables report an error. Can take the value 'alert',
		 * 'throw', 'none' or a function.
		 *
		 *  @type string|function
		 *  @default alert
		 */
		errMode: "alert",
	
	
		/**
		 * Feature plug-ins.
		 * 
		 * This is an array of objects which describe the feature plug-ins that are
		 * available to DataTables. These feature plug-ins are then available for
		 * use through the `dom` initialisation option.
		 * 
		 * Each feature plug-in is described by an object which must have the
		 * following properties:
		 * 
		 * * `fnInit` - function that is used to initialise the plug-in,
		 * * `cFeature` - a character so the feature can be enabled by the `dom`
		 *   instillation option. This is case sensitive.
		 *
		 * The `fnInit` function has the following input parameters:
		 *
		 * 1. `{object}` DataTables settings object: see
		 *    {@link DataTable.models.oSettings}
		 *
		 * And the following return is expected:
		 * 
		 * * {node|null} The element which contains your feature. Note that the
		 *   return may also be void if your plug-in does not require to inject any
		 *   DOM elements into DataTables control (`dom`) - for example this might
		 *   be useful when developing a plug-in which allows table control via
		 *   keyboard entry
		 *
		 *  @type array
		 *
		 *  @example
		 *    $.fn.dataTable.ext.features.push( {
		 *      "fnInit": function( oSettings ) {
		 *        return new TableTools( { "oDTSettings": oSettings } );
		 *      },
		 *      "cFeature": "T"
		 *    } );
		 */
		feature: [],
	
	
		/**
		 * Row searching.
		 * 
		 * This method of searching is complimentary to the default type based
		 * searching, and a lot more comprehensive as it allows you complete control
		 * over the searching logic. Each element in this array is a function
		 * (parameters described below) that is called for every row in the table,
		 * and your logic decides if it should be included in the searching data set
		 * or not.
		 *
		 * Searching functions have the following input parameters:
		 *
		 * 1. `{object}` DataTables settings object: see
		 *    {@link DataTable.models.oSettings}
		 * 2. `{array|object}` Data for the row to be processed (same as the
		 *    original format that was passed in as the data source, or an array
		 *    from a DOM data source
		 * 3. `{int}` Row index ({@link DataTable.models.oSettings.aoData}), which
		 *    can be useful to retrieve the `TR` element if you need DOM interaction.
		 *
		 * And the following return is expected:
		 *
		 * * {boolean} Include the row in the searched result set (true) or not
		 *   (false)
		 *
		 * Note that as with the main search ability in DataTables, technically this
		 * is "filtering", since it is subtractive. However, for consistency in
		 * naming we call it searching here.
		 *
		 *  @type array
		 *  @default []
		 *
		 *  @example
		 *    // The following example shows custom search being applied to the
		 *    // fourth column (i.e. the data[3] index) based on two input values
		 *    // from the end-user, matching the data in a certain range.
		 *    $.fn.dataTable.ext.search.push(
		 *      function( settings, data, dataIndex ) {
		 *        var min = document.getElementById('min').value * 1;
		 *        var max = document.getElementById('max').value * 1;
		 *        var version = data[3] == "-" ? 0 : data[3]*1;
		 *
		 *        if ( min == "" && max == "" ) {
		 *          return true;
		 *        }
		 *        else if ( min == "" && version < max ) {
		 *          return true;
		 *        }
		 *        else if ( min < version && "" == max ) {
		 *          return true;
		 *        }
		 *        else if ( min < version && version < max ) {
		 *          return true;
		 *        }
		 *        return false;
		 *      }
		 *    );
		 */
		search: [],
	
	
		/**
		 * Selector extensions
		 *
		 * The `selector` option can be used to extend the options available for the
		 * selector modifier options (`selector-modifier` object data type) that
		 * each of the three built in selector types offer (row, column and cell +
		 * their plural counterparts). For example the Select extension uses this
		 * mechanism to provide an option to select only rows, columns and cells
		 * that have been marked as selected by the end user (`{selected: true}`),
		 * which can be used in conjunction with the existing built in selector
		 * options.
		 *
		 * Each property is an array to which functions can be pushed. The functions
		 * take three attributes:
		 *
		 * * Settings object for the host table
		 * * Options object (`selector-modifier` object type)
		 * * Array of selected item indexes
		 *
		 * The return is an array of the resulting item indexes after the custom
		 * selector has been applied.
		 *
		 *  @type object
		 */
		selector: {
			cell: [],
			column: [],
			row: []
		},
	
	
		/**
		 * Internal functions, exposed for used in plug-ins.
		 * 
		 * Please note that you should not need to use the internal methods for
		 * anything other than a plug-in (and even then, try to avoid if possible).
		 * The internal function may change between releases.
		 *
		 *  @type object
		 *  @default {}
		 */
		internal: {},
	
	
		/**
		 * Legacy configuration options. Enable and disable legacy options that
		 * are available in DataTables.
		 *
		 *  @type object
		 */
		legacy: {
			/**
			 * Enable / disable DataTables 1.9 compatible server-side processing
			 * requests
			 *
			 *  @type boolean
			 *  @default null
			 */
			ajax: null
		},
	
	
		/**
		 * Pagination plug-in methods.
		 * 
		 * Each entry in this object is a function and defines which buttons should
		 * be shown by the pagination rendering method that is used for the table:
		 * {@link DataTable.ext.renderer.pageButton}. The renderer addresses how the
		 * buttons are displayed in the document, while the functions here tell it
		 * what buttons to display. This is done by returning an array of button
		 * descriptions (what each button will do).
		 *
		 * Pagination types (the four built in options and any additional plug-in
		 * options defined here) can be used through the `paginationType`
		 * initialisation parameter.
		 *
		 * The functions defined take two parameters:
		 *
		 * 1. `{int} page` The current page index
		 * 2. `{int} pages` The number of pages in the table
		 *
		 * Each function is expected to return an array where each element of the
		 * array can be one of:
		 *
		 * * `first` - Jump to first page when activated
		 * * `last` - Jump to last page when activated
		 * * `previous` - Show previous page when activated
		 * * `next` - Show next page when activated
		 * * `{int}` - Show page of the index given
		 * * `{array}` - A nested array containing the above elements to add a
		 *   containing 'DIV' element (might be useful for styling).
		 *
		 * Note that DataTables v1.9- used this object slightly differently whereby
		 * an object with two functions would be defined for each plug-in. That
		 * ability is still supported by DataTables 1.10+ to provide backwards
		 * compatibility, but this option of use is now decremented and no longer
		 * documented in DataTables 1.10+.
		 *
		 *  @type object
		 *  @default {}
		 *
		 *  @example
		 *    // Show previous, next and current page buttons only
		 *    $.fn.dataTableExt.oPagination.current = function ( page, pages ) {
		 *      return [ 'previous', page, 'next' ];
		 *    };
		 */
		pager: {},
	
	
		renderer: {
			pageButton: {},
			header: {}
		},
	
	
		/**
		 * Ordering plug-ins - custom data source
		 * 
		 * The extension options for ordering of data available here is complimentary
		 * to the default type based ordering that DataTables typically uses. It
		 * allows much greater control over the the data that is being used to
		 * order a column, but is necessarily therefore more complex.
		 * 
		 * This type of ordering is useful if you want to do ordering based on data
		 * live from the DOM (for example the contents of an 'input' element) rather
		 * than just the static string that DataTables knows of.
		 * 
		 * The way these plug-ins work is that you create an array of the values you
		 * wish to be ordering for the column in question and then return that
		 * array. The data in the array much be in the index order of the rows in
		 * the table (not the currently ordering order!). Which order data gathering
		 * function is run here depends on the `dt-init columns.orderDataType`
		 * parameter that is used for the column (if any).
		 *
		 * The functions defined take two parameters:
		 *
		 * 1. `{object}` DataTables settings object: see
		 *    {@link DataTable.models.oSettings}
		 * 2. `{int}` Target column index
		 *
		 * Each function is expected to return an array:
		 *
		 * * `{array}` Data for the column to be ordering upon
		 *
		 *  @type array
		 *
		 *  @example
		 *    // Ordering using `input` node values
		 *    $.fn.dataTable.ext.order['dom-text'] = function  ( settings, col )
		 *    {
		 *      return this.api().column( col, {order:'index'} ).nodes().map( function ( td, i ) {
		 *        return $('input', td).val();
		 *      } );
		 *    }
		 */
		order: {},
	
	
		/**
		 * Type based plug-ins.
		 *
		 * Each column in DataTables has a type assigned to it, either by automatic
		 * detection or by direct assignment using the `type` option for the column.
		 * The type of a column will effect how it is ordering and search (plug-ins
		 * can also make use of the column type if required).
		 *
		 * @namespace
		 */
		type: {
			/**
			 * Type detection functions.
			 *
			 * The functions defined in this object are used to automatically detect
			 * a column's type, making initialisation of DataTables super easy, even
			 * when complex data is in the table.
			 *
			 * The functions defined take two parameters:
			 *
		     *  1. `{*}` Data from the column cell to be analysed
		     *  2. `{settings}` DataTables settings object. This can be used to
		     *     perform context specific type detection - for example detection
		     *     based on language settings such as using a comma for a decimal
		     *     place. Generally speaking the options from the settings will not
		     *     be required
			 *
			 * Each function is expected to return:
			 *
			 * * `{string|null}` Data type detected, or null if unknown (and thus
			 *   pass it on to the other type detection functions.
			 *
			 *  @type array
			 *
			 *  @example
			 *    // Currency type detection plug-in:
			 *    $.fn.dataTable.ext.type.detect.push(
			 *      function ( data, settings ) {
			 *        // Check the numeric part
			 *        if ( ! data.substring(1).match(/[0-9]/) ) {
			 *          return null;
			 *        }
			 *
			 *        // Check prefixed by currency
			 *        if ( data.charAt(0) == '$' || data.charAt(0) == '&pound;' ) {
			 *          return 'currency';
			 *        }
			 *        return null;
			 *      }
			 *    );
			 */
			detect: [],
	
	
			/**
			 * Type based search formatting.
			 *
			 * The type based searching functions can be used to pre-format the
			 * data to be search on. For example, it can be used to strip HTML
			 * tags or to de-format telephone numbers for numeric only searching.
			 *
			 * Note that is a search is not defined for a column of a given type,
			 * no search formatting will be performed.
			 * 
			 * Pre-processing of searching data plug-ins - When you assign the sType
			 * for a column (or have it automatically detected for you by DataTables
			 * or a type detection plug-in), you will typically be using this for
			 * custom sorting, but it can also be used to provide custom searching
			 * by allowing you to pre-processing the data and returning the data in
			 * the format that should be searched upon. This is done by adding
			 * functions this object with a parameter name which matches the sType
			 * for that target column. This is the corollary of <i>afnSortData</i>
			 * for searching data.
			 *
			 * The functions defined take a single parameter:
			 *
		     *  1. `{*}` Data from the column cell to be prepared for searching
			 *
			 * Each function is expected to return:
			 *
			 * * `{string|null}` Formatted string that will be used for the searching.
			 *
			 *  @type object
			 *  @default {}
			 *
			 *  @example
			 *    $.fn.dataTable.ext.type.search['title-numeric'] = function ( d ) {
			 *      return d.replace(/\n/g," ").replace( /<.*?>/g, "" );
			 *    }
			 */
			search: {},
	
	
			/**
			 * Type based ordering.
			 *
			 * The column type tells DataTables what ordering to apply to the table
			 * when a column is sorted upon. The order for each type that is defined,
			 * is defined by the functions available in this object.
			 *
			 * Each ordering option can be described by three properties added to
			 * this object:
			 *
			 * * `{type}-pre` - Pre-formatting function
			 * * `{type}-asc` - Ascending order function
			 * * `{type}-desc` - Descending order function
			 *
			 * All three can be used together, only `{type}-pre` or only
			 * `{type}-asc` and `{type}-desc` together. It is generally recommended
			 * that only `{type}-pre` is used, as this provides the optimal
			 * implementation in terms of speed, although the others are provided
			 * for compatibility with existing Javascript sort functions.
			 *
			 * `{type}-pre`: Functions defined take a single parameter:
			 *
		     *  1. `{*}` Data from the column cell to be prepared for ordering
			 *
			 * And return:
			 *
			 * * `{*}` Data to be sorted upon
			 *
			 * `{type}-asc` and `{type}-desc`: Functions are typical Javascript sort
			 * functions, taking two parameters:
			 *
		     *  1. `{*}` Data to compare to the second parameter
		     *  2. `{*}` Data to compare to the first parameter
			 *
			 * And returning:
			 *
			 * * `{*}` Ordering match: <0 if first parameter should be sorted lower
			 *   than the second parameter, ===0 if the two parameters are equal and
			 *   >0 if the first parameter should be sorted height than the second
			 *   parameter.
			 * 
			 *  @type object
			 *  @default {}
			 *
			 *  @example
			 *    // Numeric ordering of formatted numbers with a pre-formatter
			 *    $.extend( $.fn.dataTable.ext.type.order, {
			 *      "string-pre": function(x) {
			 *        a = (a === "-" || a === "") ? 0 : a.replace( /[^\d\-\.]/g, "" );
			 *        return parseFloat( a );
			 *      }
			 *    } );
			 *
			 *  @example
			 *    // Case-sensitive string ordering, with no pre-formatting method
			 *    $.extend( $.fn.dataTable.ext.order, {
			 *      "string-case-asc": function(x,y) {
			 *        return ((x < y) ? -1 : ((x > y) ? 1 : 0));
			 *      },
			 *      "string-case-desc": function(x,y) {
			 *        return ((x < y) ? 1 : ((x > y) ? -1 : 0));
			 *      }
			 *    } );
			 */
			order: {}
		},
	
		/**
		 * Unique DataTables instance counter
		 *
		 * @type int
		 * @private
		 */
		_unique: 0,
	
	
		//
		// Depreciated
		// The following properties are retained for backwards compatibility only.
		// The should not be used in new projects and will be removed in a future
		// version
		//
	
		/**
		 * Version check function.
		 *  @type function
		 *  @depreciated Since 1.10
		 */
		fnVersionCheck: DataTable.fnVersionCheck,
	
	
		/**
		 * Index for what 'this' index API functions should use
		 *  @type int
		 *  @deprecated Since v1.10
		 */
		iApiIndex: 0,
	
	
		/**
		 * jQuery UI class container
		 *  @type object
		 *  @deprecated Since v1.10
		 */
		oJUIClasses: {},
	
	
		/**
		 * Software version
		 *  @type string
		 *  @deprecated Since v1.10
		 */
		sVersion: DataTable.version
	};
	
	
	//
	// Backwards compatibility. Alias to pre 1.10 Hungarian notation counter parts
	//
	$.extend( _ext, {
		afnFiltering: _ext.search,
		aTypes:       _ext.type.detect,
		ofnSearch:    _ext.type.search,
		oSort:        _ext.type.order,
		afnSortData:  _ext.order,
		aoFeatures:   _ext.feature,
		oApi:         _ext.internal,
		oStdClasses:  _ext.classes,
		oPagination:  _ext.pager
	} );
	
	
	$.extend( DataTable.ext.classes, {
		"sTable": "dataTable",
		"sNoFooter": "no-footer",
	
		/* Paging buttons */
		"sPageButton": "paginate_button",
		"sPageButtonActive": "current",
		"sPageButtonDisabled": "disabled",
	
		/* Striping classes */
		"sStripeOdd": "odd",
		"sStripeEven": "even",
	
		/* Empty row */
		"sRowEmpty": "dataTables_empty",
	
		/* Features */
		"sWrapper": "dataTables_wrapper",
		"sFilter": "dataTables_filter",
		"sInfo": "dataTables_info",
		"sPaging": "dataTables_paginate paging_", /* Note that the type is postfixed */
		"sLength": "dataTables_length",
		"sProcessing": "dataTables_processing",
	
		/* Sorting */
		"sSortAsc": "sorting_asc",
		"sSortDesc": "sorting_desc",
		"sSortable": "sorting", /* Sortable in both directions */
		"sSortableAsc": "sorting_desc_disabled",
		"sSortableDesc": "sorting_asc_disabled",
		"sSortableNone": "sorting_disabled",
		"sSortColumn": "sorting_", /* Note that an int is postfixed for the sorting order */
	
		/* Filtering */
		"sFilterInput": "",
	
		/* Page length */
		"sLengthSelect": "",
	
		/* Scrolling */
		"sScrollWrapper": "dataTables_scroll",
		"sScrollHead": "dataTables_scrollHead",
		"sScrollHeadInner": "dataTables_scrollHeadInner",
		"sScrollBody": "dataTables_scrollBody",
		"sScrollFoot": "dataTables_scrollFoot",
		"sScrollFootInner": "dataTables_scrollFootInner",
	
		/* Misc */
		"sHeaderTH": "",
		"sFooterTH": "",
	
		// Deprecated
		"sSortJUIAsc": "",
		"sSortJUIDesc": "",
		"sSortJUI": "",
		"sSortJUIAscAllowed": "",
		"sSortJUIDescAllowed": "",
		"sSortJUIWrapper": "",
		"sSortIcon": "",
		"sJUIHeader": "",
		"sJUIFooter": ""
	} );
	
	
	var extPagination = DataTable.ext.pager;
	
	function _numbers ( page, pages ) {
		var
			numbers = [],
			buttons = extPagination.numbers_length,
			half = Math.floor( buttons / 2 ),
			i = 1;
	
		if ( pages <= buttons ) {
			numbers = _range( 0, pages );
		}
		else if ( page <= half ) {
			numbers = _range( 0, buttons-2 );
			numbers.push( 'ellipsis' );
			numbers.push( pages-1 );
		}
		else if ( page >= pages - 1 - half ) {
			numbers = _range( pages-(buttons-2), pages );
			numbers.splice( 0, 0, 'ellipsis' ); // no unshift in ie6
			numbers.splice( 0, 0, 0 );
		}
		else {
			numbers = _range( page-half+2, page+half-1 );
			numbers.push( 'ellipsis' );
			numbers.push( pages-1 );
			numbers.splice( 0, 0, 'ellipsis' );
			numbers.splice( 0, 0, 0 );
		}
	
		numbers.DT_el = 'span';
		return numbers;
	}
	
	
	$.extend( extPagination, {
		simple: function ( page, pages ) {
			return [ 'previous', 'next' ];
		},
	
		full: function ( page, pages ) {
			return [  'first', 'previous', 'next', 'last' ];
		},
	
		numbers: function ( page, pages ) {
			return [ _numbers(page, pages) ];
		},
	
		simple_numbers: function ( page, pages ) {
			return [ 'previous', _numbers(page, pages), 'next' ];
		},
	
		full_numbers: function ( page, pages ) {
			return [ 'first', 'previous', _numbers(page, pages), 'next', 'last' ];
		},
		
		first_last_numbers: function (page, pages) {
	 		return ['first', _numbers(page, pages), 'last'];
	 	},
	
		// For testing and plug-ins to use
		_numbers: _numbers,
	
		// Number of number buttons (including ellipsis) to show. _Must be odd!_
		numbers_length: 7
	} );
	
	
	$.extend( true, DataTable.ext.renderer, {
		pageButton: {
			_: function ( settings, host, idx, buttons, page, pages ) {
				var classes = settings.oClasses;
				var lang = settings.oLanguage.oPaginate;
				var aria = settings.oLanguage.oAria.paginate || {};
				var btnDisplay, btnClass, counter=0;
	
				var attach = function( container, buttons ) {
					var i, ien, node, button, tabIndex;
					var disabledClass = classes.sPageButtonDisabled;
					var clickHandler = function ( e ) {
						_fnPageChange( settings, e.data.action, true );
					};
	
					for ( i=0, ien=buttons.length ; i<ien ; i++ ) {
						button = buttons[i];
	
						if ( Array.isArray( button ) ) {
							var inner = $( '<'+(button.DT_el || 'div')+'/>' )
								.appendTo( container );
							attach( inner, button );
						}
						else {
							btnDisplay = null;
							btnClass = button;
							tabIndex = settings.iTabIndex;
	
							switch ( button ) {
								case 'ellipsis':
									container.append('<span class="ellipsis">&#x2026;</span>');
									break;
	
								case 'first':
									btnDisplay = lang.sFirst;
	
									if ( page === 0 ) {
										tabIndex = -1;
										btnClass += ' ' + disabledClass;
									}
									break;
	
								case 'previous':
									btnDisplay = lang.sPrevious;
	
									if ( page === 0 ) {
										tabIndex = -1;
										btnClass += ' ' + disabledClass;
									}
									break;
	
								case 'next':
									btnDisplay = lang.sNext;
	
									if ( pages === 0 || page === pages-1 ) {
										tabIndex = -1;
										btnClass += ' ' + disabledClass;
									}
									break;
	
								case 'last':
									btnDisplay = lang.sLast;
	
									if ( pages === 0 || page === pages-1 ) {
										tabIndex = -1;
										btnClass += ' ' + disabledClass;
									}
									break;
	
								default:
									btnDisplay = settings.fnFormatNumber( button + 1 );
									btnClass = page === button ?
										classes.sPageButtonActive : '';
									break;
							}
	
							if ( btnDisplay !== null ) {
								node = $('<a>', {
										'class': classes.sPageButton+' '+btnClass,
										'aria-controls': settings.sTableId,
										'aria-label': aria[ button ],
										'data-dt-idx': counter,
										'tabindex': tabIndex,
										'id': idx === 0 && typeof button === 'string' ?
											settings.sTableId +'_'+ button :
											null
									} )
									.html( btnDisplay )
									.appendTo( container );
	
								_fnBindAction(
									node, {action: button}, clickHandler
								);
	
								counter++;
							}
						}
					}
				};
	
				// IE9 throws an 'unknown error' if document.activeElement is used
				// inside an iframe or frame. Try / catch the error. Not good for
				// accessibility, but neither are frames.
				var activeEl;
	
				try {
					// Because this approach is destroying and recreating the paging
					// elements, focus is lost on the select button which is bad for
					// accessibility. So we want to restore focus once the draw has
					// completed
					activeEl = $(host).find(document.activeElement).data('dt-idx');
				}
				catch (e) {}
	
				attach( $(host).empty(), buttons );
	
				if ( activeEl !== undefined ) {
					$(host).find( '[data-dt-idx='+activeEl+']' ).trigger('focus');
				}
			}
		}
	} );
	
	
	
	// Built in type detection. See model.ext.aTypes for information about
	// what is required from this methods.
	$.extend( DataTable.ext.type.detect, [
		// Plain numbers - first since V8 detects some plain numbers as dates
		// e.g. Date.parse('55') (but not all, e.g. Date.parse('22')...).
		function ( d, settings )
		{
			var decimal = settings.oLanguage.sDecimal;
			return _isNumber( d, decimal ) ? 'num'+decimal : null;
		},
	
		// Dates (only those recognised by the browser's Date.parse)
		function ( d, settings )
		{
			// V8 tries _very_ hard to make a string passed into `Date.parse()`
			// valid, so we need to use a regex to restrict date formats. Use a
			// plug-in for anything other than ISO8601 style strings
			if ( d && !(d instanceof Date) && ! _re_date.test(d) ) {
				return null;
			}
			var parsed = Date.parse(d);
			return (parsed !== null && !isNaN(parsed)) || _empty(d) ? 'date' : null;
		},
	
		// Formatted numbers
		function ( d, settings )
		{
			var decimal = settings.oLanguage.sDecimal;
			return _isNumber( d, decimal, true ) ? 'num-fmt'+decimal : null;
		},
	
		// HTML numeric
		function ( d, settings )
		{
			var decimal = settings.oLanguage.sDecimal;
			return _htmlNumeric( d, decimal ) ? 'html-num'+decimal : null;
		},
	
		// HTML numeric, formatted
		function ( d, settings )
		{
			var decimal = settings.oLanguage.sDecimal;
			return _htmlNumeric( d, decimal, true ) ? 'html-num-fmt'+decimal : null;
		},
	
		// HTML (this is strict checking - there must be html)
		function ( d, settings )
		{
			return _empty( d ) || (typeof d === 'string' && d.indexOf('<') !== -1) ?
				'html' : null;
		}
	] );
	
	
	
	// Filter formatting functions. See model.ext.ofnSearch for information about
	// what is required from these methods.
	// 
	// Note that additional search methods are added for the html numbers and
	// html formatted numbers by `_addNumericSort()` when we know what the decimal
	// place is
	
	
	$.extend( DataTable.ext.type.search, {
		html: function ( data ) {
			return _empty(data) ?
				data :
				typeof data === 'string' ?
					data
						.replace( _re_new_lines, " " )
						.replace( _re_html, "" ) :
					'';
		},
	
		string: function ( data ) {
			return _empty(data) ?
				data :
				typeof data === 'string' ?
					data.replace( _re_new_lines, " " ) :
					data;
		}
	} );
	
	
	
	var __numericReplace = function ( d, decimalPlace, re1, re2 ) {
		if ( d !== 0 && (!d || d === '-') ) {
			return -Infinity;
		}
	
		// If a decimal place other than `.` is used, it needs to be given to the
		// function so we can detect it and replace with a `.` which is the only
		// decimal place Javascript recognises - it is not locale aware.
		if ( decimalPlace ) {
			d = _numToDecimal( d, decimalPlace );
		}
	
		if ( d.replace ) {
			if ( re1 ) {
				d = d.replace( re1, '' );
			}
	
			if ( re2 ) {
				d = d.replace( re2, '' );
			}
		}
	
		return d * 1;
	};
	
	
	// Add the numeric 'deformatting' functions for sorting and search. This is done
	// in a function to provide an easy ability for the language options to add
	// additional methods if a non-period decimal place is used.
	function _addNumericSort ( decimalPlace ) {
		$.each(
			{
				// Plain numbers
				"num": function ( d ) {
					return __numericReplace( d, decimalPlace );
				},
	
				// Formatted numbers
				"num-fmt": function ( d ) {
					return __numericReplace( d, decimalPlace, _re_formatted_numeric );
				},
	
				// HTML numeric
				"html-num": function ( d ) {
					return __numericReplace( d, decimalPlace, _re_html );
				},
	
				// HTML numeric, formatted
				"html-num-fmt": function ( d ) {
					return __numericReplace( d, decimalPlace, _re_html, _re_formatted_numeric );
				}
			},
			function ( key, fn ) {
				// Add the ordering method
				_ext.type.order[ key+decimalPlace+'-pre' ] = fn;
	
				// For HTML types add a search formatter that will strip the HTML
				if ( key.match(/^html\-/) ) {
					_ext.type.search[ key+decimalPlace ] = _ext.type.search.html;
				}
			}
		);
	}
	
	
	// Default sort methods
	$.extend( _ext.type.order, {
		// Dates
		"date-pre": function ( d ) {
			var ts = Date.parse( d );
			return isNaN(ts) ? -Infinity : ts;
		},
	
		// html
		"html-pre": function ( a ) {
			return _empty(a) ?
				'' :
				a.replace ?
					a.replace( /<.*?>/g, "" ).toLowerCase() :
					a+'';
		},
	
		// string
		"string-pre": function ( a ) {
			// This is a little complex, but faster than always calling toString,
			// http://jsperf.com/tostring-v-check
			return _empty(a) ?
				'' :
				typeof a === 'string' ?
					a.toLowerCase() :
					! a.toString ?
						'' :
						a.toString();
		},
	
		// string-asc and -desc are retained only for compatibility with the old
		// sort methods
		"string-asc": function ( x, y ) {
			return ((x < y) ? -1 : ((x > y) ? 1 : 0));
		},
	
		"string-desc": function ( x, y ) {
			return ((x < y) ? 1 : ((x > y) ? -1 : 0));
		}
	} );
	
	
	// Numeric sorting types - order doesn't matter here
	_addNumericSort( '' );
	
	
	$.extend( true, DataTable.ext.renderer, {
		header: {
			_: function ( settings, cell, column, classes ) {
				// No additional mark-up required
				// Attach a sort listener to update on sort - note that using the
				// `DT` namespace will allow the event to be removed automatically
				// on destroy, while the `dt` namespaced event is the one we are
				// listening for
				$(settings.nTable).on( 'order.dt.DT', function ( e, ctx, sorting, columns ) {
					if ( settings !== ctx ) { // need to check this this is the host
						return;               // table, not a nested one
					}
	
					var colIdx = column.idx;
	
					cell
						.removeClass(
							classes.sSortAsc +' '+
							classes.sSortDesc
						)
						.addClass( columns[ colIdx ] == 'asc' ?
							classes.sSortAsc : columns[ colIdx ] == 'desc' ?
								classes.sSortDesc :
								column.sSortingClass
						);
				} );
			},
	
			jqueryui: function ( settings, cell, column, classes ) {
				$('<div/>')
					.addClass( classes.sSortJUIWrapper )
					.append( cell.contents() )
					.append( $('<span/>')
						.addClass( classes.sSortIcon+' '+column.sSortingClassJUI )
					)
					.appendTo( cell );
	
				// Attach a sort listener to update on sort
				$(settings.nTable).on( 'order.dt.DT', function ( e, ctx, sorting, columns ) {
					if ( settings !== ctx ) {
						return;
					}
	
					var colIdx = column.idx;
	
					cell
						.removeClass( classes.sSortAsc +" "+classes.sSortDesc )
						.addClass( columns[ colIdx ] == 'asc' ?
							classes.sSortAsc : columns[ colIdx ] == 'desc' ?
								classes.sSortDesc :
								column.sSortingClass
						);
	
					cell
						.find( 'span.'+classes.sSortIcon )
						.removeClass(
							classes.sSortJUIAsc +" "+
							classes.sSortJUIDesc +" "+
							classes.sSortJUI +" "+
							classes.sSortJUIAscAllowed +" "+
							classes.sSortJUIDescAllowed
						)
						.addClass( columns[ colIdx ] == 'asc' ?
							classes.sSortJUIAsc : columns[ colIdx ] == 'desc' ?
								classes.sSortJUIDesc :
								column.sSortingClassJUI
						);
				} );
			}
		}
	} );
	
	/*
	 * Public helper functions. These aren't used internally by DataTables, or
	 * called by any of the options passed into DataTables, but they can be used
	 * externally by developers working with DataTables. They are helper functions
	 * to make working with DataTables a little bit easier.
	 */
	
	var __htmlEscapeEntities = function ( d ) {
		if (Array.isArray(d)) {
			d = d.join(',');
		}
	
		return typeof d === 'string' ?
			d
				.replace(/&/g, '&amp;')
				.replace(/</g, '&lt;')
				.replace(/>/g, '&gt;')
				.replace(/"/g, '&quot;') :
			d;
	};
	
	// Common logic for moment, luxon or a date action
	function __mld( dt, momentFn, luxonFn, dateFn, arg1 ) {
		if (window.moment) {
			return dt[momentFn]( arg1 );
		}
		else if (window.luxon) {
			return dt[luxonFn]( arg1 );
		}
		
		return dateFn ? dt[dateFn]( arg1 ) : dt;
	}
	
	
	var __mlWarning = false;
	function __mldObj (d, format, locale) {
		var dt;
	
		if (window.moment) {
			dt = window.moment.utc( d, format, locale, true );
	
			if (! dt.isValid()) {
				return null;
			}
		}
		else if (window.luxon) {
			dt = format
				? window.luxon.DateTime.fromFormat( d, format )
				: window.luxon.DateTime.fromISO( d );
	
			if (! dt.isValid) {
				return null;
			}
	
			dt.setLocale(locale);
		}
		else if (! format) {
			// No format given, must be ISO
			dt = new Date(d);
		}
		else {
			if (! __mlWarning) {
				alert('DataTables warning: Formatted date without Moment.js or Luxon - https://datatables.net/tn/17');
			}
	
			__mlWarning = true;
		}
	
		return dt;
	}
	
	// Wrapper for date, datetime and time which all operate the same way with the exception of
	// the output string for auto locale support
	function __mlHelper (localeString) {
		return function ( from, to, locale, def ) {
			// Luxon and Moment support
			// Argument shifting
			if ( arguments.length === 0 ) {
				locale = 'en';
				to = null; // means toLocaleString
				from = null; // means iso8601
			}
			else if ( arguments.length === 1 ) {
				locale = 'en';
				to = from;
				from = null;
			}
			else if ( arguments.length === 2 ) {
				locale = to;
				to = from;
				from = null;
			}
	
			var typeName = 'datetime-' + to;
	
			// Add type detection and sorting specific to this date format - we need to be able to identify
			// date type columns as such, rather than as numbers in extensions. Hence the need for this.
			if (! DataTable.ext.type.order[typeName]) {
				// The renderer will give the value to type detect as the type!
				DataTable.ext.type.detect.unshift(function (d) {
					return d === typeName ? typeName : false;
				});
	
				// The renderer gives us Moment, Luxon or Date obects for the sorting, all of which have a
				// `valueOf` which gives milliseconds epoch
				DataTable.ext.type.order[typeName + '-asc'] = function (a, b) {
					var x = a.valueOf();
					var y = b.valueOf();
	
					return x === y
						? 0
						: x < y
							? -1
							: 1;
				}
	
				DataTable.ext.type.order[typeName + '-desc'] = function (a, b) {
					var x = a.valueOf();
					var y = b.valueOf();
	
					return x === y
						? 0
						: x > y
							? -1
							: 1;
				}
			}
		
			return function ( d, type ) {
				// Allow for a default value
				if (d === null || d === undefined) {
					if (def === '--now') {
						// We treat everything as UTC further down, so no changes are
						// made, as such need to get the local date / time as if it were
						// UTC
						var local = new Date();
						d = new Date( Date.UTC(
							local.getFullYear(), local.getMonth(), local.getDate(),
							local.getHours(), local.getMinutes(), local.getSeconds()
						) );
					}
					else {
						d = '';
					}
				}
	
				if (type === 'type') {
					// Typing uses the type name for fast matching
					return typeName;
				}
	
				if (d === '') {
					return type !== 'sort'
						? ''
						: __mldObj('0000-01-01 00:00:00', null, locale);
				}
	
				// Shortcut. If `from` and `to` are the same, we are using the renderer to
				// format for ordering, not display - its already in the display format.
				if ( to !== null && from === to && type !== 'sort' && type !== 'type' && ! (d instanceof Date) ) {
					return d;
				}
	
				var dt = __mldObj(d, from, locale);
	
				if (dt === null) {
					return d;
				}
	
				if (type === 'sort') {
					return dt;
				}
				
				var formatted = to === null
					? __mld(dt, 'toDate', 'toJSDate', '')[localeString]()
					: __mld(dt, 'format', 'toFormat', 'toISOString', to);
	
				// XSS protection
				return type === 'display' ?
					__htmlEscapeEntities( formatted ) :
					formatted;
			};
		}
	}
	
	// Based on locale, determine standard number formatting
	// Fallback for legacy browsers is US English
	var __thousands = ',';
	var __decimal = '.';
	
	if (Intl) {
		try {
			var num = new Intl.NumberFormat().formatToParts(100000.1);
		
			for (var i=0 ; i<num.length ; i++) {
				if (num[i].type === 'group') {
					__thousands = num[i].value;
				}
				else if (num[i].type === 'decimal') {
					__decimal = num[i].value;
				}
			}
		}
		catch (e) {
			// noop
		}
	}
	
	// Formatted date time detection - use by declaring the formats you are going to use
	DataTable.datetime = function ( format, locale ) {
		var typeName = 'datetime-detect-' + format;
	
		if (! locale) {
			locale = 'en';
		}
	
		if (! DataTable.ext.type.order[typeName]) {
			DataTable.ext.type.detect.unshift(function (d) {
				var dt = __mldObj(d, format, locale);
				return d === '' || dt ? typeName : false;
			});
	
			DataTable.ext.type.order[typeName + '-pre'] = function (d) {
				return __mldObj(d, format, locale) || 0;
			}
		}
	}
	
	/**
	 * Helpers for `columns.render`.
	 *
	 * The options defined here can be used with the `columns.render` initialisation
	 * option to provide a display renderer. The following functions are defined:
	 *
	 * * `number` - Will format numeric data (defined by `columns.data`) for
	 *   display, retaining the original unformatted data for sorting and filtering.
	 *   It takes 5 parameters:
	 *   * `string` - Thousands grouping separator
	 *   * `string` - Decimal point indicator
	 *   * `integer` - Number of decimal points to show
	 *   * `string` (optional) - Prefix.
	 *   * `string` (optional) - Postfix (/suffix).
	 * * `text` - Escape HTML to help prevent XSS attacks. It has no optional
	 *   parameters.
	 *
	 * @example
	 *   // Column definition using the number renderer
	 *   {
	 *     data: "salary",
	 *     render: $.fn.dataTable.render.number( '\'', '.', 0, '$' )
	 *   }
	 *
	 * @namespace
	 */
	DataTable.render = {
		date: __mlHelper('toLocaleDateString'),
		datetime: __mlHelper('toLocaleString'),
		time: __mlHelper('toLocaleTimeString'),
		number: function ( thousands, decimal, precision, prefix, postfix ) {
			// Auto locale detection
			if (thousands === null || thousands === undefined) {
				thousands = __thousands;
			}
	
			if (decimal === null || decimal === undefined) {
				decimal = __decimal;
			}
	
			return {
				display: function ( d ) {
					if ( typeof d !== 'number' && typeof d !== 'string' ) {
						return d;
					}
	
					if (d === '' || d === null) {
						return d;
					}
	
					var negative = d < 0 ? '-' : '';
					var flo = parseFloat( d );
	
					// If NaN then there isn't much formatting that we can do - just
					// return immediately, escaping any HTML (this was supposed to
					// be a number after all)
					if ( isNaN( flo ) ) {
						return __htmlEscapeEntities( d );
					}
	
					flo = flo.toFixed( precision );
					d = Math.abs( flo );
	
					var intPart = parseInt( d, 10 );
					var floatPart = precision ?
						decimal+(d - intPart).toFixed( precision ).substring( 2 ):
						'';
	
					// If zero, then can't have a negative prefix
					if (intPart === 0 && parseFloat(floatPart) === 0) {
						negative = '';
					}
	
					return negative + (prefix||'') +
						intPart.toString().replace(
							/\B(?=(\d{3})+(?!\d))/g, thousands
						) +
						floatPart +
						(postfix||'');
				}
			};
		},
	
		text: function () {
			return {
				display: __htmlEscapeEntities,
				filter: __htmlEscapeEntities
			};
		}
	};
	
	
	/*
	 * This is really a good bit rubbish this method of exposing the internal methods
	 * publicly... - To be fixed in 2.0 using methods on the prototype
	 */
	
	
	/**
	 * Create a wrapper function for exporting an internal functions to an external API.
	 *  @param {string} fn API function name
	 *  @returns {function} wrapped function
	 *  @memberof DataTable#internal
	 */
	function _fnExternApiFunc (fn)
	{
		return function() {
			var args = [_fnSettingsFromNode( this[DataTable.ext.iApiIndex] )].concat(
				Array.prototype.slice.call(arguments)
			);
			return DataTable.ext.internal[fn].apply( this, args );
		};
	}
	
	
	/**
	 * Reference to internal functions for use by plug-in developers. Note that
	 * these methods are references to internal functions and are considered to be
	 * private. If you use these methods, be aware that they are liable to change
	 * between versions.
	 *  @namespace
	 */
	$.extend( DataTable.ext.internal, {
		_fnExternApiFunc: _fnExternApiFunc,
		_fnBuildAjax: _fnBuildAjax,
		_fnAjaxUpdate: _fnAjaxUpdate,
		_fnAjaxParameters: _fnAjaxParameters,
		_fnAjaxUpdateDraw: _fnAjaxUpdateDraw,
		_fnAjaxDataSrc: _fnAjaxDataSrc,
		_fnAddColumn: _fnAddColumn,
		_fnColumnOptions: _fnColumnOptions,
		_fnAdjustColumnSizing: _fnAdjustColumnSizing,
		_fnVisibleToColumnIndex: _fnVisibleToColumnIndex,
		_fnColumnIndexToVisible: _fnColumnIndexToVisible,
		_fnVisbleColumns: _fnVisbleColumns,
		_fnGetColumns: _fnGetColumns,
		_fnColumnTypes: _fnColumnTypes,
		_fnApplyColumnDefs: _fnApplyColumnDefs,
		_fnHungarianMap: _fnHungarianMap,
		_fnCamelToHungarian: _fnCamelToHungarian,
		_fnLanguageCompat: _fnLanguageCompat,
		_fnBrowserDetect: _fnBrowserDetect,
		_fnAddData: _fnAddData,
		_fnAddTr: _fnAddTr,
		_fnNodeToDataIndex: _fnNodeToDataIndex,
		_fnNodeToColumnIndex: _fnNodeToColumnIndex,
		_fnGetCellData: _fnGetCellData,
		_fnSetCellData: _fnSetCellData,
		_fnSplitObjNotation: _fnSplitObjNotation,
		_fnGetObjectDataFn: _fnGetObjectDataFn,
		_fnSetObjectDataFn: _fnSetObjectDataFn,
		_fnGetDataMaster: _fnGetDataMaster,
		_fnClearTable: _fnClearTable,
		_fnDeleteIndex: _fnDeleteIndex,
		_fnInvalidate: _fnInvalidate,
		_fnGetRowElements: _fnGetRowElements,
		_fnCreateTr: _fnCreateTr,
		_fnBuildHead: _fnBuildHead,
		_fnDrawHead: _fnDrawHead,
		_fnDraw: _fnDraw,
		_fnReDraw: _fnReDraw,
		_fnAddOptionsHtml: _fnAddOptionsHtml,
		_fnDetectHeader: _fnDetectHeader,
		_fnGetUniqueThs: _fnGetUniqueThs,
		_fnFeatureHtmlFilter: _fnFeatureHtmlFilter,
		_fnFilterComplete: _fnFilterComplete,
		_fnFilterCustom: _fnFilterCustom,
		_fnFilterColumn: _fnFilterColumn,
		_fnFilter: _fnFilter,
		_fnFilterCreateSearch: _fnFilterCreateSearch,
		_fnEscapeRegex: _fnEscapeRegex,
		_fnFilterData: _fnFilterData,
		_fnFeatureHtmlInfo: _fnFeatureHtmlInfo,
		_fnUpdateInfo: _fnUpdateInfo,
		_fnInfoMacros: _fnInfoMacros,
		_fnInitialise: _fnInitialise,
		_fnInitComplete: _fnInitComplete,
		_fnLengthChange: _fnLengthChange,
		_fnFeatureHtmlLength: _fnFeatureHtmlLength,
		_fnFeatureHtmlPaginate: _fnFeatureHtmlPaginate,
		_fnPageChange: _fnPageChange,
		_fnFeatureHtmlProcessing: _fnFeatureHtmlProcessing,
		_fnProcessingDisplay: _fnProcessingDisplay,
		_fnFeatureHtmlTable: _fnFeatureHtmlTable,
		_fnScrollDraw: _fnScrollDraw,
		_fnApplyToChildren: _fnApplyToChildren,
		_fnCalculateColumnWidths: _fnCalculateColumnWidths,
		_fnThrottle: _fnThrottle,
		_fnConvertToWidth: _fnConvertToWidth,
		_fnGetWidestNode: _fnGetWidestNode,
		_fnGetMaxLenString: _fnGetMaxLenString,
		_fnStringToCss: _fnStringToCss,
		_fnSortFlatten: _fnSortFlatten,
		_fnSort: _fnSort,
		_fnSortAria: _fnSortAria,
		_fnSortListener: _fnSortListener,
		_fnSortAttachListener: _fnSortAttachListener,
		_fnSortingClasses: _fnSortingClasses,
		_fnSortData: _fnSortData,
		_fnSaveState: _fnSaveState,
		_fnLoadState: _fnLoadState,
		_fnImplementState: _fnImplementState,
		_fnSettingsFromNode: _fnSettingsFromNode,
		_fnLog: _fnLog,
		_fnMap: _fnMap,
		_fnBindAction: _fnBindAction,
		_fnCallbackReg: _fnCallbackReg,
		_fnCallbackFire: _fnCallbackFire,
		_fnLengthOverflow: _fnLengthOverflow,
		_fnRenderer: _fnRenderer,
		_fnDataSource: _fnDataSource,
		_fnRowAttributes: _fnRowAttributes,
		_fnExtend: _fnExtend,
		_fnCalculateEnd: function () {} // Used by a lot of plug-ins, but redundant
		                                // in 1.10, so this dead-end function is
		                                // added to prevent errors
	} );
	
	
	// jQuery access
	$.fn.dataTable = DataTable;
	
	// Provide access to the host jQuery object (circular reference)
	DataTable.$ = $;
	
	// Legacy aliases
	$.fn.dataTableSettings = DataTable.settings;
	$.fn.dataTableExt = DataTable.ext;
	
	// With a capital `D` we return a DataTables API instance rather than a
	// jQuery object
	$.fn.DataTable = function ( opts ) {
		return $(this).dataTable( opts ).api();
	};
	
	// All properties that are available to $.fn.dataTable should also be
	// available on $.fn.DataTable
	$.each( DataTable, function ( prop, val ) {
		$.fn.DataTable[ prop ] = val;
	} );
	
	return DataTable;
}
\")).default;\n",
+       "    dt(window.$);\n",
+       "  }\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>table.dataTable td.dt-control{text-align:center;cursor:pointer}table.dataTable td.dt-control:before{height:1em;width:1em;margin-top:-9px;display:inline-block;color:white;border:.15em solid white;border-radius:1em;box-shadow:0 0 .2em #444;box-sizing:content-box;text-align:center;text-indent:0 !important;font-family:\"Courier New\",Courier,monospace;line-height:1em;content:\"+\";background-color:#31b131}table.dataTable tr.dt-hasChild td.dt-control:before{content:\"-\";background-color:#d33333}table.dataTable thead>tr>th.sorting,table.dataTable thead>tr>th.sorting_asc,table.dataTable thead>tr>th.sorting_desc,table.dataTable thead>tr>th.sorting_asc_disabled,table.dataTable thead>tr>th.sorting_desc_disabled,table.dataTable thead>tr>td.sorting,table.dataTable thead>tr>td.sorting_asc,table.dataTable thead>tr>td.sorting_desc,table.dataTable thead>tr>td.sorting_asc_disabled,table.dataTable 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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:itables.downsample:showing 744x11 of 6645x11 as nbytes=584760>65536=maxBytes. See https://mwouts.github.io/itables/downsampling.html\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table id=\"9fc29ee0-ffb5-490c-b657-fa42043f998b\" class=\"display\"style=\"max-width:100%\"><thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Number of times repeated_x</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Level</th>\n",
+       "      <th>Number of times repeated_y</th>\n",
+       "    </tr>\n",
+       "  </thead><tbody><tr><td>Loading... (need <a href=https://mwouts.github.io/itables/troubleshooting.html>help</a>?)</td></tr></tbody></table>\n",
+       "<style>\n",
+       "table td {\n",
+       "    text-overflow: ellipsis;\n",
+       "    overflow: hidden;\n",
+       "}\n",
+       "\n",
+       "table th {\n",
+       "    text-overflow: ellipsis;\n",
+       "    overflow: hidden;\n",
+       "}\n",
+       "\n",
+       "thead input {\n",
+       "    width: 100%;\n",
+       "    padding: 3px;\n",
+       "    box-sizing: border-box;\n",
+       "}\n",
+       "\n",
+       "tfoot input {\n",
+       "    width: 100%;\n",
+       "    padding: 3px;\n",
+       "    box-sizing: border-box;\n",
+       "}\n",
+       "\n",
+       "</style>\n",
+       "<script>\n",
+       "    // We use a namespace per table (= the next curly bracket)\n",
+       "    // to avoid conflicts between the various tables' data & dt_args\n",
+       "    {\n",
+       "        // Define the table data\n",
+       "        const data = [[0, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.817804, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [1, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.759333, \"Negative\", \"SWE\", \"3\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [2, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.936963, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [3, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.786708, \"Negative\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [4, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.924056, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [5, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.768604, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [6, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.849236, \"Negative\", \"AUT\", \"Does not apply\", \"Logarithmic\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [7, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.847466, \"Negative\", \"NLD\", \"13\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [8, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.98225, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [9, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.977356, \"Negative\", \"ARE\", \"21\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [10, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.98225, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [11, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.992105, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [12, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.927189, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [13, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.897966, \"Negative\", \"OMN\", \"21\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [14, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941656, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [15, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.984804, \"Negative\", \"ISR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [16, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941309, \"Negative\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [17, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.902682, \"Negative\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [18, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.805222, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [19, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.863071, \"Negative\", \"LBR\", \"2\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [20, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.939188, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [21, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.821756, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [22, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.934848, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [23, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.844272, \"Negative\", \"GHA\", \"1\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [24, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.99911, \"Negative\", \"BGD\", \"2\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [25, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.992671, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [26, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941656, \"Negative\", \"VNM\", \"21\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [27, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.781154, \"Negative\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [28, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.951008, \"Negative\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [29, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.942998, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [30, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.777929, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [31, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.838543, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [32, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.940761, \"Negative\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [33, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.950571, \"Negative\", \"VEN\", \"1\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [34, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.89959, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [35, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.895844, \"Negative\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [36, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999778, \"Negative\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [37, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999123, \"Negative\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [38, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999123, \"Negative\", \"USA\", \"13\", \"Does not apply\", \"Pair\", 22, \"Demography\", \"secondary\", 40], [39, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.995455, \"Positive\", \"CHN\", \"21\", \"Does not apply\", \"Pair\", 22, \"Demography\", \"secondary\", 40], [40, \"Commercial bank branches (per 100,000 adults)\", 0.801333, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [41, \"Commercial bank branches (per 100,000 adults)\", 0.75667, \"Negative\", \"FRA\", \"5\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [42, \"Commercial bank branches (per 100,000 adults)\", 0.782831, \"Negative\", \"GBR\", \"5\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [43, \"Commercial bank branches (per 100,000 adults)\", 0.838543, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [44, \"Commercial bank branches (per 100,000 adults)\", 0.836928, \"Negative\", \"NLD\", \"13\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [45, \"Commercial bank branches (per 100,000 adults)\", 0.933522, \"Negative\", \"QAT\", \"5\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [46, \"Commercial bank branches (per 100,000 adults)\", 0.755661, \"Negative\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [47, \"Commercial bank branches (per 100,000 adults)\", 0.875117, \"Negative\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [48, \"Commercial bank branches (per 100,000 adults)\", 0.895191, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [49, \"Commercial bank branches (per 100,000 adults)\", 0.76892, \"Positive\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [50, \"Commercial bank branches (per 100,000 adults)\", 0.81853, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [51, \"Commercial bank branches (per 100,000 adults)\", 0.835641, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [52, \"Commercial bank branches (per 100,000 adults)\", 0.843377, \"Positive\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [53, \"Commercial bank branches (per 100,000 adults)\", 0.877827, \"Positive\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [54, \"Commercial bank branches (per 100,000 adults)\", 0.881445, \"Positive\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [55, \"Commercial bank branches (per 100,000 adults)\", 0.787284, \"Positive\", \"ZAF\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [56, \"Commercial bank branches (per 100,000 adults)\", 0.907985, \"Positive\", \"MOZ\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [57, \"Commercial bank branches (per 100,000 adults)\", 0.892506, \"Positive\", \"CMR\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [58, \"Commercial bank branches (per 100,000 adults)\", 0.869419, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [59, \"Commercial bank branches (per 100,000 adults)\", 0.960022, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [60, \"Commercial bank branches (per 100,000 adults)\", 0.980779, \"Positive\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [61, \"Commercial bank branches (per 100,000 adults)\", 0.806534, \"Positive\", \"IDN\", \"2\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [62, \"Commercial bank branches (per 100,000 adults)\", 0.814298, \"Positive\", \"MEX\", \"Does not apply\", \"Logarithmic\", \"Latam\", 11, \"Economy\", \"secondary\", 26], [63, \"Commercial bank branches (per 100,000 adults)\", 0.977356, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 11, \"Economy\", \"secondary\", 26], [64, \"Commercial bank branches (per 100,000 adults)\", 0.977356, \"Negative\", \"USA\", \"21\", \"Does not apply\", \"Pair\", 11, \"Economy\", \"secondary\", 26], [65, \"Commercial bank branches (per 100,000 adults)\", 0.768043, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 11, \"Economy\", \"secondary\", 26], [66, \"CO2 emissions (kt)\", 0.794276, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [67, \"CO2 emissions (kt)\", 0.761173, \"Negative\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [68, \"CO2 emissions (kt)\", 0.905449, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [69, \"CO2 emissions (kt)\", 0.971453, \"Positive\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [70, \"CO2 emissions (kt)\", 0.984775, \"Positive\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [71, \"CO2 emissions (kt)\", 0.960933, \"Positive\", \"SAU\", \"3\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [72, \"CO2 emissions (kt)\", 0.97328, \"Positive\", \"OMN\", \"5\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [73, \"CO2 emissions (kt)\", 0.829432, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [74, \"CO2 emissions (kt)\", 0.921508, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [75, \"CO2 emissions (kt)\", 0.941573, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [76, \"CO2 emissions (kt)\", 0.952939, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [77, \"CO2 emissions (kt)\", 0.789979, \"Positive\", \"SEN\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [78, \"CO2 emissions (kt)\", 0.783163, \"Positive\", \"ZAF\", \"1\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [79, \"CO2 emissions (kt)\", 0.833274, \"Positive\", \"LBR\", \"Does not apply\", \"Linear\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [80, \"CO2 emissions (kt)\", 0.930963, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [81, \"CO2 emissions (kt)\", 0.78301, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [82, \"CO2 emissions (kt)\", 0.85301, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [83, \"CO2 emissions (kt)\", 1.0, \"Positive\", \"BGD\", \"Does not apply\", \"Cuadratic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [84, \"CO2 emissions (kt)\", 0.992493, \"Positive\", \"IND\", \"Does not apply\", \"Linear\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [85, \"CO2 emissions (kt)\", 0.993956, \"Positive\", \"VNM\", \"1\", \"Does not apply\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [86, \"CO2 emissions (kt)\", 0.863207, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [87, \"CO2 emissions (kt)\", 0.902509, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [88, \"CO2 emissions (kt)\", 0.903608, \"Positive\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [89, \"CO2 emissions (kt)\", 0.959865, \"Positive\", \"KOR\", \"Does not apply\", \"Cubic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [90, \"CO2 emissions (kt)\", 0.821451, \"Positive\", \"MEX\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [91, \"CO2 emissions (kt)\", 0.855111, \"Positive\", \"BRA\", \"2\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [92, \"CO2 emissions (kt)\", 0.753552, \"Positive\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [93, \"CO2 emissions (kt)\", 0.944664, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [94, \"CO2 emissions (kt)\", 0.768742, \"Positive\", \"COL\", \"1\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [95, \"CO2 emissions (kt)\", 0.925663, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [96, \"CO2 emissions (kt)\", 0.900419, \"Positive\", \"PAN\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [97, \"CO2 emissions (kt)\", 0.964184, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [98, \"CO2 emissions (kt)\", 0.98957, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 30, \"Environment\", \"secondary\", 33], [99, \"International migrant stock (% of population)\", 0.782956, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [100, \"International migrant stock (% of population)\", 0.807906, \"Positive\", \"FRA\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [101, \"International migrant stock (% of population)\", 0.770511, \"Positive\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [102, \"International migrant stock (% of population)\", 0.907327, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [103, \"International migrant stock (% of population)\", 0.900471, \"Positive\", \"ESP\", \"3\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [104, \"International migrant stock (% of population)\", 0.835596, \"Positive\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [105, \"International migrant stock (% of population)\", 0.968708, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [106, \"International migrant stock (% of population)\", 0.841846, \"Positive\", \"GRC\", \"5\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [107, \"International migrant stock (% of population)\", 0.831819, \"Positive\", \"AUT\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [108, \"International migrant stock (% of population)\", 0.898961, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [109, \"International migrant stock (% of population)\", 0.929941, \"Positive\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [110, \"International migrant stock (% of population)\", 0.927564, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [111, \"International migrant stock (% of population)\", 0.972132, \"Negative\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [112, \"International migrant stock (% of population)\", 0.903976, \"Positive\", \"YEM\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [113, \"International migrant stock (% of population)\", 0.833059, \"Negative\", \"DZA\", \"3\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [114, \"International migrant stock (% of population)\", 0.972723, \"Negative\", \"ISR\", \"1\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [115, \"International migrant stock (% of population)\", 0.906777, \"Negative\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [116, \"International migrant stock (% of population)\", 0.783531, \"Positive\", \"MAR\", \"13\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [117, \"International migrant stock (% of population)\", 0.775082, \"Negative\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [118, \"International migrant stock (% of population)\", 0.943912, \"Negative\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [119, \"International migrant stock (% of population)\", 0.971948, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [120, \"International migrant stock (% of population)\", 0.98406, \"Negative\", \"IND\", \"Does not apply\", \"Logarithmic\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [121, \"International migrant stock (% of population)\", 0.90581, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [122, \"International migrant stock (% of population)\", 0.874691, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [123, \"International migrant stock (% of population)\", 0.945693, \"Positive\", \"KOR\", \"Does not apply\", \"Linear\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [124, \"International migrant stock (% of population)\", 0.933482, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [125, \"International migrant stock (% of population)\", 0.912187, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [126, \"International migrant stock (% of population)\", 0.892956, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [127, \"International migrant stock (% of population)\", 0.989918, \"Positive\", \"PAN\", \"Does not apply\", \"Cubic\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [128, \"International migrant stock (% of population)\", 0.987719, \"Positive\", \"USA\", \"Does not apply\", \"Cubic\", \"Pair\", 16, \"Migration\", \"secondary\", 31], [129, \"International migrant stock (% of population)\", 0.989918, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 16, \"Migration\", \"secondary\", 31], [130, \"CO2 emissions (metric tons per capita)\", 0.782806, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [131, \"CO2 emissions (metric tons per capita)\", 0.768845, \"Negative\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [132, \"CO2 emissions (metric tons per capita)\", 0.915538, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [133, \"CO2 emissions (metric tons per capita)\", 0.78562, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [134, \"CO2 emissions (metric tons per capita)\", 0.759154, \"Negative\", \"NLD\", \"5\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [135, \"CO2 emissions (metric tons per capita)\", 0.806797, \"Positive\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 18, \"Environment\", \"secondary\", 29], [136, \"CO2 emissions (metric tons per capita)\", 0.841863, \"Positive\", \"OMN\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Environment\", \"secondary\", 29], [137, \"CO2 emissions (metric tons per capita)\", 0.846222, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [138, \"CO2 emissions (metric tons per capita)\", 0.884204, \"Positive\", \"EGY\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [139, \"CO2 emissions (metric tons per capita)\", 0.788288, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [140, \"CO2 emissions (metric tons per capita)\", 0.895687, \"Positive\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [141, \"CO2 emissions (metric tons per capita)\", 0.936433, \"Positive\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [142, \"CO2 emissions (metric tons per capita)\", 0.782676, \"Positive\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [143, \"CO2 emissions (metric tons per capita)\", 0.905693, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [144, \"CO2 emissions (metric tons per capita)\", 0.786576, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [145, \"CO2 emissions (metric tons per capita)\", 0.997798, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [146, \"CO2 emissions (metric tons per capita)\", 0.98957, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [147, \"CO2 emissions (metric tons per capita)\", 0.991031, \"Positive\", \"VNM\", \"Does not apply\", \"Linear\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [148, \"CO2 emissions (metric tons per capita)\", 0.858441, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [149, \"CO2 emissions (metric tons per capita)\", 0.903207, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [150, \"CO2 emissions (metric tons per capita)\", 0.840909, \"Positive\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [151, \"CO2 emissions (metric tons per capita)\", 0.948395, \"Positive\", \"KOR\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [152, \"CO2 emissions (metric tons per capita)\", 0.866209, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [153, \"CO2 emissions (metric tons per capita)\", 0.880682, \"Positive\", \"PER\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [154, \"CO2 emissions (metric tons per capita)\", 0.895551, \"Positive\", \"CHL\", \"Does not apply\", \"Cubic\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [155, \"CO2 emissions (metric tons per capita)\", 0.757305, \"Positive\", \"PAN\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [156, \"CO2 emissions (metric tons per capita)\", 0.753095, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [157, \"CO2 emissions (metric tons per capita)\", 0.913924, \"Negative\", \"USA\", \"8\", \"Does not apply\", \"Pair\", 18, \"Environment\", \"secondary\", 29], [158, \"CO2 emissions (metric tons per capita)\", 0.976473, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 18, \"Environment\", \"secondary\", 29], [159, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.779372, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [160, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.798438, \"Positive\", \"FRA\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [161, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.782956, \"Positive\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [162, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.907327, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [163, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.800012, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [164, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.791365, \"Positive\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [165, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.968708, \"Positive\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [166, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.82583, \"Positive\", \"AUT\", \"3\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [167, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.843098, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [168, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.935625, \"Positive\", \"AZE\", \"5\", \"Does not apply\", \"Persian Gulf\", 29, \"Principal\", \"primary\", 35], [169, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.932399, \"Positive\", \"EGY\", \"2\", \"Does not apply\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [170, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.936453, \"Positive\", \"ISR\", \"Does not apply\", \"Cubic\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [171, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.920491, \"Positive\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [172, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.780022, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [173, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.820787, \"Positive\", \"ZAF\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [174, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.937635, \"Positive\", \"MOZ\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [175, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.848688, \"Positive\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [176, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.941264, \"Positive\", \"NGA\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [177, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.838169, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [178, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.98201, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [179, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.950622, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [180, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.967217, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [181, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.848688, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [182, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.927815, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 29, \"Principal\", \"primary\", 35], [183, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.93913, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [184, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.941701, \"Positive\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 29, \"Principal\", \"primary\", 35], [185, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.925869, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [186, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.831081, \"Positive\", \"BRA\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [187, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.752212, \"Positive\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [188, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.912985, \"Positive\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [189, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.853454, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [190, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.946165, \"Positive\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [191, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.899891, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [192, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.987719, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 29, \"Principal\", \"primary\", 35], [193, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.981854, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 29, \"Principal\", \"primary\", 35], [194, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.759092, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [195, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.898766, \"Positive\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [196, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.775622, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [197, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.801511, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [198, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.788969, \"Positive\", \"AUT\", \"2\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [199, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.857483, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [200, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.908522, \"Negative\", \"QAT\", \"1\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [201, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.892232, \"Negative\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [202, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.994777, \"Negative\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [203, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.947125, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [204, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.984385, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [205, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.891518, \"Negative\", \"DZA\", \"1\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [206, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.92803, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [207, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.819845, \"Positive\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [208, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.876163, \"Negative\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [209, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.902778, \"Negative\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [210, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.809567, \"Negative\", \"MOZ\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [211, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.907764, \"Negative\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [212, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.96926, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [213, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.86686, \"Negative\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [214, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.993913, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [215, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.987845, \"Negative\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [216, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.815973, \"Positive\", \"VNM\", \"21\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [217, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.904913, \"Negative\", \"PHL\", \"Does not apply\", \"Logarithmic\", \"Asia\", 18, \"Health\", \"secondary\", 33], [218, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.933133, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [219, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.754175, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [220, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.799358, \"Negative\", \"PER\", \"13\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [221, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.893013, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [222, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.896554, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [223, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.996522, \"Negative\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [224, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.821128, \"Negative\", \"CRI\", \"Does not apply\", \"Logarithmic\", \"Latam\", 18, \"Health\", \"secondary\", 33], [225, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.886364, \"Positive\", \"USA\", \"21\", \"Does not apply\", \"Pair\", 18, \"Health\", \"secondary\", 33], [226, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.833357, \"Negative\", \"CHN\", \"Does not apply\", \"Logarithmic\", \"Pair\", 18, \"Health\", \"secondary\", 33], [227, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.809179, \"Positive\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [228, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.773946, \"Positive\", \"FRA\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [229, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.815998, \"Positive\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [230, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.8799, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [231, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.751187, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [232, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.909632, \"Positive\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [233, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.873516, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [234, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.799967, \"Positive\", \"AUT\", \"Does not apply\", \"Logarithmic\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [235, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.829252, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [236, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.889718, \"Positive\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [237, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.977084, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [238, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.959094, \"Positive\", \"AZE\", \"21\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [239, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.847827, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [240, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.859402, \"Positive\", \"EGY\", \"Does not apply\", \"Linear\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [241, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.962303, \"Positive\", \"ISR\", \"Does not apply\", \"Cuadratic\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [242, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.932453, \"Positive\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [243, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.920393, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [244, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.904112, \"Positive\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [245, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.82775, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [246, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.784068, \"Positive\", \"NGA\", \"13\", \"Does not apply\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [247, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.843838, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [248, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.972127, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [249, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.955771, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [250, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.914644, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [251, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.864774, \"Positive\", \"THA\", \"1\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [252, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.897196, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [253, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.937594, \"Positive\", \"PHL\", \"2\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [254, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.918708, \"Positive\", \"KOR\", \"3\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [255, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.951463, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [256, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.751235, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [257, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.883617, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [258, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.893338, \"Positive\", \"VEN\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [259, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.904546, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [260, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.903191, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [261, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.979415, \"Positive\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [262, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.971504, \"Positive\", \"CRI\", \"2\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [263, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.949698, \"Positive\", \"USA\", \"2\", \"Does not apply\", \"Pair\", 28, \"Imports\", \"secondary\", 38], [264, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.925899, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 28, \"Imports\", \"secondary\", 38], [265, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.785897, \"Positive\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [266, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.832712, \"Positive\", \"FRA\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [267, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.792293, \"Positive\", \"SWE\", \"Does not apply\", \"Cuadratic\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [268, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.890853, \"Positive\", \"GBR\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [269, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.846758, \"Positive\", \"ESP\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [270, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.796993, \"Positive\", \"POL\", \"Does not apply\", \"Cuadratic\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [271, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.855869, \"Positive\", \"AUT\", \"2\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [272, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.940436, \"Positive\", \"SAU\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 15], [273, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.933687, \"Positive\", \"ISR\", \"Does not apply\", \"Cubic\", \"North Africa\", 12, \"Environment\", \"secondary\", 15], [274, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.836127, \"Positive\", \"ZAF\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 12, \"Environment\", \"secondary\", 15], [275, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.813553, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Environment\", \"secondary\", 15], [276, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.813553, \"Positive\", \"MEX\", \"Does not apply\", \"Linear\", \"Latam\", 12, \"Environment\", \"secondary\", 15], [277, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.7618, \"Positive\", \"VEN\", \"5\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 15], [278, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.945556, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 12, \"Environment\", \"secondary\", 15], [279, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.947754, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 12, \"Environment\", \"secondary\", 15], [280, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.768636, \"Negative\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [281, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.797226, \"Negative\", \"FRA\", \"3\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [282, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.785944, \"Negative\", \"SWE\", \"3\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [283, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.879221, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [284, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.868737, \"Negative\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [285, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.834752, \"Negative\", \"HRV\", \"2\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [286, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.944398, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [287, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.852272, \"Negative\", \"AUT\", \"1\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [288, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.863656, \"Negative\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [289, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.977434, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [290, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.977717, \"Negative\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [291, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.979206, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [292, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.909342, \"Negative\", \"AZE\", \"1\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [293, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.871188, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [294, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.922552, \"Negative\", \"OMN\", \"2\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [295, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.813387, \"Negative\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [296, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.95019, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [297, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.956329, \"Negative\", \"ISR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [298, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.945261, \"Negative\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [299, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.953349, \"Negative\", \"MAR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [300, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.939033, \"Negative\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [301, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.779079, \"Negative\", \"LBR\", \"1\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [302, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.939188, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [303, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.845139, \"Negative\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [304, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.934848, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [305, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.858043, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [306, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.842518, \"Negative\", \"BGD\", \"1\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [307, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.992671, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [308, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.993951, \"Negative\", \"VNM\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [309, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.900637, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [310, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.78477, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [311, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.947448, \"Negative\", \"KOR\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [312, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.898692, \"Negative\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [313, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.780573, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [314, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.801076, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [315, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.934719, \"Negative\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [316, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.887514, \"Negative\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [317, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.892492, \"Negative\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [318, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.927253, \"Negative\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [319, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.970669, \"Negative\", \"CRI\", \"Does not apply\", \"Logarithmic\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [320, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.823122, \"Negative\", \"USA\", \"Does not apply\", \"Logarithmic\", \"Pair\", 25, \"Mortality\", \"secondary\", 42], [321, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.989378, \"Negative\", \"CHN\", \"Does not apply\", \"Logarithmic\", \"Pair\", 25, \"Mortality\", \"secondary\", 42], [322, \"Time required to start a business (days)\", 0.792869, \"Negative\", \"DEU\", \"3\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [323, \"Time required to start a business (days)\", 0.762356, \"Negative\", \"FRA\", \"5\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [324, \"Time required to start a business (days)\", 0.847713, \"Negative\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [325, \"Time required to start a business (days)\", 0.810464, \"Negative\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [326, \"Time required to start a business (days)\", 0.905217, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [327, \"Time required to start a business (days)\", 0.816949, \"Negative\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [328, \"Time required to start a business (days)\", 0.885547, \"Negative\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [329, \"Time required to start a business (days)\", 0.841579, \"Negative\", \"SAU\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [330, \"Time required to start a business (days)\", 0.932239, \"Negative\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [331, \"Time required to start a business (days)\", 0.76496, \"Negative\", \"YEM\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [332, \"Time required to start a business (days)\", 0.873136, \"Negative\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [333, \"Time required to start a business (days)\", 0.830084, \"Negative\", \"DZA\", \"3\", \"Does not apply\", \"North Africa\", 11, \"Industry\", \"secondary\", 29], [334, \"Time required to start a business (days)\", 0.811387, \"Negative\", \"MAR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Industry\", \"secondary\", 29], [335, \"Time required to start a business (days)\", 0.782024, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [336, \"Time required to start a business (days)\", 0.793607, \"Negative\", \"ZAF\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [337, \"Time required to start a business (days)\", 0.751747, \"Negative\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [338, \"Time required to start a business (days)\", 0.820023, \"Negative\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [339, \"Time required to start a business (days)\", 0.874331, \"Negative\", \"CMR\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [340, \"Time required to start a business (days)\", 0.806947, \"Negative\", \"IND\", \"21\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [341, \"Time required to start a business (days)\", 0.965942, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [342, \"Time required to start a business (days)\", 0.83733, \"Negative\", \"THA\", \"5\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [343, \"Time required to start a business (days)\", 0.797064, \"Negative\", \"KOR\", \"5\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [344, \"Time required to start a business (days)\", 0.818574, \"Negative\", \"PER\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [345, \"Time required to start a business (days)\", 0.751607, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [346, \"Time required to start a business (days)\", 0.84776, \"Negative\", \"COL\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [347, \"Time required to start a business (days)\", 0.866647, \"Negative\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [348, \"Time required to start a business (days)\", 0.876316, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [349, \"Time required to start a business (days)\", 0.85138, \"Negative\", \"CRI\", \"8\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [350, \"Time required to start a business (days)\", 0.931818, \"Negative\", \"CHN\", \"21\", \"Does not apply\", \"Pair\", 11, \"Industry\", \"secondary\", 29], [351, \"Combustible renewables and waste (% of total energy)\", 0.759597, \"Positive\", \"DEU\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [352, \"Combustible renewables and waste (% of total energy)\", 0.793758, \"Positive\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [353, \"Combustible renewables and waste (% of total energy)\", 0.905255, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [354, \"Combustible renewables and waste (% of total energy)\", 0.915732, \"Positive\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [355, \"Combustible renewables and waste (% of total energy)\", 0.776496, \"Positive\", \"AUT\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [356, \"Combustible renewables and waste (% of total energy)\", 0.810504, \"Positive\", \"NLD\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [357, \"Combustible renewables and waste (% of total energy)\", 0.935853, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 25], [358, \"Combustible renewables and waste (% of total energy)\", 0.763706, \"Positive\", \"AZE\", \"Does not apply\", \"Linear\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 25], [359, \"Combustible renewables and waste (% of total energy)\", 0.780104, \"Negative\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [360, \"Combustible renewables and waste (% of total energy)\", 0.941407, \"Negative\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [361, \"Combustible renewables and waste (% of total energy)\", 0.78341, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [362, \"Combustible renewables and waste (% of total energy)\", 0.890534, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Environment\", \"secondary\", 25], [363, \"Combustible renewables and waste (% of total energy)\", 0.822012, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 12, \"Environment\", \"secondary\", 25], [364, \"Combustible renewables and waste (% of total energy)\", 0.982244, \"Negative\", \"BGD\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [365, \"Combustible renewables and waste (% of total energy)\", 0.978927, \"Negative\", \"IND\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [366, \"Combustible renewables and waste (% of total energy)\", 0.860249, \"Negative\", \"VNM\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [367, \"Combustible renewables and waste (% of total energy)\", 0.803047, \"Negative\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [368, \"Combustible renewables and waste (% of total energy)\", 0.918827, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [369, \"Combustible renewables and waste (% of total energy)\", 0.784859, \"Positive\", \"KOR\", \"3\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [370, \"Combustible renewables and waste (% of total energy)\", 0.915132, \"Negative\", \"MEX\", \"1\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 25], [371, \"Combustible renewables and waste (% of total energy)\", 0.930378, \"Negative\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 25], [6273, \"Food production index (2014-2016 = 100)\", 0.868933, \"Positive\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6274, \"Food production index (2014-2016 = 100)\", 0.834377, \"Positive\", \"MOZ\", \"Does not apply\", \"Linear\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6275, \"Food production index (2014-2016 = 100)\", 0.821876, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6276, \"Food production index (2014-2016 = 100)\", 0.858764, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6277, \"Food production index (2014-2016 = 100)\", 0.860215, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6278, \"Food production index (2014-2016 = 100)\", 0.995078, \"Positive\", \"BGD\", \"3\", \"Does not apply\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6279, \"Food production index (2014-2016 = 100)\", 0.98233, \"Positive\", \"IND\", \"1\", \"Does not apply\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6280, \"Food production index (2014-2016 = 100)\", 0.994324, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6281, \"Food production index (2014-2016 = 100)\", 0.807971, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6282, \"Food production index (2014-2016 = 100)\", 0.943786, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6283, \"Food production index (2014-2016 = 100)\", 0.942178, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6284, \"Food production index (2014-2016 = 100)\", 0.93495, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6285, \"Food production index (2014-2016 = 100)\", 0.76444, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6286, \"Food production index (2014-2016 = 100)\", 0.936928, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6287, \"Food production index (2014-2016 = 100)\", 0.896305, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6288, \"Food production index (2014-2016 = 100)\", 0.878859, \"Positive\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6289, \"Food production index (2014-2016 = 100)\", 0.966456, \"Positive\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6290, \"Food production index (2014-2016 = 100)\", 0.985531, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6291, \"Food production index (2014-2016 = 100)\", 0.946677, \"Positive\", \"USA\", \"1\", \"Does not apply\", \"Pair\", 24, \"Industry\", \"secondary\", 35], [6292, \"Food production index (2014-2016 = 100)\", 0.995055, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 24, \"Industry\", \"secondary\", 35], [6293, \"Time to obtain an electrical connection (days)\", 0.963721, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 11, \"R&D\", \"secondary\", 24], [6294, \"Time to obtain an electrical connection (days)\", 0.83291, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 11, \"R&D\", \"secondary\", 24], [6295, \"Time to obtain an electrical connection (days)\", 0.804939, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6296, \"Time to obtain an electrical connection (days)\", 0.75968, \"Negative\", \"TUR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6297, \"Time to obtain an electrical connection (days)\", 0.876713, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6298, \"Time to obtain an electrical connection (days)\", 0.852294, \"Positive\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6299, \"Time to obtain an electrical connection (days)\", 0.805169, \"Negative\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6300, \"Time to obtain an electrical connection (days)\", 0.92942, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6301, \"Time to obtain an electrical connection (days)\", 0.881377, \"Negative\", \"CMR\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6302, \"Time to obtain an electrical connection (days)\", 0.891739, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6303, \"Time to obtain an electrical connection (days)\", 0.843733, \"Positive\", \"GHA\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6304, \"Time to obtain an electrical connection (days)\", 0.877193, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6305, \"Time to obtain an electrical connection (days)\", 0.917544, \"Negative\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6306, \"Time to obtain an electrical connection (days)\", 0.901754, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6307, \"Time to obtain an electrical connection (days)\", 0.817729, \"Negative\", \"IDN\", \"8\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6308, \"Time to obtain an electrical connection (days)\", 0.810745, \"Negative\", \"PHL\", \"5\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6309, \"Time to obtain an electrical connection (days)\", 0.815051, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6310, \"Time to obtain an electrical connection (days)\", 0.909091, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6311, \"Time to obtain an electrical connection (days)\", 0.786273, \"Positive\", \"PER\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6312, \"Time to obtain an electrical connection (days)\", 0.818161, \"Negative\", \"VEN\", \"2\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6313, \"Time to obtain an electrical connection (days)\", 0.855364, \"Positive\", \"COL\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6314, \"Time to obtain an electrical connection (days)\", 0.817488, \"Negative\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6315, \"Time to obtain an electrical connection (days)\", 0.825397, \"Negative\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6316, \"Time to obtain an electrical connection (days)\", 0.845543, \"Negative\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6317, \"Suicide mortality rate (per 100,000 population)\", 0.855364, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 17, \"Mortality\", \"primary\", 25], [6318, \"Suicide mortality rate (per 100,000 population)\", 0.827624, \"Negative\", \"AUT\", \"13\", \"Does not apply\", \"Europe\", 17, \"Mortality\", \"primary\", 25], [6319, \"Suicide mortality rate (per 100,000 population)\", 0.773321, \"Negative\", \"QAT\", \"21\", \"Does not apply\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6320, \"Suicide mortality rate (per 100,000 population)\", 0.78697, \"Negative\", \"ARE\", \"3\", \"Does not apply\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6321, \"Suicide mortality rate (per 100,000 population)\", 0.872247, \"Positive\", \"SAU\", \"Does not apply\", \"Logarithmic\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6322, \"Suicide mortality rate (per 100,000 population)\", 0.827891, \"Negative\", \"YEM\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6323, \"Suicide mortality rate (per 100,000 population)\", 0.878636, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6324, \"Suicide mortality rate (per 100,000 population)\", 0.886364, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6325, \"Suicide mortality rate (per 100,000 population)\", 0.897716, \"Negative\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6326, \"Suicide mortality rate (per 100,000 population)\", 0.90973, \"Negative\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6327, \"Suicide mortality rate (per 100,000 population)\", 0.897565, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 17, \"Mortality\", \"primary\", 25], [6328, \"Suicide mortality rate (per 100,000 population)\", 0.966749, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 17, \"Mortality\", \"primary\", 25], [6329, \"Suicide mortality rate (per 100,000 population)\", 0.878685, \"Negative\", \"BGD\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6330, \"Suicide mortality rate (per 100,000 population)\", 0.915641, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6331, \"Suicide mortality rate (per 100,000 population)\", 0.869184, \"Positive\", \"VNM\", \"3\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6332, \"Suicide mortality rate (per 100,000 population)\", 0.882422, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6333, \"Suicide mortality rate (per 100,000 population)\", 0.831818, \"Negative\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6334, \"Suicide mortality rate (per 100,000 population)\", 0.952612, \"Positive\", \"MEX\", \"3\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6335, \"Suicide mortality rate (per 100,000 population)\", 0.756344, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6336, \"Suicide mortality rate (per 100,000 population)\", 0.902423, \"Negative\", \"VEN\", \"8\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6337, \"Suicide mortality rate (per 100,000 population)\", 0.776677, \"Negative\", \"COL\", \"Does not apply\", \"Logarithmic\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6338, \"Suicide mortality rate (per 100,000 population)\", 0.755201, \"Negative\", \"CHL\", \"21\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6339, \"Suicide mortality rate (per 100,000 population)\", 0.920313, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6340, \"Suicide mortality rate (per 100,000 population)\", 0.991231, \"Positive\", \"USA\", \"13\", \"Does not apply\", \"Pair\", 17, \"Mortality\", \"primary\", 25], [6341, \"Suicide mortality rate (per 100,000 population)\", 0.95768, \"Negative\", \"CHN\", \"5\", \"Does not apply\", \"Pair\", 17, \"Mortality\", \"primary\", 25], [6342, \"Firms using banks to finance investment (% of firms)\", 0.841751, \"Positive\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 12, \"Economy\", \"secondary\", 28], [6343, \"Firms using banks to finance investment (% of firms)\", 0.83291, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 12, \"Economy\", \"secondary\", 28], [6344, \"Firms using banks to finance investment (% of firms)\", 0.945216, \"Negative\", \"AZE\", \"21\", \"Does not apply\", \"Persian Gulf\", 12, \"Economy\", \"secondary\", 28], [6345, \"Firms using banks to finance investment (% of firms)\", 0.789777, \"Negative\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 12, \"Economy\", \"secondary\", 28], [6346, \"Firms using banks to finance investment (% of firms)\", 0.941656, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6347, \"Firms using banks to finance investment (% of firms)\", 0.75968, \"Negative\", \"TUR\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6348, \"Firms using banks to finance investment (% of firms)\", 0.876713, \"Positive\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6349, \"Firms using banks to finance investment (% of firms)\", 0.852294, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6350, \"Firms using banks to finance investment (% of firms)\", 0.805169, \"Negative\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6351, \"Firms using banks to finance investment (% of firms)\", 0.882199, \"Positive\", \"LBR\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6352, \"Firms using banks to finance investment (% of firms)\", 0.92942, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6353, \"Firms using banks to finance investment (% of firms)\", 0.759974, \"Negative\", \"CMR\", \"13\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6354, \"Firms using banks to finance investment (% of firms)\", 0.891739, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6355, \"Firms using banks to finance investment (% of firms)\", 0.843733, \"Positive\", \"GHA\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6356, \"Firms using banks to finance investment (% of firms)\", 0.877193, \"Negative\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6357, \"Firms using banks to finance investment (% of firms)\", 0.917544, \"Negative\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6358, \"Firms using banks to finance investment (% of firms)\", 0.901754, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6359, \"Firms using banks to finance investment (% of firms)\", 0.870956, \"Negative\", \"THA\", \"3\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6360, \"Firms using banks to finance investment (% of firms)\", 0.817729, \"Positive\", \"IDN\", \"8\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6361, \"Firms using banks to finance investment (% of firms)\", 0.810745, \"Negative\", \"PHL\", \"5\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6362, \"Firms using banks to finance investment (% of firms)\", 0.815051, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6363, \"Firms using banks to finance investment (% of firms)\", 0.909091, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6364, \"Firms using banks to finance investment (% of firms)\", 0.88294, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6365, \"Firms using banks to finance investment (% of firms)\", 0.818161, \"Negative\", \"VEN\", \"2\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6366, \"Firms using banks to finance investment (% of firms)\", 0.855364, \"Positive\", \"COL\", \"21\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6367, \"Firms using banks to finance investment (% of firms)\", 0.817488, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6368, \"Firms using banks to finance investment (% of firms)\", 0.825397, \"Negative\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6369, \"Firms using banks to finance investment (% of firms)\", 0.845543, \"Positive\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6370, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.768265, \"Negative\", \"GRC\", \"5\", \"Does not apply\", \"Europe\", 12, \"Imports\", \"secondary\", 21], [6371, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.933568, \"Negative\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6372, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.946379, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6373, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.829932, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6374, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.893727, \"Negative\", \"OMN\", \"Does not apply\", \"Logarithmic\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6375, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.827434, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Imports\", \"secondary\", 21], [6376, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.759141, \"Negative\", \"TUR\", \"2\", \"Does not apply\", \"North Africa\", 12, \"Imports\", \"secondary\", 21], [6377, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.904546, \"Negative\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Imports\", \"secondary\", 21], [6378, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.937705, \"Negative\", \"BGD\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6379, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.9145, \"Negative\", \"VNM\", \"8\", \"Does not apply\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6380, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.865531, \"Negative\", \"THA\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6381, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.898586, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6382, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.945048, \"Negative\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6383, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.929542, \"Negative\", \"KOR\", \"5\", \"Does not apply\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6384, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.949767, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6385, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.752078, \"Negative\", \"BRA\", \"1\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6386, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.919187, \"Negative\", \"VEN\", \"Does not apply\", \"Logarithmic\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6387, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.797346, \"Negative\", \"COL\", \"Does not apply\", \"Logarithmic\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6388, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.8055, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6389, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.831757, \"Negative\", \"USA\", \"Does not apply\", \"Logarithmic\", \"Pair\", 12, \"Imports\", \"secondary\", 21], [6390, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.857664, \"Negative\", \"CHN\", \"1\", \"Does not apply\", \"Pair\", 12, \"Imports\", \"secondary\", 21], [6391, \"People practicing open defecation (% of population)\", 0.85947, \"Negative\", \"GRC\", \"8\", \"Does not apply\", \"Europe\", 27, \"Health\", \"secondary\", 32], [6392, \"People practicing open defecation (% of population)\", 0.764246, \"Negative\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6393, \"People practicing open defecation (% of population)\", 0.961673, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6394, \"People practicing open defecation (% of population)\", 0.955118, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6395, \"People practicing open defecation (% of population)\", 0.905975, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6396, \"People practicing open defecation (% of population)\", 0.850746, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6397, \"People practicing open defecation (% of population)\", 0.913668, \"Negative\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6398, \"People practicing open defecation (% of population)\", 0.94435, \"Negative\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6399, \"People practicing open defecation (% of population)\", 0.924176, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6400, \"People practicing open defecation (% of population)\", 0.903327, \"Negative\", \"SEN\", \"Does not apply\", \"Linear\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6401, \"People practicing open defecation (% of population)\", 0.756276, \"Negative\", \"ZAF\", \"8\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6402, \"People practicing open defecation (% of population)\", 0.889166, \"Negative\", \"LBR\", \"1\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6403, \"People practicing open defecation (% of population)\", 0.948441, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6404, \"People practicing open defecation (% of population)\", 0.909027, \"Negative\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6405, \"People practicing open defecation (% of population)\", 0.964139, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6406, \"People practicing open defecation (% of population)\", 0.867409, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6407, \"People practicing open defecation (% of population)\", 0.996522, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6408, \"People practicing open defecation (% of population)\", 0.989149, \"Negative\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6409, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6410, \"People practicing open defecation (% of population)\", 0.865868, \"Negative\", \"THA\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6411, \"People practicing open defecation (% of population)\", 0.908314, \"Negative\", \"IDN\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6412, \"People practicing open defecation (% of population)\", 0.903327, \"Negative\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6413, \"People practicing open defecation (% of population)\", 0.939572, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6414, \"People practicing open defecation (% of population)\", 0.750815, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6415, \"People practicing open defecation (% of population)\", 0.751955, \"Negative\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6416, \"People practicing open defecation (% of population)\", 0.90475, \"Negative\", \"PER\", \"Does not apply\", \"Linear\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6417, \"People practicing open defecation (% of population)\", 0.890679, \"Negative\", \"VEN\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6418, \"People practicing open defecation (% of population)\", 0.897855, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6419, \"People practicing open defecation (% of population)\", 0.909091, \"Negative\", \"CHL\", \"21\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6420, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6421, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6422, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 27, \"Health\", \"secondary\", 32], [6423, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.829371, \"Negative\", \"AUT\", \"8\", \"Does not apply\", \"Europe\", 11, \"Environment\", \"secondary\", 18], [6424, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.848986, \"Negative\", \"QAT\", \"3\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6425, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.855782, \"Positive\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6426, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.964478, \"Negative\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6427, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.767146, \"Positive\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6428, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.805752, \"Positive\", \"MAR\", \"2\", \"Does not apply\", \"North Africa\", 11, \"Environment\", \"secondary\", 18], [6429, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.909825, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6430, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.855509, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6431, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.900159, \"Negative\", \"NGA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6432, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.853751, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6433, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.967802, \"Negative\", \"IND\", \"1\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6434, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.879127, \"Negative\", \"VNM\", \"5\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6435, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.922596, \"Negative\", \"IDN\", \"2\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6436, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.941576, \"Negative\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6437, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.895958, \"Negative\", \"MEX\", \"Does not apply\", \"Logarithmic\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6438, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.804632, \"Negative\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6439, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.87797, \"Negative\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6440, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.874924, \"Negative\", \"CHN\", \"1\", \"Does not apply\", \"Pair\", 11, \"Environment\", \"secondary\", 18], [6441, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.843001, \"Negative\", \"NLD\", \"Does not apply\", \"Logarithmic\", \"Europe\", 13, \"Agriculture\", \"secondary\", 21], [6442, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.853721, \"Positive\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6443, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.956311, \"Positive\", \"ARE\", \"3\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6444, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.983928, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6445, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.805605, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6446, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.972728, \"Positive\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6447, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.831716, \"Positive\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6448, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.795868, \"Positive\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6449, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.841358, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6450, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.790698, \"Positive\", \"LBR\", \"2\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6451, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.920335, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6452, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.774495, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6453, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.983907, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6454, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.961303, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6455, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.896246, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6456, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.936072, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6457, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.831515, \"Positive\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6458, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.758321, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6459, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.828788, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6460, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.792582, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6461, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.80934, \"Positive\", \"USA\", \"Does not apply\", \"Cubic\", \"Pair\", 13, \"Agriculture\", \"secondary\", 21], [6462, \"Access to clean fuels and technologies for cooking (% of population)\", 0.766483, \"Positive\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6463, \"Access to clean fuels and technologies for cooking (% of population)\", 0.996083, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6464, \"Access to clean fuels and technologies for cooking (% of population)\", 0.9509, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6465, \"Access to clean fuels and technologies for cooking (% of population)\", 0.853684, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6466, \"Access to clean fuels and technologies for cooking (% of population)\", 0.897511, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6467, \"Access to clean fuels and technologies for cooking (% of population)\", 0.957737, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6468, \"Access to clean fuels and technologies for cooking (% of population)\", 0.923174, \"Positive\", \"MAR\", \"8\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6469, \"Access to clean fuels and technologies for cooking (% of population)\", 0.878984, \"Negative\", \"SEN\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6470, \"Access to clean fuels and technologies for cooking (% of population)\", 0.756276, \"Positive\", \"ZAF\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6471, \"Access to clean fuels and technologies for cooking (% of population)\", 0.960204, \"Positive\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6472, \"Access to clean fuels and technologies for cooking (% of population)\", 0.872263, \"Positive\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6473, \"Access to clean fuels and technologies for cooking (% of population)\", 0.962628, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6474, \"Access to clean fuels and technologies for cooking (% of population)\", 0.864074, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6475, \"Access to clean fuels and technologies for cooking (% of population)\", 0.995617, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6476, \"Access to clean fuels and technologies for cooking (% of population)\", 0.989149, \"Positive\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6477, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6478, \"Access to clean fuels and technologies for cooking (% of population)\", 0.880003, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6479, \"Access to clean fuels and technologies for cooking (% of population)\", 0.908314, \"Positive\", \"IDN\", \"Does not apply\", \"Linear\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6480, \"Access to clean fuels and technologies for cooking (% of population)\", 0.826918, \"Positive\", \"PHL\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6481, \"Access to clean fuels and technologies for cooking (% of population)\", 0.753409, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6482, \"Access to clean fuels and technologies for cooking (% of population)\", 0.853102, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6483, \"Access to clean fuels and technologies for cooking (% of population)\", 0.90475, \"Positive\", \"PER\", \"Does not apply\", \"Linear\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6484, \"Access to clean fuels and technologies for cooking (% of population)\", 0.897855, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6485, \"Access to clean fuels and technologies for cooking (% of population)\", 0.945724, \"Positive\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6486, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6487, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 17, \"Internet\", \"secondary\", 26], [6488, \"Access to electricity (% of population)\", 0.755466, \"Positive\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6489, \"Access to electricity (% of population)\", 0.844248, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6490, \"Access to electricity (% of population)\", 0.825795, \"Positive\", \"YEM\", \"3\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6491, \"Access to electricity (% of population)\", 0.870107, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 16, \"Environment\", \"primary\", 26], [6492, \"Access to electricity (% of population)\", 0.914656, \"Positive\", \"MAR\", \"2\", \"Does not apply\", \"North Africa\", 16, \"Environment\", \"primary\", 26], [6493, \"Access to electricity (% of population)\", 0.780617, \"Positive\", \"SEN\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6494, \"Access to electricity (% of population)\", 0.798854, \"Positive\", \"ZAF\", \"2\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6495, \"Access to electricity (% of population)\", 0.848246, \"Positive\", \"LBR\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6496, \"Access to electricity (% of population)\", 0.924489, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6497, \"Access to electricity (% of population)\", 0.811965, \"Positive\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6498, \"Access to electricity (% of population)\", 0.829545, \"Positive\", \"NGA\", \"3\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6499, \"Access to electricity (% of population)\", 0.824265, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6500, \"Access to electricity (% of population)\", 0.993779, \"Positive\", \"BGD\", \"2\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6501, \"Access to electricity (% of population)\", 0.979171, \"Positive\", \"IND\", \"3\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6502, \"Access to electricity (% of population)\", 0.968614, \"Positive\", \"VNM\", \"3\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6503, \"Access to electricity (% of population)\", 0.872662, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6504, \"Access to electricity (% of population)\", 0.823824, \"Positive\", \"IDN\", \"5\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6505, \"Access to electricity (% of population)\", 0.909312, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6506, \"Access to electricity (% of population)\", 0.845838, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6507, \"Access to electricity (% of population)\", 0.776361, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6508, \"Access to electricity (% of population)\", 0.928139, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6509, \"Access to electricity (% of population)\", 0.780517, \"Positive\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6510, \"Access to electricity (% of population)\", 0.871062, \"Positive\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6511, \"Access to electricity (% of population)\", 0.977356, \"Positive\", \"PAN\", \"21\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6512, \"Access to electricity (% of population)\", 0.937042, \"Positive\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6513, \"Access to electricity (% of population)\", 0.963675, \"Positive\", \"CHN\", \"5\", \"Does not apply\", \"Pair\", 16, \"Environment\", \"primary\", 26], [6514, \"Prevalence of undernourishment (% of population)\", 0.766965, \"Positive\", \"IRQ\", \"3\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6515, \"Prevalence of undernourishment (% of population)\", 0.774883, \"Negative\", \"AZE\", \"2\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6516, \"Prevalence of undernourishment (% of population)\", 0.7825, \"Positive\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6517, \"Prevalence of undernourishment (% of population)\", 0.896934, \"Negative\", \"OMN\", \"Does not apply\", \"Cuadratic\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6518, \"Prevalence of undernourishment (% of population)\", 0.874165, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 14, \"Health\", \"secondary\", 21], [6519, \"Prevalence of undernourishment (% of population)\", 0.863113, \"Negative\", \"MAR\", \"8\", \"Does not apply\", \"North Africa\", 14, \"Health\", \"secondary\", 21], [6520, \"Prevalence of undernourishment (% of population)\", 0.877935, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6521, \"Prevalence of undernourishment (% of population)\", 0.809735, \"Negative\", \"MOZ\", \"Does not apply\", \"Cubic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6522, \"Prevalence of undernourishment (% of population)\", 0.912315, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6523, \"Prevalence of undernourishment (% of population)\", 0.959901, \"Positive\", \"NGA\", \"13\", \"Does not apply\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6524, \"Prevalence of undernourishment (% of population)\", 0.839859, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6525, \"Prevalence of undernourishment (% of population)\", 0.891, \"Negative\", \"BGD\", \"21\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6526, \"Prevalence of undernourishment (% of population)\", 0.996491, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6527, \"Prevalence of undernourishment (% of population)\", 0.836744, \"Negative\", \"THA\", \"Does not apply\", \"Logarithmic\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6528, \"Prevalence of undernourishment (% of population)\", 0.872487, \"Negative\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6529, \"Prevalence of undernourishment (% of population)\", 0.827297, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6530, \"Prevalence of undernourishment (% of population)\", 0.840925, \"Positive\", \"MEX\", \"3\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6531, \"Prevalence of undernourishment (% of population)\", 0.759618, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6532, \"Prevalence of undernourishment (% of population)\", 0.893013, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6533, \"Prevalence of undernourishment (% of population)\", 0.961103, \"Negative\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6534, \"Prevalence of undernourishment (% of population)\", 0.9, \"Negative\", \"CHN\", \"3\", \"Does not apply\", \"Pair\", 14, \"Health\", \"secondary\", 21], [6535, \"Cereal yield (kg per hectare)\", 0.779595, \"Positive\", \"QAT\", \"1\", \"Does not apply\", \"Persian Gulf\", 12, \"Agriculture\", \"secondary\", 18], [6536, \"Cereal yield (kg per hectare)\", 0.888932, \"Positive\", \"SAU\", \"5\", \"Does not apply\", \"Persian Gulf\", 12, \"Agriculture\", \"secondary\", 18], [6537, \"Cereal yield (kg per hectare)\", 0.788288, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Agriculture\", \"secondary\", 18], [6538, \"Cereal yield (kg per hectare)\", 0.87744, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Agriculture\", \"secondary\", 18], [6539, \"Cereal yield (kg per hectare)\", 0.774258, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 12, \"Agriculture\", \"secondary\", 18], [6540, \"Cereal yield (kg per hectare)\", 0.9936, \"Positive\", \"BGD\", \"3\", \"Does not apply\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6541, \"Cereal yield (kg per hectare)\", 0.961303, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6542, \"Cereal yield (kg per hectare)\", 0.983739, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6543, \"Cereal yield (kg per hectare)\", 0.894857, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6544, \"Cereal yield (kg per hectare)\", 0.939127, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6545, \"Cereal yield (kg per hectare)\", 0.903125, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6546, \"Cereal yield (kg per hectare)\", 0.804904, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6547, \"Cereal yield (kg per hectare)\", 0.907475, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6548, \"Cereal yield (kg per hectare)\", 0.794782, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6549, \"Cereal yield (kg per hectare)\", 0.836149, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6550, \"Cereal yield (kg per hectare)\", 0.750296, \"Positive\", \"PAN\", \"1\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6551, \"Cereal yield (kg per hectare)\", 0.911779, \"Positive\", \"USA\", \"3\", \"Does not apply\", \"Pair\", 12, \"Agriculture\", \"secondary\", 18], [6552, \"Cereal yield (kg per hectare)\", 0.977243, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 12, \"Agriculture\", \"secondary\", 18], [6553, \"Pregnant women receiving prenatal care (%)\", 0.803913, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6554, \"Pregnant women receiving prenatal care (%)\", 0.889643, \"Positive\", \"ARE\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6555, \"Pregnant women receiving prenatal care (%)\", 0.89992, \"Positive\", \"SAU\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6556, \"Pregnant women receiving prenatal care (%)\", 0.79933, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6557, \"Pregnant women receiving prenatal care (%)\", 0.908023, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6558, \"Pregnant women receiving prenatal care (%)\", 0.855771, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6559, \"Pregnant women receiving prenatal care (%)\", 0.898054, \"Positive\", \"EGY\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6560, \"Pregnant women receiving prenatal care (%)\", 0.95029, \"Positive\", \"TUR\", \"2\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6561, \"Pregnant women receiving prenatal care (%)\", 0.954106, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6562, \"Pregnant women receiving prenatal care (%)\", 0.904693, \"Positive\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6563, \"Pregnant women receiving prenatal care (%)\", 0.919914, \"Positive\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6564, \"Pregnant women receiving prenatal care (%)\", 0.878474, \"Positive\", \"MOZ\", \"21\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6565, \"Pregnant women receiving prenatal care (%)\", 0.858423, \"Positive\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6566, \"Pregnant women receiving prenatal care (%)\", 0.814632, \"Positive\", \"NGA\", \"21\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6567, \"Pregnant women receiving prenatal care (%)\", 0.983888, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6568, \"Pregnant women receiving prenatal care (%)\", 0.966606, \"Positive\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6569, \"Pregnant women receiving prenatal care (%)\", 0.956534, \"Positive\", \"VNM\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6570, \"Pregnant women receiving prenatal care (%)\", 0.861958, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6571, \"Pregnant women receiving prenatal care (%)\", 0.810999, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6572, \"Pregnant women receiving prenatal care (%)\", 0.898514, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6573, \"Pregnant women receiving prenatal care (%)\", 0.91284, \"Positive\", \"VEN\", \"3\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6574, \"Pregnant women receiving prenatal care (%)\", 0.932079, \"Positive\", \"COL\", \"1\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6575, \"Pregnant women receiving prenatal care (%)\", 0.954545, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6576, \"Pregnant women receiving prenatal care (%)\", 0.981598, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 18, \"Health\", \"secondary\", 24], [6577, \"Crop production index (2014-2016 = 100)\", 0.766012, \"Positive\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6578, \"Crop production index (2014-2016 = 100)\", 0.963749, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6579, \"Crop production index (2014-2016 = 100)\", 0.835085, \"Positive\", \"YEM\", \"Does not apply\", \"Cuadratic\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6580, \"Crop production index (2014-2016 = 100)\", 0.951834, \"Positive\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6581, \"Crop production index (2014-2016 = 100)\", 0.814074, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6582, \"Crop production index (2014-2016 = 100)\", 0.908494, \"Positive\", \"EGY\", \"Does not apply\", \"Cubic\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6583, \"Crop production index (2014-2016 = 100)\", 0.947974, \"Positive\", \"TUR\", \"13\", \"Does not apply\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6584, \"Crop production index (2014-2016 = 100)\", 0.87045, \"Positive\", \"MAR\", \"Does not apply\", \"Cuadratic\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6585, \"Crop production index (2014-2016 = 100)\", 0.804939, \"Negative\", \"SEN\", \"21\", \"Does not apply\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6586, \"Crop production index (2014-2016 = 100)\", 0.833037, \"Positive\", \"MOZ\", \"Does not apply\", \"Linear\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6587, \"Crop production index (2014-2016 = 100)\", 0.821211, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6588, \"Crop production index (2014-2016 = 100)\", 0.854241, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6589, \"Crop production index (2014-2016 = 100)\", 0.858855, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6590, \"Crop production index (2014-2016 = 100)\", 0.989486, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6591, \"Crop production index (2014-2016 = 100)\", 0.965283, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6592, \"Crop production index (2014-2016 = 100)\", 0.995787, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6593, \"Crop production index (2014-2016 = 100)\", 0.819882, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6594, \"Crop production index (2014-2016 = 100)\", 0.913211, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6595, \"Crop production index (2014-2016 = 100)\", 0.91721, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6596, \"Crop production index (2014-2016 = 100)\", 0.908215, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6597, \"Crop production index (2014-2016 = 100)\", 0.766164, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6598, \"Crop production index (2014-2016 = 100)\", 0.936928, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6599, \"Crop production index (2014-2016 = 100)\", 0.851083, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6600, \"Crop production index (2014-2016 = 100)\", 0.882284, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6601, \"Crop production index (2014-2016 = 100)\", 0.967446, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6602, \"Crop production index (2014-2016 = 100)\", 0.845314, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 21, \"Agriculture\", \"secondary\", 27], [6603, \"Crop production index (2014-2016 = 100)\", 0.998717, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 21, \"Agriculture\", \"secondary\", 27], [6604, \"Births attended by skilled health staff (% of total)\", 0.86053, \"Negative\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6605, \"Births attended by skilled health staff (% of total)\", 0.938926, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6606, \"Births attended by skilled health staff (% of total)\", 0.842853, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6607, \"Births attended by skilled health staff (% of total)\", 0.842834, \"Positive\", \"DZA\", \"2\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6608, \"Births attended by skilled health staff (% of total)\", 0.923878, \"Positive\", \"EGY\", \"2\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6609, \"Births attended by skilled health staff (% of total)\", 0.95508, \"Positive\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6610, \"Births attended by skilled health staff (% of total)\", 0.953389, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6611, \"Births attended by skilled health staff (% of total)\", 0.797257, \"Positive\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6612, \"Births attended by skilled health staff (% of total)\", 0.783475, \"Positive\", \"MOZ\", \"Does not apply\", \"Cubic\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6613, \"Births attended by skilled health staff (% of total)\", 0.858575, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6614, \"Births attended by skilled health staff (% of total)\", 0.977507, \"Positive\", \"BGD\", \"5\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6615, \"Births attended by skilled health staff (% of total)\", 0.982218, \"Positive\", \"IND\", \"Does not apply\", \"Linear\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6616, \"Births attended by skilled health staff (% of total)\", 0.95581, \"Positive\", \"VNM\", \"2\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6617, \"Births attended by skilled health staff (% of total)\", 0.876592, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6618, \"Births attended by skilled health staff (% of total)\", 0.939552, \"Positive\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6619, \"Births attended by skilled health staff (% of total)\", 0.844034, \"Positive\", \"MEX\", \"1\", \"Does not apply\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6620, \"Births attended by skilled health staff (% of total)\", 0.924618, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6621, \"Births attended by skilled health staff (% of total)\", 0.758834, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6622, \"Births attended by skilled health staff (% of total)\", 0.794656, \"Negative\", \"USA\", \"8\", \"Does not apply\", \"Pair\", 13, \"Mortality\", \"secondary\", 20], [6623, \"Births attended by skilled health staff (% of total)\", 0.950593, \"Positive\", \"CHN\", \"3\", \"Does not apply\", \"Pair\", 13, \"Mortality\", \"secondary\", 20], [6624, \"Completeness of birth registration (%)\", 0.86815, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6625, \"Completeness of birth registration (%)\", 0.840806, \"Positive\", \"EGY\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6626, \"Completeness of birth registration (%)\", 0.898573, \"Positive\", \"TUR\", \"1\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6627, \"Completeness of birth registration (%)\", 0.932084, \"Positive\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6628, \"Completeness of birth registration (%)\", 0.909636, \"Positive\", \"LBR\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6629, \"Completeness of birth registration (%)\", 0.8862, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6630, \"Completeness of birth registration (%)\", 0.790617, \"Negative\", \"CMR\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6631, \"Completeness of birth registration (%)\", 0.81349, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6632, \"Completeness of birth registration (%)\", 0.773855, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6633, \"Completeness of birth registration (%)\", 0.792328, \"Positive\", \"BGD\", \"5\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6634, \"Completeness of birth registration (%)\", 0.810707, \"Positive\", \"IND\", \"5\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6635, \"Completeness of birth registration (%)\", 0.781322, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6636, \"Completeness of birth registration (%)\", 0.901982, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6637, \"Completeness of birth registration (%)\", 0.894689, \"Positive\", \"MEX\", \"13\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6638, \"Completeness of birth registration (%)\", 0.781913, \"Positive\", \"BRA\", \"2\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6639, \"Completeness of birth registration (%)\", 0.855313, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6640, \"Completeness of birth registration (%)\", 0.839228, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6641, \"Completeness of birth registration (%)\", 0.792184, \"Negative\", \"VEN\", \"1\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6642, \"Completeness of birth registration (%)\", 0.840648, \"Positive\", \"COL\", \"2\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6643, \"Completeness of birth registration (%)\", 0.963636, \"Positive\", \"PAN\", \"21\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6644, \"Completeness of birth registration (%)\", 0.789474, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21]];\n",
+       "\n",
+       "        // Define the dt_args\n",
+       "        let dt_args = {\"dom\": \"ftpr\"};\n",
+       "        dt_args[\"data\"] = data;\n",
+       "\n",
+       "        // Display the table\n",
+       "        $(document).ready(function () {\n",
+       "            \n",
+       "            window.__itables_render('#9fc29ee0-ffb5-490c-b657-fa42043f998b', dt_args);\n",
+       "        });\n",
+       "    }\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:itables.downsample:showing 744x11 of 6645x11 as nbytes=584760>65536=maxBytes. See https://mwouts.github.io/itables/downsampling.html\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table id=\"bfe67991-2c27-4fd3-b544-c37b65897ab3\" class=\"display nowrap\"style=\"max-width:100%\"><thead><th>None</th><th>Indicator</th><th>R^2 Spearman</th><th>Behaviour</th><th>Country</th><th>Moved</th><th>Type</th><th>Continent</th><th>Number of times repeated_x</th><th>Group</th><th>Level</th><th>Number of times repeated_y</th></thead><tbody><tr><td>Loading... (need <a href=https://mwouts.github.io/itables/troubleshooting.html>help</a>?)</td></tr></tbody></table>\n",
+       "<style>\n",
+       "table td {\n",
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+       "    overflow: hidden;\n",
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+       "    text-overflow: ellipsis;\n",
+       "    overflow: hidden;\n",
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+       "<script>\n",
+       "    // We use a namespace per table (= the next curly bracket)\n",
+       "    // to avoid conflicts between the various tables' data & dt_args\n",
+       "    {\n",
+       "        // Define the table data\n",
+       "        const data = [[0, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.817804, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [1, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.759333, \"Negative\", \"SWE\", \"3\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [2, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.936963, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [3, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.786708, \"Negative\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [4, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.924056, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [5, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.768604, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [6, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.849236, \"Negative\", \"AUT\", \"Does not apply\", \"Logarithmic\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [7, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.847466, \"Negative\", \"NLD\", \"13\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [8, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.98225, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [9, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.977356, \"Negative\", \"ARE\", \"21\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [10, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.98225, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [11, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.992105, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [12, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.927189, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [13, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.897966, \"Negative\", \"OMN\", \"21\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [14, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941656, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [15, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.984804, \"Negative\", \"ISR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [16, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941309, \"Negative\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [17, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.902682, \"Negative\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [18, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.805222, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [19, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.863071, \"Negative\", \"LBR\", \"2\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [20, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.939188, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [21, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.821756, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [22, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.934848, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [23, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.844272, \"Negative\", \"GHA\", \"1\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [24, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.99911, \"Negative\", \"BGD\", \"2\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [25, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.992671, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [26, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941656, \"Negative\", \"VNM\", \"21\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [27, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.781154, \"Negative\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [28, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.951008, \"Negative\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [29, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.942998, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [30, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.777929, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [31, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.838543, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [32, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.940761, \"Negative\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [33, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.950571, \"Negative\", \"VEN\", \"1\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [34, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.89959, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [35, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.895844, \"Negative\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [36, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999778, \"Negative\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [37, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999123, \"Negative\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [38, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999123, \"Negative\", \"USA\", \"13\", \"Does not apply\", \"Pair\", 22, \"Demography\", \"secondary\", 40], [39, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.995455, \"Positive\", \"CHN\", \"21\", \"Does not apply\", \"Pair\", 22, \"Demography\", \"secondary\", 40], [40, \"Commercial bank branches (per 100,000 adults)\", 0.801333, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [41, \"Commercial bank branches (per 100,000 adults)\", 0.75667, \"Negative\", \"FRA\", \"5\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [42, \"Commercial bank branches (per 100,000 adults)\", 0.782831, \"Negative\", \"GBR\", \"5\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [43, \"Commercial bank branches (per 100,000 adults)\", 0.838543, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [44, \"Commercial bank branches (per 100,000 adults)\", 0.836928, \"Negative\", \"NLD\", \"13\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [45, \"Commercial bank branches (per 100,000 adults)\", 0.933522, \"Negative\", \"QAT\", \"5\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [46, \"Commercial bank branches (per 100,000 adults)\", 0.755661, \"Negative\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [47, \"Commercial bank branches (per 100,000 adults)\", 0.875117, \"Negative\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [48, \"Commercial bank branches (per 100,000 adults)\", 0.895191, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [49, \"Commercial bank branches (per 100,000 adults)\", 0.76892, \"Positive\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [50, \"Commercial bank branches (per 100,000 adults)\", 0.81853, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [51, \"Commercial bank branches (per 100,000 adults)\", 0.835641, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [52, \"Commercial bank branches (per 100,000 adults)\", 0.843377, \"Positive\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [53, \"Commercial bank branches (per 100,000 adults)\", 0.877827, \"Positive\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [54, \"Commercial bank branches (per 100,000 adults)\", 0.881445, \"Positive\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [55, \"Commercial bank branches (per 100,000 adults)\", 0.787284, \"Positive\", \"ZAF\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [56, \"Commercial bank branches (per 100,000 adults)\", 0.907985, \"Positive\", \"MOZ\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [57, \"Commercial bank branches (per 100,000 adults)\", 0.892506, \"Positive\", \"CMR\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [58, \"Commercial bank branches (per 100,000 adults)\", 0.869419, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [59, \"Commercial bank branches (per 100,000 adults)\", 0.960022, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [60, \"Commercial bank branches (per 100,000 adults)\", 0.980779, \"Positive\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [61, \"Commercial bank branches (per 100,000 adults)\", 0.806534, \"Positive\", \"IDN\", \"2\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [62, \"Commercial bank branches (per 100,000 adults)\", 0.814298, \"Positive\", \"MEX\", \"Does not apply\", \"Logarithmic\", \"Latam\", 11, \"Economy\", \"secondary\", 26], [63, \"Commercial bank branches (per 100,000 adults)\", 0.977356, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 11, \"Economy\", \"secondary\", 26], [64, \"Commercial bank branches (per 100,000 adults)\", 0.977356, \"Negative\", \"USA\", \"21\", \"Does not apply\", \"Pair\", 11, \"Economy\", \"secondary\", 26], [65, \"Commercial bank branches (per 100,000 adults)\", 0.768043, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 11, \"Economy\", \"secondary\", 26], [66, \"CO2 emissions (kt)\", 0.794276, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [67, \"CO2 emissions (kt)\", 0.761173, \"Negative\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [68, \"CO2 emissions (kt)\", 0.905449, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [69, \"CO2 emissions (kt)\", 0.971453, \"Positive\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [70, \"CO2 emissions (kt)\", 0.984775, \"Positive\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [71, \"CO2 emissions (kt)\", 0.960933, \"Positive\", \"SAU\", \"3\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [72, \"CO2 emissions (kt)\", 0.97328, \"Positive\", \"OMN\", \"5\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [73, \"CO2 emissions (kt)\", 0.829432, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [74, \"CO2 emissions (kt)\", 0.921508, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [75, \"CO2 emissions (kt)\", 0.941573, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [76, \"CO2 emissions (kt)\", 0.952939, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [77, \"CO2 emissions (kt)\", 0.789979, \"Positive\", \"SEN\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [78, \"CO2 emissions (kt)\", 0.783163, \"Positive\", \"ZAF\", \"1\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [79, \"CO2 emissions (kt)\", 0.833274, \"Positive\", \"LBR\", \"Does not apply\", \"Linear\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [80, \"CO2 emissions (kt)\", 0.930963, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [81, \"CO2 emissions (kt)\", 0.78301, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [82, \"CO2 emissions (kt)\", 0.85301, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [83, \"CO2 emissions (kt)\", 1.0, \"Positive\", \"BGD\", \"Does not apply\", \"Cuadratic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [84, \"CO2 emissions (kt)\", 0.992493, \"Positive\", \"IND\", \"Does not apply\", \"Linear\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [85, \"CO2 emissions (kt)\", 0.993956, \"Positive\", \"VNM\", \"1\", \"Does not apply\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [86, \"CO2 emissions (kt)\", 0.863207, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [87, \"CO2 emissions (kt)\", 0.902509, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [88, \"CO2 emissions (kt)\", 0.903608, \"Positive\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [89, \"CO2 emissions (kt)\", 0.959865, \"Positive\", \"KOR\", \"Does not apply\", \"Cubic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [90, \"CO2 emissions (kt)\", 0.821451, \"Positive\", \"MEX\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [91, \"CO2 emissions (kt)\", 0.855111, \"Positive\", \"BRA\", \"2\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [92, \"CO2 emissions (kt)\", 0.753552, \"Positive\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [93, \"CO2 emissions (kt)\", 0.944664, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [94, \"CO2 emissions (kt)\", 0.768742, \"Positive\", \"COL\", \"1\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [95, \"CO2 emissions (kt)\", 0.925663, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [96, \"CO2 emissions (kt)\", 0.900419, \"Positive\", \"PAN\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [97, \"CO2 emissions (kt)\", 0.964184, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [98, \"CO2 emissions (kt)\", 0.98957, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 30, \"Environment\", \"secondary\", 33], [99, \"International migrant stock (% of population)\", 0.782956, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [100, \"International migrant stock (% of population)\", 0.807906, \"Positive\", \"FRA\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [101, \"International migrant stock (% of population)\", 0.770511, \"Positive\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [102, \"International migrant stock (% of population)\", 0.907327, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [103, \"International migrant stock (% of population)\", 0.900471, \"Positive\", \"ESP\", \"3\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [104, \"International migrant stock (% of population)\", 0.835596, \"Positive\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [105, \"International migrant stock (% of population)\", 0.968708, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [106, \"International migrant stock (% of population)\", 0.841846, \"Positive\", \"GRC\", \"5\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [107, \"International migrant stock (% of population)\", 0.831819, \"Positive\", \"AUT\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [108, \"International migrant stock (% of population)\", 0.898961, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [109, \"International migrant stock (% of population)\", 0.929941, \"Positive\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [110, \"International migrant stock (% of population)\", 0.927564, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [111, \"International migrant stock (% of population)\", 0.972132, \"Negative\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [112, \"International migrant stock (% of population)\", 0.903976, \"Positive\", \"YEM\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [113, \"International migrant stock (% of population)\", 0.833059, \"Negative\", \"DZA\", \"3\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [114, \"International migrant stock (% of population)\", 0.972723, \"Negative\", \"ISR\", \"1\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [115, \"International migrant stock (% of population)\", 0.906777, \"Negative\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [116, \"International migrant stock (% of population)\", 0.783531, \"Positive\", \"MAR\", \"13\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [117, \"International migrant stock (% of population)\", 0.775082, \"Negative\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [118, \"International migrant stock (% of population)\", 0.943912, \"Negative\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [119, \"International migrant stock (% of population)\", 0.971948, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [120, \"International migrant stock (% of population)\", 0.98406, \"Negative\", \"IND\", \"Does not apply\", \"Logarithmic\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [121, \"International migrant stock (% of population)\", 0.90581, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [122, \"International migrant stock (% of population)\", 0.874691, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [123, \"International migrant stock (% of population)\", 0.945693, \"Positive\", \"KOR\", \"Does not apply\", \"Linear\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [124, \"International migrant stock (% of population)\", 0.933482, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [125, \"International migrant stock (% of population)\", 0.912187, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [126, \"International migrant stock (% of population)\", 0.892956, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [127, \"International migrant stock (% of population)\", 0.989918, \"Positive\", \"PAN\", \"Does not apply\", \"Cubic\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [128, \"International migrant stock (% of population)\", 0.987719, \"Positive\", \"USA\", \"Does not apply\", \"Cubic\", \"Pair\", 16, \"Migration\", \"secondary\", 31], [129, \"International migrant stock (% of population)\", 0.989918, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 16, \"Migration\", \"secondary\", 31], [130, \"CO2 emissions (metric tons per capita)\", 0.782806, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [131, \"CO2 emissions (metric tons per capita)\", 0.768845, \"Negative\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [132, \"CO2 emissions (metric tons per capita)\", 0.915538, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [133, \"CO2 emissions (metric tons per capita)\", 0.78562, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [134, \"CO2 emissions (metric tons per capita)\", 0.759154, \"Negative\", \"NLD\", \"5\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [135, \"CO2 emissions (metric tons per capita)\", 0.806797, \"Positive\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 18, \"Environment\", \"secondary\", 29], [136, \"CO2 emissions (metric tons per capita)\", 0.841863, \"Positive\", \"OMN\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Environment\", \"secondary\", 29], [137, \"CO2 emissions (metric tons per capita)\", 0.846222, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [138, \"CO2 emissions (metric tons per capita)\", 0.884204, \"Positive\", \"EGY\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [139, \"CO2 emissions (metric tons per capita)\", 0.788288, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [140, \"CO2 emissions (metric tons per capita)\", 0.895687, \"Positive\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [141, \"CO2 emissions (metric tons per capita)\", 0.936433, \"Positive\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [142, \"CO2 emissions (metric tons per capita)\", 0.782676, \"Positive\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [143, \"CO2 emissions (metric tons per capita)\", 0.905693, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [144, \"CO2 emissions (metric tons per capita)\", 0.786576, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [145, \"CO2 emissions (metric tons per capita)\", 0.997798, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [146, \"CO2 emissions (metric tons per capita)\", 0.98957, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [147, \"CO2 emissions (metric tons per capita)\", 0.991031, \"Positive\", \"VNM\", \"Does not apply\", \"Linear\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [148, \"CO2 emissions (metric tons per capita)\", 0.858441, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [149, \"CO2 emissions (metric tons per capita)\", 0.903207, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [150, \"CO2 emissions (metric tons per capita)\", 0.840909, \"Positive\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [151, \"CO2 emissions (metric tons per capita)\", 0.948395, \"Positive\", \"KOR\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [152, \"CO2 emissions (metric tons per capita)\", 0.866209, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [153, \"CO2 emissions (metric tons per capita)\", 0.880682, \"Positive\", \"PER\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [154, \"CO2 emissions (metric tons per capita)\", 0.895551, \"Positive\", \"CHL\", \"Does not apply\", \"Cubic\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [155, \"CO2 emissions (metric tons per capita)\", 0.757305, \"Positive\", \"PAN\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [156, \"CO2 emissions (metric tons per capita)\", 0.753095, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [157, \"CO2 emissions (metric tons per capita)\", 0.913924, \"Negative\", \"USA\", \"8\", \"Does not apply\", \"Pair\", 18, \"Environment\", \"secondary\", 29], [158, \"CO2 emissions (metric tons per capita)\", 0.976473, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 18, \"Environment\", \"secondary\", 29], [159, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.779372, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [160, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.798438, \"Positive\", \"FRA\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [161, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.782956, \"Positive\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [162, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.907327, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [163, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.800012, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [164, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.791365, \"Positive\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [165, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.968708, \"Positive\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [166, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.82583, \"Positive\", \"AUT\", \"3\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [167, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.843098, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [168, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.935625, \"Positive\", \"AZE\", \"5\", \"Does not apply\", \"Persian Gulf\", 29, \"Principal\", \"primary\", 35], [169, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.932399, \"Positive\", \"EGY\", \"2\", \"Does not apply\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [170, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.936453, \"Positive\", \"ISR\", \"Does not apply\", \"Cubic\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [171, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.920491, \"Positive\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [172, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.780022, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [173, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.820787, \"Positive\", \"ZAF\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [174, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.937635, \"Positive\", \"MOZ\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [175, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.848688, \"Positive\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [176, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.941264, \"Positive\", \"NGA\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [177, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.838169, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [178, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.98201, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [179, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.950622, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [180, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.967217, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [181, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.848688, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [182, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.927815, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 29, \"Principal\", \"primary\", 35], [183, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.93913, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [184, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.941701, \"Positive\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 29, \"Principal\", \"primary\", 35], [185, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.925869, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [186, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.831081, \"Positive\", \"BRA\", 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\"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.987719, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 29, \"Principal\", \"primary\", 35], [193, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.981854, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 29, \"Principal\", \"primary\", 35], [194, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.759092, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [195, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.898766, \"Positive\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [196, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.775622, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [197, \"Prevalence of anemia among children (% of children ages 6-59 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\"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [203, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.947125, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [204, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.984385, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [205, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.891518, \"Negative\", \"DZA\", \"1\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [206, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.92803, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [207, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.819845, \"Positive\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [208, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.876163, \"Negative\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [209, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.902778, \"Negative\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [210, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.809567, \"Negative\", \"MOZ\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [211, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.907764, \"Negative\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [212, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.96926, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [213, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.86686, \"Negative\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [214, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.993913, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [215, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.987845, \"Negative\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [216, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.815973, \"Positive\", \"VNM\", \"21\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [217, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.904913, \"Negative\", \"PHL\", \"Does not apply\", \"Logarithmic\", \"Asia\", 18, \"Health\", \"secondary\", 33], [218, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.933133, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [219, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.754175, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [220, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.799358, \"Negative\", \"PER\", \"13\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [221, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.893013, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [222, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.896554, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [223, \"Prevalence of anemia among children (% of children ages 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economies in East Asia & Pacific (% of total merchandise imports)\", 0.773946, \"Positive\", \"FRA\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [229, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.815998, \"Positive\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [230, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.8799, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [231, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.751187, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [232, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise 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\"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [237, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.977084, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [238, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.959094, \"Positive\", \"AZE\", \"21\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [239, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.847827, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [240, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.859402, \"Positive\", \"EGY\", \"Does not apply\", \"Linear\", \"North 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\"secondary\", 38], [245, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.82775, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [246, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.784068, \"Positive\", \"NGA\", \"13\", \"Does not apply\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [247, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.843838, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [248, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.972127, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [249, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.955771, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [250, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.914644, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [251, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.864774, \"Positive\", \"THA\", \"1\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [252, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.897196, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [253, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.937594, \"Positive\", \"PHL\", \"2\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [254, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.918708, \"Positive\", \"KOR\", \"3\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [255, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.951463, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [256, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.751235, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [257, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.883617, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [258, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.893338, \"Positive\", \"VEN\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [259, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.904546, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [260, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.903191, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [261, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.979415, \"Positive\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [262, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.971504, \"Positive\", \"CRI\", \"2\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [263, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.949698, \"Positive\", \"USA\", \"2\", \"Does not apply\", \"Pair\", 28, \"Imports\", \"secondary\", 38], [264, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.925899, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 28, \"Imports\", \"secondary\", 38], [265, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.785897, \"Positive\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [266, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.832712, \"Positive\", \"FRA\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [267, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.792293, \"Positive\", \"SWE\", \"Does not apply\", \"Cuadratic\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [268, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.890853, \"Positive\", \"GBR\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [269, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.846758, \"Positive\", \"ESP\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [270, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.796993, \"Positive\", \"POL\", \"Does not apply\", \"Cuadratic\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [271, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.855869, \"Positive\", \"AUT\", \"2\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [272, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.940436, \"Positive\", \"SAU\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 15], [273, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.933687, \"Positive\", \"ISR\", \"Does not apply\", \"Cubic\", \"North Africa\", 12, \"Environment\", \"secondary\", 15], [274, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.836127, \"Positive\", \"ZAF\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 12, \"Environment\", \"secondary\", 15], [275, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.813553, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Environment\", \"secondary\", 15], [276, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.813553, \"Positive\", \"MEX\", \"Does not apply\", \"Linear\", \"Latam\", 12, \"Environment\", \"secondary\", 15], [277, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.7618, \"Positive\", \"VEN\", \"5\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 15], [278, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.945556, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 12, \"Environment\", \"secondary\", 15], [279, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.947754, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 12, \"Environment\", \"secondary\", 15], [280, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.768636, \"Negative\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [281, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.797226, \"Negative\", \"FRA\", \"3\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [282, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.785944, \"Negative\", \"SWE\", \"3\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [283, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.879221, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [284, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.868737, \"Negative\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [285, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.834752, \"Negative\", \"HRV\", \"2\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [286, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.944398, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [287, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.852272, \"Negative\", \"AUT\", \"1\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [288, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.863656, \"Negative\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [289, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.977434, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [290, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.977717, \"Negative\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [291, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.979206, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [292, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.909342, \"Negative\", \"AZE\", \"1\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [293, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.871188, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [294, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.922552, \"Negative\", \"OMN\", \"2\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [295, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.813387, \"Negative\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [296, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.95019, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [297, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.956329, \"Negative\", \"ISR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [298, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.945261, \"Negative\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [299, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.953349, \"Negative\", \"MAR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [300, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.939033, \"Negative\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [301, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.779079, \"Negative\", \"LBR\", \"1\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [302, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.939188, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [303, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.845139, \"Negative\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [304, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.934848, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [305, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.858043, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [306, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.842518, \"Negative\", \"BGD\", \"1\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [307, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.992671, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [308, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.993951, \"Negative\", \"VNM\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [309, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.900637, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [310, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.78477, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [311, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.947448, \"Negative\", \"KOR\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [312, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.898692, \"Negative\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [313, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.780573, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [314, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.801076, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [315, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.934719, \"Negative\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [316, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.887514, \"Negative\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [317, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.892492, \"Negative\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [318, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.927253, \"Negative\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [319, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.970669, \"Negative\", \"CRI\", \"Does not apply\", \"Logarithmic\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [320, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.823122, \"Negative\", \"USA\", \"Does not apply\", \"Logarithmic\", \"Pair\", 25, \"Mortality\", \"secondary\", 42], [321, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.989378, \"Negative\", \"CHN\", \"Does not apply\", \"Logarithmic\", \"Pair\", 25, \"Mortality\", \"secondary\", 42], [322, \"Time required to start a business (days)\", 0.792869, \"Negative\", \"DEU\", \"3\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [323, \"Time required to start a business (days)\", 0.762356, \"Negative\", \"FRA\", \"5\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [324, \"Time required to start a business (days)\", 0.847713, \"Negative\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [325, \"Time required to start a business (days)\", 0.810464, \"Negative\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [326, \"Time required to start a business (days)\", 0.905217, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [327, \"Time required to start a business (days)\", 0.816949, \"Negative\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [328, \"Time required to start a business (days)\", 0.885547, \"Negative\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [329, \"Time required to start a business (days)\", 0.841579, \"Negative\", \"SAU\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [330, \"Time required to start a business (days)\", 0.932239, \"Negative\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [331, \"Time required to start a business (days)\", 0.76496, \"Negative\", \"YEM\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [332, \"Time required to start a business (days)\", 0.873136, \"Negative\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [333, \"Time required to start a business (days)\", 0.830084, \"Negative\", \"DZA\", \"3\", \"Does not apply\", \"North Africa\", 11, \"Industry\", \"secondary\", 29], [334, \"Time required to start a business (days)\", 0.811387, \"Negative\", \"MAR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Industry\", \"secondary\", 29], [335, \"Time required to start a business (days)\", 0.782024, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [336, \"Time required to start a business (days)\", 0.793607, \"Negative\", \"ZAF\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [337, \"Time required to start a business (days)\", 0.751747, \"Negative\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [338, \"Time required to start a business (days)\", 0.820023, \"Negative\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [339, \"Time required to start a business (days)\", 0.874331, \"Negative\", \"CMR\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [340, \"Time required to start a business (days)\", 0.806947, \"Negative\", \"IND\", \"21\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [341, \"Time required to start a business (days)\", 0.965942, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [342, \"Time required to start a business (days)\", 0.83733, \"Negative\", \"THA\", \"5\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [343, \"Time required to start a business (days)\", 0.797064, \"Negative\", \"KOR\", \"5\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [344, \"Time required to start a business (days)\", 0.818574, \"Negative\", \"PER\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [345, \"Time required to start a business (days)\", 0.751607, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [346, \"Time required to start a business (days)\", 0.84776, \"Negative\", \"COL\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [347, \"Time required to start a business (days)\", 0.866647, \"Negative\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [348, \"Time required to start a business (days)\", 0.876316, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [349, \"Time required to start a business (days)\", 0.85138, \"Negative\", \"CRI\", \"8\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [350, \"Time required to start a business (days)\", 0.931818, \"Negative\", \"CHN\", \"21\", \"Does not apply\", \"Pair\", 11, \"Industry\", \"secondary\", 29], [351, \"Combustible renewables and waste (% of total energy)\", 0.759597, \"Positive\", \"DEU\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [352, \"Combustible renewables and waste (% of total energy)\", 0.793758, \"Positive\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [353, \"Combustible renewables and waste (% of total energy)\", 0.905255, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [354, \"Combustible renewables and waste (% of total energy)\", 0.915732, \"Positive\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [355, \"Combustible renewables and waste (% of total energy)\", 0.776496, \"Positive\", \"AUT\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [356, \"Combustible renewables and waste (% of total energy)\", 0.810504, \"Positive\", \"NLD\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [357, \"Combustible renewables and waste (% of total energy)\", 0.935853, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 25], [358, \"Combustible renewables and waste (% of total energy)\", 0.763706, \"Positive\", \"AZE\", \"Does not apply\", \"Linear\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 25], [359, \"Combustible renewables and waste (% of total energy)\", 0.780104, \"Negative\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [360, \"Combustible renewables and waste (% of total energy)\", 0.941407, \"Negative\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [361, \"Combustible renewables and waste (% of total energy)\", 0.78341, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [362, \"Combustible renewables and waste (% of total energy)\", 0.890534, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Environment\", \"secondary\", 25], [363, \"Combustible renewables and waste (% of total energy)\", 0.822012, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 12, \"Environment\", \"secondary\", 25], [364, \"Combustible renewables and waste (% of total energy)\", 0.982244, \"Negative\", \"BGD\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [365, \"Combustible renewables and waste (% of total energy)\", 0.978927, \"Negative\", \"IND\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [366, \"Combustible renewables and waste (% of total energy)\", 0.860249, \"Negative\", \"VNM\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [367, \"Combustible renewables and waste (% of total energy)\", 0.803047, \"Negative\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [368, \"Combustible renewables and waste (% of total energy)\", 0.918827, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [369, \"Combustible renewables and waste (% of total energy)\", 0.784859, \"Positive\", \"KOR\", \"3\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [370, \"Combustible renewables and waste (% of total energy)\", 0.915132, \"Negative\", \"MEX\", \"1\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 25], [371, \"Combustible renewables and waste (% of total energy)\", 0.930378, \"Negative\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 25], [6273, \"Food production index (2014-2016 = 100)\", 0.868933, \"Positive\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6274, \"Food production index (2014-2016 = 100)\", 0.834377, \"Positive\", \"MOZ\", \"Does not apply\", \"Linear\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6275, \"Food production index (2014-2016 = 100)\", 0.821876, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6276, \"Food production index (2014-2016 = 100)\", 0.858764, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6277, \"Food production index (2014-2016 = 100)\", 0.860215, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6278, \"Food production index (2014-2016 = 100)\", 0.995078, \"Positive\", \"BGD\", \"3\", \"Does not apply\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6279, \"Food production index (2014-2016 = 100)\", 0.98233, \"Positive\", \"IND\", \"1\", \"Does not apply\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6280, \"Food production index (2014-2016 = 100)\", 0.994324, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6281, \"Food production index (2014-2016 = 100)\", 0.807971, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6282, \"Food production index (2014-2016 = 100)\", 0.943786, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6283, \"Food production index (2014-2016 = 100)\", 0.942178, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6284, \"Food production index (2014-2016 = 100)\", 0.93495, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6285, \"Food production index (2014-2016 = 100)\", 0.76444, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6286, \"Food production index (2014-2016 = 100)\", 0.936928, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6287, \"Food production index (2014-2016 = 100)\", 0.896305, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6288, \"Food production index (2014-2016 = 100)\", 0.878859, \"Positive\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6289, \"Food production index (2014-2016 = 100)\", 0.966456, \"Positive\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6290, \"Food production index (2014-2016 = 100)\", 0.985531, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6291, \"Food production index (2014-2016 = 100)\", 0.946677, \"Positive\", \"USA\", \"1\", \"Does not apply\", \"Pair\", 24, \"Industry\", \"secondary\", 35], [6292, \"Food production index (2014-2016 = 100)\", 0.995055, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 24, \"Industry\", \"secondary\", 35], [6293, \"Time to obtain an electrical connection (days)\", 0.963721, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 11, \"R&D\", \"secondary\", 24], [6294, \"Time to obtain an electrical connection (days)\", 0.83291, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 11, \"R&D\", \"secondary\", 24], [6295, \"Time to obtain an electrical connection (days)\", 0.804939, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6296, \"Time to obtain an electrical connection (days)\", 0.75968, \"Negative\", \"TUR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6297, \"Time to obtain an electrical connection (days)\", 0.876713, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6298, \"Time to obtain an electrical connection (days)\", 0.852294, \"Positive\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6299, \"Time to obtain an electrical connection (days)\", 0.805169, \"Negative\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6300, \"Time to obtain an electrical connection (days)\", 0.92942, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6301, \"Time to obtain an electrical connection (days)\", 0.881377, \"Negative\", \"CMR\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6302, \"Time to obtain an electrical connection (days)\", 0.891739, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6303, \"Time to obtain an electrical connection (days)\", 0.843733, \"Positive\", \"GHA\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6304, \"Time to obtain an electrical connection (days)\", 0.877193, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6305, \"Time to obtain an electrical connection (days)\", 0.917544, \"Negative\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6306, \"Time to obtain an electrical connection (days)\", 0.901754, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6307, \"Time to obtain an electrical connection (days)\", 0.817729, \"Negative\", \"IDN\", \"8\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6308, \"Time to obtain an electrical connection (days)\", 0.810745, \"Negative\", \"PHL\", \"5\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6309, \"Time to obtain an electrical connection (days)\", 0.815051, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6310, \"Time to obtain an electrical connection (days)\", 0.909091, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6311, \"Time to obtain an electrical connection (days)\", 0.786273, \"Positive\", \"PER\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6312, \"Time to obtain an electrical connection (days)\", 0.818161, \"Negative\", \"VEN\", \"2\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6313, \"Time to obtain an electrical connection (days)\", 0.855364, \"Positive\", \"COL\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6314, \"Time to obtain an electrical connection (days)\", 0.817488, \"Negative\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6315, \"Time to obtain an electrical connection (days)\", 0.825397, \"Negative\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6316, \"Time to obtain an electrical connection (days)\", 0.845543, \"Negative\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6317, \"Suicide mortality rate (per 100,000 population)\", 0.855364, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 17, \"Mortality\", \"primary\", 25], [6318, \"Suicide mortality rate (per 100,000 population)\", 0.827624, \"Negative\", \"AUT\", \"13\", \"Does not apply\", \"Europe\", 17, \"Mortality\", \"primary\", 25], [6319, \"Suicide mortality rate (per 100,000 population)\", 0.773321, \"Negative\", \"QAT\", \"21\", \"Does not apply\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6320, \"Suicide mortality rate (per 100,000 population)\", 0.78697, \"Negative\", \"ARE\", \"3\", \"Does not apply\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6321, \"Suicide mortality rate (per 100,000 population)\", 0.872247, \"Positive\", \"SAU\", \"Does not apply\", \"Logarithmic\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6322, \"Suicide mortality rate (per 100,000 population)\", 0.827891, \"Negative\", \"YEM\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6323, \"Suicide mortality rate (per 100,000 population)\", 0.878636, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6324, \"Suicide mortality rate (per 100,000 population)\", 0.886364, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6325, \"Suicide mortality rate (per 100,000 population)\", 0.897716, \"Negative\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6326, \"Suicide mortality rate (per 100,000 population)\", 0.90973, \"Negative\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6327, \"Suicide mortality rate (per 100,000 population)\", 0.897565, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 17, \"Mortality\", \"primary\", 25], [6328, \"Suicide mortality rate (per 100,000 population)\", 0.966749, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 17, \"Mortality\", \"primary\", 25], [6329, \"Suicide mortality rate (per 100,000 population)\", 0.878685, \"Negative\", \"BGD\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6330, \"Suicide mortality rate (per 100,000 population)\", 0.915641, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6331, \"Suicide mortality rate (per 100,000 population)\", 0.869184, \"Positive\", \"VNM\", \"3\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6332, \"Suicide mortality rate (per 100,000 population)\", 0.882422, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6333, \"Suicide mortality rate (per 100,000 population)\", 0.831818, \"Negative\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6334, \"Suicide mortality rate (per 100,000 population)\", 0.952612, \"Positive\", \"MEX\", \"3\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6335, \"Suicide mortality rate (per 100,000 population)\", 0.756344, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6336, \"Suicide mortality rate (per 100,000 population)\", 0.902423, \"Negative\", \"VEN\", \"8\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6337, \"Suicide mortality rate (per 100,000 population)\", 0.776677, \"Negative\", \"COL\", \"Does not apply\", \"Logarithmic\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6338, \"Suicide mortality rate (per 100,000 population)\", 0.755201, \"Negative\", \"CHL\", \"21\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6339, \"Suicide mortality rate (per 100,000 population)\", 0.920313, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6340, \"Suicide mortality rate (per 100,000 population)\", 0.991231, \"Positive\", \"USA\", \"13\", \"Does not apply\", \"Pair\", 17, \"Mortality\", \"primary\", 25], [6341, \"Suicide mortality rate (per 100,000 population)\", 0.95768, \"Negative\", \"CHN\", \"5\", \"Does not apply\", \"Pair\", 17, \"Mortality\", \"primary\", 25], [6342, \"Firms using banks to finance investment (% of firms)\", 0.841751, \"Positive\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 12, \"Economy\", \"secondary\", 28], [6343, \"Firms using banks to finance investment (% of firms)\", 0.83291, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 12, \"Economy\", \"secondary\", 28], [6344, \"Firms using banks to finance investment (% of firms)\", 0.945216, \"Negative\", \"AZE\", \"21\", \"Does not apply\", \"Persian Gulf\", 12, \"Economy\", \"secondary\", 28], [6345, \"Firms using banks to finance investment (% of firms)\", 0.789777, \"Negative\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 12, \"Economy\", \"secondary\", 28], [6346, \"Firms using banks to finance investment (% of firms)\", 0.941656, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6347, \"Firms using banks to finance investment (% of firms)\", 0.75968, \"Negative\", \"TUR\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6348, \"Firms using banks to finance investment (% of firms)\", 0.876713, \"Positive\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6349, \"Firms using banks to finance investment (% of firms)\", 0.852294, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6350, \"Firms using banks to finance investment (% of firms)\", 0.805169, \"Negative\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6351, \"Firms using banks to finance investment (% of firms)\", 0.882199, \"Positive\", \"LBR\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6352, \"Firms using banks to finance investment (% of firms)\", 0.92942, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6353, \"Firms using banks to finance investment (% of firms)\", 0.759974, \"Negative\", \"CMR\", \"13\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6354, \"Firms using banks to finance investment (% of firms)\", 0.891739, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6355, \"Firms using banks to finance investment (% of firms)\", 0.843733, \"Positive\", \"GHA\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6356, \"Firms using banks to finance investment (% of firms)\", 0.877193, \"Negative\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6357, \"Firms using banks to finance investment (% of firms)\", 0.917544, \"Negative\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6358, \"Firms using banks to finance investment (% of firms)\", 0.901754, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6359, \"Firms using banks to finance investment (% of firms)\", 0.870956, \"Negative\", \"THA\", \"3\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6360, \"Firms using banks to finance investment (% of firms)\", 0.817729, \"Positive\", \"IDN\", \"8\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6361, \"Firms using banks to finance investment (% of firms)\", 0.810745, \"Negative\", \"PHL\", \"5\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6362, \"Firms using banks to finance investment (% of firms)\", 0.815051, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6363, \"Firms using banks to finance investment (% of firms)\", 0.909091, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6364, \"Firms using banks to finance investment (% of firms)\", 0.88294, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6365, \"Firms using banks to finance investment (% of firms)\", 0.818161, \"Negative\", \"VEN\", \"2\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6366, \"Firms using banks to finance investment (% of firms)\", 0.855364, \"Positive\", \"COL\", \"21\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6367, \"Firms using banks to finance investment (% of firms)\", 0.817488, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6368, \"Firms using banks to finance investment (% of firms)\", 0.825397, \"Negative\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6369, \"Firms using banks to finance investment (% of firms)\", 0.845543, \"Positive\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6370, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.768265, \"Negative\", \"GRC\", \"5\", \"Does not apply\", \"Europe\", 12, \"Imports\", \"secondary\", 21], [6371, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.933568, \"Negative\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6372, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.946379, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6373, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.829932, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6374, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.893727, \"Negative\", \"OMN\", \"Does not apply\", \"Logarithmic\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6375, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.827434, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Imports\", \"secondary\", 21], [6376, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.759141, \"Negative\", \"TUR\", \"2\", \"Does not apply\", \"North Africa\", 12, \"Imports\", \"secondary\", 21], [6377, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.904546, \"Negative\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Imports\", \"secondary\", 21], [6378, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.937705, \"Negative\", \"BGD\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6379, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.9145, \"Negative\", \"VNM\", \"8\", \"Does not apply\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6380, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.865531, \"Negative\", \"THA\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6381, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.898586, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6382, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.945048, \"Negative\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6383, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.929542, \"Negative\", \"KOR\", \"5\", \"Does not apply\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6384, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.949767, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6385, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.752078, \"Negative\", \"BRA\", \"1\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6386, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.919187, \"Negative\", \"VEN\", \"Does not apply\", \"Logarithmic\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6387, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.797346, \"Negative\", \"COL\", \"Does not apply\", \"Logarithmic\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6388, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.8055, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6389, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.831757, \"Negative\", \"USA\", \"Does not apply\", \"Logarithmic\", \"Pair\", 12, \"Imports\", \"secondary\", 21], [6390, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.857664, \"Negative\", \"CHN\", \"1\", \"Does not apply\", \"Pair\", 12, \"Imports\", \"secondary\", 21], [6391, \"People practicing open defecation (% of population)\", 0.85947, \"Negative\", \"GRC\", \"8\", \"Does not apply\", \"Europe\", 27, \"Health\", \"secondary\", 32], [6392, \"People practicing open defecation (% of population)\", 0.764246, \"Negative\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6393, \"People practicing open defecation (% of population)\", 0.961673, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6394, \"People practicing open defecation (% of population)\", 0.955118, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6395, \"People practicing open defecation (% of population)\", 0.905975, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6396, \"People practicing open defecation (% of population)\", 0.850746, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6397, \"People practicing open defecation (% of population)\", 0.913668, \"Negative\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6398, \"People practicing open defecation (% of population)\", 0.94435, \"Negative\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6399, \"People practicing open defecation (% of population)\", 0.924176, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6400, \"People practicing open defecation (% of population)\", 0.903327, \"Negative\", \"SEN\", \"Does not apply\", \"Linear\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6401, \"People practicing open defecation (% of population)\", 0.756276, \"Negative\", \"ZAF\", \"8\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6402, \"People practicing open defecation (% of population)\", 0.889166, \"Negative\", \"LBR\", \"1\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6403, \"People practicing open defecation (% of population)\", 0.948441, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6404, \"People practicing open defecation (% of population)\", 0.909027, \"Negative\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6405, \"People practicing open defecation (% of population)\", 0.964139, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6406, \"People practicing open defecation (% of population)\", 0.867409, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6407, \"People practicing open defecation (% of population)\", 0.996522, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6408, \"People practicing open defecation (% of population)\", 0.989149, \"Negative\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6409, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6410, \"People practicing open defecation (% of population)\", 0.865868, \"Negative\", \"THA\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6411, \"People practicing open defecation (% of population)\", 0.908314, \"Negative\", \"IDN\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6412, \"People practicing open defecation (% of population)\", 0.903327, \"Negative\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6413, \"People practicing open defecation (% of population)\", 0.939572, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6414, \"People practicing open defecation (% of population)\", 0.750815, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6415, \"People practicing open defecation (% of population)\", 0.751955, \"Negative\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6416, \"People practicing open defecation (% of population)\", 0.90475, \"Negative\", \"PER\", \"Does not apply\", \"Linear\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6417, \"People practicing open defecation (% of population)\", 0.890679, \"Negative\", \"VEN\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6418, \"People practicing open defecation (% of population)\", 0.897855, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6419, \"People practicing open defecation (% of population)\", 0.909091, \"Negative\", \"CHL\", \"21\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6420, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6421, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6422, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 27, \"Health\", \"secondary\", 32], [6423, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.829371, \"Negative\", \"AUT\", \"8\", \"Does not apply\", \"Europe\", 11, \"Environment\", \"secondary\", 18], [6424, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.848986, \"Negative\", \"QAT\", \"3\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6425, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.855782, \"Positive\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6426, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.964478, \"Negative\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6427, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.767146, \"Positive\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6428, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.805752, \"Positive\", \"MAR\", \"2\", \"Does not apply\", \"North Africa\", 11, \"Environment\", \"secondary\", 18], [6429, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.909825, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6430, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.855509, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6431, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.900159, \"Negative\", \"NGA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6432, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.853751, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6433, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.967802, \"Negative\", \"IND\", \"1\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6434, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.879127, \"Negative\", \"VNM\", \"5\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6435, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.922596, \"Negative\", \"IDN\", \"2\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6436, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.941576, \"Negative\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6437, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.895958, \"Negative\", \"MEX\", \"Does not apply\", \"Logarithmic\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6438, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.804632, \"Negative\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6439, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.87797, \"Negative\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6440, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.874924, \"Negative\", \"CHN\", \"1\", \"Does not apply\", \"Pair\", 11, \"Environment\", \"secondary\", 18], [6441, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.843001, \"Negative\", \"NLD\", \"Does not apply\", \"Logarithmic\", \"Europe\", 13, \"Agriculture\", \"secondary\", 21], [6442, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.853721, \"Positive\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6443, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.956311, \"Positive\", \"ARE\", \"3\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6444, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.983928, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6445, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.805605, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6446, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.972728, \"Positive\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6447, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.831716, \"Positive\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6448, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.795868, \"Positive\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6449, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.841358, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6450, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.790698, \"Positive\", \"LBR\", \"2\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6451, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.920335, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6452, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.774495, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6453, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.983907, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6454, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.961303, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6455, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.896246, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6456, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.936072, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6457, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.831515, \"Positive\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6458, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.758321, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6459, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.828788, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6460, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.792582, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6461, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.80934, \"Positive\", \"USA\", \"Does not apply\", \"Cubic\", \"Pair\", 13, \"Agriculture\", \"secondary\", 21], [6462, \"Access to clean fuels and technologies for cooking (% of population)\", 0.766483, \"Positive\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6463, \"Access to clean fuels and technologies for cooking (% of population)\", 0.996083, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6464, \"Access to clean fuels and technologies for cooking (% of population)\", 0.9509, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6465, \"Access to clean fuels and technologies for cooking (% of population)\", 0.853684, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6466, \"Access to clean fuels and technologies for cooking (% of population)\", 0.897511, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6467, \"Access to clean fuels and technologies for cooking (% of population)\", 0.957737, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6468, \"Access to clean fuels and technologies for cooking (% of population)\", 0.923174, \"Positive\", \"MAR\", \"8\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6469, \"Access to clean fuels and technologies for cooking (% of population)\", 0.878984, \"Negative\", \"SEN\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6470, \"Access to clean fuels and technologies for cooking (% of population)\", 0.756276, \"Positive\", \"ZAF\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6471, \"Access to clean fuels and technologies for cooking (% of population)\", 0.960204, \"Positive\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6472, \"Access to clean fuels and technologies for cooking (% of population)\", 0.872263, \"Positive\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6473, \"Access to clean fuels and technologies for cooking (% of population)\", 0.962628, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6474, \"Access to clean fuels and technologies for cooking (% of population)\", 0.864074, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6475, \"Access to clean fuels and technologies for cooking (% of population)\", 0.995617, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6476, \"Access to clean fuels and technologies for cooking (% of population)\", 0.989149, \"Positive\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6477, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6478, \"Access to clean fuels and technologies for cooking (% of population)\", 0.880003, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6479, \"Access to clean fuels and technologies for cooking (% of population)\", 0.908314, \"Positive\", \"IDN\", \"Does not apply\", \"Linear\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6480, \"Access to clean fuels and technologies for cooking (% of population)\", 0.826918, \"Positive\", \"PHL\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6481, \"Access to clean fuels and technologies for cooking (% of population)\", 0.753409, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6482, \"Access to clean fuels and technologies for cooking (% of population)\", 0.853102, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6483, \"Access to clean fuels and technologies for cooking (% of population)\", 0.90475, \"Positive\", \"PER\", \"Does not apply\", \"Linear\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6484, \"Access to clean fuels and technologies for cooking (% of population)\", 0.897855, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6485, \"Access to clean fuels and technologies for cooking (% of population)\", 0.945724, \"Positive\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6486, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6487, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 17, \"Internet\", \"secondary\", 26], [6488, \"Access to electricity (% of population)\", 0.755466, \"Positive\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6489, \"Access to electricity (% of population)\", 0.844248, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6490, \"Access to electricity (% of population)\", 0.825795, \"Positive\", \"YEM\", \"3\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6491, \"Access to electricity (% of population)\", 0.870107, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 16, \"Environment\", \"primary\", 26], [6492, \"Access to electricity (% of population)\", 0.914656, \"Positive\", \"MAR\", \"2\", \"Does not apply\", \"North Africa\", 16, \"Environment\", \"primary\", 26], [6493, \"Access to electricity (% of population)\", 0.780617, \"Positive\", \"SEN\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6494, \"Access to electricity (% of population)\", 0.798854, \"Positive\", \"ZAF\", \"2\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6495, \"Access to electricity (% of population)\", 0.848246, \"Positive\", \"LBR\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6496, \"Access to electricity (% of population)\", 0.924489, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6497, \"Access to electricity (% of population)\", 0.811965, \"Positive\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6498, \"Access to electricity (% of population)\", 0.829545, \"Positive\", \"NGA\", \"3\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6499, \"Access to electricity (% of population)\", 0.824265, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6500, \"Access to electricity (% of population)\", 0.993779, \"Positive\", \"BGD\", \"2\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6501, \"Access to electricity (% of population)\", 0.979171, \"Positive\", \"IND\", \"3\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6502, \"Access to electricity (% of population)\", 0.968614, \"Positive\", \"VNM\", \"3\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6503, \"Access to electricity (% of population)\", 0.872662, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6504, \"Access to electricity (% of population)\", 0.823824, \"Positive\", \"IDN\", \"5\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6505, \"Access to electricity (% of population)\", 0.909312, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6506, \"Access to electricity (% of population)\", 0.845838, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6507, \"Access to electricity (% of population)\", 0.776361, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6508, \"Access to electricity (% of population)\", 0.928139, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6509, \"Access to electricity (% of population)\", 0.780517, \"Positive\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6510, \"Access to electricity (% of population)\", 0.871062, \"Positive\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6511, \"Access to electricity (% of population)\", 0.977356, \"Positive\", \"PAN\", \"21\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6512, \"Access to electricity (% of population)\", 0.937042, \"Positive\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6513, \"Access to electricity (% of population)\", 0.963675, \"Positive\", \"CHN\", \"5\", \"Does not apply\", \"Pair\", 16, \"Environment\", \"primary\", 26], [6514, \"Prevalence of undernourishment (% of population)\", 0.766965, \"Positive\", \"IRQ\", \"3\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6515, \"Prevalence of undernourishment (% of population)\", 0.774883, \"Negative\", \"AZE\", \"2\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6516, \"Prevalence of undernourishment (% of population)\", 0.7825, \"Positive\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6517, \"Prevalence of undernourishment (% of population)\", 0.896934, \"Negative\", \"OMN\", \"Does not apply\", \"Cuadratic\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6518, \"Prevalence of undernourishment (% of population)\", 0.874165, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 14, \"Health\", \"secondary\", 21], [6519, \"Prevalence of undernourishment (% of population)\", 0.863113, \"Negative\", \"MAR\", \"8\", \"Does not apply\", \"North Africa\", 14, \"Health\", \"secondary\", 21], [6520, \"Prevalence of undernourishment (% of population)\", 0.877935, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6521, \"Prevalence of undernourishment (% of population)\", 0.809735, \"Negative\", \"MOZ\", \"Does not apply\", \"Cubic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6522, \"Prevalence of undernourishment (% of population)\", 0.912315, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6523, \"Prevalence of undernourishment (% of population)\", 0.959901, \"Positive\", \"NGA\", \"13\", \"Does not apply\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6524, \"Prevalence of undernourishment (% of population)\", 0.839859, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6525, \"Prevalence of undernourishment (% of population)\", 0.891, \"Negative\", \"BGD\", \"21\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6526, \"Prevalence of undernourishment (% of population)\", 0.996491, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6527, \"Prevalence of undernourishment (% of population)\", 0.836744, \"Negative\", \"THA\", \"Does not apply\", \"Logarithmic\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6528, \"Prevalence of undernourishment (% of population)\", 0.872487, \"Negative\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6529, \"Prevalence of undernourishment (% of population)\", 0.827297, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6530, \"Prevalence of undernourishment (% of population)\", 0.840925, \"Positive\", \"MEX\", \"3\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6531, \"Prevalence of undernourishment (% of population)\", 0.759618, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6532, \"Prevalence of undernourishment (% of population)\", 0.893013, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6533, \"Prevalence of undernourishment (% of population)\", 0.961103, \"Negative\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6534, \"Prevalence of undernourishment (% of population)\", 0.9, \"Negative\", \"CHN\", \"3\", \"Does not apply\", \"Pair\", 14, \"Health\", \"secondary\", 21], [6535, \"Cereal yield (kg per hectare)\", 0.779595, \"Positive\", \"QAT\", \"1\", \"Does not apply\", \"Persian Gulf\", 12, \"Agriculture\", \"secondary\", 18], [6536, \"Cereal yield (kg per hectare)\", 0.888932, \"Positive\", \"SAU\", \"5\", \"Does not apply\", \"Persian Gulf\", 12, \"Agriculture\", \"secondary\", 18], [6537, \"Cereal yield (kg per hectare)\", 0.788288, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Agriculture\", \"secondary\", 18], [6538, \"Cereal yield (kg per hectare)\", 0.87744, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Agriculture\", \"secondary\", 18], [6539, \"Cereal yield (kg per hectare)\", 0.774258, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 12, \"Agriculture\", \"secondary\", 18], [6540, \"Cereal yield (kg per hectare)\", 0.9936, \"Positive\", \"BGD\", \"3\", \"Does not apply\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6541, \"Cereal yield (kg per hectare)\", 0.961303, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6542, \"Cereal yield (kg per hectare)\", 0.983739, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6543, \"Cereal yield (kg per hectare)\", 0.894857, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6544, \"Cereal yield (kg per hectare)\", 0.939127, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6545, \"Cereal yield (kg per hectare)\", 0.903125, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6546, \"Cereal yield (kg per hectare)\", 0.804904, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6547, \"Cereal yield (kg per hectare)\", 0.907475, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6548, \"Cereal yield (kg per hectare)\", 0.794782, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6549, \"Cereal yield (kg per hectare)\", 0.836149, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6550, \"Cereal yield (kg per hectare)\", 0.750296, \"Positive\", \"PAN\", \"1\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6551, \"Cereal yield (kg per hectare)\", 0.911779, \"Positive\", \"USA\", \"3\", \"Does not apply\", \"Pair\", 12, \"Agriculture\", \"secondary\", 18], [6552, \"Cereal yield (kg per hectare)\", 0.977243, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 12, \"Agriculture\", \"secondary\", 18], [6553, \"Pregnant women receiving prenatal care (%)\", 0.803913, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6554, \"Pregnant women receiving prenatal care (%)\", 0.889643, \"Positive\", \"ARE\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6555, \"Pregnant women receiving prenatal care (%)\", 0.89992, \"Positive\", \"SAU\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6556, \"Pregnant women receiving prenatal care (%)\", 0.79933, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6557, \"Pregnant women receiving prenatal care (%)\", 0.908023, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6558, \"Pregnant women receiving prenatal care (%)\", 0.855771, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6559, \"Pregnant women receiving prenatal care (%)\", 0.898054, \"Positive\", \"EGY\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6560, \"Pregnant women receiving prenatal care (%)\", 0.95029, \"Positive\", \"TUR\", \"2\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6561, \"Pregnant women receiving prenatal care (%)\", 0.954106, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6562, \"Pregnant women receiving prenatal care (%)\", 0.904693, \"Positive\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6563, \"Pregnant women receiving prenatal care (%)\", 0.919914, \"Positive\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6564, \"Pregnant women receiving prenatal care (%)\", 0.878474, \"Positive\", \"MOZ\", \"21\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6565, \"Pregnant women receiving prenatal care (%)\", 0.858423, \"Positive\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6566, \"Pregnant women receiving prenatal care (%)\", 0.814632, \"Positive\", \"NGA\", \"21\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6567, \"Pregnant women receiving prenatal care (%)\", 0.983888, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6568, \"Pregnant women receiving prenatal care (%)\", 0.966606, \"Positive\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6569, \"Pregnant women receiving prenatal care (%)\", 0.956534, \"Positive\", \"VNM\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6570, \"Pregnant women receiving prenatal care (%)\", 0.861958, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6571, \"Pregnant women receiving prenatal care (%)\", 0.810999, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6572, \"Pregnant women receiving prenatal care (%)\", 0.898514, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6573, \"Pregnant women receiving prenatal care (%)\", 0.91284, \"Positive\", \"VEN\", \"3\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6574, \"Pregnant women receiving prenatal care (%)\", 0.932079, \"Positive\", \"COL\", \"1\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6575, \"Pregnant women receiving prenatal care (%)\", 0.954545, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6576, \"Pregnant women receiving prenatal care (%)\", 0.981598, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 18, \"Health\", \"secondary\", 24], [6577, \"Crop production index (2014-2016 = 100)\", 0.766012, \"Positive\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6578, \"Crop production index (2014-2016 = 100)\", 0.963749, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6579, \"Crop production index (2014-2016 = 100)\", 0.835085, \"Positive\", \"YEM\", \"Does not apply\", \"Cuadratic\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6580, \"Crop production index (2014-2016 = 100)\", 0.951834, \"Positive\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6581, \"Crop production index (2014-2016 = 100)\", 0.814074, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6582, \"Crop production index (2014-2016 = 100)\", 0.908494, \"Positive\", \"EGY\", \"Does not apply\", \"Cubic\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6583, \"Crop production index (2014-2016 = 100)\", 0.947974, \"Positive\", \"TUR\", \"13\", \"Does not apply\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6584, \"Crop production index (2014-2016 = 100)\", 0.87045, \"Positive\", \"MAR\", \"Does not apply\", \"Cuadratic\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6585, \"Crop production index (2014-2016 = 100)\", 0.804939, \"Negative\", \"SEN\", \"21\", \"Does not apply\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6586, \"Crop production index (2014-2016 = 100)\", 0.833037, \"Positive\", \"MOZ\", \"Does not apply\", \"Linear\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6587, \"Crop production index (2014-2016 = 100)\", 0.821211, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6588, \"Crop production index (2014-2016 = 100)\", 0.854241, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6589, \"Crop production index (2014-2016 = 100)\", 0.858855, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6590, \"Crop production index (2014-2016 = 100)\", 0.989486, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6591, \"Crop production index (2014-2016 = 100)\", 0.965283, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6592, \"Crop production index (2014-2016 = 100)\", 0.995787, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6593, \"Crop production index (2014-2016 = 100)\", 0.819882, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6594, \"Crop production index (2014-2016 = 100)\", 0.913211, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6595, \"Crop production index (2014-2016 = 100)\", 0.91721, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6596, \"Crop production index (2014-2016 = 100)\", 0.908215, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6597, \"Crop production index (2014-2016 = 100)\", 0.766164, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6598, \"Crop production index (2014-2016 = 100)\", 0.936928, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6599, \"Crop production index (2014-2016 = 100)\", 0.851083, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6600, \"Crop production index (2014-2016 = 100)\", 0.882284, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6601, \"Crop production index (2014-2016 = 100)\", 0.967446, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6602, \"Crop production index (2014-2016 = 100)\", 0.845314, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 21, \"Agriculture\", \"secondary\", 27], [6603, \"Crop production index (2014-2016 = 100)\", 0.998717, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 21, \"Agriculture\", \"secondary\", 27], [6604, \"Births attended by skilled health staff (% of total)\", 0.86053, \"Negative\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6605, \"Births attended by skilled health staff (% of total)\", 0.938926, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6606, \"Births attended by skilled health staff (% of total)\", 0.842853, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6607, \"Births attended by skilled health staff (% of total)\", 0.842834, \"Positive\", \"DZA\", \"2\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6608, \"Births attended by skilled health staff (% of total)\", 0.923878, \"Positive\", \"EGY\", \"2\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6609, \"Births attended by skilled health staff (% of total)\", 0.95508, \"Positive\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6610, \"Births attended by skilled health staff (% of total)\", 0.953389, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6611, \"Births attended by skilled health staff (% of total)\", 0.797257, \"Positive\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6612, \"Births attended by skilled health staff (% of total)\", 0.783475, \"Positive\", \"MOZ\", \"Does not apply\", \"Cubic\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6613, \"Births attended by skilled health staff (% of total)\", 0.858575, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6614, \"Births attended by skilled health staff (% of total)\", 0.977507, \"Positive\", \"BGD\", \"5\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6615, \"Births attended by skilled health staff (% of total)\", 0.982218, \"Positive\", \"IND\", \"Does not apply\", \"Linear\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6616, \"Births attended by skilled health staff (% of total)\", 0.95581, \"Positive\", \"VNM\", \"2\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6617, \"Births attended by skilled health staff (% of total)\", 0.876592, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6618, \"Births attended by skilled health staff (% of total)\", 0.939552, \"Positive\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6619, \"Births attended by skilled health staff (% of total)\", 0.844034, \"Positive\", \"MEX\", \"1\", \"Does not apply\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6620, \"Births attended by skilled health staff (% of total)\", 0.924618, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6621, \"Births attended by skilled health staff (% of total)\", 0.758834, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6622, \"Births attended by skilled health staff (% of total)\", 0.794656, \"Negative\", \"USA\", \"8\", \"Does not apply\", \"Pair\", 13, \"Mortality\", \"secondary\", 20], [6623, \"Births attended by skilled health staff (% of total)\", 0.950593, \"Positive\", \"CHN\", \"3\", \"Does not apply\", \"Pair\", 13, \"Mortality\", \"secondary\", 20], [6624, \"Completeness of birth registration (%)\", 0.86815, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6625, \"Completeness of birth registration (%)\", 0.840806, \"Positive\", \"EGY\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6626, \"Completeness of birth registration (%)\", 0.898573, \"Positive\", \"TUR\", \"1\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6627, \"Completeness of birth registration (%)\", 0.932084, \"Positive\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6628, \"Completeness of birth registration (%)\", 0.909636, \"Positive\", \"LBR\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6629, \"Completeness of birth registration (%)\", 0.8862, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6630, \"Completeness of birth registration (%)\", 0.790617, \"Negative\", \"CMR\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6631, \"Completeness of birth registration (%)\", 0.81349, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6632, \"Completeness of birth registration (%)\", 0.773855, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6633, \"Completeness of birth registration (%)\", 0.792328, \"Positive\", \"BGD\", \"5\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6634, \"Completeness of birth registration (%)\", 0.810707, \"Positive\", \"IND\", \"5\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6635, \"Completeness of birth registration (%)\", 0.781322, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6636, \"Completeness of birth registration (%)\", 0.901982, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6637, \"Completeness of birth registration (%)\", 0.894689, \"Positive\", \"MEX\", \"13\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6638, \"Completeness of birth registration (%)\", 0.781913, \"Positive\", \"BRA\", \"2\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6639, \"Completeness of birth registration (%)\", 0.855313, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6640, \"Completeness of birth registration (%)\", 0.839228, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6641, \"Completeness of birth registration (%)\", 0.792184, \"Negative\", \"VEN\", \"1\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6642, \"Completeness of birth registration (%)\", 0.840648, \"Positive\", \"COL\", \"2\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6643, \"Completeness of birth registration (%)\", 0.963636, \"Positive\", \"PAN\", \"21\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6644, \"Completeness of birth registration (%)\", 0.789474, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21]];\n",
+       "\n",
+       "        // Define the dt_args\n",
+       "        let dt_args = {\"columnDefs\": [{\"className\": \"dt-left\", \"targets\": \"_all\"}], \"lengthMenu\": [5, 10, 20, 50, 100, 200], \"initComplete\": function () {\n",
+       "    // Apply the search\n",
+       "    this.api()\n",
+       "        .columns()\n",
+       "        .every(function () {\n",
+       "            const that = this;\n",
+       "\n",
+       "            $('input', this.header()).on('keyup change clear', function () {\n",
+       "                if (that.search() !== this.value) {\n",
+       "                    that.search(this.value).draw();\n",
+       "                }\n",
+       "            });\n",
+       "        });\n",
+       "}\n",
+       "};\n",
+       "        dt_args[\"data\"] = data;\n",
+       "\n",
+       "        // Display the table\n",
+       "        $(document).ready(function () {\n",
+       "            // Setup - add a text input to each header or footer cell\n",
+       "$('#bfe67991-2c27-4fd3-b544-c37b65897ab3 thead th').each(function () {\n",
+       "    let title = $(this).text();\n",
+       "    $(this).html('<input type=\"text\" placeholder=\"Search ' +\n",
+       "        // We use encodeURI to avoid this LGTM error:\n",
+       "        // https://lgtm.com/rules/1511866576920/\n",
+       "        encodeURI(title).replaceAll(\"%20\", \" \") +\n",
+       "        '\" />');\n",
+       "});\n",
+       "\n",
+       "            window.__itables_render('#bfe67991-2c27-4fd3-b544-c37b65897ab3', dt_args);\n",
+       "        });\n",
+       "    }\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from itables import init_notebook_mode,show\n",
+    "\n",
+    "init_notebook_mode(all_interactive=False)\n",
+    "\n",
+    "import itables.options as opt\n",
+    "\n",
+    "show(pd.DataFrame(final),dom=\"ftpr\")\n",
+    "\n",
+    "opt.lengthMenu=[5,10,20,50,100,200]\n",
+    "\n",
+    "opt.classes=[\"display\",\"nowrap\"]\n",
+    "\n",
+    "show(final,columnDefs=[{\"className\": \"dt-left\", \"targets\": \"_all\"}],column_filters=\"header\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, there is a recount by the different columns, *Behaviour*, *Relationship* and *Time moved*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Type of behaviour</th>\n",
+       "      <th>Number of times repeated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>4723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Negative</td>\n",
+       "      <td>1922</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Type of behaviour  Number of times repeated\n",
+       "1          Positive                      4723\n",
+       "0          Negative                      1922"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columnssf=final.Behaviour.to_list()\n",
+    "columnsf=np.unique(columnssf)\n",
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "\n",
+    "final_Behaviour = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Type of behaviour','Number of times repeated'])\n",
+    "final_Behaviour=final_Behaviour.sort_values(by = 'Number of times repeated',ascending = False)\n",
+    "final_Behaviour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Type of relationship</th>\n",
+       "      <th>Number of times repeated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Linear</td>\n",
+       "      <td>1294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cubic</td>\n",
+       "      <td>419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cuadratic</td>\n",
+       "      <td>253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Type of relationship  Number of times repeated\n",
+       "3               Linear                      1294\n",
+       "4          Logarithmic                       687\n",
+       "1                Cubic                       419\n",
+       "0            Cuadratic                       253"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columnssf=final.Type.to_list()\n",
+    "columnsf=np.unique(columnssf)\n",
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "\n",
+    "final_type = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Type of relationship','Number of times repeated'])\n",
+    "final_type=final_type.sort_values(by = 'Number of times repeated',ascending = False)\n",
+    "final_type.drop([2], inplace=True)\n",
+    "final_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the table above, we can observe which is the most common type of relationship."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time moved</th>\n",
+       "      <th>Number of times repeated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>2653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8</td>\n",
+       "      <td>902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21</td>\n",
+       "      <td>474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>338</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Time moved  Number of times repeated\n",
+       "7  Does not apply                      2653\n",
+       "6               8                       902\n",
+       "5               5                       741\n",
+       "1              13                       607\n",
+       "0               1                       516\n",
+       "3              21                       474\n",
+       "2               2                       414\n",
+       "4               3                       338"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columnssf=final.Moved.to_list()\n",
+    "columnsf=np.unique(columnssf)\n",
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "\n",
+    "final_moved = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Time moved','Number of times repeated'])\n",
+    "final_moved=final_moved.sort_values(by = 'Number of times repeated',ascending = False)\n",
+    "final_moved"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As said, the temporary displacement shows us correlation of an indicator with the GDP comparing different periods of time. In this table, we have made a recount to see which range of displacement is most repeated. As we can see, no displacement is the most common case, what means correlation year-by-year is majority in our sample."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization of results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Needed imports\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import plotly.express as px\n",
+    "\n",
+    "import plotly.graph_objects as go\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we’ve loaded the data, we can start right away to create widgets. These widgets are essentials to add interactivity to our visualizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        define('plotly', function(require, exports, module) {\n",
+       "            /**\n",
+       "* plotly.js v2.9.0\n",
+       "* Copyright 2012-2022, Plotly, Inc.\n",
+       "* All rights reserved.\n",
+       "* Licensed under the MIT license\n",
+       "*/\n",
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+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <feross@feross.org> <http://feross.org>\n",
+       " * @license  MIT\n",
+       " */function i(t,e){if(t===e)return 0;for(var r=t.length,n=e.length,i=0,a=Math.min(r,n);i<a;++i)if(t[i]!==e[i]){r=t[i],n=e[i];break}return r<n?-1:n<r?1:0}function a(t){return r.Buffer&&\"function\"==typeof r.Buffer.isBuffer?r.Buffer.isBuffer(t):!(null==t||!t._isBuffer)}var o=t(\"util/\"),s=Object.prototype.hasOwnProperty,l=Array.prototype.slice,c=\"foo\"===function(){}.name;function u(t){return Object.prototype.toString.call(t)}function f(t){return!a(t)&&(\"function\"==typeof r.ArrayBuffer&&(\"function\"==typeof ArrayBuffer.isView?ArrayBuffer.isView(t):!!t&&(t instanceof DataView||!!(t.buffer&&t.buffer instanceof ArrayBuffer))))}var h=e.exports=y,p=/\\s*function\\s+([^\\(\\s]*)\\s*/;function d(t){if(o.isFunction(t)){if(c)return t.name;var e=t.toString().match(p);return e&&e[1]}}function g(t,e){return\"string\"==typeof t?t.length<e?t:t.slice(0,e):t}function m(t){if(c||!o.isFunction(t))return o.inspect(t);var e=d(t);return\"[Function\"+(e?\": \"+e:\"\")+\"]\"}function 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+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
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t[0]=s*(h*v-p*m)-f*(l*v-c*m)+g*(l*p-c*h),t[1]=-(n*(h*v-p*m)-f*(i*v-a*m)+g*(i*p-a*h)),t[2]=n*(l*v-c*m)-s*(i*v-a*m)+g*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*m)-u*(l*v-c*m)+d*(l*p-c*h)),t[5]=r*(h*v-p*m)-u*(i*v-a*m)+d*(i*p-a*h),t[6]=-(r*(l*v-c*m)-o*(i*v-a*m)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*g)-u*(s*v-c*g)+d*(s*p-c*f),t[9]=-(r*(f*v-p*g)-u*(n*v-a*g)+d*(n*p-a*f)),t[10]=r*(s*v-c*g)-o*(n*v-a*g)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*m-h*g)-u*(s*m-l*g)+d*(s*h-l*f)),t[13]=r*(f*m-h*g)-u*(n*m-i*g)+d*(n*h-i*f),t[14]=-(r*(s*m-l*g)-o*(n*m-i*g)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],196:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return 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t[0]=1-(p+g),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=h+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],203:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],204:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],205:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],206:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=g-_,h=m-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{\"./identity\":209}],213:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*g,t[1]=x*i+b*l+_*h+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*g,t[5]=x*i+b*l+_*h+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*g,t[9]=x*i+b*l+_*h+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],217:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+g*S,t[9]=l*A+h*M+m*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],219:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],220:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],221:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],222:[function(t,e,r){e.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},{}],223:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],224:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],225:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),g=t(\"detect-kerning\"),m=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);m(t.kerning,g(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var q;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=q}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var 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this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new 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r=e.parse(t[1],1,Bt);if(!r)return null;var n=[],i=null;e.expectedType&&\"value\"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,c=a+1;if(\"number\"!=typeof o)return e.error('Input/output pairs for \"step\" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for \"step\" expressions must be arranged with input values in strictly ascending order.',l);var u=e.parse(s,c,i);if(!u)return null;i=i||u.type,n.push([o,u])}return new He(i,r,n)},He.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[Ve(e,n)].evaluate(t)},He.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},He.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},He.prototype.serialize=function(){for(var t=[\"step\",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var Ge=Object.freeze({__proto__:null,number:qe,color:function(t,e,r){return new te(qe(t.r,e.r,r),qe(t.g,e.g,r),qe(t.b,e.b,r),qe(t.a,e.a,r))},array:function(t,e,r){return t.map((function(t,n){return qe(t,e[n],r)}))}}),Ye=6/29,We=3*Ye*Ye,Xe=Math.PI/180,Ze=180/Math.PI;function Je(t){return t>.008856451679035631?Math.pow(t,1/3):t/We+4/29}function Ke(t){return t>Ye?t*t*t:We*(t-4/29)}function Qe(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function $e(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function tr(t){var e=$e(t.r),r=$e(t.g),n=$e(t.b),i=Je((.4124564*e+.3575761*r+.1804375*n)/.95047),a=Je((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-Je((.0193339*e+.119192*r+.9503041*n)/1.08883)),alpha:t.a}}function er(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*Ke(e),r=.95047*Ke(r),n=1.08883*Ke(n),new te(Qe(3.2404542*r-1.5371385*e-.4985314*n),Qe(-.969266*r+1.8760108*e+.041556*n),Qe(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function rr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var nr={forward:tr,reverse:er,interpolate:function(t,e,r){return{l:qe(t.l,e.l,r),a:qe(t.a,e.a,r),b:qe(t.b,e.b,r),alpha:qe(t.alpha,e.alpha,r)}}},ir={forward:function(t){var e=tr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*Ze;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*Xe,r=t.c;return er({l:t.l,a:Math.cos(e)*r,b:Math.sin(e)*r,alpha:t.alpha})},interpolate:function(t,e,r){return{h:rr(t.h,e.h,r),c:qe(t.c,e.c,r),l:qe(t.l,e.l,r),alpha:qe(t.alpha,e.alpha,r)}}},ar=Object.freeze({__proto__:null,lab:nr,hcl:ir}),or=function(t,e,r,n,i){this.type=t,this.operator=e,this.interpolation=r,this.input=n,this.labels=[],this.outputs=[];for(var a=0,o=i;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1];this.labels.push(l),this.outputs.push(c)}};function sr(t,e,r,n){var i=n-r,a=t-r;return 0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}or.interpolationFactor=function(t,e,n,i){var a=0;if(\"exponential\"===t.name)a=sr(e,t.base,n,i);else if(\"linear\"===t.name)a=sr(e,1,n,i);else if(\"cubic-bezier\"===t.name){var o=t.controlPoints;a=new r(o[0],o[1],o[2],o[3]).solve(sr(e,1,n,i))}return a},or.parse=function(t,e){var r=t[0],n=t[1],i=t[2],a=t.slice(3);if(!Array.isArray(n)||0===n.length)return e.error(\"Expected an interpolation type expression.\",1);if(\"linear\"===n[0])n={name:\"linear\"};else if(\"exponential\"===n[0]){var o=n[1];if(\"number\"!=typeof o)return e.error(\"Exponential interpolation requires a numeric base.\",1,1);n={name:\"exponential\",base:o}}else{if(\"cubic-bezier\"!==n[0])return e.error(\"Unknown interpolation type \"+String(n[0]),1,0);var s=n.slice(1);if(4!==s.length||s.some((function(t){return\"number\"!=typeof t||t<0||t>1})))return e.error(\"Cubic bezier interpolation requires four numeric arguments with values between 0 and 1.\",1);n={name:\"cubic-bezier\",controlPoints:s}}if(t.length-1<4)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if((t.length-1)%2!=0)return e.error(\"Expected an even number of arguments.\");if(!(i=e.parse(i,2,Bt)))return null;var l=[],c=null;\"interpolate-hcl\"===r||\"interpolate-lab\"===r?c=Ut:e.expectedType&&\"value\"!==e.expectedType.kind&&(c=e.expectedType);for(var u=0;u<a.length;u+=2){var f=a[u],h=a[u+1],p=u+3,d=u+4;if(\"number\"!=typeof f)return e.error('Input/output pairs for 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?Ht:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||Ht));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,Ht])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,Ht]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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g?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,g):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var dr=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};dr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=0)return e.error(\"Expected an odd number of arguments.\");var r;e.expectedType&&\"value\"!==e.expectedType.kind&&(r=e.expectedType);for(var n=[],i=1;i<t.length-1;i+=2){var a=e.parse(t[i],i,jt);if(!a)return null;var o=e.parse(t[i+1],i+1,r);if(!o)return null;n.push([a,o]),r=r||o.type}var s=e.parse(t[t.length-1],t.length-1,r);return s?new dr(r,n,s):null},dr.prototype.evaluate=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];if(i.evaluate(t))return a.evaluate(t)}return this.otherwise.evaluate(t)},dr.prototype.eachChild=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t(i),t(a)}t(this.otherwise)},dr.prototype.outputDefined=function(){return this.branches.every((function(t){t[0];return 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or?a.interpolation:void 0;return Ir(new Zr(n?\"camera\":\"composite\",t.value,a.labels,o))}Zr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},Zr.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},Zr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?or.interpolationFactor(this.interpolationType,t,e,r):0};var Kr=function(t,e){this._parameters=t,this._specification=e,It(this,function t(e,r){var n,i,a,o=\"color\"===r.type,s=e.stops&&\"object\"==typeof e.stops[0][0],l=s||void 0!==e.property,c=s||!l,u=e.type||(Rr(r)?\"exponential\":\"interval\");if(o&&((e=It({},e)).stops&&(e.stops=e.stops.map((function(t){return[t[0],te.parse(t[1])]}))),e.default?e.default=te.parse(e.default):e.default=te.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!ar[e.colorSpace])throw new Error(\"Unknown color space: 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n&&r<n.minimum?[new Ct(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Ct(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function en(t){var e,r,n,i=t.valueSpec,a=Ot(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Fr(t.value.stops)&&\"array\"===Fr(t.value.stops[0])&&\"object\"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Ct(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,\"array must have at least one stop\"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ne(r,[\"zoom\",\"feature-state\"]))return[new Ct(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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on={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?un(t[1],t[2],\"==\"):\"!=\"===r?pn(un(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?un(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(cn))):\"all\"===r?[\"all\"].concat(t.slice(1).map(cn)):\"none\"===r?[\"all\"].concat(t.slice(1).map(cn).map(pn)):\"in\"===r?fn(t[1],t.slice(2)):\"!in\"===r?pn(fn(t[1],t.slice(2))):\"has\"===r?hn(t[1]):\"!has\"===r?pn(hn(t[1])):\"within\"!==r||t}function un(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(ln)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function hn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function pn(t){return[\"!\",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==Fr(r))return[new Ct(n,r,\"array expected, \"+Fr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat(nn({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Ot(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Ot(r[1])&&o.push(new Ct(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new Ct(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=Fr(r[1]))&&o.push(new Ct(n+\"[1]\",r[1],\"string expected, \"+i+\" 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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Kr(t,e);if(Yr(t)){var r=Jr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=te.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var gi=function(t){this.property=t,this.value=new di(t,void 0)};gi.prototype.transitioned=function(t,e){return new vi(this.property,this.value,e,u({},t.transition,this.transition),t.now)},gi.prototype.untransitioned=function(){return new vi(this.property,this.value,null,{},0)};var mi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};mi.prototype.getValue=function(t){return x(this._values[t].value.value)},mi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new gi(this._values[t].property)),this._values[t].value=new di(this._values[t].property,null===e?void 0:x(e))},mi.prototype.getTransition=function(t){return x(this._values[t].transition)},mi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new gi(this._values[t].property)),this._values[t].transition=x(e)||void 0},mi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},mi.prototype.transitioned=function(t,e){for(var r=new yi(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},mi.prototype.untransitioned=function(){for(var t=new yi(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new gi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Ai),Nn(\"ColorRampProperty\",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new mi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return m(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(m(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=g,this.uint32[x+10]=m,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=g,this.uint16[L+15]=m,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn(\"StructArrayLayout8i15ui1ul4f68\",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1f4\",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3i6\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn(\"StructArrayLayout1ul2ui8\",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2ui4\",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn(\"StructArrayLayout1ui2\",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn(\"CollisionBoxArray\",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn(\"PlacedSymbolArray\",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn(\"SymbolInstanceArray\",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn(\"GlyphOffsetArray\",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn(\"SymbolLineVertexArray\",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=Ha(a,c,\"source\");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push(\"/a_\"+a)}else{var d=Ha(a,c,\"composite\");this.binders[a]=new Ba(l,s,c,u,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof Ra&&n.setConstantPatternPositions(t,e)}},ja.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof Fa||h instanceof Ba||h instanceof Na)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},ja.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof Da||r instanceof Ra)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},ja.prototype.getPaintVertexBuffers=function(){return this._buffers},ja.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof Da||i instanceof Ra||i instanceof Ba)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},ja.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,c=o.binding;this.binders[l].setUniform(c,n,r.get(l),s)}},ja.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Na){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Fa||r instanceof Ba)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},ja.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Fa||r instanceof Ba||r instanceof Na)&&r.upload(t)}this.updatePaintBuffers()},ja.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Fa||e instanceof Ba||e instanceof Na)&&e.destroy()}};var Ua=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new ja(o,r,n,t)}this.needsUpload=!1,this._featureMap=new _a,this._bufferOffset=0};function Va(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function Ha(t,e,r){var n={color:{source:ta,composite:ea},number:{source:Zi,composite:ta}},i=function(t){return{\"line-pattern\":{source:Bi,composite:Bi},\"fill-pattern\":{source:Bi,composite:Bi},\"fill-extrusion-pattern\":{source:Bi,composite:Bi}}[t]}(t);return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(gs(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var g=0,m=0;m<d.length;m++){var v=d[m];if(m>=1){var y=d[m-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);g+b>32768&&(g=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,g),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,g),g+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,g),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,g);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,g=0,m=t;g<m.length;g+=1){for(var v=m[g],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),g=u*h-f*f,m=(h*p-f*d)/g,v=(u*d-f*p)/g,y=1-m-v,x=i.z*y+r.z*m+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,g={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(g)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var m=0,v=i;m<v.length;m+=1){var y=v[m],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=gs(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,g=void 0,m=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],m=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){m&&(g=m),u&&(p=u),u=t[v],m=d?d.sub(u)._unit()._perp():g;var y=(g=g||m).add(m);0===y.x&&0===y.y||y._unit();var x=g.x*m.x+g.y*m.y,b=y.x*m.x+y.y*m.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=g.x*m.y-g.y*m.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,g,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=m.mult(-1);else{var L=_*g.add(m).mag()/g.sub(m).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,g,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=m.sub(g)._mult(D)._add(g)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,m,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,g,0,0,h),this.addCurrentVertex(u,g,1,1,h,!0)),d&&(this.addCurrentVertex(u,m,-1,-1,h,!0),this.addCurrentVertex(u,m,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,m,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",qs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new wi(Lt.layout_line[\"line-cap\"]),\"line-join\":new Ti(Lt.layout_line[\"line-join\"]),\"line-miter-limit\":new wi(Lt.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new wi(Lt.layout_line[\"line-round-limit\"]),\"line-sort-key\":new Ti(Lt.layout_line[\"line-sort-key\"])}),Ys={paint:new Si({\"line-opacity\":new Ti(Lt.paint_line[\"line-opacity\"]),\"line-color\":new Ti(Lt.paint_line[\"line-color\"]),\"line-translate\":new wi(Lt.paint_line[\"line-translate\"]),\"line-translate-anchor\":new wi(Lt.paint_line[\"line-translate-anchor\"]),\"line-width\":new Ti(Lt.paint_line[\"line-width\"]),\"line-gap-width\":new Ti(Lt.paint_line[\"line-gap-width\"]),\"line-offset\":new Ti(Lt.paint_line[\"line-offset\"]),\"line-blur\":new Ti(Lt.paint_line[\"line-blur\"]),\"line-dasharray\":new Ai(Lt.paint_line[\"line-dasharray\"]),\"line-pattern\":new ki(Lt.paint_line[\"line-pattern\"]),\"line-gradient\":new Mi(Lt.paint_line[\"line-gradient\"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),g=d.x*p.x+d.y*p.y;d._mult(1/g),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,g=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*g},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&ul(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),il(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),il(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,g=d.x+1,m=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:g,y:m},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:g,y:m-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:g,y:m+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:g-1,y:m},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:g+v,y:m},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn(\"ImagePosition\",Sl),Nn(\"ImageAtlas\",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){m=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);m.push(T)}}else if(x){m=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,m.push(E)}}else m=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,g=0,m=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn[\"Arabic Supplement\"](Y)||Yn[\"Arabic Extended-A\"](Y)||Yn[\"Arabic Presentation Forms-A\"](Y)||Yn[\"Arabic Presentation Forms-B\"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,m,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,g=Math.max(G,g),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,g=p.positionedGlyphs;d<g.length;d+=1){var m=g[d];m.x+=c,m.y+=u}})(t.positionedLines,m,Z,J,d,g,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,m,o,s,l,f,c,h,g),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var 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e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function gc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function mc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,g){var m=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,g=p.positionedGlyphs;d<g.length;d+=1){var m=g[d];if(m.rect){var v=m.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&m.vertical,T=m.metrics.advance*m.scale/2;if(l&&e.verticalizable){var k=24*(m.scale-1),A=(24-m.metrics.width*m.scale)/2;_=p.lineOffset/2-(m.imageName?-A:k)}if(m.imageName){var M=s[m.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[m.x+T,m.y]:[0,0],E=a?[0,0]:[m.x+T+r[0],m.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(m.metrics.left-y)*m.scale-T+E[0],P=(-m.metrics.top-y)*m.scale+E[1],I=C+v.w*m.scale/b,O=P+v.h*m.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=m.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:m.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'):\"composite\"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},g),128*d.compositeTextSizes[1].evaluate(s,{},g)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'),t.addSymbols(t.text,m,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,g);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*m.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),g=0,m=t;g<m.length;g+=1){var v=m[g],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,f),m=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[m].geometry,!0)]=m,n[g].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),g=this.glyphOffsetArray.length,m=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,g,this.glyphOffsetArray.length-g,m,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var g=t.get(d);c.iconBox={x1:g.x1,y1:g.y1,x2:g.x2,y2:g.y2,anchorPointX:g.anchorPointX,anchorPointY:g.anchorPointY},c.iconFeatureIndex=g.featureIndex;break}for(var m=s;m<l;m++){var v=t.get(m);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Mc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Ai(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g([\"receive\",\"process\"],this),this.invoker=new Hc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),g=0,m=d;g<m.length;g+=1){var v=m[g];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),g=0;g<f.length;g++){var m=f[g];if(!(a&&a.indexOf(m)<0)){var v=s[m];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[m];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[m];void 0===w&&(w=t[m]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),g=new su(p,l,c,u,d);g.tile=f,t.push(g)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var 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r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=go,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new 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m=(c+1)%2;(0===c?r-i<=d:n-i<=g)&&(o.push(f),o.push(p-1),o.push(m)),(0===c?r+i>=d:n+i>=g)&&(o.push(p+1),o.push(u),o.push(m))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var j={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},U=function(t){this.options=X(Object.create(j),t),this.trees=new Array(this.options.maxZoom+1)};function V(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function H(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function q(t){return{type:\"Feature\",id:t.id,properties:G(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function G(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function tt(t,e,r,n){if(e.geometry){var i=e.geometry.coordinates,a=e.geometry.type,o=Math.pow(r.tolerance/((1<<r.maxZoom)*r.extent),2),s=[],l=e.id;if(r.promoteId?l=e.properties[r.promoteId]:r.generateId&&(l=n||0),\"Point\"===a)et(i,s);else if(\"MultiPoint\"===a)for(var c=0;c<i.length;c++)et(i[c],s);else if(\"LineString\"===a)rt(i,s,o,!1);else if(\"MultiLineString\"===a){if(r.lineMetrics){for(c=0;c<i.length;c++)s=[],rt(i[c],s,o,!1),t.push(Q(l,\"LineString\",s,e.properties));return}nt(i,s,o,!1)}else if(\"Polygon\"===a)nt(i,s,o,!0);else{if(\"MultiPolygon\"!==a){if(\"GeometryCollection\"===a){for(c=0;c<e.geometry.geometries.length;c++)tt(t,{id:l,geometry:e.geometry.geometries[c],properties:e.properties},r,n);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(c=0;c<i.length;c++){var 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u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var g=[];if(\"Point\"===h||\"MultiPoint\"===h)st(f,g,r,n,i);else if(\"LineString\"===h)lt(f,g,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ut(f,g,r,n,i,!1);else if(\"Polygon\"===h)ut(f,g,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var m=0;m<f.length;m++){var v=[];ut(f[m],v,r,n,i,!0),v.length&&g.push(v)}if(g.length){if(s.lineMetrics&&\"LineString\"===h){for(m=0;m<g.length;m++)l.push(Q(u.id,h,g[m],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h||(1===g.length?(h=\"LineString\",g=g[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h||(h=3===g.length?\"Point\":\"MultiPoint\"),l.push(Q(u.id,h,g,u.tags))}}}return l.length?l:null}function st(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function lt(t,e,r,n,i,a,o){for(var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=gt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(gt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(gt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function gt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function mt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function 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u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new m(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,g=o&&h>1?this._map(c,!0):null,m=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=m,o&&(g||(g=this._map(c,!0)),o(g,this._map(b)))}}1===h?r.push(c):(c.parentId=m,r.push(V(p/h,d/h,m,h,g)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;g=m=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(g=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),m=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?mt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],g=t.createExpression(d),m=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=g.value,a[f]=m.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON 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t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=m,d=m,g=1e20;function m(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-g,n[1]=+g;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+g}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}m.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?g:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?g:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var g=n-Math.abs(d);p=Math.sqrt(h*h+g*g)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var 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e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new mt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],g=this._tiles[p];if(g&&!(g.fadeEndTime&&g.fadeEndTime<=t.browser.now())){var m=this.findLoadedParent(d,o);m&&(this._addTile(m.tileID),c[m.tileID.key]=m.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],g=this._tiles[d.key];if(!g.hasData()){if(e+1>this._source.maxzoom){var m=d.children(this._source.maxzoom)[0],v=this.getTile(m);if(v&&v.hasData()){n[m.key]=m;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=g.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,g=0,m=c;g<m.length;g+=1){var v=m[g];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,g=Math.pow(2,o.zoom-n.tileID.overscaledZ),m=r*n.queryPadding*t.EXTENT/n.tileSize/g,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-m<t.EXTENT&&v[0].y-m<t.EXTENT&&v[1].x+m>=0&&v[1].y+m>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:g})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),g=f.slice(),m=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],g[g.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,c=g[g.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),g.splice(g.length-i,0,o),m[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!m[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!m)ue(y.numGlyphs,h);else{m=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,g);m=S.useVertical,(S.notEnoughRoom||m||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,g).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),g=e.getoffsetX(f-1),m=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!m)return null;var v=le(t*g,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:m,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=r/24,v=e.lineOffsetX*m,y=e.lineOffsetY*m;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(m,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}g=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)g.push(le(m*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));g.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(m*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};g=[O]}for(var z=0,D=g;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,g=0;i&&(d*=-1,g=Math.PI),d<0&&(g+=Math.PI);for(var m=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((m+=d)<l||m>=c)return null;if(y=v,w.push(v),void 0===(v=h[m])){var T=new t.Point(u.getx(m),u.gety(m)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[m]=k.point;else{var A=m-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=g+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(m[b],m[b+1],m[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=m[b],w=m[b+1],T=m[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var g=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[g+0],f[g+1],f[g+2],f[g+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var m=this.circleCells[i];if(null!==m)for(var v=this.circles,y=0,x=m;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=new t.Point(r.anchorX,r.anchorY),m=te(g,o),v=ee(this.transform.cameraToCenterDistance,m.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(g,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,g,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],m=g.key;if(void 0===f[m.bucketInstanceId]&&(f[m.bucketInstanceId]={}),!f[m.bucketInstanceId][m.featureIndex]){var v=[new t.Point(g.x1,g.y1),new t.Point(g.x2,g.y1),new t.Point(g.x2,g.y2),new t.Point(g.x1,g.y2)];t.polygonIntersectsPolygon(r,v)&&(f[m.bucketInstanceId][m.featureIndex]=!0,void 0===h[m.bucketInstanceId]&&(h[m.bucketInstanceId]=[]),h[m.bucketInstanceId].push(m.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var ge=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};ge.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var me=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),g=Qt(f,h,p,this.transform,d),m=null;if(h){var v=$t(f,h,p,this.transform,d);m=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:g,labelToScreenMatrix:m,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=[f.textOffset0,f.textOffset1],v=be(t,r,n,m,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(g=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:m,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:g},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,g=a.collisionGroup,m=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||m);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new me(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var m=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(m,t,a,s,c,b,_,f,l,g,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,g.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,g.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,g.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=m||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,g.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,g.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,g.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,g.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new me(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new ge(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new ge(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new ge(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new ge(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new ge(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},g=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,g=a.crossTileID,m=r[g],v=i.opacities[g];m?v=o:v||(v=p,i.opacities[g]=v),r[g]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[g];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},m=0;m<e.symbolInstances.length;m++)g(m);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,g=l.stretchX,m=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:g,stretchY:m,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var g=this._order[d];if(r(g))for(var m=i.length-1;m>=0;m--){var v=i[m].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][g];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=g[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,g=c;d<g.length;d+=1)p();var m=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)m(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var g=0,m=this._order;g<m.length;g+=1){var v=m[g],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),gr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),mr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:gr,symbolSDF:mr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var m=g[d];m.bind(),m.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var g=a.getActiveAttrib(this.program,d);g&&(this.attributes[g.name]=a.getAttribLocation(this.program,g.name))}for(var m=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<m;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(m={},m[v.LINES]=2,m[v.TRIANGLES]=3,m[v.LINE_STRIP]=1,m)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var g=i[d],m=r.getTile(g),v=m.getBucket(n);if(v){var y=g.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(g.posMatrix,m,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,m),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,g={};p.clear();for(var m=0;m<h.length;m++){var v=h.get(m),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(g[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=g[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,g=e.transform,m=\"map\"===l,v=\"map\"===c,y=m&&\"point\"!==n.layout.get(\"symbol-placement\"),x=m&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==g.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,g.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,m,e.transform,q),Y=$t(M.posMatrix,v,m,e.transform,q),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),g=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var m=0,v=n;m<v.length;m+=1){var y=v[m],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(g));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,g,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,g,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var g=d[p],m=e.getTile(g),v=m.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),m.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&m.imageAtlas){var _=m.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(g.posMatrix,m,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=u?kr(k,t,A,h,g,f,m):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var g=r.fbo;if(!g){var m=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);m.bind(c.LINEAR,c.CLAMP_TO_EDGE),(g=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(m.texture)}l.bindFramebuffer.set(g.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function gn(t){var e=t.transform.padding;mn(t,t.transform.height-(e.top||0),3,un),mn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function mn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,g=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,m=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(m+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,m+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,g),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),g=d.getBucket(n);if(g&&g.text&&g.text.segments.get().length){var m=g.textSizeData,v=t.evaluateSizeForZoom(m,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&g.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(g,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],g=0;g<i.length;g++){var m=i[g],v=r.getTile(m),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,m,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),g=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,g,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",g=e.context,m=g.gl,v=!0;if(h){g.activeTexture.set(m.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(g,n.gradient,m.RGBA)),y.bind(m.LINEAR,m.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(g.activeTexture.set(m.TEXTURE0),w.imageAtlasTexture.bind(m.LINEAR,m.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(g.activeTexture.set(m.TEXTURE0),e.lineAtlas.bind(g)),M.draw(g,m.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,g=f;d<g.length;d+=1){var m=g[d],v=t.depthModeForSublayer(m.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(m),x=t.transform.calculatePosMatrix(m.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(m,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[m.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),g=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var m=r.getCrossfadeParameters(),v=0,y=g;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},m):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o=\"3d\"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var g=0;g<i.length;g++){var m=i[g];if(this.style._layers[m].is3D()){this.opaquePassCutoff=g;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&gn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var g=u.pop(),m=g.x,v=g.y,y=g.fullyVisible;if(!y){var x=g.aabb.intersects(s);if(0===x)continue;y=2===x}var b=g.aabb.distanceX(o),_=g.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-g.zoom)-2;if(g.zoom===h||w>T&&g.zoom>=l)f.push({tileID:new t.OverscaledTileID(g.zoom===h?p:g.zoom,g.wrap,g.zoom,m,v),distanceSq:t.sqrLen([o[0]-.5-m,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(m<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:g.aabb.quadrant(k),zoom:g.zoom+1,x:A,y:M,wrap:g.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var g=p.y,m=c.y/2;g-m<a&&(i=a+m),g+m>o&&(i=o-m)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),g=Math.sin(this.angle),m=s-Math.round(s)+d*h+g*p,v=l-Math.round(l)+d*p+g*h,y=new Float64Array(f);if(t.translate(y,y,[m>.5?m-1:m,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var gi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},mi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var g=e.keyboard=new oi;this._add(\"keyboard\",g),this._add(\"blockableMapEvent\",new jn(e));for(var m=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];m<v.length;m+=1){var y=v[m];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(gi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var g=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(g=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,g,h,n),g&&g.needsRenderFrame&&this._triggerRenderFrame()),(g||p.isActive())&&(o[h]=p)}}var m={};for(var v in this._previousActiveHandlers)o[v]||(m[v]=n);this._previousActiveHandlers=o,(Object.keys(m).length||vi(i))&&(this._changes.push([i,a,m]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=gi(this._eventsInProgress),a=gi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var g=this._eventsInProgress[d],m=g.handlerName,v=g.originalEvent;this._handlersById[m].isActive()||(delete this._eventsInProgress[d],h=r[m]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=gi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new mi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),g=p.sub(d),m=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/g.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/g.y;if(!(v<0||m<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(m,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),g=i.pointLocation(d),m=t.LngLat.convert(e.center||g);this._normalizeCenter(m);var v,y,x=i.project(g),b=i.project(m).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var g=i.zoomScale(i.zoom-a),m=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(m,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(g));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),g=t.Point.convert(e.offset),m=a.centerPoint.add(g),v=a.pointLocation(m),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),m=a.centerPoint.add(g));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,m),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ai))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,g=r.data,m=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(g)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:m}),m.onAdd&&m.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");g.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(g);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"opacity\",\"0.25\"),m.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),m.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(m),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Hi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],240:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return\"altKey\"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),\"shiftKey\"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),\"ctrlKey\"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),\"metaKey\"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);\"buttons\"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function g(t){c(r&~n.buttons(t),t)}function m(){s||(s=!0,t.addEventListener(\"mousemove\",p),t.addEventListener(\"mousedown\",d),t.addEventListener(\"mouseup\",g),t.addEventListener(\"mouseleave\",u),t.addEventListener(\"mouseenter\",u),t.addEventListener(\"mouseout\",u),t.addEventListener(\"mouseover\",u),t.addEventListener(\"blur\",f),t.addEventListener(\"keyup\",h),t.addEventListener(\"keydown\",h),t.addEventListener(\"keypress\",h),t!==window&&(window.addEventListener(\"blur\",f),window.addEventListener(\"keyup\",h),window.addEventListener(\"keydown\",h),window.addEventListener(\"keypress\",h)))}m();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?m():function(){if(!s)return;s=!1,t.removeEventListener(\"mousemove\",p),t.removeEventListener(\"mousedown\",d),t.removeEventListener(\"mouseup\",g),t.removeEventListener(\"mouseleave\",u),t.removeEventListener(\"mouseenter\",u),t.removeEventListener(\"mouseout\",u),t.removeEventListener(\"mouseover\",u),t.removeEventListener(\"blur\",f),t.removeEventListener(\"keyup\",h),t.removeEventListener(\"keydown\",h),t.removeEventListener(\"keypress\",h),t!==window&&(window.removeEventListener(\"blur\",f),window.removeEventListener(\"keyup\",h),window.removeEventListener(\"keydown\",h),window.removeEventListener(\"keypress\",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t(\"mouse-event\")},{\"mouse-event\":243}],242:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],243:[function(t,e,r){\"use strict\";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if(\"object\"==typeof t){if(\"buttons\"in t)return t.buttons;if(\"which\"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if(\"button\"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if(\"object\"==typeof t){if(\"offsetX\"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if(\"object\"==typeof t){if(\"offsetY\"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],244:[function(t,e,r){\"use strict\";var n=t(\"to-px\");e.exports=function(t,e,r){\"function\"==typeof t&&(r=!!e,e=t,t=window);var i=n(\"ex\",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener(\"wheel\",a),a}},{\"to-px\":314}],245:[function(t,e,r){(function(t,r){(function(){\n",
+       "/*! Native Promise Only\n",
+       "    v0.8.1 (c) Kyle Simpson\n",
+       "    MIT License: http://getify.mit-license.org\n",
+       "*/\n",
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+       "/*\n",
+       "object-assign\n",
+       "(c) Sindre Sorhus\n",
+       "@license MIT\n",
+       "*/\n",
+       "\"use strict\";var n=Object.getOwnPropertySymbols,i=Object.prototype.hasOwnProperty,a=Object.prototype.propertyIsEnumerable;function o(t){if(null==t)throw new TypeError(\"Object.assign cannot be called with null or undefined\");return Object(t)}e.exports=function(){try{if(!Object.assign)return!1;var t=new String(\"abc\");if(t[5]=\"de\",\"5\"===Object.getOwnPropertyNames(t)[0])return!1;for(var e={},r=0;r<10;r++)e[\"_\"+String.fromCharCode(r)]=r;if(\"0123456789\"!==Object.getOwnPropertyNames(e).map((function(t){return e[t]})).join(\"\"))return!1;var n={};return\"abcdefghijklmnopqrst\".split(\"\").forEach((function(t){n[t]=t})),\"abcdefghijklmnopqrst\"===Object.keys(Object.assign({},n)).join(\"\")}catch(t){return!1}}()?Object.assign:function(t,e){for(var r,s,l=o(t),c=1;c<arguments.length;c++){for(var u in r=Object(arguments[c]))i.call(r,u)&&(l[u]=r[u]);if(n){s=n(r);for(var f=0;f<s.length;f++)a.call(r,s[f])&&(l[s[f]]=r[s[f]])}}return l}},{}],248:[function(t,e,r){\"use strict\";function n(t,e){if(\"string\"!=typeof t)return[t];var r=[t];\"string\"==typeof e||Array.isArray(e)?e={brackets:e}:e||(e={});var n=e.brackets?Array.isArray(e.brackets)?e.brackets:[e.brackets]:[\"{}\",\"[]\",\"()\"],i=e.escape||\"___\",a=!!e.flat;n.forEach((function(t){var e=new RegExp([\"\\\\\",t[0],\"[^\\\\\",t[0],\"\\\\\",t[1],\"]*\\\\\",t[1]].join(\"\")),n=[];function a(e,a,o){var s=r.push(e.slice(t[0].length,-t[1].length))-1;return n.push(s),i+s+i}r.forEach((function(t,n){for(var i,o=0;t!=i;)if(i=t,t=t.replace(e,a),o++>1e4)throw Error(\"References have circular dependency. 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+       "/*\n",
+       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
+       " * @license MIT\n",
+       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
+       " */\n",
+       "var n,i=t(\"./lib/build-log\"),a=t(\"./lib/epsilon\"),o=t(\"./lib/intersecter\"),s=t(\"./lib/segment-chainer\"),l=t(\"./lib/segment-selector\"),c=t(\"./lib/geojson\"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return 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r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:\"Polygon\",coordinates:[]}:1==u.length?{type:\"Polygon\",coordinates:u[0]}:{type:\"MultiPolygon\",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t(\"./linked-list\");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),g=!p&&e.pointBetween(s,c,u);if(h)return g?l(n,s):l(t,u),n;d&&(p||(g?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,g=p.after?p.after.ev:null;function m(){if(d){var t=u(h,d);if(t)return t}return!!g&&u(h,g)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!g&&g.seg);var v,y=m();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=g?g.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=g?h.primary===g.primary?g.seg.otherFill.above:g.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!g&&g.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{\"./linked-list\":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(m=n[u])[0],h=(m[1],m[m.length-1]);m[m.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,g=s.matches_head,m=n[p],v=g?m[0]:m[m.length-1],y=g?m[1]:m[m.length-2],x=g?m[m.length-1]:m[0],b=g?m[m.length-2]:m[1];return e.pointsCollinear(y,v,d)&&(g?(r&&r.chainRemoveHead(s.index,d),m.shift()):(r&&r.chainRemoveTail(s.index,d),m.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(g?(r&&r.chainRemoveTail(s.index,v),m.pop()):(r&&r.chainRemoveHead(s.index,v),m.shift())),r&&r.chainClose(s.index),void i.push(m)):void(g?(r&&r.chainAddHead(s.index,d),m.unshift(d)):(r&&r.chainAddTail(s.index,d),m.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return n(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],e)}};e.exports=i},{}],262:[function(t,e,r){\"use strict\";var n=t(\"stream\").Transform,i=t(\"stream-parser\");function a(){n.call(this,{readableObjectMode:!0})}function o(t,e,r){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=(new Error).stack||\"\",this.name=this.constructor.name,this.message=t,e&&(this.code=e),r&&(this.statusCode=r)}a.prototype=Object.create(n.prototype),a.prototype.constructor=a,i(a.prototype),r.ParserStream=a,r.sliceEq=function(t,e,r){for(var n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,\"stream-parser\":301}],263:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],g=h,m=0;m<c;m++,g+=u){var v=this.exif_format_read(l,g);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),g=s(t,l+r,i);e.item_loc[u]={length:g,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var 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n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt32BE,o=n(\"8BPS\\0\\x01\");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:\"psd\",mime:\"image/vnd.adobe.photoshop\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":262}],272:[function(t,e,r){\"use strict\";function n(t){return\"number\"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\\s/,o=/[^-]\\bwidth=\"([^%]+?)\"|[^-]\\bwidth='([^%]+?)'/,s=/\\bheight=\"([^%]+?)\"|\\bheight='([^%]+?)'/,l=/\\bview[bB]ox=\"(.+?)\"|\\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:\"px\"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e=\"\",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[\"\"])[0];if(a.test(c)){var 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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":71,\"color-normalize\":89,\"flatten-vertex-data\":191,\"object-assign\":247,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],280:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),g=t(\"parse-rect\"),m=t(\"array-find-index\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=m(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct=\"transparent\");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&\"number\"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],\"uint8\");ut.set(ht,4*ft)}ut.set(n(ct[0],\"uint8\"),4*lt)}else for(var pt=n(ct,\"uint8\"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return 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mix(color, fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),m&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),g=0;g<r.length;g++){var m=r[g];d[m]=p?p[m]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=f({},b.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var m=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),m+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),m+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=g(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:v.fract32(t,p),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:\"dynamic\"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":59,\"array-bounds\":71,\"color-id\":87,\"color-normalize\":89,\"flatten-vertex-data\":191,glslify:227,\"is-iexplorer\":233,\"object-assign\":247,\"parse-rect\":249,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],282:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag showDiagonal\",upper:\"upper up top upperhalf upperHalf showupperhalf showUpper showUpperHalf\",lower:\"lower low bottom lowerhalf lowerHalf showlowerhalf showLowerHalf showLower\"}),s=this.traces[t]||(this.traces[t]={id:t,buffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(c(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var g=s.columns,m=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/g,w=y/g;s.passes=[];for(var T=0;T<g;T++)for(var k=0;k<g;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:m,stride:g},y:{buffer:s.buffer,offset:T,count:m,stride:g}}:M.positions={x:{buffer:s.buffer,offset:k*m,count:m},y:{buffer:s.buffer,offset:T*m,count:m}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],I=E[3];M.viewport=[x+L*v+S[0],b+C*y+S[1],x+P*v-S[2],b+I*y-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":346,\"object-assign\":247}],335:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":346,\"object-assign\":247}],339:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":346,\"object-assign\":247}],340:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return 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9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638}],352:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),g=r.hoverlabel||{};if(d){var m=a(\"hoverlabel.bgcolor\",g.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",g.bordercolor||i.contrast(m));n.coerceFont(a,\"hoverlabel.font\",{family:g.font.family,size:g.font.size,color:g.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":503,\"../color\":366}],353:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":531,\"fast-isnumeric\":190}],354:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var g=f[d],m=i.coerceRef(t,e,p,g,\"\",\"paper\");if(\"paper\"!==m)i.getFromId(p,m)._annIndices.push(e._index);if(i.coercePosition(e,p,a,m,g,.5),u){var v=\"a\"+g,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==m&&(y=e[v]=\"pixel\");var x=\"pixel\"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(g+\"anchor\"),a(g+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),u&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),c){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:\"annotations\",handleItemDefaults:l})}},{\"../../lib\":503,\"../../plots/array_container_defaults\":549,\"../../plots/cartesian/axes\":554,\"./attributes\":349,\"./common_defaults\":352}],355:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../registry\"),a=t(\"../../plots/plots\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../plots/cartesian/axes\"),c=t(\"../color\"),u=t(\"../drawing\"),f=t(\"../fx\"),h=t(\"../../lib/svg_text_utils\"),p=t(\"../../lib/setcursor\"),d=t(\"../dragelement\"),g=t(\"../../plot_api/plot_template\").arrayEditor,m=t(\"./draw_arrow_head\");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var A=g(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),g=u.bBox((f?n:G).node()),b=g.width,_=g.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var gt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var mt=l.getRefType(nt);\"domain\"===mt?(\"y\"===et&&(gt=1-gt),ft.tail=it._offset+it._length*gt):\"paper\"===mt?\"y\"===et?(gt=1-gt,ft.tail=T.t+T.h*gt):ft.tail=T.l+T.w*gt:ft.tail=it._offset+it.r2p(gt),K=tt}else ft.tail=J+gt,K=tt+gt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-g.top,Tt=N+bt-g.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,g=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,g,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=g-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(H)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(H));if(m(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-W)+\",\"+(p-X),transform:s(W,X)}).style(\"stroke-width\",V+6+\"px\").call(c.stroke,\"rgba(0,0,0,0)\").call(c.fill,\"rgba(0,0,0,0)\");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S(\"x\",y(v,t,\"x\",T,e)),S(\"y\",y(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",y(v,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",y(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),O.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",y(v,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",y(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,\"x\",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,\"y\",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Y(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{\"../../lib\":503,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/axes\":554,\"../../plots/plots\":619,\"../../registry\":638,\"../color\":366,\"../dragelement\":385,\"../drawing\":388,\"../fx\":406,\"./draw_arrow_head\":356,\"@plotly/d3\":58}],356:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../color\"),a=t(\"./arrow_paths\"),o=t(\"../../lib\"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],g=a[r.startarrowhead||0],m=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*m+r.standoff,_=g.backoff*v+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},u={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var 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n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../plots/cartesian/axes\"),l=t(\"../dragelement\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../lib/setcursor\"),p=t(\"../drawing\"),d=t(\"../color\"),g=t(\"../titles\"),m=t(\"../../lib/svg_text_utils\"),v=t(\"../colorscale/helpers\").flipScale,y=t(\"../../plots/cartesian/axis_defaults\"),x=t(\"../../plots/cartesian/position_defaults\"),b=t(\"../../plots/cartesian/layout_attributes\"),_=t(\"../../constants/alignment\"),w=_.LINE_SPACING,T=_.FROM_TL,k=_.FROM_BR,A=t(\"./constants\").cn;e.exports={draw:function(t){var e=t._fullLayout._infolayer.selectAll(\"g.\"+A.colorbar).data(function(t){var e,r,n,i,a=t._fullLayout,o=t.calcdata,s=[];function l(t){return f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function c(){\"function\"==typeof 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r=n.select(this);c.ensureSingle(r,\"rect\",A.cbbg),c.ensureSingle(r,\"g\",A.cbfills),c.ensureSingle(r,\"g\",A.cblines),c.ensureSingle(r,\"g\",A.cbaxis,(function(t){t.classed(A.crisp,!0)})),c.ensureSingle(r,\"g\",A.cbtitleunshift,(function(t){t.append(\"g\").classed(A.cbtitle,!0)})),c.ensureSingle(r,\"rect\",A.cboutline);var v=function(t,e,r){var o=\"v\"===e.orientation,l=e.len,h=e.lenmode,v=e.thickness,_=e.thicknessmode,M=e.outlinewidth,S=e.borderwidth,E=e.bgcolor,L=e.xanchor,C=e.yanchor,P=e.xpad,I=e.ypad,O=e.x,z=o?e.y:1-e.y,D=r._fullLayout,R=D._size,F=e._fillcolor,B=e._line,N=e.title,j=N.side,U=e._zrange||n.extent((\"function\"==typeof F?F:B.color).domain()),V=\"function\"==typeof B.color?B.color:function(){return B.color},H=\"function\"==typeof F?F:function(){return F},q=e._levels,G=function(t,e,r){var n,i,a=e._levels,o=[],s=[],l=a.end+a.size/100,c=a.size,u=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(i=0;i<1e5&&(n=a.start+i*c,!(c>0?n>=l:n<=l));i++)n>u&&n<f&&o.push(n);if(e._fillgradient)s=[0];else if(\"function\"==typeof e._fillcolor){var h=e._filllevels;if(h)for(l=h.end+h.size/100,c=h.size,i=0;i<1e5&&(n=h.start+i*c,!(c>0?n>=l:n<=l));i++)n>r[0]&&n<r[1]&&s.push(n);else(s=o.map((function(t){return t-a.size/2}))).push(s[s.length-1]+a.size)}else e._fillcolor&&\"string\"==typeof e._fillcolor&&(s=[0]);a.size<0&&(o.reverse(),s.reverse());return{line:o,fill:s}}(0,e,U),Y=G.fill,W=G.line,X=Math.round(v*(\"fraction\"===_?o?R.w:R.h:1)),Z=X/(o?R.w:R.h),J=Math.round(l*(\"fraction\"===h?o?R.h:R.w:1)),K=J/(o?R.h:R.w),Q=Math.round(o?O*R.w+P:z*R.h+I),$={center:.5,right:1}[L]||0,tt={top:1,middle:.5}[C]||0,et=o?O-$*Z:z-tt*Z,rt=o?z-tt*K:O-$*K,nt=Math.round(o?R.h*(1-rt):R.w*rt);e._lenFrac=K,e._thickFrac=Z,e._uFrac=et,e._vFrac=rt;var it=e._axis=function(t,e,r){var n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noHover:!0,noTickson:!0,noTicklabelmode:!0,calendar:n.calendar};function u(t,e){return c.coerce(a,s,b,t,e)}return y(a,s,u,l,n),x(a,s,u,l),s}(r,e,U);it.position=Z+(o?O+P/R.w:z+I/R.h);var at=-1!==[\"top\",\"bottom\"].indexOf(j);o&&at&&(it.title.side=j,it.titlex=O+P/R.w,it.titley=rt+(\"top\"===N.side?K-I/R.h:I/R.h));o||at||(it.title.side=j,it.titley=z+I/R.h,it.titlex=rt+P/R.w);if(B.color&&\"auto\"===e.tickmode){it.tickmode=\"linear\",it.tick0=q.start;var ot=q.size,st=c.constrain(J/50,4,15)+1,lt=(U[1]-U[0])/((e.nticks||st)*ot);if(lt>1){var ct=Math.pow(10,Math.floor(Math.log(lt)/Math.LN10));ot*=ct*c.roundUp(lt/ct,[2,5,10]),(Math.abs(q.start)/q.size+1e-6)%1<2e-6&&(it.tick0=0)}it.dtick=ot}it.domain=o?[rt+I/R.h,rt+K-I/R.h]:[rt+P/R.w,rt+K-P/R.w],it.setScale(),t.attr(\"transform\",u(Math.round(R.l),Math.round(R.t)));var ut,ft=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",u(-Math.round(R.l),-Math.round(R.t))),ht=it.ticklabelposition,pt=it.title.font.size,dt=t.select(\".\"+A.cbaxis),gt=0,mt=0;function vt(n,i){var a={propContainer:it,propName:e._propPrefix+\"title\",traceIndex:e._traceIndex,_meta:e._meta,placeholder:D._dfltTitle.colorbar,containerGroup:t.select(\".\"+A.cbtitle)},o=\"h\"===n.charAt(0)?n.substr(1):\"h\"+n;t.selectAll(\".\"+o+\",.\"+o+\"-math-group\").remove(),g.draw(r,n,f(a,i||{}))}return c.syncOrAsync([a.previousPromises,function(){var t,e;(o&&at||!o&&!at)&&(\"top\"===j&&(t=P+R.l+R.w*O,e=I+R.t+R.h*(1-rt-K)+3+.75*pt),\"bottom\"===j&&(t=P+R.l+R.w*O,e=I+R.t+R.h*(1-rt)-3-.25*pt),\"right\"===j&&(e=I+R.t+R.h*z+3+.75*pt,t=P+R.l+R.w*rt),vt(it._id+\"title\",{attributes:{x:t,y:e,\"text-anchor\":o?\"start\":\"middle\"}}))},function(){if(!o&&!at||o&&at){var a,l=t.select(\".\"+A.cbtitle),f=l.select(\"text\"),h=[-M/2,M/2],d=l.select(\".h\"+it._id+\"title-math-group\").node(),g=15.6;if(f.node()&&(g=parseInt(f.node().style.fontSize,10)*w),d?(a=p.bBox(d),mt=a.width,(gt=a.height)>g&&(h[1]-=(gt-g)/2)):f.node()&&!f.classed(A.jsPlaceholder)&&(a=p.bBox(f.node()),mt=a.width,gt=a.height),o){if(gt){if(gt+=5,\"top\"===j)it.domain[1]-=gt/R.h,h[1]*=-1;else{it.domain[0]+=gt/R.h;var v=m.lineCount(f);h[1]+=(1-v)*g}l.attr(\"transform\",u(h[0],h[1])),it.setScale()}}else mt&&(\"right\"===j&&(it.domain[0]+=(mt+pt/2)/R.w),l.attr(\"transform\",u(h[0],h[1])),it.setScale())}t.selectAll(\".\"+A.cbfills+\",.\"+A.cblines).attr(\"transform\",o?u(0,Math.round(R.h*(1-it.domain[1]))):u(Math.round(R.w*it.domain[0]),0)),dt.attr(\"transform\",o?u(0,Math.round(-R.t)):u(Math.round(-R.l),0));var y=t.select(\".\"+A.cbfills).selectAll(\"rect.\"+A.cbfill).attr(\"style\",\"\").data(Y);y.enter().append(\"rect\").classed(A.cbfill,!0).style(\"stroke\",\"none\"),y.exit().remove();var x=U.map(it.c2p).map(Math.round).sort((function(t,e){return t-e}));y.each((function(t,a){var s=[0===a?U[0]:(Y[a]+Y[a-1])/2,a===Y.length-1?U[1]:(Y[a]+Y[a+1])/2].map(it.c2p).map(Math.round);o&&(s[1]=c.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",Q).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var u=H(t).replace(\"e-\",\"\");l.attr(\"fill\",i(u).toHexString())}}));var b=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(B.color&&B.width?W:[]);b.enter().append(\"path\").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=Q,r=Math.round(it.c2p(t))+B.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+X).call(p.lineGroupStyle,B.width,V(t),B.dash)})),dt.selectAll(\"g.\"+it._id+\"tick,path\").remove();var _=Q+X+(M||0)/2-(\"outside\"===e.ticks?1:0),T=s.calcTicks(it),k=s.getTickSigns(it)[2];return s.drawTicks(r,it,{vals:\"inside\"===it.ticks?s.clipEnds(it,T):T,layer:dt,path:s.makeTickPath(it,_,k),transFn:s.makeTransTickFn(it)}),s.drawLabels(r,it,{vals:T,layer:dt,transFn:s.makeTransTickLabelFn(it),labelFns:s.makeLabelFns(it,_)})},function(){if(o&&!at||!o&&at){var t,i,a=it.position||0,s=it._offset+it._length/2;if(\"right\"===j)i=s,t=R.l+R.w*a+10+pt*(it.showticklabels?1:.5);else if(t=s,\"bottom\"===j&&(i=R.t+R.h*a+10+(-1===ht.indexOf(\"inside\")?it.tickfont.size:0)+(\"intside\"!==it.ticks&&e.ticklen||0)),\"top\"===j){var l=N.text.split(\"<br>\").length;i=R.t+R.h*a+10-X-w*pt*l}vt((o?\"h\":\"v\")+it._id+\"title\",{avoid:{selection:n.select(r).selectAll(\"g.\"+it._id+\"tick\"),side:j,offsetTop:o?0:R.t,offsetLeft:o?R.l:0,maxShift:o?D.width:D.height},attributes:{x:t,y:i,\"text-anchor\":\"middle\"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var n,s=X+M/2;-1===ht.indexOf(\"inside\")&&(n=p.bBox(dt.node()),s+=o?n.width:n.height),ut=ft.select(\"text\");var c=0,f=o&&\"top\"===j,g=!o&&\"right\"===j,m=0;if(ut.node()&&!ut.classed(A.jsPlaceholder)){var y,x=ft.select(\".h\"+it._id+\"title-math-group\").node();x&&(o&&at||!o&&!at)?(c=(n=p.bBox(x)).width,y=n.height):(c=(n=p.bBox(ft.node())).right-R.l-(o?Q:nt),y=n.bottom-R.t-(o?nt:Q),o||\"top\"!==j||(s+=n.height,m=n.height)),g&&(ut.attr(\"transform\",u(c/2+pt/2,0)),c*=2),s=Math.max(s,o?c:y)}var b=2*(o?P:I)+s+S+M/2,w=0;!o&&N.text&&\"bottom\"===C&&z<=0&&(b+=w=b/2,m+=w),D._hColorbarMoveTitle=w,D._hColorbarMoveCBTitle=m;var F=S+M;t.select(\".\"+A.cbbg).attr(\"x\",(o?Q:nt)-F/2-(o?P:0)).attr(\"y\",(o?nt:Q)-(o?J:I+m-w)).attr(o?\"width\":\"height\",Math.max(b-w,2)).attr(o?\"height\":\"width\",Math.max(J+F,2)).call(d.fill,E).call(d.stroke,e.bordercolor).style(\"stroke-width\",S);var B=g?Math.max(c-10,0):0;if(t.selectAll(\".\"+A.cboutline).attr(\"x\",(o?Q:nt+P)+B).attr(\"y\",(o?nt+I-J:Q)+(f?gt:0)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(J-(o?2*I+gt:2*P+B),2)).call(d.stroke,e.outlinecolor).style({fill:\"none\",\"stroke-width\":M}),t.attr(\"transform\",u(R.l-(o?$*b:0),R.t-(o?0:(1-tt)*b-m))),!o&&(S||i(E).getAlpha()&&!i.equals(D.paper_bgcolor,E))){var U=dt.selectAll(\"text\"),V=U[0].length,H=t.select(\".\"+A.cbbg).node(),q=p.bBox(H),G=p.getTranslate(t);U.each((function(t,e){var r=V-1;if(0===e||e===r){var n,i=p.bBox(this),a=p.getTranslate(this);if(e===r){var o=i.right+a.x;(n=q.right+G.x+nt-S-2+O-o)>0&&(n=0)}else if(0===e){var s=i.left+a.x;(n=q.left+G.x+nt+S+2-s)<0&&(n=0)}n&&(V<3?this.setAttribute(\"transform\",\"translate(\"+n+\",0) \"+this.getAttribute(\"transform\")):this.setAttribute(\"visibility\",\"hidden\"))}}))}var Y={},W=T[L],Z=k[L],K=T[C],et=k[C],rt=b-X;o?(\"pixels\"===h?(Y.y=z,Y.t=J*K,Y.b=J*et):(Y.t=Y.b=0,Y.yt=z+l*K,Y.yb=z-l*et),\"pixels\"===_?(Y.x=O,Y.l=b*W,Y.r=b*Z):(Y.l=rt*W,Y.r=rt*Z,Y.xl=O-v*W,Y.xr=O+v*Z)):(\"pixels\"===h?(Y.x=O,Y.l=J*W,Y.r=J*Z):(Y.l=Y.r=0,Y.xl=O+l*W,Y.xr=O-l*Z),\"pixels\"===_?(Y.y=1-z,Y.t=b*K,Y.b=b*et):(Y.t=rt*K,Y.b=rt*et,Y.yt=z-v*K,Y.yb=z+v*et)),a.autoMargin(r,e._id,Y)}],r)}(r,e,t);v&&v.then&&(t._promises||[]).push(v),t._context.edits.colorbarPosition&&function(t,e,r){var n,i,a,s=\"v\"===e.orientation,c=r._fullLayout._size;l.init({element:t.node(),gd:r,prepFn:function(){n=t.attr(\"transform\"),h(t)},moveFn:function(r,o){t.attr(\"transform\",n+u(r,o)),i=l.align((s?e._uFrac:e._vFrac)+r/c.w,s?e._thickFrac:e._lenFrac,0,1,e.xanchor),a=l.align((s?e._vFrac:1-e._uFrac)-o/c.h,s?e._lenFrac:e._thickFrac,0,1,e.yanchor);var f=l.getCursor(i,a,e.xanchor,e.yanchor);h(t,f)},doneFn:function(){if(h(t),void 0!==i&&void 0!==a){var n={};n[e._propPrefix+\"x\"]=i,n[e._propPrefix+\"y\"]=a,void 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o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ct=1/0;function ut(t,r){for(G=0;G<rt.length;G++)if((Y=rt[G])&&Y[0]&&Y[0].trace&&!0===(W=Y[0].trace).visible&&0!==W._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(W._module.name)){if(\"splom\"===W.type?X=l[Z=0]:(X=v.getSubplot(W),Z=l.indexOf(X)),J=P,v.isUnifiedHover(J)&&(J=J.charAt(0)),$={cd:Y,trace:W,xa:_[Z],ya:w[Z],maxHoverDistance:O,maxSpikeDistance:B,index:!1,distance:Math.min(ct,O),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:W.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[X]&&($.subplot=u[X]._subplot),u._splomScenes&&u._splomScenes[W.uid]&&($.scene=u._splomScenes[W.uid]),tt=et.length,\"array\"===J){var n=e[G];\"pointNumber\"in n?($.index=n.pointNumber,J=\"closest\"):(J=\"\",\"xval\"in n&&(K=n.xval,J=\"x\"),\"yval\"in n&&(Q=n.yval,J=J?\"closest\":\"y\"))}else void 0!==t&&void 0!==r?(K=t,Q=r):(K=V[Z],Q=H[Z]);if(0!==O)if(W._module&&W._module.hoverPoints){var a=W._module.hoverPoints($,K,Q,J,{finiteRange:!0,hoverLayer:u._hoverlayer});if(a)for(var s,c=0;c<a.length;c++)s=a[c],i(s.x0)&&i(s.y0)&&et.push(D(s,P))}else o.log(\"Unrecognized trace type in hover:\",W);if(\"closest\"===P&&et.length>tt&&(et.splice(0,tt),ct=et[0].distance),y&&0!==B&&0===et.length){$.distance=B,$.index=!1;var f=W._module.hoverPoints($,K,Q,\"closest\",{hoverLayer:u._hoverlayer});if(f&&(f=f.filter((function(t){return t.spikeDistance<=B}))),f&&f.length){var h,d=f.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(d.length){var g=d[0];i(g.x0)&&i(g.y0)&&(h=ht(g),(!nt.vLinePoint||nt.vLinePoint.spikeDistance>h.spikeDistance)&&(nt.vLinePoint=h))}var m=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(m.length){var x=m[0];i(x.x0)&&i(x.y0)&&(h=ht(x),(!nt.hLinePoint||nt.hLinePoint.spikeDistance>h.spikeDistance)&&(nt.hLinePoint=h))}}}}}function ft(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function ht(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ut();var pt={fullLayout:u,container:u._hoverlayer,event:e},dt=t._spikepoints,gt={vLinePoint:nt.vLinePoint,hLinePoint:nt.hLinePoint};t._spikepoints=gt;var mt=function(){et.sort((function(t,e){return t.distance-e.distance})),et=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];m.traceIs(s.trace,\"bar-like\")||m.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(et,P)};mt();var vt=P.charAt(0),yt=(\"x\"===vt||\"y\"===vt)&&et[0]&&L[et[0].trace.type];if(y&&0!==B&&0!==et.length){var xt=ft(et.filter((function(t){return t.ya.showspikes})),B,yt);nt.hLinePoint=ht(xt);var bt=ft(et.filter((function(t){return t.xa.showspikes})),B,yt);nt.vLinePoint=ht(bt)}if(0===et.length){var _t=d.unhoverRaw(t,e);return!y||null===nt.hLinePoint&&null===nt.vLinePoint||F(dt)&&R(t,nt,pt),_t}y&&F(dt)&&R(t,nt,pt);if(v.isXYhover(J)&&0!==et[0].length&&\"splom\"!==et[0].trace.type){var wt=et[0],Tt=(et=E[wt.trace.type]?et.filter((function(t){return t.trace.index===wt.trace.index})):[wt]).length,kt=N(\"x\",wt,u),At=N(\"y\",wt,u);ut(kt,At);var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var r=Et[e]-1,n=St[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(St[r]=t)}else Lt++,Et[e]=Lt,St.push(t)};for(Mt=0;Mt<Tt;Mt++)Ct(et[Mt]);for(Mt=et.length-1;Mt>Tt-1;Mt--)Ct(et[Mt]);et=St,mt()}var Pt=t._hoverdata,It=[],Ot=j(t),zt=U(t);for(q=0;q<et.length;q++){var Dt=et[q],Rt=v.makeEventData(Dt,Dt.trace,Dt.cd);if(!1!==Dt.hovertemplate){var Ft=!1;Dt.cd[Dt.index]&&Dt.cd[Dt.index].ht&&(Ft=Dt.cd[Dt.index].ht),Dt.hovertemplate=Ft||Dt.trace.hovertemplate||!1}if(Dt.xa&&Dt.ya){var Bt=Dt.x0+Dt.xa._offset,Nt=Dt.x1+Dt.xa._offset,jt=Dt.y0+Dt.ya._offset,Ut=Dt.y1+Dt.ya._offset,Vt=Math.min(Bt,Nt),Ht=Math.max(Bt,Nt),qt=Math.min(jt,Ut),Gt=Math.max(jt,Ut);Rt.bbox={x0:Vt+zt,x1:Ht+zt,y0:qt+Ot,y1:Gt+Ot}}Dt.eventData=[Rt],It.push(Rt)}t._hoverdata=It;var Yt=\"y\"===P&&(rt.length>1||et.length>1)||\"closest\"===P&&it&&et.length>1,Wt=p.combine(u.plot_bgcolor||p.background,u.paper_bgcolor),Xt=I(et,{gd:t,hovermode:P,rotateLabels:Yt,bgColor:Wt,container:u._hoverlayer,outerContainer:u._paper.node(),commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance});v.isUnifiedHover(P)||(!function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.size(),p=new Array(h),d=0;function g(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var c=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&c++;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,c--);for(o=0;o<t.length&&!(c<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,c--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,c--)}}}t.each((function(t){var n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(f=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?T:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=h;){for(u++,n=!0,o=0;o<p.length-1;){var m=p[o],v=p[o+1],y=m[m.length-1],x=v[0];if((i=y.pos+y.dp+y.size-x.pos-x.dp+x.size)>.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(m.push.apply(m,v),p.splice(o+1,1),c=0,s=m.length-1;s>=0;s--)c+=m[s].dp;for(a=c/m.length,s=m.length-1;s>=0;s--)m[s].dp-=a;n=!1}else o++}p.forEach(g)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var _=b[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(Xt,Yt?\"xa\":\"ya\",u),z(Xt,Yt,u._invScaleX,u._invScaleY));if(s&&s.tagName){var Zt=m.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,It);f(n.select(s),Zt?\"pointer\":\"\")}if(!s||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Pt))return;Pt&&t.emit(\"plotly_unhover\",{event:e,points:Pt});t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:_,yaxes:w,xvals:V,yvals:H})}(t,e,r,a,s)}))},r.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=j(i),o=U(i),s=I(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,c=t.eventData;if(c){var u=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),d=Math.max(s,l),g=t.trace;if(m.traceIs(g,\"gl3d\")){var v=i._fullLayout[g.scene]._scene.container,y=v.offsetLeft,x=v.offsetTop;u+=y,f+=y,h+=x,d+=x}c.bbox={x0:u+o,x1:f+o,y0:h+a,y1:d+a},e.inOut_bbox&&e.inOut_bbox.push(c.bbox)}else c=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}),l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(c=t.offset)})).each((function(t){t.offset-=c})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function I(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,g=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||y.HOVERFONT,k=e.fontSize||y.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),I=A[P+\"Label\"],z=V(r,g),D=z.top,R=z.width,F=z.height,B=void 0!==I&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(B){var N,j,U=!0;for(N=0;N<t.length;N++)if(U&&void 0===t[N].zLabel&&(U=!1),j=t[N].hoverinfo||t[N].trace.hoverinfo){var H=Array.isArray(j)?j:j.split(\"+\");if(-1===H.indexOf(\"all\")&&-1===H.indexOf(a)){B=!1;break}}U&&(B=!1)}var q=d.selectAll(\"g.axistext\").data(B?[0]:[]);if(q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove(),q.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=w.bgcolor||p.defaultLine,f=w.bordercolor||p.contrast(c),d=p.contrast(c),g={family:w.font.family||T,size:w.font.size||k,color:w.font.color||d};e.style({fill:c,stroke:f}),l.text(I).call(h.font,g).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var m,v,y=V(r,l.node());if(\"x\"===a){var x=\"top\"===E.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===E.side?D-y.bottom-M-S:D-y.top+M+S),m=E._offset+(A.x0+A.x1)/2,v=L._offset+(\"top\"===E.side?0:L._length);var b=y.width/2+S;m<b?(m=b,e.attr(\"d\",\"M-\"+(b-M)+\",0L-\"+(b-2*M)+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"Z\")):m>i.width-b?(m=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+(S+y.width/2)+\"V\"+x+M+\"H-\"+M+\"Z\")}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",m=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",m=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+y.height/2)+\"h\"+P+(2*S+y.width)+\"V-\"+(S+y.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\");var O,z=y.height/2,R=D-y.top-z,F=\"clip\"+i._uid+\"commonlabel\"+L._id;if(m<y.width+2*S+M){O=\"M-\"+(M+S)+\"-\"+z+\"h-\"+(y.width-S)+\"V\"+z+\"h\"+(y.width-S)+\"Z\";var B=y.width-m+S;u.positionText(l,B,R),\"end\"===_&&l.selectAll(\"tspan\").each((function(){var t=n.select(this),e=h.tester.append(\"text\").text(t.text()).call(h.font,g),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr(\"x\",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll(\"#\"+F).data(O?[0]:[]);N.enter().append(\"clipPath\").attr(\"id\",F).append(\"path\"),N.exit().remove(),N.select(\"path\").attr(\"d\",O),h.setClipUrl(l,O?F:null,r)}t.attr(\"transform\",s(m,v))})),v.isUnifiedHover(a)){d.selectAll(\"g.hovertext\").remove();var G=t.filter((function(t){return\"none\"!==t.hoverinfo}));if(0===G.length)return;var Y=i.hoverlabel,W=Y.font,X={showlegend:!0,legend:{title:{text:I,font:W},font:W,bgcolor:Y.bgcolor,bordercolor:Y.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},Z={font:W};x(X,Z,r._fullData);var J=Z.legend;J.entries=[];for(var K=0;K<G.length;K++){var Q=G[K];if(\"none\"!==Q.hoverinfo){var $=O(Q,!0,a,i,I),tt=$[0],et=$[1];Q.name=et,Q.text=\"\"!==et?et+\" : 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../lib/events\"),l=t(\"../dragelement\"),c=t(\"../drawing\"),u=t(\"../color\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"./handle_click\"),p=t(\"./constants\"),d=t(\"../../constants/alignment\"),g=d.LINE_SPACING,m=d.FROM_TL,v=d.FROM_BR,y=t(\"./get_legend_data\"),x=t(\"./style\"),b=t(\"./helpers\");function _(t,e,r,n,i){var a=r.data()[0][0].trace,l={event:i,node:r.node(),curveNumber:a.index,expandedIndex:a._expandedIndex,data:t.data,layout:t.layout,frames:t._transitionData._frames,config:t._context,fullData:t._fullData,fullLayout:t._fullLayout};if(a._group&&(l.group=a._group),o.traceIs(a,\"pie-like\")&&(l.label=r.datum()[0].label),!1!==s.triggerHandler(t,\"plotly_legendclick\",l))if(1===n)e._clickTimeout=setTimeout((function(){t._fullLayout&&h(r,t,n)}),t._context.doubleClickDelay);else 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axes\")},attr:\"zoom\",val:\"reset\",icon:o.home,click:f},u.hoverClosestCartesian={name:\"hoverClosestCartesian\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:f},u.hoverCompareCartesian={name:\"hoverCompareCartesian\",_cat:\"hoverCompare\",title:function(t){return c(t,\"Compare data on hover\")},attr:\"hovermode\",val:function(t){return t._fullLayout._isHoriz?\"y\":\"x\"},icon:o.tooltip_compare,gravity:\"ne\",click:f},u.zoom3d={name:\"zoom3d\",_cat:\"zoom\",title:function(t){return c(t,\"Zoom\")},attr:\"scene.dragmode\",val:\"zoom\",icon:o.zoombox,click:h},u.pan3d={name:\"pan3d\",_cat:\"pan\",title:function(t){return c(t,\"Pan\")},attr:\"scene.dragmode\",val:\"pan\",icon:o.pan,click:h},u.orbitRotation={name:\"orbitRotation\",title:function(t){return c(t,\"Orbital 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hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:v},u.resetViewSankey={name:\"resetSankeyGroup\",title:function(t){return c(t,\"Reset view\")},icon:o.home,click:function(t){for(var e={\"node.groups\":[],\"node.x\":[],\"node.y\":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e[\"node.groups\"].push(i.node.groups.slice()),e[\"node.x\"].push(i.node.x.slice()),e[\"node.y\"].push(i.node.y.slice())}n.call(\"restyle\",t,e)}},u.toggleHover={name:\"toggleHover\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);r.hovermode=m(t),n.call(\"_guiRelayout\",t,r)}},u.resetViews={name:\"resetViews\",title:function(t){return c(t,\"Reset views\")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom 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n=t(\"../../lib\"),i=t(\"../color\"),a=t(\"../../plot_api/plot_template\"),o=t(\"./attributes\");e.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(c,.3)),l(\"activecolor\",i.addOpacity(c,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../color\":366,\"./attributes\":425}],429:[function(t,e,r){\"use strict\";e.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),manage:t(\"./manage\")}},{\"./attributes\":425,\"./defaults\":428,\"./manage\":430}],430:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axis_ids\"),i=t(\"../../traces/scatter/subtypes\"),a=t(\"../../registry\"),o=t(\"../fx/helpers\").isUnifiedHover,s=t(\"./modebar\"),l=t(\"./buttons\"),c=t(\"./constants\").DRAW_MODES;e.exports=function(t){var e=t._fullLayout,r=t._context,u=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var f,h=r.modeBarButtons;f=Array.isArray(h)&&h.length?function(t){for(var e=0;e<t.length;e++)for(var r=t[e],n=0;n<r.length;n++){var i=r[n];if(\"string\"==typeof i){if(void 0===l[i])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));t[e][n]=l[i]}}return t}(h):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),g=e._has(\"cartesian\"),m=e._has(\"gl3d\"),v=e._has(\"geo\"),y=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,c=0;c<d.length;c++){var u=d[c].toLowerCase();if(u===a||u===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\");L(C);var P=[],I=[],O=[],z=[];(g||b||y||x||_)+v+m+w+T+k>1?(I=[\"toggleHover\"],O=[\"resetViews\"]):v?(P=[\"zoomInGeo\",\"zoomOutGeo\"],I=[\"hoverClosestGeo\"],O=[\"resetGeo\"]):m?(I=[\"hoverClosest3d\"],O=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],I=[\"toggleHover\"],O=[\"resetViewMapbox\"]):b?I=[\"hoverClosestGl2d\"]:y?I=[\"hoverClosestPie\"]:A?(I=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],O=[\"resetViewSankey\"]):I=[\"toggleHover\"];g&&(I=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]);(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(I=[]);!g&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==O[0]&&(O=[\"resetScale2d\"]));m?z=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(g||b)&&!M||_?z=[\"zoom2d\",\"pan2d\"]:w||v?z=[\"pan2d\"]:T&&(z=[\"zoom2d\"]);(function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var 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r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this._uid=i,this.element.className=\"modebar\",\"hover\"===r.displayModeBar&&(this.element.className+=\" modebar--hover ease-bg\"),\"v\"===n.modebar.orientation&&(this.element.className+=\" vertical\",e=e.reverse());var o=n.modebar,s=\"hover\"===r.displayModeBar?\".js-plotly-plot .plotly:hover \":\"\";a.deleteRelatedStyleRule(i),a.addRelatedStyleRule(i,s+\"#\"+i+\" .modebar-group\",\"background-color: \"+o.bgcolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn .icon path\",\"fill: \"+o.color),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn:hover .icon path\",\"fill: \"+o.activecolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn.active .icon path\",\"fill: \"+o.activecolor);var l=!this.hasButtons(e),c=this.hasLogo!==r.displaylogo,u=this.locale!==r.locale;if(this.locale=r.locale,(l||c||u)&&(this.removeAllButtons(),this.updateButtons(e),r.watermark||r.displaylogo)){var f=this.getLogo();r.watermark&&(f.className=f.className+\" watermark\"),\"v\"===n.modebar.orientation?this.element.insertBefore(f,this.element.childNodes[0]):this.element.appendChild(f),this.hasLogo=!0}this.updateActiveButton()},u.updateButtons=function(t){var e=this;this.buttons=t,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach((function(t){var r=e.createGroup();t.forEach((function(t){var n=t.name;if(!n)throw new Error(\"must provide button 'name' in button config\");if(-1!==e.buttonsNames.indexOf(n))throw new Error(\"button name '\"+n+\"' is taken\");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},u.createGroup=function(){var t=document.createElement(\"div\");return t.className=\"modebar-group\",t},u.createButton=function(t){var e=this,r=document.createElement(\"a\");r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var i=t.title;void 0===i?i=t.name:\"function\"==typeof 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a=document.createElementNS(n,\"path\");a.setAttribute(\"d\",t.path),t.transform?a.setAttribute(\"transform\",t.transform):void 0!==t.ascent&&a.setAttribute(\"transform\",\"matrix(1 0 0 -1 0 \"+t.ascent+\")\"),e.appendChild(a)}t.svg&&(e=l.parseFromString(t.svg,\"application/xml\").childNodes[0]);return e.setAttribute(\"height\",\"1em\"),e.setAttribute(\"width\",\"1em\"),e},u.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute(\"data-attr\"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute(\"data-val\")||!0,o=t.getAttribute(\"data-attr\"),s=\"true\"===t.getAttribute(\"data-toggle\"),l=n.select(t);if(s)o===r&&l.classed(\"active\",!l.classed(\"active\"));else{var c=null===o?o:a.nestedProperty(e,o).get();l.classed(\"active\",c===i)}}))},u.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},u.getLogo=function(){var t=this.createGroup(),e=document.createElement(\"a\");return e.href=\"https://plotly.com/\",e.target=\"_blank\",e.setAttribute(\"data-title\",a._(this.graphInfo,\"Produced with Plotly.js\")+\" (v\"+s+\")\"),e.className=\"modebar-btn plotlyjsicon modebar-btn--logo\",e.appendChild(this.createIcon(o.newplotlylogo)),t.appendChild(e),t},u.removeAllButtons=function(){for(;this.element.firstChild;)this.element.removeChild(this.element.firstChild);this.hasLogo=!1},u.destroy=function(){a.removeElement(this.container.querySelector(\".modebar\")),a.deleteRelatedStyleRule(this._uid)},e.exports=function(t,e){var r=t._fullLayout,i=new c({graphInfo:t,container:r._modebardiv.node(),buttons:e});return r._privateplot&&n.select(i.element).append(\"span\").classed(\"badge-private 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if(\"log\"===t.type){t.tick0=0;var c=s.simpleMap(t.range,t.r2l);if(e>.7)t.dtick=Math.ceil(e);else if(Math.abs(c[1]-c[0])<1){var u=1.5*Math.abs((c[1]-c[0])/e);e=Math.abs(Math.pow(10,c[1])-Math.pow(10,c[0]))/u,r=n(10),t.dtick=\"L\"+ot(e,r,$)}else t.dtick=e>.3?\"D2\":\"D1\"}else\"category\"===t.type||\"multicategory\"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):At(t)?(t.tick0=0,r=1,t.dtick=ot(e,r,at)):(t.tick0=0,r=n(10),t.dtick=ot(e,r,$));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&\"string\"!=typeof t.dtick){var f=t.dtick;throw t.dtick=1,\"ax.dtick error: \"+String(f)}},V.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),c=o*Number(e.substr(1));if(\"M\"===l)return s.incrementMonth(t,c,a);if(\"L\"===l)return Math.log(Math.pow(10,t)+c)/Math.LN10;if(\"D\"===l){var u=\"D2\"===e?it:nt,f=t+.01*o,h=s.roundUp(s.mod(f,1),u,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw\"unrecognized dtick 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a=n._fullLayout._zoomlayer.selectAll(\".select-outline-\"+r.id);if(a&&n._fullLayout._drawing){var o=v(a,t);o&&i.call(\"_guiRelayout\",n,{shapes:o}),n._fullLayout._drawing=!1}}r.selection={},r.selection.selectionDefs=t.selectionDefs=[],r.selection.mergedPolygons=t.mergedPolygons=[]}function N(t,e,r,n){var i,a,o,s=[],l=e.map((function(t){return t._id})),c=r.map((function(t){return t._id}));for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!n||a.subplot!==n&&a.geo!==n)if(\"splom\"===a.type&&a._xaxes[l[0]]&&a._yaxes[c[0]]){var u=h(a._module,i,e[0],r[0]);u.scene=t._fullLayout._splomScenes[a.uid],s.push(u)}else if(\"sankey\"===a.type){var f=h(a._module,i,e[0],r[0]);s.push(f)}else{if(-1===l.indexOf(a.xaxis))continue;if(-1===c.indexOf(a.yaxis))continue;s.push(h(a._module,i,_(t,a.xaxis),_(t,a.yaxis)))}else s.push(h(a._module,i,e[0],r[0]));return s;function h(t,e,r,n){return{_module:t,cd:e,xaxis:r,yaxis:n}}}function j(t){var 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n=t(\"@plotly/d3\"),i=t(\"d3-geo\"),a=i.geoPath,o=i.geoDistance,s=t(\"d3-geo-projection\"),l=t(\"../../registry\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../components/fx\"),d=t(\"../plots\"),g=t(\"../cartesian/axes\"),m=t(\"../cartesian/autorange\").getAutoRange,v=t(\"../../components/dragelement\"),y=t(\"../cartesian/select\").prepSelect,x=t(\"../cartesian/select\").clearSelect,b=t(\"../cartesian/select\").selectOnClick,_=t(\"./zoom\"),w=t(\"./constants\"),T=t(\"../../lib/geo_location_utils\"),k=t(\"../../lib/topojson_utils\"),A=t(\"topojson-client\").feature;function M(t){this.id=t.id,this.graphDiv=t.graphDiv,this.container=t.container,this.topojsonURL=t.topojsonURL,this.isStatic=t.staticPlot,this.topojsonName=null,this.topojson=null,this.projection=null,this.scope=null,this.viewInitial=null,this.fitScale=null,this.bounds=null,this.midPt=null,this.hasChoropleth=!1,this.traceHash={},this.layers={},this.basePaths={},this.dataPaths={},this.dataPoints={},this.clipDef=null,this.clipRect=null,this.bgRect=null,this.makeFramework()}var S=M.prototype;function E(t,e){var r=w.clipPad,n=t[0]+r,i=t[1]-r,a=e[0]+r,o=e[1]-r;n>0&&i<0&&(i+=360);var s=(i-n)/4;return{type:\"Polygon\",coordinates:[[[n,a],[n,o],[n+s,o],[n+2*s,o],[n+3*s,o],[i,o],[i,a],[i-s,a],[i-2*s,a],[i-3*s,a],[n,a]]]}}e.exports=function(t){return new M(t)},S.plot=function(t,e,r){var n=this,i=e[this.id],a=[],o=!1;for(var s in w.layerNameToAdjective)if(\"frame\"!==s&&i[\"show\"+s]){o=!0;break}for(var l=0;l<t.length;l++)if(t[0][0].trace.locationmode){o=!0;break}if(o){var 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i=t[n],a=i[0].trace;\"choropleth\"===a.type&&(this.hasChoropleth=!0),!0===a.visible&&a._length>0&&a._module.calcGeoJSON(i,e)}if(!this.updateProjection(t,e)){this.viewInitial&&this.scope===r.scope||this.saveViewInitial(r),this.scope=r.scope,this.updateBaseLayers(e,r),this.updateDims(e,r),this.updateFx(e,r),d.generalUpdatePerTraceModule(this.graphDiv,this,t,r);var o=this.layers.frontplot.select(\".scatterlayer\");this.dataPoints.point=o.selectAll(\".point\"),this.dataPoints.text=o.selectAll(\"text\"),this.dataPaths.line=o.selectAll(\".js-line\");var s=this.layers.backplot.select(\".choroplethlayer\");this.dataPaths.choropleth=s.selectAll(\"path\"),this.render()}},S.updateProjection=function(t,e){var r=this.graphDiv,n=e[this.id],l=e._size,u=n.domain,f=n.projection,h=n.lonaxis,p=n.lataxis,d=h._ax,g=p._ax,v=this.projection=function(t){var e=t.projection,r=e.type,n=w.projNames[r];n=\"geo\"+c.titleCase(n);for(var l=(i[n]||s[n])(),u=t._isSatellite?180*Math.acos(1/e.distance)/Math.PI:t._isClipped?w.lonaxisSpan[r]/2:null,f=[\"center\",\"rotate\",\"parallels\",\"clipExtent\"],h=function(t){return t?l:[]},p=0;p<f.length;p++){var d=f[p];\"function\"!=typeof l[d]&&(l[d]=h)}l.isLonLatOverEdges=function(t){if(null===l(t))return!0;if(u){var e=l.rotate();return o(t,[-e[0],-e[1]])>u*Math.PI/180}return!1},l.getPath=function(){return a().projection(l)},l.getBounds=function(t){return l.getPath().bounds(t)},l.precision(w.precision),t._isSatellite&&l.tilt(e.tilt).distance(e.distance);u&&l.clipAngle(u-w.clipPad);return l}(n),y=[[l.l+l.w*u.x[0],l.t+l.h*(1-u.y[1])],[l.l+l.w*u.x[1],l.t+l.h*(1-u.y[0])]],x=n.center||{},b=f.rotation||{},_=h.range||[],T=p.range||[];if(n.fitbounds){d._length=y[1][0]-y[0][0],g._length=y[1][1]-y[0][1],d.range=m(r,d),g.range=m(r,g);var k=(d.range[0]+d.range[1])/2,A=(g.range[0]+g.range[1])/2;if(n._isScoped)x={lon:k,lat:A};else if(n._isClipped){x={lon:k,lat:A},b={lon:k,lat:A,roll:b.roll};var M=f.type,S=w.lonaxisSpan[M]/2||180,L=w.lataxisSpan[M]/2||90;_=[k-S,k+S],T=[A-L,A+L]}else x={lon:k,lat:A},b={lon:k,lat:b.lat,roll:b.roll}}v.center([x.lon-b.lon,x.lat-b.lat]).rotate([-b.lon,-b.lat,b.roll]).parallels(f.parallels);var C=E(_,T);v.fitExtent(y,C);var P=this.bounds=v.getBounds(C),I=this.fitScale=v.scale(),O=v.translate();if(n.fitbounds){var z=v.getBounds(E(d.range,g.range)),D=Math.min((P[1][0]-P[0][0])/(z[1][0]-z[0][0]),(P[1][1]-P[0][1])/(z[1][1]-z[0][1]));isFinite(D)?v.scale(D*I):c.warn(\"Something went wrong during\"+this.id+\"fitbounds computations.\")}else v.scale(f.scale*I);var R=this.midPt=[(P[0][0]+P[1][0])/2,(P[0][1]+P[1][1])/2];if(v.translate([O[0]+(R[0]-O[0]),O[1]+(R[1]-O[1])]).clipExtent(P),n._isAlbersUsa){var F=v([x.lon,x.lat]),B=v.translate();v.translate([B[0]-(F[0]-B[0]),B[1]-(F[1]-B[1])])}},S.updateBaseLayers=function(t,e){var r=this,i=r.topojson,a=r.layers,o=r.basePaths;function s(t){return\"lonaxis\"===t||\"lataxis\"===t}function l(t){return Boolean(w.lineLayers[t])}function c(t){return Boolean(w.fillLayers[t])}var u=(this.hasChoropleth?w.layersForChoropleth:w.layers).filter((function(t){return l(t)||c(t)?e[\"show\"+t]:!s(t)||e[t].showgrid})),p=r.framework.selectAll(\".layer\").data(u,String);p.exit().each((function(t){delete a[t],delete o[t],n.select(this).remove()})),p.enter().append(\"g\").attr(\"class\",(function(t){return\"layer \"+t})).each((function(t){var e=a[t]=n.select(this);\"bg\"===t?r.bgRect=e.append(\"rect\").style(\"pointer-events\",\"all\"):s(t)?o[t]=e.append(\"path\").style(\"fill\",\"none\"):\"backplot\"===t?e.append(\"g\").classed(\"choroplethlayer\",!0):\"frontplot\"===t?e.append(\"g\").classed(\"scatterlayer\",!0):l(t)?o[t]=e.append(\"path\").style(\"fill\",\"none\").style(\"stroke-miterlimit\",2):c(t)&&(o[t]=e.append(\"path\").style(\"stroke\",\"none\"))})),p.order(),p.each((function(r){var n=o[r],a=w.layerNameToAdjective[r];\"frame\"===r?n.datum(w.sphereSVG):l(r)||c(r)?n.datum(A(i,i.objects[r])):s(r)&&n.datum(function(t,e,r){var n,i,a,o=e[t],s=w.scopeDefaults[e.scope];\"lonaxis\"===t?(n=s.lonaxisRange,i=s.lataxisRange,a=function(t,e){return[t,e]}):\"lataxis\"===t&&(n=s.lataxisRange,i=s.lonaxisRange,a=function(t,e){return[e,t]});var l={type:\"linear\",range:[n[0],n[1]-1e-6],tick0:o.tick0,dtick:o.dtick};g.setConvert(l,r);var c=g.calcTicks(l);e.isScoped||\"lonaxis\"!==t||c.pop();for(var u=c.length,f=new Array(u),h=0;h<u;h++)for(var p=c[h].x,d=f[h]=[],m=i[0];m<i[1]+2.5;m+=2.5)d.push(a(p,m));return{type:\"MultiLineString\",coordinates:f}}(r,e,t)).call(f.stroke,e[r].gridcolor).call(h.dashLine,\"\",e[r].gridwidth),l(r)?n.call(f.stroke,e[a+\"color\"]).call(h.dashLine,\"\",e[a+\"width\"]):c(r)&&n.call(f.fill,e[a+\"color\"])}))},S.updateDims=function(t,e){var r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;\"select\"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:\"lasso\"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};\"pan\"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on(\"dblclick.zoom\",(function(){var t=r.viewInitial,e={};for(var n in 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v=n/(f[2]-f[0])*(l[2]-l[0])+l[0],y=i/(f[3]-f[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=v,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=v,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<g,b=Math.abs(c.dragStart[1]-i)<g;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(m(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(m(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener(\"touchstart\",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":561,\"has-passive-events\":229,\"mouse-change\":241,\"mouse-event-offset\":242,\"mouse-wheel\":244}],595:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":528,\"../cartesian/axes\":554}],596:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../plot_api/edit_types\":536,\"../cartesian\":568,\"../cartesian/attributes\":552,\"../cartesian/constants\":561,\"../get_data\":593,\"../layout_attributes\":610,\"./scene2d\":597}],597:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"../../../stackgl_modules\").gl_plot2d,c=t(\"../../../stackgl_modules\").gl_spikes2d,u=t(\"../../../stackgl_modules\").gl_select_box,f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),g=t(\"../cartesian/constraints\"),m=g.enforce,v=g.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();m(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var m=this.fullData[g.trace.index]||{},v=g.pointIndex,y=s.castHoverinfo(m,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(m,v,\"bgcolor\")||g.color,borderColor:s.castHoverOption(m,v,\"bordercolor\"),fontFamily:s.castHoverOption(m,v,\"font.family\"),fontSize:s.castHoverOption(m,v,\"font.size\"),fontColor:s.castHoverOption(m,v,\"font.color\"),nameLength:s.castHoverOption(m,v,\"namelength\"),textAlign:s.castHoverOption(m,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../../stackgl_modules\":1120,\"../../components/dragelement/helpers\":384,\"../../components/fx\":406,\"../../lib/show_no_webgl_msg\":525,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"../cartesian/autorange\":553,\"../cartesian/constants\":561,\"../cartesian/constraints\":562,\"./camera\":594,\"./convert\":595,\"webgl-context\":331}],598:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../plot_api/edit_types\":536,\"../get_data\":593,\"./layout/attributes\":599,\"./layout/defaults\":603,\"./layout/layout_attributes\":604,\"./scene\":608}],599:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],600:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":366,\"../../../lib/extend\":493,\"../../../plot_api/edit_types\":536,\"../../cartesian/layout_attributes\":569}],601:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":503,\"../../../plot_api/plot_template\":543,\"../../cartesian/axis_defaults\":556,\"../../cartesian/type_defaults\":582,\"./axis_attributes\":600,tinycolor2:312}],602:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":503,\"../../../lib/str2rgbarray\":528}],603:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var m=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(m&&v&&y?y/Math.sqrt(m*m+v*v+y*y)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":366,\"../../../lib\":503,\"../../../registry\":638,\"../../get_data\":593,\"../../subplot_defaults\":632,\"./axis_defaults\":601,\"./layout_attributes\":604}],604:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":503,\"../../../lib/extend\":493,\"../../domain\":584,\"./axis_attributes\":600}],605:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":528}],606:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":503,\"../../cartesian/axes\":554}],607:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],608:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../../stackgl_modules\").gl_plot3d,o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),g=t(\"../../lib/str2rgbarray\"),m=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return m(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,g=0;g<c.length;++g)\"skip\"!==(t=e.traces[c[g]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function m(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:m(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:m(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:m(\"z\",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||\"\").split(\"+\"),M=k&&\"all\"===k;_.hovertemplate||M||(-1===A.indexOf(\"x\")&&(T.xLabel=void 0),-1===A.indexOf(\"y\")&&(T.yLabel=void 0),-1===A.indexOf(\"z\")&&(T.zLabel=void 0),-1===A.indexOf(\"text\")&&(h.textLabel=void 0),-1===A.indexOf(\"name\")&&(u.name=void 0));var S=[];\"cone\"===t.type||\"streamtube\"===t.type?(T.uLabel=m(\"x\",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf(\"u\"))&&S.push(\"u: \"+T.uLabel),T.vLabel=m(\"y\",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf(\"v\"))&&S.push(\"v: \"+T.vLabel),T.wLabel=m(\"z\",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf(\"w\"))&&S.push(\"w: \"+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf(\"norm\"))&&S.push(\"norm: \"+T.normLabel),\"streamtube\"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf(\"divergence\"))&&S.push(\"divergence: \"+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push(\"value: \"+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,\"bgcolor\")||u.color,borderColor:d.castHoverOption(_,w,\"bordercolor\"),fontFamily:d.castHoverOption(_,w,\"font.family\"),fontSize:d.castHoverOption(_,w,\"font.size\"),fontColor:d.castHoverOption(_,w,\"font.color\"),nameLength:d.castHoverOption(_,w,\"namelength\"),textAlign:d.castHoverOption(_,w,\"align\"),hovertemplate:f.castOption(_,w,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(g(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,m=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){m[0][a]=1/0,m[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?m[0][a]=Math.min(m[0][a],s._lowerLogErrorBound):m[0][a]=Math.min(m[0][a],T[0][a]/h[a]-M),m[1][a]=Math.max(m[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);m[0][a]=Math.min(m[0][a],E),m[1][a]=Math.max(m[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var L=m[1][a]-m[0][a];m[0][a]-=L/32,m[1][a]+=L/32}if(\"reversed\"===s.autorange){var C=m[0][a];m[0][a]=m[1][a],m[1][a]=C}}else{var P=s.range;m[0][a]=s.r2l(P[0]),m[1][a]=s.r2l(P[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.setBounds(a,{min:m[0][a]*h[a],max:m[1][a]*h[a]})}var I=c.aspectmode;if(\"cube\"===I)d=[1,1,1];else if(\"manual\"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call(\"_storeDirectGUIEdit\",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement(\"canvas\");o.width=r,o.height=i;var 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n=t(\"../../lib/sort_object_keys\"),i='\\xa9 <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">OpenStreetMap</a> contributors',a=['\\xa9 <a target=\"_blank\" href=\"https://carto.com/\">Carto</a>',i].join(\" \"),o=['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">ODbL</a>'].join(\" 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g=c.select(s._subplot.div);if(!(null===g.select(\".mapboxgl-ctrl-logo\").node().offsetParent)){var m=e._glimages.append(\"g\");m.attr(\"transform\",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),m.append(\"path\").attr(\"d\",p.mapboxLogo.path0).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"}),m.append(\"path\").attr(\"d\",p.mapboxLogo.path1).style(\"opacity\",.35).style(\"enable-background\",\"new\"),m.append(\"path\").attr(\"d\",p.mapboxLogo.path2).style(\"opacity\",.35).style(\"enable-background\",\"new\"),m.append(\"polygon\").attr(\"points\",p.mapboxLogo.polygon).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"})}var v=g.select(\".mapboxgl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),y=e._glimages.append(\"g\"),x=y.append(\"text\");x.text(v).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":v});var 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n=t(\"../../lib\"),i=t(\"../../lib/svg_text_utils\").sanitizeHTML,a=t(\"./convert_text_opts\"),o=t(\"./constants\");function s(t,e){this.subplot=t,this.uid=t.uid+\"-\"+e,this.index=e,this.idSource=\"source-\"+this.uid,this.idLayer=o.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var l=s.prototype;function c(t){if(!t.visible)return!1;var e=t.source;if(Array.isArray(e)&&e.length>0){for(var r=0;r<e.length;r++)if(\"string\"!=typeof e[r]||0===e[r].length)return!1;return!0}return n.isPlainObject(e)||\"string\"==typeof e&&e.length>0}function u(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity,\"line-dasharray\":t.line.dash});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{\"icon-image\":i.icon+\"-15\",\"icon-size\":i.iconsize/10,\"text-field\":i.text,\"text-size\":i.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset,\"symbol-placement\":i.placement}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":i.textfont.color,\"text-opacity\":t.opacity});break;case\"raster\":n.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates);a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution));return a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapboxLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r<e.length;r++){var n=e[r].id;if(\"string\"==typeof n&&0===n.indexOf(o.traceLayerPrefix)){t=n;break}}return t},l.updateLayer=function(t){var e=this.subplot,r=u(t),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),c(t)&&e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer||\"\",type:t.type,minzoom:t.minzoom,maxzoom:t.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=t.type,this.below=n},l.updateStyle=function(t){if(c(t)){var e=u(t);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",e.paint)}},l.removeLayer=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},l.dispose=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer),t.getSource(this.idSource)&&t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new s(t,e);return n.update(r),n}},{\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"./constants\":611,\"./convert_text_opts\":612}],615:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../domain\").attributes,o=t(\"../font_attributes\"),s=t(\"../../traces/scatter/attributes\").textposition,l=t(\"../../plot_api/edit_types\").overrideAll,c=t(\"../../plot_api/plot_template\").templatedArray,u=t(\"./constants\"),f=o({});f.family.dflt=\"Open Sans Regular, Arial Unicode MS 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n=t(\"mapbox-gl/dist/mapbox-gl-unminified\"),i=t(\"../../lib\"),a=t(\"../../lib/geo_location_utils\"),o=t(\"../../registry\"),s=t(\"../cartesian/axes\"),l=t(\"../../components/dragelement\"),c=t(\"../../components/fx\"),u=t(\"../../components/dragelement/helpers\"),f=u.rectMode,h=u.drawMode,p=u.selectMode,d=t(\"../cartesian/select\").prepSelect,g=t(\"../cartesian/select\").clearSelect,m=t(\"../cartesian/select\").clearSelectionsCache,v=t(\"../cartesian/select\").selectOnClick,y=t(\"./constants\"),x=t(\"./layers\");function b(t,e){this.id=e,this.gd=t;var r=t._fullLayout,n=t._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+\"-\"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var _=b.prototype;_.plot=function(t,e,r){var n,i=this,a=e[i.id];i.map&&a.accesstoken!==i.accessToken&&(i.map.remove(),i.map=null,i.styleObj=null,i.traceHash={},i.layerList=[]),n=i.map?new Promise((function(r,n){i.updateMap(t,e,r,n)})):new Promise((function(r,n){i.createMap(t,e,r,n)})),r.push(n)},_.createMap=function(t,e,r,i){var o=this,s=e[o.id],l=o.styleObj=T(s.style);o.accessToken=s.accesstoken;var c=o.map=new n.Map({container:o.div,style:l.style,center:A(s.center),zoom:s.zoom,bearing:s.bearing,pitch:s.pitch,interactive:!o.isStatic,preserveDrawingBuffer:o.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new n.AttributionControl({compact:!0}));c._canvas.style.left=\"0px\",c._canvas.style.top=\"0px\",o.rejectOnError(i),o.isStatic||o.initFx(t,e);var u=[];u.push(new Promise((function(t){c.once(\"load\",t)}))),u=u.concat(a.fetchTraceGeoData(t)),Promise.all(u).then((function(){o.fillBelowLookup(t,e),o.updateData(t),o.updateLayout(e),o.resolveOnRender(r)})).catch(i)},_.updateMap=function(t,e,r,n){var i=this,o=i.map,s=e[this.id];i.rejectOnError(n);var l=[],c=T(s.style);JSON.stringify(i.styleObj)!==JSON.stringify(c)&&(i.styleObj=c,o.setStyle(c.style),i.traceHash={},l.push(new Promise((function(t){o.once(\"styledata\",t)})))),l=l.concat(a.fetchTraceGeoData(t)),Promise.all(l).then((function(){i.fillBelowLookup(t,e),i.updateData(t),i.updateLayout(e),i.resolveOnRender(r)})).catch(n)},_.fillBelowLookup=function(t,e){var r,n,i=e[this.id].layers,a=this.belowLookup={},o=!1;for(r=0;r<t.length;r++){var s=t[r][0].trace,l=s._module;\"string\"==typeof s.below?n=s.below:l.getBelow&&(n=l.getBelow(s,this)),\"\"===n&&(o=!0),a[\"trace-\"+s.uid]=n||\"\"}for(r=0;r<i.length;r++){var c=i[r];n=\"string\"==typeof c.below?c.below:o?\"traces\":\"\",a[\"layout-\"+r]=n}var u,f,h={};for(u in a)h[n=a[u]]?h[n].push(u):h[n]=[u];for(n in h){var p=h[n];if(p.length>1)for(r=0;r<p.length;r++)0===(u=p[r]).indexOf(\"trace-\")?(f=u.split(\"trace-\")[1],this.traceHash[f]&&(this.traceHash[f].below=null)):0===u.indexOf(\"layout-\")&&(f=u.split(\"layout-\")[1],this.layerList[f]&&(this.layerList[f].below=null))}};var w={choroplethmapbox:0,densitymapbox:1,scattermapbox:2};function T(t){var e={};return i.isPlainObject(t)?(e.id=t.id,e.style=t):\"string\"==typeof t?(e.id=t,-1!==y.styleValuesMapbox.indexOf(t)?e.style=k(t):y.stylesNonMapbox[t]?e.style=y.stylesNonMapbox[t]:e.style=t):(e.id=y.styleValueDflt,e.style=k(y.styleValueDflt)),e.transition={duration:0,delay:0},e}function k(t){return y.styleUrlPrefix+t+\"-\"+y.styleUrlSuffix}function A(t){return[t.lon,t.lat]}_.updateData=function(t){var e,r,n,i,a=this.traceHash,o=t.slice().sort((function(t,e){return w[t[0].trace.type]-w[e[0].trace.type]}));for(n=0;n<o.length;n++){var s=o[n],l=!1;(e=a[(r=s[0].trace).uid])&&(e.type===r.type?(e.update(s),l=!0):e.dispose()),!l&&r._module&&(a[r.uid]=r._module.plot(this,s))}var c=Object.keys(a);t:for(n=0;n<c.length;n++){var u=c[n];for(i=0;i<t.length;i++)if(u===(r=t[i][0].trace).uid)continue t;(e=a[u]).dispose(),delete a[u]}},_.updateLayout=function(t){var e=this.map,r=t[this.id];this.dragging||this.wheeling||(e.setCenter(A(r.center)),e.setZoom(r.zoom),e.setBearing(r.bearing),e.setPitch(r.pitch)),this.updateLayers(t),this.updateFramework(t),this.updateFx(t),this.map.resize(),this.gd._context._scrollZoom.mapbox?e.scrollZoom.enable():e.scrollZoom.disable()},_.resolveOnRender=function(t){var e=this.map;e.on(\"render\",(function r(){e.loaded()&&(e.off(\"render\",r),setTimeout(t,10))}))},_.rejectOnError=function(t){var e=this.map;function r(){t(new Error(y.mapOnErrorMsg))}e.once(\"error\",r),e.once(\"style.error\",r),e.once(\"source.error\",r),e.once(\"tile.error\",r),e.once(\"layer.error\",r)},_.createFramework=function(t){var e=this,r=e.div=document.createElement(\"div\");r.id=e.uid,r.style.position=\"absolute\",e.container.appendChild(r),e.xaxis={_id:\"x\",c2p:function(t){return e.project(t).x}},e.yaxis={_id:\"y\",c2p:function(t){return e.project(t).y}},e.updateFramework(t),e.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},s.setConvert(e.mockAxis,t)},_.initFx=function(t,e){var r=this,n=r.gd,i=r.map;function a(){c.loneUnhover(e._hoverlayer)}function s(){var t=r.getView();n.emit(\"plotly_relayouting\",r.getViewEditsWithDerived(t))}i.on(\"moveend\",(function(t){if(r.map){var e=n._fullLayout;if(t.originalEvent||r.wheeling){var i=e[r.id];o.call(\"_storeDirectGUIEdit\",n.layout,e._preGUI,r.getViewEdits(i));var 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n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strRotate,l=o.strTranslate,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../plots\"),h=t(\"../../plots/cartesian/axes\"),p=t(\"../cartesian/set_convert\"),d=t(\"./set_convert\"),g=t(\"../cartesian/autorange\").doAutoRange,m=t(\"../cartesian/dragbox\"),v=t(\"../../components/dragelement\"),y=t(\"../../components/fx\"),x=t(\"../../components/titles\"),b=t(\"../cartesian/select\").prepSelect,_=t(\"../cartesian/select\").selectOnClick,w=t(\"../cartesian/select\").clearSelect,T=t(\"../../lib/setcursor\"),k=t(\"../../lib/clear_gl_canvases\"),A=t(\"../../plot_api/subroutines\").redrawReglTraces,M=t(\"../../constants/alignment\").MID_SHIFT,S=t(\"./constants\"),E=t(\"./helpers\"),L=t(\"../smith/helpers\"),C=L.smith,P=L.reactanceArc,I=L.resistanceArc,O=L.smithTransform,z=o._,D=o.mod,R=o.deg2rad,F=o.rad2deg;function B(t,e,r){this.isSmith=r||!1,this.id=e,this.gd=t,this._hasClipOnAxisFalse=null,this.vangles=null,this.radialAxisAngle=null,this.traceHash={},this.layers={},this.clipPaths={},this.clipIds={},this.viewInitial={};var n=t._fullLayout,i=\"clip\"+n._uid+e;this.clipIds.forTraces=i+\"-for-traces\",this.clipPaths.forTraces=n._clips.append(\"clipPath\").attr(\"id\",this.clipIds.forTraces),this.clipPaths.forTraces.append(\"path\"),this.framework=n[\"_\"+(r?\"smith\":\"polar\")+\"layer\"].append(\"g\").attr(\"class\",e),this.getHole=function(t){return this.isSmith?0:t.hole},this.getSector=function(t){return this.isSmith?[0,360]:t.sector},this.getRadial=function(t){return this.isSmith?t.realaxis:t.radialaxis},this.getAngular=function(t){return this.isSmith?t.imaginaryaxis:t.angularaxis},r||(this.radialTickLayout=null,this.angularTickLayout=null)}var N=B.prototype;function j(t){var e=t.ticks+String(t.ticklen)+String(t.showticklabels);return\"side\"in t&&(e+=t.side),e}function U(t,e){return 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z=this.pathSubplot();this.clipPaths.forTraces.select(\"path\").attr(\"d\",z).attr(\"transform\",l(P,I)),r.frontplot.attr(\"transform\",l(A,M)).call(u.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces,this.gd),r.bg.attr(\"d\",z).attr(\"transform\",l(L,C)).call(c.fill,e.bgcolor)},N.mockAxis=function(t,e,r,n){var i=o.extendFlat({},r,n);return d(i,e,t),i},N.mockCartesianAxis=function(t,e,r){var n=this,i=n.isSmith,a=r._id,s=o.extendFlat({type:\"linear\"},r);p(s,t);var l={x:[0,2],y:[1,3]};return s.setRange=function(){var t=n.sectorBBox,r=l[a],i=n.radialAxis._rl,o=(i[1]-i[0])/(1-n.getHole(e));s.range=[t[r[0]]*o,t[r[1]]*o]},s.isPtWithinRange=\"x\"!==a||i?function(){return!0}:function(t){return n.isPtInside(t)},s.setRange(),s.setScale(),s},N.doAutoRange=function(t,e){var r=this.gd,n=this.radialAxis,i=this.getRadial(e);g(r,n);var 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n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../cartesian/layout_attributes\"),o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../lib/extend\").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,ticklabelstep:a.ticklabelstep,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:\"number\",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:\"ternary\"}),bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},\"plot\",\"from-root\");c.uirevision={valType:\"any\",editType:\"none\"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":365,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../cartesian/layout_attributes\":569,\"../domain\":584}],636:[function(t,e,r){\"use 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r,n,i,a,o,l,p=this,d=t.domain,g=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*g-i/2,n=e.t+e.h*(1-m)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[g-o/2,g+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[m-l/2,m+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var 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n=t(\"../../components/fx\"),i=t(\"../../registry\"),a=t(\"../../components/color\"),o=t(\"../../lib\").fillText,s=t(\"./helpers\").getLineWidth,l=t(\"../../plots/cartesian/axes\").hoverLabelText,c=t(\"../../constants/numerical\").BADNUM;function u(t,e,r,i,a){var s,u,f,h,p,d,g,m=t.cd,v=m[0].trace,y=m[0].t,x=\"closest\"===i,b=\"waterfall\"===v.type,_=t.maxHoverDistance,w=t.maxSpikeDistance;\"h\"===v.orientation?(s=r,u=e,f=\"y\",h=\"x\",p=z,d=I):(s=e,u=r,f=\"x\",h=\"y\",d=z,p=I);var T=v[f+\"period\"],k=x||T;function A(t){return S(t,-1)}function M(t){return S(t,1)}function S(t,e){var r=t.w;return t[f]+e*r/2}function E(t){return t[f+\"End\"]-t[f+\"Start\"]}var L=x?A:T?function(t){return t.p-E(t)/2}:function(t){return Math.min(A(t),t.p-y.bardelta/2)},C=x?M:T?function(t){return t.p+E(t)/2}:function(t){return Math.max(M(t),t.p+y.bardelta/2)};function P(t,e,r){return a.finiteRange&&(r=0),n.inbox(t-s,e-s,r+Math.min(1,Math.abs(e-t)/g)-1)}function I(t){return P(L(t),C(t),_)}function O(t){var 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e=I(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},O.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var D=function(t){return l(b[0]+m*Math.cos(t),b[1]-m*Math.sin(t))};f=function(t){return D(I(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h=\"inside\"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:\"M\"+h*R+\",0h\"+h*o.ticklen,transFn:function(t){var e=I(t);return D(e)+\"rotate(\"+-c(e)+\")\"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:O})}var F=[y].concat(p.gauge.steps),B=_.selectAll(\"g.bg-arc\").data(F);B.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),B.select(\"path\").call(P).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll(\"g.value-arc\").data([p.gauge.bar]);j.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var U=j.select(\"path\");w(M)?(U.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){S&&S()})).each(\"interrupt\",(function(){S&&S()})).attrTween(\"d\",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr(\"d\",\"number\"==typeof r[0].y?N.endAngle(L(r[0].y)):\"M0,0Z\");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll(\"g.threshold-arc\").data(F);Y.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),Y.select(\"path\").call(P).call(T),Y.exit().remove();var W=_.selectAll(\"g.gauge-outline\").data([x]);W.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:m});var X=O.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=O.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,g=n.gaugeBg,m=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(v.l,v.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}var C=[g].concat(u.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var I=f.selectAll(\"g.value-bullet\").data([u.gauge.bar]);I.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),I.select(\"rect\").attr(\"height\",M).attr(\"y\",(A-M)/2).call(T),w(b)?I.select(\"rect\").transition().duration(b.duration).ease(b.easing).each(\"end\",(function(){_&&_()})).each(\"interrupt\",(function(){_&&_()})).attr(\"width\",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll(\"g.threshold-bullet\").data(O);z.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),z.select(\"line\").attr(\"x1\",i.c2p(u.gauge.threshold.value)).attr(\"x2\",i.c2p(u.gauge.threshold.value)).attr(\"y1\",(1-u.gauge.threshold.thickness)/2*A).attr(\"y2\",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style(\"stroke-width\",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll(\"g.gauge-outline\").data([m]);D.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),D.select(\"rect\").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:m});var J=O.selectAll(\"text.title\").data(e);J.exit().remove(),J.enter().append(\"text\").classed(\"title\",!0),J.attr(\"text-anchor\",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr(\"transform\",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../constants/alignment\":471,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../../plots/cartesian/axes\":554,\"../../plots/cartesian/axis_defaults\":556,\"../../plots/cartesian/layout_attributes\":569,\"../../plots/cartesian/position_defaults\":572,\"./constants\":858,\"@plotly/d3\":58,\"d3-interpolate\":116}],862:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../mesh3d/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;var 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0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374,\"../streamtube/calc\":1041}],864:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_mesh3d,i=t(\"../../lib/gl_format_color\").parseColorScale,a=t(\"../../lib/str2rgbarray\"),o=t(\"../../components/colorscale\").extractOpts,s=t(\"../../plots/gl3d/zip3\"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var e,r,n,i,a,o,s,c=t.surface.show,u=t.spaceframe.show,h=t.surface.fill,p=t.spaceframe.fill,d=!1,g=!1,m=0,v=t._Xs,y=t._Ys,x=t._Zs,b=v.length,_=y.length,w=x.length,T=f.indexOf(t._gridFill.replace(/-/g,\"\").replace(/\\+/g,\"\")),k=function(t,e,r){switch(T){case 5:return r+w*e+w*_*t;case 4:return r+w*t+w*b*e;case 3:return e+_*r+_*w*t;case 2:return e+_*t+_*b*r;case 1:return t+b*r+b*w*e;default:return t+b*e+b*_*r}},A=t._minValues,M=t._maxValues,S=t._vMin,E=t._vMax;function L(t,e,s){for(var l=o.length,c=r;c<l;c++)if(t===n[c]&&e===i[c]&&s===a[c])return c;return-1}function C(){r=e}function P(){n=[],i=[],a=[],o=[],e=0,C()}function I(t,r,s,l){return n.push(t),i.push(r),a.push(s),o.push(l),++e-1}function O(t,e,r){for(var n=[],i=0;i<t.length;i++)n[i]=t[i]*(1-r)+r*e[i];return n}function z(t){s=t}function D(t,e){return\"all\"===t||null===t||t.indexOf(e)>-1}function R(t,e){return null===t?e:t}function F(e,r,n){C();var i,a,o,l=[r],c=[n];if(s>=1)l=[r],c=[n];else if(s>0){var u=function(t,e){var 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r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,m[e])}));var v,y=e.line;y?(i(e,\"line\")&&a(t,e,{vals:e.line.color,containerStr:\"line\",cLetter:\"c\"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,A={},M=m.map((function(t){return t.inds}));for(g=0,x=0;x<k;x++){var S=[];for(b=0;b<M.length;b++)S.push(M[b][x]);d=p[x%p.length],g+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),L=S+\"-\"+E.rawColor;void 0===A[L]&&(A[L]=f(S,E.color,E.rawColor)),h(A[L],x,d)}var C,P=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,g)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<P.length;b++){var I=P[b].containerInd,O=m[b].inds[x],z=P[b].categories;if(void 0===z[O]){var D=e.dimensions[I]._categoryarray[O],R=e.dimensions[I]._ticktext[O];z[O]={dimensionInd:b,categoryInd:C=O,categoryValue:D,displayInd:C,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(z[O],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(P,A,g))}},{\"../../components/colorscale/calc\":374,\"../../components/colorscale/helpers\":377,\"../../components/drawing\":388,\"../../lib\":503,\"../../lib/filter_unique.js\":494,\"../../lib/gup\":500,\"fast-isnumeric\":190}],884:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"./attributes\"),c=t(\"../parcoords/merge_length\");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var 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g={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",g);var m={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",m)}},{\"../../components/colorscale/defaults\":376,\"../../components/colorscale/helpers\":377,\"../../lib\":503,\"../../plots/array_container_defaults\":549,\"../../plots/domain\":584,\"../parcoords/merge_length\":897,\"./attributes\":881}],885:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":881,\"./base_plot\":882,\"./calc\":883,\"./defaults\":884,\"./plot\":887}],886:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"d3-interpolate\").interpolateNumber,a=t(\"../../plot_api/plot_api\"),o=t(\"../../components/fx\"),s=t(\"../../lib\"),l=s.strTranslate,c=t(\"../../components/drawing\"),u=t(\"tinycolor2\"),f=t(\"../../lib/svg_text_utils\");function h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll(\"g.parcatslayer\").data([null]);o.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var u=o.selectAll(\"g.trace.parcats\").data(a,p),h=u.enter().append(\"g\").attr(\"class\",\"trace parcats\");u.attr(\"transform\",(function(t){return l(t.x,t.y)})),h.append(\"g\").attr(\"class\",\"paths\");var y=u.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),p);y.attr(\"fill\",(function(t){return t.model.color}));var x=y.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);_(x),y.attr(\"d\",(function(t){return t.svgD})),x.empty()||y.sort(g),y.exit().remove(),y.on(\"mouseover\",m).on(\"mouseout\",v).on(\"click\",b),h.append(\"g\").attr(\"class\",\"dimensions\");var w=u.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),p);w.enter().append(\"g\").attr(\"class\",\"dimension\"),w.attr(\"transform\",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll(\"g.category\").data((function(t){return t.categories}),p),M=A.enter().append(\"g\").attr(\"class\",\"category\");A.attr(\"transform\",(function(t){return l(0,t.y)})),M.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),A.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),T(M);var S=A.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr(\"fill\",(function(t){return t.color}));var E=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(E),S.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;A.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),A.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",I).on(\"dragend\",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function g(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function 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b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+g.left,y:_-d.top+g.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(g),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return u.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var g=u.model.count,m=u.model.categoryLabel,v=g/u.parcatsViewModel.model.count,y={countLabel:g,categoryLabel:m,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", 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e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=E(r,a,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,g=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var m=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:m,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color \\u2229 \"+m+\"): \"+k.probabilityLabel),A.push(\"P(\"+m+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+m+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=u.mostReadable(c.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(g-e.top),text:M,color:c.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:m,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(g),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr(\"transform\",(function(t){return l(t.x,0)})),r(i).attr(\"transform\",(function(t){return l(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p),o=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var g={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(g),N(g)),g}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),g=0;g<c.length;g++){var m,v=c[g];m=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/layout_attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/layout_attributes\":569,\"../../plots/domain\":584,\"../../plots/font_attributes\":585}],889:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"@plotly/d3\"),a=t(\"../../lib/gup\").keyFun,o=t(\"../../lib/gup\").repeat,s=t(\"../../lib\").sorterAsc,l=t(\"../../lib\").strTranslate,c=n.bar.snapRatio;function u(t,e){return t*(1-c)+e*c}var f=n.bar.snapClose;function h(t,e){return t*(1-f)+e*f}function p(t,e,r,n){if(function(t,e){for(var r=0;r<e.length;r++)if(t>=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],c=l,f=a;i*f<i*o;f+=i){var p=f+i,d=e[p];if(i*r<i*h(l,d))return u(l,c);if(i*r<i*d||p===o)return u(d,l);c=l,l=d}}function d(t){t.attr(\"x\",-n.bar.captureWidth/2).attr(\"width\",n.bar.captureWidth)}function g(t){t.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function m(t){if(!t.brush.filterSpecified)return\"0,\"+t.height;for(var 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k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||y()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e){var r=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);r.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t,e){var r=t.selectAll(\".background\").data(o);r.enter().append(\"rect\").classed(\"background\",!0).call(d).call(g).style(\"pointer-events\",\"auto\").attr(\"transform\",l(0,n.verticalPadding)),r.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var i=t.selectAll(\".highlight-shadow\").data(o);i.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",e).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",(function(t){return t.height})).call(x);var a=t.selectAll(\".highlight\").data(o);a.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),a.attr(\"y1\",(function(t){return t.height})).call(x)}(r,e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":503,\"../../lib/gup\":500,\"./constants\":892,\"@plotly/d3\":58}],890:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/get_data\").getModuleCalcData,a=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":480,\"../../plots/get_data\":593,\"./plot\":899,\"@plotly/d3\":58}],891:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,i=t(\"../../components/colorscale\"),a=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{\"../../components/colorscale\":378,\"../../lib\":503,\"../../lib/gup\":500}],892:[function(t,e,r){\"use strict\";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:[\"contextLineLayer\",\"focusLineLayer\",\"pickLineLayer\"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:\"#777\",bar:{width:4,captureWidth:10,fillColor:\"magenta\",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:\"axis-extent-text\",parcoordsLineLayers:\"parcoords-line-layers\",parcoordsLineLayer:\"parcoords-lines\",parcoords:\"parcoords\",parcoordsControlView:\"parcoords-control-view\",yAxis:\"y-axis\",axisOverlays:\"axis-overlays\",axis:\"axis\",axisHeading:\"axis-heading\",axisTitle:\"axis-title\",axisExtent:\"axis-extent\",axisExtentTop:\"axis-extent-top\",axisExtentTopText:\"axis-extent-top-text\",axisExtentBottom:\"axis-extent-bottom\",axisExtentBottomText:\"axis-extent-bottom-text\",axisBrush:\"axis-brush\"},id:{filterBarPattern:\"filter-bar-pattern\"}}},{}],893:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"../../plots/cartesian/axes\"),c=t(\"./attributes\"),u=t(\"./axisbrush\"),f=t(\"./constants\").maxDimensionCount,h=t(\"./merge_length\");function p(t,e,r,i){function a(r,i){return n.coerce(t,e,c.dimensions,r,i)}var o=a(\"values\"),s=a(\"visible\");if(o&&o.length||(s=e.visible=!1),s){a(\"label\"),a(\"tickvals\"),a(\"ticktext\"),a(\"tickformat\");var f=a(\"range\");e._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:f},l.setConvert(e._ax,i.layout),a(\"multiselect\");var h=a(\"constraintrange\");h&&(e.constraintrange=u.cleanRanges(h,e))}}e.exports=function(t,e,r,l){function u(r,i){return n.coerce(t,e,c,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log(\"parcoords traces support up to \"+f+\" dimensions at the 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":503}],895:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":888,\"./base_plot\":890,\"./calc\":891,\"./defaults\":893,\"./plot\":899}],896:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\nuniform sampler2D maskTexture, palette;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function g(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,c=r[64*a+l];63===l&&0===o&&(c*=-1),n[i++]=c}return n}function m(t){var e=\"0\"+t;return e.substr(e.length-2)}function v(t){return t<o?\"p\"+m(t+1)+\"_\"+m(t+4):\"colors\"}function y(t,e,r,n,i,a,o,l,c,u,f,h,p,d){for(var g=[[],[]],m=0;m<64;m++)g[0][m]=m===i?1:0,g[1][m]=m===a?1:0;o*=d,l*=d,c*=d,u*=d;var v=t.lines.canvasOverdrag*d,y=t.domain,x=t.canvasWidth*d,b=t.canvasHeight*d,_=t.pad.l*d,w=t.pad.b*d,T=t.layoutHeight*d,k=t.layoutWidth*d,A=t.deselectedLines.color;return s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+v,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:g[0].slice(0,16),dim0B:g[0].slice(16,32),dim0C:g[0].slice(32,48),dim0D:g[0].slice(48,64),dim1A:g[1].slice(0,16),dim1B:g[1].slice(16,32),dim1C:g[1].slice(32,48),dim1D:g[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,A[3]<1?A[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+v)+(_-v)+k*y.x[0],scissorWidth:(n===r?x-o+v:c+.5)+(n===e?o+v:0),scissorY:l+w+T*y.y[0],scissorHeight:u,viewportX:_-v+k*y.x[0],viewportY:w+T*y.y[0],viewportWidth:x,viewportHeight:b},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,m,b,_=e.context,w=e.pick,T=e.regl,k=T._gl,A=k.getParameter(k.ALIASED_LINE_WIDTH_RANGE),M=Math.max(A[0],Math.min(A[1],e.viewModel.plotGlPixelRatio)),S={currentRafs:{},drawCompleted:!0,clearOnly:!1},E=function(t){for(var e={},r=0;r<=o;r+=4)e[v(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(T),L=T.texture(u),C=[];I(e);var P=T({profile:!1,blend:{enable:_,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"plot\"},editType:\"plot\"},domain:i({name:\"pie\",trace:!0,editType:\"calc\"}),hole:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},sort:{valType:\"boolean\",dflt:!0,editType:\"calc\"},direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"],dflt:\"counterclockwise\",editType:\"calc\"},rotation:{valType:\"number\",min:-360,max:360,dflt:0,editType:\"calc\"},pull:{valType:\"number\",min:0,max:1,dflt:0,arrayOk:!0,editType:\"calc\"},_deprecated:{title:{valType:\"string\",dflt:\"\",editType:\"calc\"},titlefont:c({},u,{}),titleposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":365,\"../../lib/extend\":493,\"../../plots/attributes\":550,\"../../plots/domain\":584,\"../../plots/font_attributes\":585,\"../../plots/template_attributes\":633}],901:[function(t,e,r){\"use strict\";var n=t(\"../../plots/plots\");r.name=\"pie\",r.plot=function(t,e,i,a){n.plotBasePlot(r.name,t,e,i,a)},r.clean=function(t,e,i,a){n.cleanBasePlot(r.name,t,e,i,a)}},{\"../../plots/plots\":619}],902:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"tinycolor2\"),a=t(\"../../components/color\"),o={};function s(t){return function(e,r){return!!e&&(!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e))}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}e.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},g=s(o[\"_\"+e.type+\"colormap\"]),m=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;y=+y}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(m+=y),a.push({v:y,label:x,color:g(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(m+=y),!1===i.color&&u[r]&&(i.color=g(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=m),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":366,\"fast-isnumeric\":190,tinycolor2:312}],903:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var g=c(\"textposition\");s(t,e,n,c,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&c(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&c(\"insidetextorientation\")}o(e,n,c);var m=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",m?\"middle center\":\"top center\");m||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"./attributes\":900,\"fast-isnumeric\":190}],904:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":402}],905:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":503}],906:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":900,\"./base_plot\":901,\"./calc\":902,\"./defaults\":903,\"./layout_attributes\":907,\"./layout_defaults\":908,\"./plot\":909,\"./style\":910,\"./style_one\":911}],907:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],908:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":503,\"./layout_attributes\":907}],909:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,g=t(\"../bar/constants\").TEXTPAD,m=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u=\"funnelarea\"===c.type;\"_hasHoverLabel\"in c||(c._hasHoverLabel=!1),\"_hasHoverEvent\"in c||(c._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[m.castOption(h,t.pts)],_module:c._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),g=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=m.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=m.formatPieValue(t.v,y),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=m.formatPiePercent(t.percent,y),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:g,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:g-p*i.r,_y1:u?s+t.BL[1]:g+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:m.castOption(_.bgcolor,t.pts)||t.color,borderColor:m.castOption(_.bordercolor,t.pts),fontFamily:m.castOption(w.family,t.pts),fontSize:m.castOption(w.size,t.pts),fontColor:m.castOption(w.color,t.pts),nameLength:m.castOption(_.namelength,t.pts),textAlign:m.castOption(_.align,t.pts),hovertemplate:m.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=m.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=m.castOption(t._input.textfont.color,e.pts));var i=m.castOption(t.insidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.insidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,g=[];if(!d){var m,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,g.push(a)}};if(f||h){for(m=4;m>=-4;m-=2)v(Math.PI*m,\"tan\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+1),\"tan\")}if(f||p){for(m=4;m>=-4;m-=2)v(Math.PI*(m+1.5),\"rad\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;g.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a));for(var x=0,b=0,_=0;_<g.length;_++){var w=g[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return g[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),g=n.separators;if(s=f?[e.label]:[],h){var v=m.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(m.formatPieValue(e.v,g)),d&&s.push(m.formatPiePercent(e.v/r.vTotal,g)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:m.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:m.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=m.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],g=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=m.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=g.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),g.pull){var v=+m.castOption(g.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=g.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+M*i.px0[0]+\",\"+M*i.px0[1]+A+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+A+\"Z\")}D(t,i,d);var S=m.castOption(g.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=m.castOption(t.outsidetextfont.color,e.pts)||m.castOption(t.textfont.color,e.pts)||r.color,i=m.castOption(t.outsidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.outsidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(g,i,r.font):b(g,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,g.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(g.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(g.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=g.title.text;g._meta&&(i=l.templateString(i,g._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,g.title.font).call(f.convertToTspans,t),e=\"middle center\"===g.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function g(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,g=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(m.castOption(e.pull,t.pts)||0)>=(m.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(g+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?g:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,g),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr(\"transform\",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+c):s+=\"l\"+t.labelExtraX+\",\"+u+\"v\"+(f-u)+\"h\"+c}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+c;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(v,g),_&&g.automargin){var k=s.bBox(h.node()),A=g.domain,M=a.w*(A.x[1]-A.x[0]),S=a.h*(A.y[1]-A.y[0]),E=(.5*M-d.r)/a.w,P=(.5*S-d.r)/a.h;i.autoMargin(t,\"pie.\"+g.uid+\".automargin\",{xl:A.x[0]-E,xr:A.x[1]+E,yb:A.y[0]-P,yt:A.y[1]+P,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:O,attachFxHandlers:x,computeTransform:R}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../components/fx\":406,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../../plots/plots\":619,\"../bar/constants\":650,\"../bar/uniform_text\":664,\"./event_data\":904,\"./helpers\":905,\"@plotly/d3\":58}],910:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"./style_one\"),a=t(\"../bar/uniform_text\").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each((function(t){n.select(this).call(i,t,e)}))}))}},{\"../bar/uniform_text\":664,\"./style_one\":911,\"@plotly/d3\":58}],911:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"./helpers\").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},{\"../../components/color\":366,\"./helpers\":905}],912:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},{\"../scatter/attributes\":926}],913:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_pointcloud2d,i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new Int32Array(e),l=0;l<e;l++)o=c[l],s=u[l],r[l]=l,n[2*l]=o,n[2*l+1]=s,o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var g=i(t.marker.color),m=i(t.marker.border.color),v=t.opacity*t.marker.opacity;g[3]*=v,this.pointcloudOptions.color=g;var y=t.marker.blend;if(null===y){y=c.length<100||u.length<100}this.pointcloudOptions.blend=y,m[3]*=v,this.pointcloudOptions.borderColor=m;var x=t.marker.sizemin,b=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=b,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var _=this.scene.xaxis,w=this.scene.yaxis,T=b/2||.5;t._extremes[_._id]=a(_,[d[0],d[2]],{ppad:T}),t._extremes[w._id]=a(w,[d[1],d[3]],{ppad:T})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{\"../../../stackgl_modules\":1120,\"../../lib/str2rgbarray\":528,\"../../plots/cartesian/autorange\":553,\"../scatter/get_trace_color\":936}],914:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},{\"../../lib\":503,\"./attributes\":912}],915:[function(t,e,r){\"use strict\";[\"*pointcloud* trace is deprecated!\",\"Please consider switching to the *scattergl* trace type.\"].join(\" 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../plots/template_attributes\").hovertemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../plot_api/plot_template\").templatedArray,f=t(\"../../plots/cartesian/axis_format_attributes\").descriptionOnlyNumbers,h=t(\"../../lib/extend\").extendFlat,p=t(\"../../plot_api/edit_types\").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\",description:f(\"value\")},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),customdata:void 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n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,g=t.width*(u.x[1]-u.x[0]),m=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[g,m]:[m,g]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*g,a.node.y[_]*m];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:g,height:m,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?m:g,dragPerpendicular:f?g:m,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return\"M\"+a+\",\"+u+\"C\"+l+\",\"+u+\" \"+c+\",\"+h+\" \"+o+\",\"+h+\"L\"+o+\",\"+p+\"C\"+c+\",\"+p+\" \"+l+\",\"+f+\" \"+a+\",\"+f+\"Z\"}}function S(t,e){var r=c(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s=\"node_\"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr(\"d\",M())}function P(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function I(t){return t.link.width>1||t.linkLineWidth>0}function O(t){return p(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function z(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function D(t,e,r,i){var o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(a){if(\"fixed\"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),\"snap\"===a.arrangement)){var o=a.traceId+\"|\"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force(\"collide\",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force(\"constrain\",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var s=r.node.originalX;r.node.x0=s-r.visibleWidth/2,r.node.x1=s+r.visibleWidth/2,R(r,i)}}))}(t,e,a,o,i)}})).on(\"drag\",(function(r){if(\"fixed\"!==r.arrangement){var n=a.event.x,i=a.event.y;\"snap\"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2):(\"freeform\"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),i=Math.max(0,Math.min(r.size-r.visibleHeight/2,i)),r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2),F(r.node),\"snap\"!==r.arrangement&&(r.sankey.update(r.graph),C(t.filter(B(r)),e))}})).on(\"dragend\",(function(t){if(\"fixed\"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;\"snap\"!==t.arrangement&&R(t,i)}}));t.on(\".drag\",null).call(o)}function R(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}b.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function F(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function B(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,n,i){var o=!1;h.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){o=!0}));var s=t._fullLayout._dragCover,g=r.filter((function(t){return y(t).trace.visible})).map(k.bind(null,n)),b=e.selectAll(\".\"+l.cn.sankey).data(g,m);b.exit().remove(),b.enter().append(\"g\").classed(l.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").attr(\"transform\",O),b.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;h.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=a.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),b.transition().ease(l.ease).duration(l.duration).attr(\"transform\",O);var _=b.selectAll(\".\"+l.cn.sankeyLinks).data(v,m);_.enter().append(\"g\").classed(l.cn.sankeyLinks,!0).style(\"fill\",\"none\");var L=_.selectAll(\".\"+l.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(A.bind(null,t))}),m);L.enter().append(\"path\").classed(l.cn.sankeyLink,!0).call(z,b,i.linkEvents),L.style(\"stroke\",(function(t){return I(t)?u.tinyRGB(c(t.linkLineColor)):t.tinyColorHue})).style(\"stroke-opacity\",(function(t){return I(t)?u.opacity(t.linkLineColor):t.tinyColorAlpha})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})).style(\"stroke-width\",(function(t){return I(t)?t.linkLineWidth:1})).attr(\"d\",M()),L.style(\"opacity\",(function(){return t._context.staticPlot||o||s?1:0})).transition().ease(l.ease).duration(l.duration).style(\"opacity\",1),L.exit().transition().ease(l.ease).duration(l.duration).style(\"opacity\",0).remove();var C=b.selectAll(\".\"+l.cn.sankeyNodeSet).data(v,m);C.enter().append(\"g\").classed(l.cn.sankeyNodeSet,!0),C.style(\"cursor\",(function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}}));var R=C.selectAll(\".\"+l.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return t-e})),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.map(S.bind(null,t))}),m);R.enter().append(\"g\").classed(l.cn.sankeyNode,!0).call(E).style(\"opacity\",(function(e){return!t._context.staticPlot&&!o||e.partOfGroup?0:1})),R.call(z,b,i.nodeEvents).call(D,L,i,t),R.transition().ease(l.ease).duration(l.duration).call(E).style(\"opacity\",(function(t){return t.partOfGroup?0:1})),R.exit().transition().ease(l.ease).duration(l.duration).style(\"opacity\",0).remove();var F=R.selectAll(\".\"+l.cn.nodeRect).data(v);F.enter().append(\"rect\").classed(l.cn.nodeRect,!0).call(P),F.style(\"stroke-width\",(function(t){return t.nodeLineWidth})).style(\"stroke\",(function(t){return u.tinyRGB(c(t.nodeLineColor))})).style(\"stroke-opacity\",(function(t){return u.opacity(t.nodeLineColor)})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),F.transition().ease(l.ease).duration(l.duration).call(P);var B=R.selectAll(\".\"+l.cn.nodeLabel).data(v);B.enter().append(\"text\").classed(l.cn.nodeLabel,!0).style(\"cursor\",\"default\"),B.attr(\"data-notex\",1).text((function(t){return t.node.label})).each((function(e){var r=a.select(this);f.font(r,e.textFont),x.convertToTspans(r,t)})).style(\"text-shadow\",x.makeTextShadow(t._fullLayout.paper_bgcolor)).attr(\"text-anchor\",(function(t){return t.horizontal&&t.left?\"end\":\"start\"})).attr(\"transform\",(function(t){var e=a.select(this),r=x.lineCount(e),n=t.textFont.size*((r-1)*T-w),i=t.nodeLineWidth/2+3,o=((t.horizontal?t.visibleHeight:t.visibleWidth)-n)/2;t.horizontal&&(t.left?i=-i:i+=t.visibleWidth);var s=t.horizontal?\"\":\"scale(-1,1)\"+d(90);return p(t.horizontal?i:o,t.horizontal?o:i)+s})),B.transition().ease(l.ease).duration(l.duration)}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../constants/alignment\":471,\"../../lib\":503,\"../../lib/gup\":500,\"../../lib/svg_text_utils\":529,\"../../registry\":638,\"./constants\":919,\"@plotly/d3\":58,\"@plotly/d3-sankey\":57,\"@plotly/d3-sankey-circular\":56,\"d3-force\":111,\"d3-interpolate\":116,tinycolor2:312}],924:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=[],n=t.cd[0].trace,i=n._sankey.graph.nodes,a=0;a<i.length;a++){var o=i[a];if(!o.partOfGroup){var 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),g=p(\"b\"),m=Math.min(d.length,g.length);if(m){e._length=m,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",m<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var 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n=t(\"../scatter/hover\"),i=t(\"../../lib\").fillText;e.exports=function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,c=t.xa._length,u=c*l/2,f=c-u;return s.x0=Math.max(Math.min(s.x0,f),u),s.x1=Math.max(Math.min(s.x1,f),u),o}var h=s.cd[s.index];s.a=h.a,s.b=h.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var p=s.trace,d=p._carpet,g=p._module.formatLabels(h,p);s.yLabel=g.yLabel,delete s.text;var m=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split(\"+\");-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"text\"]),-1!==v.indexOf(\"a\")&&y(d.aaxis,h.a),-1!==v.indexOf(\"b\")&&y(d.baxis,h.b),m.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,m),s.extraText=m.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,m.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":503,\"../scatter/hover\":937}],966:[function(t,e,r){\"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":388,\"../../plots/cartesian/axes\":554,\"../scatter/plot\":947}],968:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,g=f(\"geojson\");(\"string\"==typeof g&&\"\"!==g||n.isPlainObject(g))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var m=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(m.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":503,\"../scatter/fillcolor_defaults\":934,\"../scatter/line_defaults\":939,\"../scatter/marker_defaults\":945,\"../scatter/subtypes\":951,\"../scatter/text_defaults\":952,\"./attributes\":968}],971:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],972:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},{\"../../plots/cartesian/axes\":554}],973:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/get_trace_color\"),o=t(\"../../lib\").fillText,s=t(\"./attributes\");e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=h.projection.isLonLatOverEdges,d=h.project;if(n.getClosest(l,(function(t){var n=t.lonlat;if(n[0]===i)return 1/0;if(p(n))return 1/0;var a=d(n),o=d([e,r]),s=Math.abs(a[0]-o[0]),l=Math.abs(a[1]-o[1]),c=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-c,1-3/c)}),t),!1!==t.index){var g=l[t.index],m=g.lonlat,v=[u.c2p(m),f.c2p(m)],y=g.mrc||1;t.x0=v[0]-y,t.x1=v[0]+y,t.y0=v[1]-y,t.y1=v[1]+y,t.loc=g.loc,t.lon=m[0],t.lat=m[1];var x={};x[c.geo]={_subplot:h};var b=c._module.formatLabels(g,c,x);return 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n;if(f.hasText(e)&&(n.text=y(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(e),n.markerSel=b(e,e.selected),n.markerUnsel=b(e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=g*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&\"none\"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var 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if(\"vh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var c=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){c=!0;break}var u=c||n.length>d.TOO_MANY_POINTS||f.hasMarkers(e)?\"rect\":\"round\";if(c&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=h,n[i+1]=p):(h=n[i],p=n[i+1])}return{join:u,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod(\"errorbars\",\"makeComputeError\"),l=c.getFromId(t,e.xaxis),u=c.getFromId(t,e.yaxis),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e[\"error_\"+a];if(o&&o.visible&&(\"linear\"===i.type||\"log\"===i.type)){for(var l=s(o),c={x:0,y:1}[a],u={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,g=-1/0,m=0,v=0;m<f;m++,v+=4){var y=t[m];if(n(y)){var x=r[2*m+c],b=l(y,m),_=b[0],w=b[1];if(n(_)&&n(w)){var T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),g=Math.max(g,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,g]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,g=m[p],v=m[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[g*y/h,x/h]}}return o}}},{\"../../components/drawing\":388,\"../../components/fx/helpers\":402,\"../../constants/interactions\":478,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../plots/cartesian/axis_ids\":558,\"../../registry\":638,\"../scatter/make_bubble_size_func\":943,\"../scatter/subtypes\":951,\"./constants\":980,\"./helpers\":985,\"color-normalize\":89,\"fast-isnumeric\":190,\"svg-path-sdf\":310}],982:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,g){function m(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,g,m);if(x){u(t,e,g,m),m(\"xhoverformat\"),m(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";m(\"text\"),m(\"hovertext\"),m(\"hovertemplate\"),m(\"mode\",b),l.hasLines(e)&&(m(\"connectgaps\"),h(t,e,r,g,m),m(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,g,m),m(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(m(\"texttemplate\"),d(t,e,g,m));var _=(e.line||{}).color,w=(e.marker||{}).color;m(\"fill\"),\"none\"!==e.fill&&p(t,e,r,m);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,m)}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/constants\":930,\"../scatter/fillcolor_defaults\":934,\"../scatter/line_defaults\":939,\"../scatter/marker_defaults\":945,\"../scatter/period_defaults\":946,\"../scatter/subtypes\":951,\"../scatter/text_defaults\":952,\"../scatter/xy_defaults\":953,\"./attributes\":978,\"./helpers\":985}],983:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},g=c.textUnselectedOptions[u]||{},m=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=g.color,x=p.color,b=Array.isArray(x);for(m.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],m.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;m.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(m)}}},{\"../../components/color\":366,\"../../constants/interactions\":478,\"../../lib\":503}],984:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":935}],985:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":980}],986:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var g=d&&d.line;g&&(h.mlc=Array.isArray(g.color)?g.color[f]:g.color,h.mlw=i.isArrayOrTypedArray(g.width)?g.width[f]:g.width);var m=d&&d.gradient;m&&\"none\"!==m.type&&(h.mgt=Array.isArray(m.type)?m.type[f]:m.type,h.mgc=Array.isArray(m.color)?m.color[f]:m.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,g=t.cd,m=g[0].t,v=g[0].trace,y=t.xa,x=t.ya,b=m.x,_=m.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(m.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?m.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):m.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=m.ids;var L=k;if(\"x\"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/get_trace_color\":936}],987:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":568,\"../scatter/cross_trace_defaults\":932,\"../scatter/marker_colorbar\":944,\"./attributes\":978,\"./calc\":979,\"./defaults\":982,\"./format_labels\":984,\"./hover\":986,\"./plot\":988,\"./select\":990}],988:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}e.exports=function(t,e,r){if(r.length){var d,g,m=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=m._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_)),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var g=s._nexttrace;if(g){var m=v.lineOptions[e+1];if(m){var y=m.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=g.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var A=m.dragmode,M=l(A),S=m.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],L=E.trace,C=E.t,P=C.index,I=L._length,O=C.x,z=C.y;if(L.selectedpoints||M||S){if(M||(M=!0),L.selectedpoints){var D=v.selectBatch[P]=s.selIndices2selPoints(L),R={};for(g=0;g<D.length;g++)R[D[g]]=1;var F=[];for(g=0;g<I;g++)R[g]||F.push(g);v.unselectBatch[P]=F}var B=C.xpx=new Array(I),N=C.ypx=new Array(I);for(g=0;g<I;g++)B[g]=y.c2p(O[g]),N[g]=x.c2p(z[g])}else C.xpx=C.ypx=null}if(M){if(v.select2d||(v.select2d=n(m._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(m,y,x,t._context.plotGlPixelRatio),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":384,\"../../lib\":503,\"../../lib/prepare_regl\":516,\"../scatter/link_traces\":942,\"../scatter/subtypes\":951,\"./edit_style\":983,\"gl-text\":225,\"regl-error2d\":279,\"regl-line2d\":280,\"regl-scatter2d\":281}],989:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var 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_.remove();var D=null,R={};v&&_.each((function(t){R[b.getPtId(t)]={rpx0:t.rpx0,rpx1:t.rpx1,x0:t.x0,x1:t.x1,transform:t.transform},!D&&b.isEntry(t)&&(D=t)}));var F=function(t){return i.partition().size([2*Math.PI,t.height+1])(t)}(M).descendants(),B=M.height+1,N=0,j=S;T.hasMultipleRoots&&b.isHierarchyRoot(M)&&(F=F.slice(1),B-=1,N=1,j+=1),F=F.filter((function(t){return t.y1<=j}));var U=p(k.rotation);U&&F.forEach((function(t){t.x0+=U,t.x1+=U}));var V=Math.min(B,S),H=function(t){return(t-N)/V*I},q=function(t,e){return[t*Math.cos(e),-t*Math.sin(e)]},G=function(t){return s.pathAnnulus(t.rpx0,t.rpx1,t.x0,t.x1,O,z)},Y=function(t){return O+w(t)[0]*(t.transform.rCenter||0)+(t.transform.x||0)},W=function(t){return z+w(t)[1]*(t.transform.rCenter||0)+(t.transform.y||0)};(_=_.data(F,b.getPtId)).enter().append(\"g\").classed(\"slice\",!0),v?_.exit().transition().each((function(){var t=n.select(this);t.select(\"path.surface\").transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=b.getPtId(t),n=R[r],i=R[b.getPtId(M)];if(i){var o=(t.x1>i.x1?2*Math.PI:0)+U;e=t.rpx1<i.rpx1?{x0:t.x0,x1:t.x1,rpx0:0,rpx1:0}:{x0:o,x1:o,rpx0:t.rpx0,rpx1:t.rpx1}}else{var s,l=b.getPtId(t.parent);_.each((function(t){if(b.getPtId(t)===l)return s=t}));var c,u=s.children;u.forEach((function(t,e){if(b.getPtId(t)===r)return c=e}));var f=u.length,h=a(s.x0,s.x1);e={rpx0:I,rpx1:I,x0:h(c/f),x1:h((c+1)/f)}}return a(n,e)}(t);return function(t){return G(e(t))}})),t.select(\"g.slicetext\").attr(\"opacity\",0)})).remove():_.exit().remove(),_.order();var X=null;if(v&&D){var Z=b.getPtId(D);_.each((function(t){null===X&&b.getPtId(t)===Z&&(X=t.x1)}))}var J=_;function K(t){var e=t.parent,r=R[b.getPtId(e)],n={};if(r){var i=e.children,o=i.indexOf(t),s=i.length,l=a(r.x0,r.x1);n.x0=l(o/s),n.x1=l(o/s)}else n.x0=n.x1=0;return n}v&&(J=J.transition().each(\"end\",(function(){var e=n.select(this);b.setSliceCursor(e,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})}))),J.each((function(i){var c=n.select(this),f=s.ensureSingle(c,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",\"all\")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr(\"d\",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(m,i,k);var p=s.ensureSingle(c,\"g\",\"slicetext\"),_=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=g(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),g=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,m=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return m(Math.pow(t,g))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr(\"transform\",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var g,m=o.split(\"+\"),v=function(t){return-1!==m.indexOf(t)},y=[];if(v(\"label\")&&u.label&&y.push(u.label),u.hasOwnProperty(\"v\")&&v(\"value\")&&y.push(b.formatValue(u.v,l)),!h){v(\"current path\")&&y.push(b.getPath(t.data));var x=0;v(\"percent parent\")&&x++,v(\"percent entry\")&&x++,v(\"percent root\")&&x++;var _=x>1;if(x){var w,T=function(t){g=b.formatPercent(w,l),_&&(g+=\" of \"+t),y.push(g)};v(\"percent parent\")&&!h&&(w=d/b.getValue(p),T(\"parent\")),v(\"percent entry\")&&(w=d/b.getValue(e),T(\"entry\")),v(\"percent root\")&&(w=d/b.getValue(f),T(\"root\"))}}return v(\"text\")&&(g=s.castOption(r,u.i,\"text\"),s.isValidTextValue(g)&&y.push(g)),y.join(\"<br>\")}var k=s.castOption(r,u.i,\"texttemplate\");if(!k)return\"\";var A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty(\"color\")&&(A.color=u.color);var M=s.castOption(r,u.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{\"../../components/drawing\":388,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../bar/style\":662,\"../bar/uniform_text\":664,\"../pie/helpers\":905,\"../pie/plot\":909,\"./constants\":1048,\"./fx\":1050,\"./helpers\":1051,\"./style\":1056,\"@plotly/d3\":58,\"d3-hierarchy\":115,\"d3-interpolate\":116}],1056:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],1057:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;function u(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),\"calc\",\"nested\");f.x.editType=f.y.editType=f.z.editType=\"calc+clearAxisTypes\",f.transforms=void 0},{\"../../components/color\":366,\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/cartesian/axis_format_attributes\":557,\"../../plots/template_attributes\":633}],1058:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],1059:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_surface3d,i=t(\"../../../stackgl_modules\").ndarray,a=t(\"../../../stackgl_modules\").ndarray_linear_interpolate.d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var 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x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<m;r++)x[e][r]=[];for(r=0;r<m;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<m;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=g[e];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v]),i(new 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A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in 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i=n(\"opacityscale\");\"max\"===i?e.opacityscale=[[0,.1],[1,1]]:\"min\"===i?e.opacityscale=[[0,1],[1,.1]]:\"extremes\"===i?e.opacityscale=function(t,e){for(var r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var 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\",cn:{table:\"table\",tableControlView:\"table-control-view\",scrollBackground:\"scroll-background\",yColumn:\"y-column\",columnBlock:\"column-block\",scrollAreaClip:\"scroll-area-clip\",scrollAreaClipRect:\"scroll-area-clip-rect\",columnBoundary:\"column-boundary\",columnBoundaryClippath:\"column-boundary-clippath\",columnBoundaryRect:\"column-boundary-rect\",columnCells:\"column-cells\",columnCell:\"column-cell\",cellRect:\"cell-rect\",cellText:\"cell-text\",cellTextHolder:\"cell-text-holder\",scrollbarKit:\"scrollbar-kit\",scrollbar:\"scrollbar\",scrollbarSlider:\"scrollbar-slider\",scrollbarGlyph:\"scrollbar-glyph\",scrollbarCaptureZone:\"scrollbar-capture-zone\"}}},{}],1066:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"../../lib/extend\").extendFlat,a=t(\"fast-isnumeric\");function o(t){if(Array.isArray(t)){for(var e=0,r=0;r<t.length;r++)e=Math.max(e,o(t[r]));return e}return t}function s(t,e){return t+e}function l(t){var e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(c(a)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=\"\";return e}function u(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function f(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function h(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((a+=r)>=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[\"\"],d=l(d));var g=d.concat(p(r).map((function(){return c((d[0]||[\"\"]).length)}))),m=e.domain,v=Math.floor(t._fullLayout._size.w*(m.x[1]-m.x[0])),y=Math.floor(t._fullLayout._size.h*(m.y[1]-m.y[0])),x=e.header.values.length?g[0].map((function(){return e.header.height})):[n.emptyHeaderHeight],b=r.length?r[0].map((function(){return e.cells.height})):[],_=x.reduce(s,0),w=h(b,y-_+n.uplift),T=f(h(x,_),[]),k=f(w,T),A={},M=e._fullInput.columnorder.concat(p(r.map((function(t,e){return e})))),S=g.map((function(t,r){var n=Array.isArray(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return a(n)?Number(n):1})),E=S.reduce(s,0);S=S.map((function(t){return t/E*v}));var L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:m.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-m.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:g}),gdColumns:g.map((function(t){return t[0]})),gdColumnsOriginalOrder:g.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:g.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+\"__\"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{\"../../lib/extend\":493,\"./constants\":1065,\"fast-isnumeric\":190}],1067:[function(t,e,r){\"use strict\";var n=t(\"../../lib/extend\").extendFlat;r.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:\"header\",type:\"header\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:\"cells1\",type:\"cells\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:\"cells2\",type:\"cells\",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},r.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0,n=e?r+e.rows.length:0;return[r,n]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+(\"string\"==typeof r&&r.match(/[<$&> 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a.indexOf(t)})),s=o.length;s<n;s++)o.push(s);e(\"columnorder\",o)}(e,s),s(\"cells.values\"),s(\"cells.format\"),s(\"cells.align\"),s(\"cells.prefix\"),s(\"cells.suffix\"),s(\"cells.height\"),s(\"cells.line.width\"),s(\"cells.line.color\"),s(\"cells.fill.color\"),n.coerceFont(s,\"cells.font\",n.extendFlat({},o.font)),e._length=null}},{\"../../lib\":503,\"../../plots/domain\":584,\"./attributes\":1062}],1069:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),moduleType:\"trace\",name:\"table\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":1062,\"./base_plot\":1063,\"./calc\":1064,\"./defaults\":1068,\"./plot\":1070}],1070:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"@plotly/d3\"),a=t(\"../../lib\").numberFormat,o=t(\"../../lib/gup\"),s=t(\"../../components/drawing\"),l=t(\"../../lib/svg_text_utils\"),c=t(\"../../lib\").raiseToTop,u=t(\"../../lib\").strTranslate,f=t(\"../../lib\").cancelTransition,h=t(\"./data_preparation_helper\"),p=t(\"./data_split_helpers\"),d=t(\"../../components/color\");function g(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function m(t,e){return\"clip\"+t._fullLayout._uid+\"_scrollAreaBottomClip_\"+e.key}function v(t,e){return\"clip\"+t._fullLayout._uid+\"_columnBoundaryClippath_\"+e.calcdata.key+\"_\"+e.specIndex}function y(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function x(t,e,r){var a=t.selectAll(\".\"+n.cn.scrollbarKit).data(o.repeat,o.keyFun);a.enter().append(\"g\").classed(n.cn.scrollbarKit,!0).style(\"shape-rendering\",\"geometricPrecision\"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return D(e,e.length-1)+(e.length?R(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-S(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr(\"transform\",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return u(e,S(t))}));var s=a.selectAll(\".\"+n.cn.scrollbar).data(o.repeat,o.keyFun);s.enter().append(\"g\").classed(n.cn.scrollbar,!0);var l=s.selectAll(\".\"+n.cn.scrollbarSlider).data(o.repeat,o.keyFun);l.enter().append(\"g\").classed(n.cn.scrollbarSlider,!0),l.attr(\"transform\",(function(t){return u(0,t.scrollbarState.topY||0)}));var c=l.selectAll(\".\"+n.cn.scrollbarGlyph).data(o.repeat,o.keyFun);c.enter().append(\"line\").classed(n.cn.scrollbarGlyph,!0).attr(\"stroke\",\"black\").attr(\"stroke-width\",n.scrollbarWidth).attr(\"stroke-linecap\",\"round\").attr(\"y1\",n.scrollbarWidth/2),c.attr(\"y2\",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr(\"stroke-opacity\",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),c.transition().delay(0).duration(0),c.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr(\"stroke-opacity\",0);var f=s.selectAll(\".\"+n.cn.scrollbarCaptureZone).data(o.repeat,o.keyFun);f.enter().append(\"line\").classed(n.cn.scrollbarCaptureZone,!0).attr(\"stroke\",\"white\").attr(\"stroke-opacity\",.01).attr(\"stroke-width\",n.scrollbarCaptureWidth).attr(\"stroke-linecap\",\"butt\").attr(\"y1\",0).on(\"mousedown\",(function(r){var n=i.event.y,a=this.getBoundingClientRect(),o=r.scrollbarState,s=n-a.top,l=i.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||L(e,t,null,l(s-o.barLength/2))(r)})).call(i.behavior.drag().origin((function(t){return i.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t})).on(\"drag\",L(e,t)).on(\"dragend\",(function(){}))),f.attr(\"y2\",(function(t){return t.scrollbarState.scrollableAreaHeight})),e._context.staticPlot&&(c.remove(),f.remove())}function b(t,e,r,a){var l=function(t){var e=t.selectAll(\".\"+n.cn.columnCell).data(p.splitToCells,(function(t){return t.keyWithinBlock}));return e.enter().append(\"g\").classed(n.cn.columnCell,!0),e.exit().remove(),e}(function(t){var e=t.selectAll(\".\"+n.cn.columnCells).data(o.repeat,o.keyFun);return e.enter().append(\"g\").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r));!function(t){t.each((function(t,e){var 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n=t[Math.min(e,t.length-1)];return Array.isArray(n)?n[Math.min(r,n.length-1)]:n}return t}function k(t,e,r){t.transition().ease(n.releaseTransitionEase).duration(n.releaseTransitionDuration).attr(\"transform\",u(e.x,r))}function A(t){return\"cells\"===t.type}function M(t){return\"header\"===t.type}function S(t){return(t.rowBlocks.length?t.rowBlocks[0].auxiliaryBlocks:[]).reduce((function(t,e){return t+R(e,1/0)}),0)}function E(t,e,r){var n=y(e)[0];if(void 0!==n){var i=n.rowBlocks,a=n.calcdata,o=D(i,i.length),s=n.calcdata.groupHeight-S(n),l=a.scrollY=Math.max(0,Math.min(o-s,a.scrollY)),c=function(t,e,r){for(var n=[],i=0,a=0;a<t.length;a++){for(var o=t[a],s=o.rows,l=0,c=0;c<s.length;c++)l+=s[c].rowHeight;o.allRowsHeight=l;e<i+l&&e+r>i&&n.push(a),i+=l}return n}(i,l,s);1===c.length&&(c[0]===i.length-1?c.unshift(c[0]-1):c.push(c[0]+1)),c[0]%2&&c.reverse(),e.each((function(t,e){t.page=c[e],t.scrollY=l})),e.attr(\"transform\",(function(t){var e=D(t.rowBlocks,t.page)-t.scrollY;return 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this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return 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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(g,U)|0,a=Math.imul(g,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,q)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(g,J)|0,a=a+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(g,ft)|0,a=a+Math.imul(g,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,gt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,gt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function g(t,e,r){return(new m).mulp(t,e,r)}function m(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):g(this,t,e)},m.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},m.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},m.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},m.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=f*m-h*v;v=f*v+h*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},m.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},m.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},m.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},m.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},m.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},m.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),g(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number 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fffffffe fffffc2f\")}function b(){y.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){y.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){y.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var g=p,m=0;0!==g.cmp(s);m++)g=g.redSqr();n(m<d);var v=this.pow(f,new a(1).iushln(d-m-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=m}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(void 0===e||e,this)},{buffer:2}],34:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],35:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function s(t,e){for(var r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),g=i.mallocInt32(c);(c=l(e,u,d,g))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,g):o(u,r,n,s,h,p,c,d,g),i.free(d),i.free(g))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{\"./lib/intersect\":37,\"./lib/sweep\":41,\"typedarray-pool\":308}],36:[function(t,e,r){\"use strict\";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var 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e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function g(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],m={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=g;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,F=1,\"auto\"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),\"auto\"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":71,\"./lib/cube.js\":72,\"./lib/lines.js\":73,\"./lib/text.js\":75,\"./lib/ticks.js\":76}],71:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var g=-1;g<=1;g+=2)f[u]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var m=c;c=u,u=m}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],72:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=g;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=m,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{\"bit-twiddle\":32,\"gl-mat4/multiply\":100,\"robust-orientation\":284,\"split-polygon\":300}],73:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;s[f]=h,l[f]=m-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){g=+r[f][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}m=o.length/3|0;c[f]=h,u[f]=m-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],74:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,g=p.length;d<g;++d)for(var m=p[d],v=2;v>=0;--v){var y=h[m[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var g=0;g<n[d].length;++g)n[d][g].text&&s(n[d][g].x,n[d][g].text,n[d][g].font||i,n[d][g].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":74,_process:5,\"gl-buffer\":78,\"gl-vao\":150,\"vectorize-text\":311}],76:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var f=\"\"+c;f.length<i;)f=\"0\"+f;return u+\".\"+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=g,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=m;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(m,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],m=[0,0,0]},{\"./lib/cube.js\":72,\"extract-frustum-planes\":67,\"gl-mat4/multiply\":100,\"gl-mat4/transpose\":109,\"gl-vec4/transformMat4\":221,\"split-polygon\":300}],78:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,\"ndarray-ops\":254,\"typedarray-pool\":308}],79:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,g=[],m=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(m=Math.min(m,_),v=!1):v=!0}v||(p=x,d=b),g.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(m)||(m=1),a.vectorScale=m;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=g[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":80,\"./lib/shaders\":81,\"gl-vec3\":169}],80:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function g(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var m=0;m<p.length;++m){var v=p[m];d=Math.min(d,v),g=Math.max(g,v)}else for(m=0;m<n.length;++m){v=n[m][2];d=Math.min(d,v),g=Math.max(g,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(m=0;m<n.length;++m)for(var y=n[m],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(m=0;m<r.length;++m){var _=r[m];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(A[0],A[1]),f.push(m)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=g(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,m,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],81:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORM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t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var g=d.prototype;function m(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,m(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":146}],87:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));h[g]=d}}var m=o(e).split(\"\\n\");for(p=0;p<m.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=m[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":9,\"gl-constants/lookup\":83,\"glsl-shader-name\":223,\"sprintf-js\":301}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],g=t.colorValues||[0,0,0,1],m=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=g[0],B=g[1],N=g[2],j=g[3];else if(q===m-1)F=g[4*m-4],B=g[4*m-3],N=g[4*m-2],j=g[4*m-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*g[W]+G*g[X],B=Y*g[W+1]+G*g[X+1],N=Y*g[W+2]+G*g[X+2],j=Y*g[W+3]+G*g[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":89,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"iota-array\":235,\"typedarray-pool\":308}],89:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:231}],90:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":132,glslify:231}],91:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var g=new v(e,r,o,s,l,d);return g.update(t),g};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function g(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function m(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:g(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:g(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,m=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,m=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var T=s;if(s+=d(b,_),m){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,m=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new m(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new m(e,u,h,this.points[h])}},{\"./lib/shaders\":90,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return 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t[0]=1-(p+g),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=h+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*g-f*d,A=u*m-h*d,M=u*v-p*d,S=f*m-h*g,E=f*v-p*g,L=h*v-p*m,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(g*T-m*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(m*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-g*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(g*x-d*_-m*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,g=e[0],m=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=g-_,h=m-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{\"./identity\":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*g,t[1]=x*i+b*l+_*h+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*g,t[5]=x*i+b*l+_*h+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*g,t[9]=x*i+b*l+_*h+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+g*S,t[9]=l*A+h*M+m*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,g=0;g<2;++g)d+=Math.pow(u[f][g]-e[g],2);d<p&&(p=d,h=f)}var m=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(m[f]<-.001||m[f]>1.0001)return null;v+=m[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,m),m]}},{barycentric:14,\"polytope-closest-point/lib/closest_point_2d.js\":270}],111:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:231}],112:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),m=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],m=i[f],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],g.push(Z[0],Z[1]),m.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=g(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,g,m,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":110,\"./lib/shaders\":111,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,normals:261,\"simplicial-complex-contour\":291,\"typedarray-pool\":308}],113:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],114:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=h[g];var _=6*n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=L[1]*m/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=L[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=L[3]*m/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=L[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132}],115:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],116:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:231}],117:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,m=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*g[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*g[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],g=a[o+2]-f,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,h[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,g=p.text,m=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(m,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"text-cache\":303}],118:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":113,\"./lib/grid\":114,\"./lib/line\":115,\"./lib/text\":117,\"gl-select-static\":131}],119:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,g=!!a.shift,m=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&m&&!h&&!p&&!g||m&&!h&&!p&&g)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&m&&!h&&!p&&!g||v||m&&h&&!p&&!g)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&m&&!h&&!p&&!g||y||m&&!h&&p&&!g)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":7,\"has-passive-events\":232,\"mouse-change\":247,\"mouse-event-offset\":248,\"mouse-wheel\":250,\"right-now\":278}],120:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":132,glslify:231}],121:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function m(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new g,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var g=.05*(i[p]-n[p]);n[p]=n[p]-g,i[p]=i[p]+g}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=m((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var g=0;for(d=0;d<3;++d)g=Math.max(g,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/g,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":119,\"./lib/shader\":120,\"a-big-triangle\":8,\"gl-axes3d\":70,\"gl-axes3d/properties\":77,\"gl-fbo\":86,\"gl-mat4/ortho\":101,\"gl-mat4/perspective\":102,\"gl-select-static\":131,\"gl-spikes3d\":141,\"is-mobile\":238,\"mouse-change\":247}],122:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:231}],123:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":122,\"gl-buffer\":78,\"gl-shader\":132,\"typedarray-pool\":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+f*g+h*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*g,t[3]=s*h+l*m,t}},{}],125:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],126:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":311}],127:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{\"gl-shader\":132,glslify:231}],128:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t||t>1?1:t}function m(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),g=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new m(e,r,n,o,f,h,p,d,g,s,c,u);return v.update(t),v};var v=m.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,g=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*m]=0,i[m]<0?v[12+m]=d[0][m]:v[12+m]=d[1][m],s(v,c,v),l.model=v;var C=(m+1)%3,P=(m+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,g[0],m,-1e8),l.fragClipBounds[1]=E(k,g[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,m=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(m)?H=n<m.length?+m[n]:0:m&&(H=+m);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":125,\"./lib/glyphs\":126,\"./lib/shaders\":127,\"gl-buffer\":78,\"gl-mat4/multiply\":100,\"gl-vao\":150,\"is-string-blank\":239,\"typedarray-pool\":308}],129:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:231}],130:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var g=s[2]-s[0],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,h,g,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":129,\"gl-buffer\":78,\"gl-shader\":132}],131:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,g=r-c,m=d*d+g*g;m<n&&(n=m,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":32,\"gl-fbo\":86,ndarray:259,\"typedarray-pool\":308}],132:[function(t,e,r){\"use strict\";var n=t(\"./lib/create-uniforms\"),i=t(\"./lib/create-attributes\"),a=t(\"./lib/reflect\"),o=t(\"./lib/shader-cache\"),s=t(\"./lib/runtime-reflect\"),l=t(\"./lib/GLError\");function c(t){this.gl=t,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var u=c.prototype;function f(t,e){return t.name<e.name?-1:1}u.bind=function(){var t;this.program||this._relink();var e=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),r=this.gl.lastAttribCount;if(e>r)for(t=r;t<e;t++)this.gl.enableVertexAttribArray(t);else if(r>e)for(t=e;t<r;t++)this.gl.disableVertexAttribArray(t);this.gl.lastAttribCount=e,this.gl.useProgram(this.program)},u.dispose=function(){for(var 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w=c[y];if(w.type.indexOf(\"mat\")>=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A<T;++A)k[A]=_.length,b.push(w.name+\"[\"+A+\"]\"),\"number\"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===T&&\"number\"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:k})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),\"number\"==typeof w.location?_.push(0|w.location):_.push(-1)}var M=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(M)>=0;)M+=1;_[y]=M}var S=new Array(r.length);function E(){h.program=o.program(p,h._vref,h._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(h.program,r[t].name)}E(),h._relink=E,h.types={uniforms:a(r),attributes:a(c)},h.attributes=i(p,h,x,_),Object.defineProperty(h,\"uniforms\",n(p,h,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return 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h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":140,\"gl-buffer\":78,\"gl-vao\":150}],142:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:231}],143:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],g=0,m=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),m=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(g,m,m,m,g,g);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=g;g=m,m=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var g=u[s],m=f.length;f=f.concat(g.positions),p=p.concat(g.vectors),d=d.concat(g.vertexIntensity);for(c=0;c<g.cells.length;c++){var v=g.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+m)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),g=s(a[1],c),m=s(a[2],u),v=d+1,y=g+1,x=m+1;if(d=l(d,0,f-1),v=l(v,0,f-1),g=l(g,0,h-1),y=l(y,0,h-1),m=l(m,0,p-1),x=l(x,0,p-1),d<0||g<0||m<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][g],L=a[1][y],C=a[2][m],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(g=h-1-g,y=h-1-y),r.reversedZ&&(m=p-1-m,x=p-1-x),r.filled){case 5:k=m,A=x,w=g*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=m,A=x,b=d*p,_=v*p,w=g*p*f,T=y*p*f;break;case 3:w=g,T=y,k=m*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=g,T=y,b=d*h,_=v*h,k=m*h*f,A=x*h*f;break;case 1:b=d,_=v,k=m*f,A=x*f,w=g*f*p,T=y*f*p;break;default:b=d,_=v,w=g*f,T=y*f,k=m*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},g=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},m=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),g=c(r),m=c(n),v=Math.min(d,g,m);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=g(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),m.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=g(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(m,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":142,\"gl-cone3d\":79,\"gl-vec3\":169,\"gl-vec4\":205}],144:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":132,glslify:231}],145:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",m.update(v),m};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),m=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,g,m){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=m,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,m=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var g=[0,0];g[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],g,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)m(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var gt=($+dt+1)%3;for(V=0;V<2;++V){var mt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[gt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=mt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":144,\"binary-search-bounds\":31,\"bit-twiddle\":32,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,\"ndarray-gradient\":252,\"ndarray-ops\":254,\"ndarray-pack\":255,\"surface-nets\":302,\"typedarray-pool\":308}],146:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function g(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function m(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=g(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var x=n(h,o,y,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):f(x,e),u=h.subarray(0,v)}var b=m(t);return t.texImage2D(t.TEXTURE_2D,0,d,o[0],o[1],0,d,c,u),l||a.free(h),new p(t,b,o[0],o[1],d,c)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var 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+       "/*!\n",
+       " * Determine if an object is a Buffer\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
+       "e.exports=function(t){return null!=t&&(n(t)||function(t){return\"function\"==typeof t.readFloatLE&&\"function\"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],238:[function(t,e,r){\"use strict\";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var 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l=0;l<3;++l){for(var c=0,f=0;f<3;++f)c+=r[l+4*f]*i[f];r[12+l]=-c}},h.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},h.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},h.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},h.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],c=l(a,o,s);a/=c,o/=c,s/=c;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=l(u-=a*p,f-=o*p,h-=s*p);u/=d,f/=d,h/=d;var g=i[2],m=i[6],v=i[10],y=g*a+m*o+v*s,x=g*u+m*f+v*h,b=l(g-=y*a+x*u,m-=y*o+x*f,v-=y*s+x*h);g/=b,m/=b,v/=b;var _=u*e+a*r,w=f*e+o*r,T=h*e+s*r;this.center.move(t,_,w,T);var k=Math.exp(this.computedRadius[0]);k=Math.max(1e-4,k+n),this.radius.set(t,Math.log(k))},h.rotate=function(t,e,r,n){this.recalcMatrix(t),e=e||0,r=r||0;var i=this.computedMatrix,a=i[0],o=i[4],s=i[8],u=i[1],f=i[5],h=i[9],p=i[2],d=i[6],g=i[10],m=e*a+r*u,v=e*o+r*f,y=e*s+r*h,x=-(d*y-g*v),b=-(g*m-p*y),_=-(p*v-d*m),w=Math.sqrt(Math.max(0,1-Math.pow(x,2)-Math.pow(b,2)-Math.pow(_,2))),T=c(x,b,_,w);T>1e-6?(x/=T,b/=T,_/=T,w/=T):(x=b=_=0,w=1);var k=this.computedRotation,A=k[0],M=k[1],S=k[2],E=k[3],L=A*w+E*x+M*_-S*b,C=M*w+E*b+S*x-A*_,P=S*w+E*_+A*b-M*x,I=E*w-A*x-M*b-S*_;if(n){x=p,b=d,_=g;var O=Math.sin(n)/l(x,b,_);x*=O,b*=O,_*=O,I=I*(w=Math.cos(e))-(L=L*w+I*x+C*_-P*b)*x-(C=C*w+I*b+P*x-L*_)*b-(P=P*w+I*_+L*b-C*x)*_}var z=c(L,C,P,I);z>1e-6?(L/=z,C/=z,P/=z,I/=z):(L=C=P=0,I=1),this.rotation.set(t,L,C,P,I)},h.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var a=this.computedMatrix;i(a,e,r,n);var o=this.computedRotation;s(o,a[0],a[1],a[2],a[4],a[5],a[6],a[8],a[9],a[10]),u(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var l=0,c=0;c<3;++c)l+=Math.pow(r[c]-e[c],2);this.radius.set(t,.5*Math.log(Math.max(l,1e-6))),this.center.set(t,r[0],r[1],r[2])},h.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},h.setMatrix=function(t,e){var r=this.computedRotation;s(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),u(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;o(n,e);var i=n[15];if(Math.abs(i)>1e-6){var a=n[12]/i,l=n[13]/i,c=n[14]/i;this.recalcMatrix(t);var f=Math.exp(this.computedRadius[0]);this.center.set(t,a-n[2]*f,l-n[6]*f,c-n[10]*f),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},h.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},h.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},h.getDistanceLimits=function(t){var e=this.radius.bounds;return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},h.toJSON=function(){return this.recalcMatrix(this.lastT()),{center:this.computedCenter.slice(),rotation:this.computedRotation.slice(),distance:Math.log(this.computedRadius[0]),zoomMin:this.radius.bounds[0][0],zoomMax:this.radius.bounds[1][0]}},h.fromJSON=function(t){var e=this.lastT(),r=t.center;r&&this.center.set(e,r[0],r[1],r[2]);var n=t.rotation;n&&this.rotation.set(e,n[0],n[1],n[2],n[3]);var i=t.distance;i&&i>0&&this.radius.set(e,Math.log(i)),this.setDistanceLimits(t.zoomMin,t.zoomMax)}},{\"./lib/quatFromFrame\":262,\"filtered-vector\":68,\"gl-mat4/fromQuat\":95,\"gl-mat4/invert\":98,\"gl-mat4/lookAt\":99}],264:[function(t,e,r){\n",
+       "/*!\n",
+       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT license.\n",
+       " */\n",
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+       "/*!\n",
+       " * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT License.\n",
+       " */\n",
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o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),g=Math.max(h,p,d);h===g?(s=s<0?-1:1,l=f=0):d===g?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var m=c(s,l,f);s/=m,l/=m,f/=m}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,g=d[0],m=d[1],v=d[2],y=i*g+a*m+o*v,x=c(g-=y*i,m-=y*a,v-=y*o);if(!(x<.01&&(x=c(g=a*h-o*f,m=o*l-i*h,v=i*f-a*l))<1e-6)){g/=x,m/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,g,m,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*m,_=o*g-i*v,w=i*m-a*g,T=c(b,_,w),k=i*l+a*f+o*h,A=g*l+m*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{\"filtered-vector\":68,\"gl-mat4/invert\":98,\"gl-mat4/rotate\":103,\"gl-vec3/cross\":157,\"gl-vec3/dot\":162,\"gl-vec3/normalize\":179}],306:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){\"use strict\";var 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null},r.mallocArrayBuffer=p,r.mallocUint8=d,r.mallocUint16=g,r.mallocUint32=m,r.mallocInt8=v,r.mallocInt16=y,r.mallocInt32=x,r.mallocFloat32=r.mallocFloat=b,r.mallocFloat64=r.mallocDouble=_,r.mallocUint8Clamped=w,r.mallocBigUint64=T,r.mallocBigInt64=k,r.mallocDataView=A,r.mallocBuffer=M,r.clearCache=function(){for(var t=0;t<32;++t)c.UINT8[t].length=0,c.UINT16[t].length=0,c.UINT32[t].length=0,c.INT8[t].length=0,c.INT16[t].length=0,c.INT32[t].length=0,c.FLOAT[t].length=0,c.DOUBLE[t].length=0,c.BIGUINT64[t].length=0,c.BIGINT64[t].length=0,c.UINT8C[t].length=0,u[t].length=0,f[t].length=0}}).call(this)}).call(this,void 0!==n?n:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{\"bit-twiddle\":32,buffer:3,dup:65}],309:[function(t,e,r){\"use strict\";function n(t){this.roots=new Array(t),this.ranks=new Array(t);for(var e=0;e<t;++e)this.roots[e]=e,this.ranks[e]=0}e.exports=n;var i=n.prototype;Object.defineProperty(i,\"length\",{get:function(){return this.roots.length}}),i.makeSet=function(){var t=this.roots.length;return this.roots.push(t),this.ranks.push(0),t},i.find=function(t){for(var e=t,r=this.roots;r[t]!==t;)t=r[t];for(;r[e]!==t;){var n=r[e];r[e]=t,e=n}return t},i.link=function(t,e){var r=this.find(t),n=this.find(e);if(r!==n){var i=this.ranks,a=this.roots,o=i[r],s=i[n];o<s?a[r]=n:s<o?a[n]=r:(a[n]=r,++i[r])}}},{}],310:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){return 0===t.length?t:e?(r||t.sort(e),function(t,e){for(var r=1,n=t.length,i=t[0],a=t[0],o=1;o<n;++o)if(a=i,e(i=t[o],a)){if(o===r){r++;continue}t[r++]=i}return t.length=r,t}(t,e)):(r||t.sort(),function(t){for(var e=1,r=t.length,n=t[0],i=t[0],a=1;a<r;++a,i=n)if(i=n,(n=t[a])!==i){if(a===e){e++;continue}t[e++]=n}return t.length=e,t}(t))}},{}],311:[function(t,e,r){\"use strict\";e.exports=function(t,e){\"object\"==typeof e&&null!==e||(e={});return n(t,e.canvas||i,e.context||a,e)};var n=t(\"./lib/vtext\"),i=null,a=null;\"undefined\"!=typeof 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n=t(\"surface-nets\"),i=t(\"ndarray\"),a=t(\"simplify-planar-graph\"),o=t(\"clean-pslg\"),s=t(\"cdt2d\"),l=t(\"planar-graph-to-polyline\");function c(t,e,r,n){for(var i=\"<\"+t+\">\",a=\"</\"+t+\">\",o=i.length,s=a.length,l=\"+\"===e[0]||\"-\"===e[0],c=0,u=-s;c>-1&&-1!==(c=r.indexOf(i,c))&&-1!==(u=r.indexOf(a,c+o))&&!(u<=c);){for(var f=c;f<u+s;++f)if(f<c+o||f>=u)n[f]=null,r=r.substr(0,f)+\" \"+r.substr(f+1);else if(null!==n[f]){var h=n[f].indexOf(e[0]);-1===h?n[f]+=e:l&&(n[f]=n[f].substr(0,h+1)+(1+parseInt(n[f][h+1]))+n[f].substr(h+2))}var p=c+o,d=r.substr(p,u-p).indexOf(i);c=-1!==d?d:u+s}return n}function u(t,e){var r=n(t,128);return e?a(r.cells,r.positions,.25):{edges:r.cells,positions:r.positions}}function f(t,e,r,n){var i=u(t,n),a=function(t,e,r){for(var n=e.textAlign||\"start\",i=e.textBaseline||\"alphabetic\",a=[1<<30,1<<30],o=[0,0],s=t.length,l=0;l<s;++l)for(var c=t[l],u=0;u<2;++u)a[u]=0|Math.min(a[u],c[u]),o[u]=0|Math.max(o[u],c[u]);var 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+        "title": {
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+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
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+                "Current health expenditure per capita (current US$)",
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+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)"
+               ],
+               [
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
+               ],
+               [
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "North Africa",
+            "method": "update"
+           },
+           {
+            "args": [
+             {
+              "x": [
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+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)"
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+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)"
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+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)"
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+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
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+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
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+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
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+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
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+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
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+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
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+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
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+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)"
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+               [
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+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
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+                "CO2 emissions (kg per PPP $ of GDP)",
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+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "South Africa",
+            "method": "update"
+           },
+           {
+            "args": [
+             {
+              "x": [
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+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)"
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+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)"
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+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
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+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)"
+               ],
+               [
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+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
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+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
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+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
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+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)"
+               ],
+               [
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
+               ],
+               [
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "Asia",
+            "method": "update"
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+           {
+            "args": [
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+              "x": [
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+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)"
+               ],
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+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)"
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+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of infant deaths",
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+                "Number of infant deaths",
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+                "Number of infant deaths",
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+                "Number of infant deaths",
+                "Number of infant deaths",
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+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)"
+               ],
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+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
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+                "Households and NPISHs Final consumption expenditure (current US$)",
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+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
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+                "Current health expenditure per capita (current US$)",
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+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
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+                "Prevalence of current tobacco use (% of adults)",
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+                "Prevalence of current tobacco use (% of adults)",
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+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)"
+               ],
+               [
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
+               ],
+               [
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+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
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+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#COUNT HISTOGRAM: Graph for seeing the frequency of the primary indicators for each region.\n",
+    "selected_primary=final.loc[final['Level']=='primary']\n",
+    "\n",
+    "fig=px.histogram(selected_primary,x='Indicator',histfunc=\"count\",height=700,color='Group',text_auto=True,title=\"Indicators frequency by continents\").update_xaxes(categoryorder=\"total descending\")\n",
+    "continents=list(selected_primary['Continent'].unique())\n",
+    "buttons = []\n",
+    "\n",
+    "for continent in continents:\n",
+    "    selected_primary_c = selected_primary.loc[(selected_primary['Continent'] == continent)]\n",
+    "    fig_continent = px.histogram(selected_primary_c, x='Indicator', color='Group').update_xaxes(categoryorder=\"total descending\")\n",
+    "    buttons.append(\n",
+    "        dict(\n",
+    "            label=continent,\n",
+    "            method=\"update\",\n",
+    "            args=[\n",
+    "                {\n",
+    "                    \"x\": [trace['x'] for trace in fig_continent._data],\n",
+    "                }\n",
+    "            ]\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    updatemenus=[\n",
+    "        dict(\n",
+    "            type=\"dropdown\",\n",
+    "            direction=\"down\",\n",
+    "            showactive=True,\n",
+    "            buttons=buttons\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
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+          "Export value index (2000 = 100)/Persian Gulf/ARE",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/ARE",
+          "GNI (current US$)/Persian Gulf/ARE",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/ARE",
+          "Industry (including construction), value added (current US$)/Persian Gulf/ARE",
+          "Number of deaths ages 5-9 years/Persian Gulf/ARE",
+          "Number of infant deaths/Persian Gulf/ARE",
+          "Population in largest city/Persian Gulf/ARE",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/ARE",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf/ARE",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/ARE",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf/ARE",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf/ARE",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/ARG",
+          "Current health expenditure (% of GDP)/Latam/ARG",
+          "Current health expenditure per capita (current US$)/Latam/ARG",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam/ARG",
+          "Fixed broadband subscriptions (per 100 people)/Latam/ARG",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/ARG",
+          "GNI (current US$)/Latam/ARG",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/ARG",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/ARG",
+          "Industry (including construction), value added (current US$)/Latam/ARG",
+          "Number of deaths ages 5-9 years/Latam/ARG",
+          "Number of infant deaths/Latam/ARG",
+          "Population in largest city/Latam/ARG",
+          "Prevalence of current tobacco use (% of adults)/Latam/ARG",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/ARG",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/ARG",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/ARG",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/ARG",
+          "Agricultural land (% of land area)/Europe/AUT",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/AUT",
+          "Consumer price index (2010 = 100)/Europe/AUT",
+          "Current health expenditure (% of GDP)/Europe/AUT",
+          "Current health expenditure per capita (current US$)/Europe/AUT",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/AUT",
+          "Export value index (2000 = 100)/Europe/AUT",
+          "Fixed broadband subscriptions (per 100 people)/Europe/AUT",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/AUT",
+          "GNI (current US$)/Europe/AUT",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/AUT",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/AUT",
+          "Industry (including construction), value added (current US$)/Europe/AUT",
+          "Number of deaths ages 5-9 years/Europe/AUT",
+          "Number of infant deaths/Europe/AUT",
+          "Population in largest city/Europe/AUT",
+          "Prevalence of current tobacco use (% of adults)/Europe/AUT",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/AUT",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/AUT",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/AUT",
+          "Suicide mortality rate (per 100,000 population)/Europe/AUT",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/AUT",
+          "Access to electricity (% of population)/Persian Gulf/AZE",
+          "Agricultural land (% of land area)/Persian Gulf/AZE",
+          "CO2 emissions (kg per PPP $ of GDP)/Persian Gulf/AZE",
+          "Consumer price index (2010 = 100)/Persian Gulf/AZE",
+          "Current health expenditure per capita (current US$)/Persian Gulf/AZE",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf/AZE",
+          "Export value index (2000 = 100)/Persian Gulf/AZE",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/AZE",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Persian Gulf/AZE",
+          "GNI (current US$)/Persian Gulf/AZE",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/AZE",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/AZE",
+          "Industry (including construction), value added (current US$)/Persian Gulf/AZE",
+          "Number of deaths ages 5-9 years/Persian Gulf/AZE",
+          "Number of infant deaths/Persian Gulf/AZE",
+          "Population in largest city/Persian Gulf/AZE",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/AZE",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/AZE",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf/AZE",
+          "Access to electricity (% of population)/Asia/BGD",
+          "Agricultural land (% of land area)/Asia/BGD",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/BGD",
+          "Consumer price index (2010 = 100)/Asia/BGD",
+          "Current health expenditure per capita (current US$)/Asia/BGD",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/BGD",
+          "Export value index (2000 = 100)/Asia/BGD",
+          "Fixed broadband subscriptions (per 100 people)/Asia/BGD",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/BGD",
+          "GNI (current US$)/Asia/BGD",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/BGD",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/BGD",
+          "Industry (including construction), value added (current US$)/Asia/BGD",
+          "Number of deaths ages 5-9 years/Asia/BGD",
+          "Number of infant deaths/Asia/BGD",
+          "Population in largest city/Asia/BGD",
+          "Prevalence of current tobacco use (% of adults)/Asia/BGD",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia/BGD",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia/BGD",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/BGD",
+          "Suicide mortality rate (per 100,000 population)/Asia/BGD",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/BGD",
+          "Access to electricity (% of population)/Latam/BRA",
+          "Agricultural land (% of land area)/Latam/BRA",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/BRA",
+          "Consumer price index (2010 = 100)/Latam/BRA",
+          "Current health expenditure per capita (current US$)/Latam/BRA",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam/BRA",
+          "Export value index (2000 = 100)/Latam/BRA",
+          "Fixed broadband subscriptions (per 100 people)/Latam/BRA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/BRA",
+          "GNI (current US$)/Latam/BRA",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/BRA",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/BRA",
+          "Industry (including construction), value added (current US$)/Latam/BRA",
+          "Number of deaths ages 5-9 years/Latam/BRA",
+          "Number of infant deaths/Latam/BRA",
+          "Population in largest city/Latam/BRA",
+          "Prevalence of current tobacco use (% of adults)/Latam/BRA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/BRA",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/BRA",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/BRA",
+          "Suicide mortality rate (per 100,000 population)/Latam/BRA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/BRA",
+          "Access to electricity (% of population)/Latam/CHL",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/CHL",
+          "Consumer price index (2010 = 100)/Latam/CHL",
+          "Current health expenditure (% of GDP)/Latam/CHL",
+          "Current health expenditure per capita (current US$)/Latam/CHL",
+          "Export value index (2000 = 100)/Latam/CHL",
+          "Fixed broadband subscriptions (per 100 people)/Latam/CHL",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/CHL",
+          "GNI (current US$)/Latam/CHL",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/CHL",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/CHL",
+          "Industry (including construction), value added (current US$)/Latam/CHL",
+          "Number of deaths ages 5-9 years/Latam/CHL",
+          "Number of infant deaths/Latam/CHL",
+          "Population in largest city/Latam/CHL",
+          "Prevalence of current tobacco use (% of adults)/Latam/CHL",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/CHL",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/CHL",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/CHL",
+          "Suicide mortality rate (per 100,000 population)/Latam/CHL",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/CHL",
+          "Access to electricity (% of population)/Pair/CHN",
+          "Agricultural land (% of land area)/Pair/CHN",
+          "CO2 emissions (kg per PPP $ of GDP)/Pair/CHN",
+          "Consumer price index (2010 = 100)/Pair/CHN",
+          "Current health expenditure (% of GDP)/Pair/CHN",
+          "Current health expenditure per capita (current US$)/Pair/CHN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Pair/CHN",
+          "Export value index (2000 = 100)/Pair/CHN",
+          "Fixed broadband subscriptions (per 100 people)/Pair/CHN",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Pair/CHN",
+          "GNI (current US$)/Pair/CHN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Pair/CHN",
+          "Industry (including construction), value added (current US$)/Pair/CHN",
+          "Number of deaths ages 5-9 years/Pair/CHN",
+          "Number of infant deaths/Pair/CHN",
+          "Population in largest city/Pair/CHN",
+          "Prevalence of current tobacco use (% of adults)/Pair/CHN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Pair/CHN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Pair/CHN",
+          "Suicide mortality rate (per 100,000 population)/Pair/CHN",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Pair/CHN",
+          "Access to electricity (% of population)/South Africa/CMR",
+          "Agricultural land (% of land area)/South Africa/CMR",
+          "Consumer price index (2010 = 100)/South Africa/CMR",
+          "Current health expenditure (% of GDP)/South Africa/CMR",
+          "Current health expenditure per capita (current US$)/South Africa/CMR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa/CMR",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/CMR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/CMR",
+          "GNI (current US$)/South Africa/CMR",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/CMR",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/CMR",
+          "Industry (including construction), value added (current US$)/South Africa/CMR",
+          "Number of deaths ages 5-9 years/South Africa/CMR",
+          "Number of infant deaths/South Africa/CMR",
+          "Population in largest city/South Africa/CMR",
+          "Prevalence of current tobacco use (% of adults)/South Africa/CMR",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/CMR",
+          "Access to electricity (% of population)/Latam/COL",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/COL",
+          "Consumer price index (2010 = 100)/Latam/COL",
+          "Current health expenditure (% of GDP)/Latam/COL",
+          "Current health expenditure per capita (current US$)/Latam/COL",
+          "Export value index (2000 = 100)/Latam/COL",
+          "Fixed broadband subscriptions (per 100 people)/Latam/COL",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/COL",
+          "GNI (current US$)/Latam/COL",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/COL",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/COL",
+          "Industry (including construction), value added (current US$)/Latam/COL",
+          "Number of deaths ages 5-9 years/Latam/COL",
+          "Number of infant deaths/Latam/COL",
+          "Population in largest city/Latam/COL",
+          "Prevalence of current tobacco use (% of adults)/Latam/COL",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/COL",
+          "Suicide mortality rate (per 100,000 population)/Latam/COL",
+          "Access to electricity (% of population)/Latam/CRI",
+          "Agricultural land (% of land area)/Latam/CRI",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/CRI",
+          "Consumer price index (2010 = 100)/Latam/CRI",
+          "Current health expenditure (% of GDP)/Latam/CRI",
+          "Current health expenditure per capita (current US$)/Latam/CRI",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam/CRI",
+          "Export value index (2000 = 100)/Latam/CRI",
+          "Fixed broadband subscriptions (per 100 people)/Latam/CRI",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/CRI",
+          "GNI (current US$)/Latam/CRI",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/CRI",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/CRI",
+          "Industry (including construction), value added (current US$)/Latam/CRI",
+          "Number of deaths ages 5-9 years/Latam/CRI",
+          "Number of infant deaths/Latam/CRI",
+          "Population in largest city/Latam/CRI",
+          "Prevalence of current tobacco use (% of adults)/Latam/CRI",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/CRI",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/CRI",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/CRI",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/CRI",
+          "Agricultural land (% of land area)/Europe/DEU",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/DEU",
+          "Consumer price index (2010 = 100)/Europe/DEU",
+          "Current health expenditure per capita (current US$)/Europe/DEU",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/DEU",
+          "Export value index (2000 = 100)/Europe/DEU",
+          "Fixed broadband subscriptions (per 100 people)/Europe/DEU",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/DEU",
+          "GNI (current US$)/Europe/DEU",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/DEU",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/DEU",
+          "Industry (including construction), value added (current US$)/Europe/DEU",
+          "Number of deaths ages 5-9 years/Europe/DEU",
+          "Prevalence of current tobacco use (% of adults)/Europe/DEU",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/DEU",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/DEU",
+          "Agricultural land (% of land area)/North Africa/DZA",
+          "Consumer price index (2010 = 100)/North Africa/DZA",
+          "Current health expenditure per capita (current US$)/North Africa/DZA",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa/DZA",
+          "Export value index (2000 = 100)/North Africa/DZA",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/DZA",
+          "GNI (current US$)/North Africa/DZA",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/DZA",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/DZA",
+          "Industry (including construction), value added (current US$)/North Africa/DZA",
+          "Number of deaths ages 5-9 years/North Africa/DZA",
+          "Population in largest city/North Africa/DZA",
+          "Prevalence of current tobacco use (% of adults)/North Africa/DZA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa/DZA",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa/DZA",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/DZA",
+          "Suicide mortality rate (per 100,000 population)/North Africa/DZA",
+          "Access to electricity (% of population)/North Africa/EGY",
+          "Agricultural land (% of land area)/North Africa/EGY",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa/EGY",
+          "Consumer price index (2010 = 100)/North Africa/EGY",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa/EGY",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/EGY",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa/EGY",
+          "GNI (current US$)/North Africa/EGY",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/EGY",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/EGY",
+          "Industry (including construction), value added (current US$)/North Africa/EGY",
+          "Number of deaths ages 5-9 years/North Africa/EGY",
+          "Number of infant deaths/North Africa/EGY",
+          "Population in largest city/North Africa/EGY",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/EGY",
+          "Suicide mortality rate (per 100,000 population)/North Africa/EGY",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa/EGY",
+          "Agricultural land (% of land area)/Europe/ESP",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/ESP",
+          "Consumer price index (2010 = 100)/Europe/ESP",
+          "Current health expenditure (% of GDP)/Europe/ESP",
+          "Current health expenditure per capita (current US$)/Europe/ESP",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/ESP",
+          "Export value index (2000 = 100)/Europe/ESP",
+          "Fixed broadband subscriptions (per 100 people)/Europe/ESP",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/ESP",
+          "GNI (current US$)/Europe/ESP",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/ESP",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/ESP",
+          "Industry (including construction), value added (current US$)/Europe/ESP",
+          "Number of deaths ages 5-9 years/Europe/ESP",
+          "Population in largest city/Europe/ESP",
+          "Prevalence of current tobacco use (% of adults)/Europe/ESP",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/ESP",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/ESP",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/ESP",
+          "Agricultural land (% of land area)/Europe/FRA",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/FRA",
+          "Consumer price index (2010 = 100)/Europe/FRA",
+          "Current health expenditure (% of GDP)/Europe/FRA",
+          "Current health expenditure per capita (current US$)/Europe/FRA",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/FRA",
+          "Export value index (2000 = 100)/Europe/FRA",
+          "Fixed broadband subscriptions (per 100 people)/Europe/FRA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/FRA",
+          "GNI (current US$)/Europe/FRA",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/FRA",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/FRA",
+          "Industry (including construction), value added (current US$)/Europe/FRA",
+          "Number of deaths ages 5-9 years/Europe/FRA",
+          "Number of infant deaths/Europe/FRA",
+          "Population in largest city/Europe/FRA",
+          "Prevalence of current tobacco use (% of adults)/Europe/FRA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/FRA",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/FRA",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/FRA",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/GBR",
+          "Consumer price index (2010 = 100)/Europe/GBR",
+          "Current health expenditure (% of GDP)/Europe/GBR",
+          "Current health expenditure per capita (current US$)/Europe/GBR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/GBR",
+          "Export value index (2000 = 100)/Europe/GBR",
+          "Fixed broadband subscriptions (per 100 people)/Europe/GBR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/GBR",
+          "GNI (current US$)/Europe/GBR",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/GBR",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/GBR",
+          "Industry (including construction), value added (current US$)/Europe/GBR",
+          "Number of deaths ages 5-9 years/Europe/GBR",
+          "Number of infant deaths/Europe/GBR",
+          "Population in largest city/Europe/GBR",
+          "Prevalence of current tobacco use (% of adults)/Europe/GBR",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/GBR",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/GBR",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/GBR",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/GBR",
+          "Access to electricity (% of population)/South Africa/GHA",
+          "Consumer price index (2010 = 100)/South Africa/GHA",
+          "Current health expenditure per capita (current US$)/South Africa/GHA",
+          "Export value index (2000 = 100)/South Africa/GHA",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/GHA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/GHA",
+          "GNI (current US$)/South Africa/GHA",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/GHA",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/GHA",
+          "Industry (including construction), value added (current US$)/South Africa/GHA",
+          "Number of deaths ages 5-9 years/South Africa/GHA",
+          "Number of infant deaths/South Africa/GHA",
+          "Population in largest city/South Africa/GHA",
+          "Prevalence of current tobacco use (% of adults)/South Africa/GHA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/GHA",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/GHA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa/GHA",
+          "Agricultural land (% of land area)/Europe/GRC",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/GRC",
+          "Consumer price index (2010 = 100)/Europe/GRC",
+          "Current health expenditure (% of GDP)/Europe/GRC",
+          "Current health expenditure per capita (current US$)/Europe/GRC",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/GRC",
+          "Fixed broadband subscriptions (per 100 people)/Europe/GRC",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/GRC",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/GRC",
+          "Industry (including construction), value added (current US$)/Europe/GRC",
+          "Number of deaths ages 5-9 years/Europe/GRC",
+          "Number of infant deaths/Europe/GRC",
+          "Population in largest city/Europe/GRC",
+          "Prevalence of current tobacco use (% of adults)/Europe/GRC",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/GRC",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/GRC",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/HRV",
+          "Current health expenditure per capita (current US$)/Europe/HRV",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/HRV",
+          "Fixed broadband subscriptions (per 100 people)/Europe/HRV",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/HRV",
+          "GNI (current US$)/Europe/HRV",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/HRV",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/HRV",
+          "Industry (including construction), value added (current US$)/Europe/HRV",
+          "Number of deaths ages 5-9 years/Europe/HRV",
+          "Number of infant deaths/Europe/HRV",
+          "Population in largest city/Europe/HRV",
+          "Prevalence of current tobacco use (% of adults)/Europe/HRV",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/HRV",
+          "Access to electricity (% of population)/Asia/IDN",
+          "Agricultural land (% of land area)/Asia/IDN",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/IDN",
+          "Consumer price index (2010 = 100)/Asia/IDN",
+          "Current health expenditure (% of GDP)/Asia/IDN",
+          "Current health expenditure per capita (current US$)/Asia/IDN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/IDN",
+          "Export value index (2000 = 100)/Asia/IDN",
+          "Fixed broadband subscriptions (per 100 people)/Asia/IDN",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/IDN",
+          "GNI (current US$)/Asia/IDN",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/IDN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/IDN",
+          "Industry (including construction), value added (current US$)/Asia/IDN",
+          "Number of deaths ages 5-9 years/Asia/IDN",
+          "Number of infant deaths/Asia/IDN",
+          "Population in largest city/Asia/IDN",
+          "Prevalence of current tobacco use (% of adults)/Asia/IDN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/IDN",
+          "Suicide mortality rate (per 100,000 population)/Asia/IDN",
+          "Access to electricity (% of population)/Asia/IND",
+          "Agricultural land (% of land area)/Asia/IND",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/IND",
+          "Consumer price index (2010 = 100)/Asia/IND",
+          "Current health expenditure (% of GDP)/Asia/IND",
+          "Current health expenditure per capita (current US$)/Asia/IND",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/IND",
+          "Export value index (2000 = 100)/Asia/IND",
+          "Fixed broadband subscriptions (per 100 people)/Asia/IND",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/IND",
+          "GNI (current US$)/Asia/IND",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/IND",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/IND",
+          "Industry (including construction), value added (current US$)/Asia/IND",
+          "Number of deaths ages 5-9 years/Asia/IND",
+          "Number of infant deaths/Asia/IND",
+          "Population in largest city/Asia/IND",
+          "Prevalence of current tobacco use (% of adults)/Asia/IND",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia/IND",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/IND",
+          "Suicide mortality rate (per 100,000 population)/Asia/IND",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/IND",
+          "Access to electricity (% of population)/Persian Gulf/IRQ",
+          "Export value index (2000 = 100)/Persian Gulf/IRQ",
+          "GNI (current US$)/Persian Gulf/IRQ",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/IRQ",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/IRQ",
+          "Industry (including construction), value added (current US$)/Persian Gulf/IRQ",
+          "Number of infant deaths/Persian Gulf/IRQ",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/IRQ",
+          "Agricultural land (% of land area)/North Africa/ISR",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa/ISR",
+          "Consumer price index (2010 = 100)/North Africa/ISR",
+          "Current health expenditure per capita (current US$)/North Africa/ISR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa/ISR",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/ISR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa/ISR",
+          "GNI (current US$)/North Africa/ISR",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/ISR",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/ISR",
+          "Industry (including construction), value added (current US$)/North Africa/ISR",
+          "Number of deaths ages 5-9 years/North Africa/ISR",
+          "Number of infant deaths/North Africa/ISR",
+          "Population in largest city/North Africa/ISR",
+          "Prevalence of current tobacco use (% of adults)/North Africa/ISR",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa/ISR",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa/ISR",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/ISR",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa/ISR",
+          "Agricultural land (% of land area)/Asia/KOR",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/KOR",
+          "Consumer price index (2010 = 100)/Asia/KOR",
+          "Current health expenditure (% of GDP)/Asia/KOR",
+          "Current health expenditure per capita (current US$)/Asia/KOR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/KOR",
+          "Export value index (2000 = 100)/Asia/KOR",
+          "Fixed broadband subscriptions (per 100 people)/Asia/KOR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/KOR",
+          "GNI (current US$)/Asia/KOR",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/KOR",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/KOR",
+          "Industry (including construction), value added (current US$)/Asia/KOR",
+          "Number of deaths ages 5-9 years/Asia/KOR",
+          "Number of infant deaths/Asia/KOR",
+          "Prevalence of current tobacco use (% of adults)/Asia/KOR",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia/KOR",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia/KOR",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/KOR",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/KOR",
+          "Access to electricity (% of population)/South Africa/LBR",
+          "Agricultural land (% of land area)/South Africa/LBR",
+          "Consumer price index (2010 = 100)/South Africa/LBR",
+          "Current health expenditure per capita (current US$)/South Africa/LBR",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/LBR",
+          "GNI (current US$)/South Africa/LBR",
+          "Industry (including construction), value added (current US$)/South Africa/LBR",
+          "Number of infant deaths/South Africa/LBR",
+          "Prevalence of current tobacco use (% of adults)/South Africa/LBR",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/LBR",
+          "Access to electricity (% of population)/North Africa/MAR",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa/MAR",
+          "Consumer price index (2010 = 100)/North Africa/MAR",
+          "Current health expenditure per capita (current US$)/North Africa/MAR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa/MAR",
+          "Export value index (2000 = 100)/North Africa/MAR",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/MAR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa/MAR",
+          "GNI (current US$)/North Africa/MAR",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/MAR",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/MAR",
+          "Industry (including construction), value added (current US$)/North Africa/MAR",
+          "Number of deaths ages 5-9 years/North Africa/MAR",
+          "Number of infant deaths/North Africa/MAR",
+          "Population in largest city/North Africa/MAR",
+          "Prevalence of current tobacco use (% of adults)/North Africa/MAR",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/MAR",
+          "Suicide mortality rate (per 100,000 population)/North Africa/MAR",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa/MAR",
+          "Access to electricity (% of population)/Latam/MEX",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/MEX",
+          "Consumer price index (2010 = 100)/Latam/MEX",
+          "Current health expenditure per capita (current US$)/Latam/MEX",
+          "Export value index (2000 = 100)/Latam/MEX",
+          "Fixed broadband subscriptions (per 100 people)/Latam/MEX",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/MEX",
+          "GNI (current US$)/Latam/MEX",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/MEX",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/MEX",
+          "Industry (including construction), value added (current US$)/Latam/MEX",
+          "Number of deaths ages 5-9 years/Latam/MEX",
+          "Number of infant deaths/Latam/MEX",
+          "Population in largest city/Latam/MEX",
+          "Prevalence of current tobacco use (% of adults)/Latam/MEX",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/MEX",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/MEX",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/MEX",
+          "Suicide mortality rate (per 100,000 population)/Latam/MEX",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/MEX",
+          "Access to electricity (% of population)/South Africa/MOZ",
+          "Agricultural land (% of land area)/South Africa/MOZ",
+          "Consumer price index (2010 = 100)/South Africa/MOZ",
+          "Current health expenditure (% of GDP)/South Africa/MOZ",
+          "Current health expenditure per capita (current US$)/South Africa/MOZ",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa/MOZ",
+          "Export value index (2000 = 100)/South Africa/MOZ",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/MOZ",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/MOZ",
+          "GNI (current US$)/South Africa/MOZ",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/MOZ",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/MOZ",
+          "Industry (including construction), value added (current US$)/South Africa/MOZ",
+          "Number of deaths ages 5-9 years/South Africa/MOZ",
+          "Number of infant deaths/South Africa/MOZ",
+          "Population in largest city/South Africa/MOZ",
+          "Prevalence of current tobacco use (% of adults)/South Africa/MOZ",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/MOZ",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa/MOZ",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/MOZ",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa/MOZ",
+          "Access to electricity (% of population)/South Africa/NGA",
+          "Agricultural land (% of land area)/South Africa/NGA",
+          "CO2 emissions (kg per PPP $ of GDP)/South Africa/NGA",
+          "Consumer price index (2010 = 100)/South Africa/NGA",
+          "Current health expenditure per capita (current US$)/South Africa/NGA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/NGA",
+          "GNI (current US$)/South Africa/NGA",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/NGA",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/NGA",
+          "Industry (including construction), value added (current US$)/South Africa/NGA",
+          "Number of deaths ages 5-9 years/South Africa/NGA",
+          "Population in largest city/South Africa/NGA",
+          "Prevalence of current tobacco use (% of adults)/South Africa/NGA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/NGA",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/NGA",
+          "Suicide mortality rate (per 100,000 population)/South Africa/NGA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa/NGA",
+          "Agricultural land (% of land area)/Europe/NLD",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/NLD",
+          "Consumer price index (2010 = 100)/Europe/NLD",
+          "Current health expenditure (% of GDP)/Europe/NLD",
+          "Current health expenditure per capita (current US$)/Europe/NLD",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/NLD",
+          "Export value index (2000 = 100)/Europe/NLD",
+          "Fixed broadband subscriptions (per 100 people)/Europe/NLD",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/NLD",
+          "GNI (current US$)/Europe/NLD",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/NLD",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/NLD",
+          "Industry (including construction), value added (current US$)/Europe/NLD",
+          "Number of deaths ages 5-9 years/Europe/NLD",
+          "Number of infant deaths/Europe/NLD",
+          "Population in largest city/Europe/NLD",
+          "Prevalence of current tobacco use (% of adults)/Europe/NLD",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/NLD",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/NLD",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/NLD",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/NLD",
+          "CO2 emissions (kg per PPP $ of GDP)/Persian Gulf/OMN",
+          "Consumer price index (2010 = 100)/Persian Gulf/OMN",
+          "Current health expenditure per capita (current US$)/Persian Gulf/OMN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf/OMN",
+          "Export value index (2000 = 100)/Persian Gulf/OMN",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/OMN",
+          "GNI (current US$)/Persian Gulf/OMN",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/OMN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/OMN",
+          "Industry (including construction), value added (current US$)/Persian Gulf/OMN",
+          "Number of deaths ages 5-9 years/Persian Gulf/OMN",
+          "Number of infant deaths/Persian Gulf/OMN",
+          "Population in largest city/Persian Gulf/OMN",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/OMN",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/OMN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf/OMN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/OMN",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf/OMN",
+          "Access to electricity (% of population)/Latam/PAN",
+          "Agricultural land (% of land area)/Latam/PAN",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/PAN",
+          "Consumer price index (2010 = 100)/Latam/PAN",
+          "Current health expenditure per capita (current US$)/Latam/PAN",
+          "Export value index (2000 = 100)/Latam/PAN",
+          "Fixed broadband subscriptions (per 100 people)/Latam/PAN",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/PAN",
+          "GNI (current US$)/Latam/PAN",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/PAN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/PAN",
+          "Industry (including construction), value added (current US$)/Latam/PAN",
+          "Number of deaths ages 5-9 years/Latam/PAN",
+          "Number of infant deaths/Latam/PAN",
+          "Population in largest city/Latam/PAN",
+          "Prevalence of current tobacco use (% of adults)/Latam/PAN",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/PAN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/PAN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/PAN",
+          "Suicide mortality rate (per 100,000 population)/Latam/PAN",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/PAN",
+          "Access to electricity (% of population)/Latam/PER",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/PER",
+          "Consumer price index (2010 = 100)/Latam/PER",
+          "Current health expenditure (% of GDP)/Latam/PER",
+          "Current health expenditure per capita (current US$)/Latam/PER",
+          "Export value index (2000 = 100)/Latam/PER",
+          "Fixed broadband subscriptions (per 100 people)/Latam/PER",
+          "GNI (current US$)/Latam/PER",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/PER",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/PER",
+          "Industry (including construction), value added (current US$)/Latam/PER",
+          "Number of deaths ages 5-9 years/Latam/PER",
+          "Number of infant deaths/Latam/PER",
+          "Population in largest city/Latam/PER",
+          "Prevalence of current tobacco use (% of adults)/Latam/PER",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/PER",
+          "Access to electricity (% of population)/Asia/PHL",
+          "Agricultural land (% of land area)/Asia/PHL",
+          "Consumer price index (2010 = 100)/Asia/PHL",
+          "Current health expenditure per capita (current US$)/Asia/PHL",
+          "Export value index (2000 = 100)/Asia/PHL",
+          "Fixed broadband subscriptions (per 100 people)/Asia/PHL",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/PHL",
+          "GNI (current US$)/Asia/PHL",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/PHL",
+          "Industry (including construction), value added (current US$)/Asia/PHL",
+          "Number of infant deaths/Asia/PHL",
+          "Population in largest city/Asia/PHL",
+          "Prevalence of current tobacco use (% of adults)/Asia/PHL",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/PHL",
+          "Suicide mortality rate (per 100,000 population)/Asia/PHL",
+          "Agricultural land (% of land area)/Europe/POL",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/POL",
+          "Consumer price index (2010 = 100)/Europe/POL",
+          "Current health expenditure (% of GDP)/Europe/POL",
+          "Current health expenditure per capita (current US$)/Europe/POL",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/POL",
+          "Export value index (2000 = 100)/Europe/POL",
+          "Fixed broadband subscriptions (per 100 people)/Europe/POL",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/POL",
+          "GNI (current US$)/Europe/POL",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/POL",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/POL",
+          "Industry (including construction), value added (current US$)/Europe/POL",
+          "Number of deaths ages 5-9 years/Europe/POL",
+          "Number of infant deaths/Europe/POL",
+          "Population in largest city/Europe/POL",
+          "Prevalence of current tobacco use (% of adults)/Europe/POL",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/POL",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/POL",
+          "Suicide mortality rate (per 100,000 population)/Europe/POL",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/POL",
+          "Consumer price index (2010 = 100)/Persian Gulf/QAT",
+          "Current health expenditure per capita (current US$)/Persian Gulf/QAT",
+          "Export value index (2000 = 100)/Persian Gulf/QAT",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/QAT",
+          "GNI (current US$)/Persian Gulf/QAT",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/QAT",
+          "Number of deaths ages 5-9 years/Persian Gulf/QAT",
+          "Population in largest city/Persian Gulf/QAT",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/QAT",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/QAT",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf/QAT",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/QAT",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf/QAT",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf/QAT",
+          "Consumer price index (2010 = 100)/Persian Gulf/SAU",
+          "Current health expenditure per capita (current US$)/Persian Gulf/SAU",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf/SAU",
+          "Export value index (2000 = 100)/Persian Gulf/SAU",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/SAU",
+          "GNI (current US$)/Persian Gulf/SAU",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/SAU",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/SAU",
+          "Industry (including construction), value added (current US$)/Persian Gulf/SAU",
+          "Number of deaths ages 5-9 years/Persian Gulf/SAU",
+          "Number of infant deaths/Persian Gulf/SAU",
+          "Population in largest city/Persian Gulf/SAU",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/SAU",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf/SAU",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/SAU",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf/SAU",
+          "Access to electricity (% of population)/South Africa/SEN",
+          "Consumer price index (2010 = 100)/South Africa/SEN",
+          "Current health expenditure per capita (current US$)/South Africa/SEN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa/SEN",
+          "Export value index (2000 = 100)/South Africa/SEN",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/SEN",
+          "GNI (current US$)/South Africa/SEN",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/SEN",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/SEN",
+          "Industry (including construction), value added (current US$)/South Africa/SEN",
+          "Number of deaths ages 5-9 years/South Africa/SEN",
+          "Number of infant deaths/South Africa/SEN",
+          "Population in largest city/South Africa/SEN",
+          "Prevalence of current tobacco use (% of adults)/South Africa/SEN",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/SEN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa/SEN",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/SEN",
+          "Suicide mortality rate (per 100,000 population)/South Africa/SEN",
+          "Agricultural land (% of land area)/Europe/SWE",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/SWE",
+          "Consumer price index (2010 = 100)/Europe/SWE",
+          "Current health expenditure (% of GDP)/Europe/SWE",
+          "Current health expenditure per capita (current US$)/Europe/SWE",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/SWE",
+          "Export value index (2000 = 100)/Europe/SWE",
+          "Fixed broadband subscriptions (per 100 people)/Europe/SWE",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/SWE",
+          "GNI (current US$)/Europe/SWE",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/SWE",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/SWE",
+          "Industry (including construction), value added (current US$)/Europe/SWE",
+          "Number of infant deaths/Europe/SWE",
+          "Prevalence of current tobacco use (% of adults)/Europe/SWE",
+          "Access to electricity (% of population)/Asia/THA",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/THA",
+          "Consumer price index (2010 = 100)/Asia/THA",
+          "Current health expenditure (% of GDP)/Asia/THA",
+          "Current health expenditure per capita (current US$)/Asia/THA",
+          "Export value index (2000 = 100)/Asia/THA",
+          "Fixed broadband subscriptions (per 100 people)/Asia/THA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/THA",
+          "GNI (current US$)/Asia/THA",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/THA",
+          "Industry (including construction), value added (current US$)/Asia/THA",
+          "Number of deaths ages 5-9 years/Asia/THA",
+          "Number of infant deaths/Asia/THA",
+          "Population in largest city/Asia/THA",
+          "Prevalence of current tobacco use (% of adults)/Asia/THA",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/THA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/THA",
+          "Agricultural land (% of land area)/North Africa/TUR",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa/TUR",
+          "Consumer price index (2010 = 100)/North Africa/TUR",
+          "Export value index (2000 = 100)/North Africa/TUR",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/TUR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa/TUR",
+          "GNI (current US$)/North Africa/TUR",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/TUR",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/TUR",
+          "Industry (including construction), value added (current US$)/North Africa/TUR",
+          "Number of deaths ages 5-9 years/North Africa/TUR",
+          "Number of infant deaths/North Africa/TUR",
+          "Population in largest city/North Africa/TUR",
+          "Prevalence of current tobacco use (% of adults)/North Africa/TUR",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa/TUR",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa/TUR",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/TUR",
+          "Suicide mortality rate (per 100,000 population)/North Africa/TUR",
+          "Agricultural land (% of land area)/Pair/USA",
+          "CO2 emissions (kg per PPP $ of GDP)/Pair/USA",
+          "Consumer price index (2010 = 100)/Pair/USA",
+          "Current health expenditure (% of GDP)/Pair/USA",
+          "Current health expenditure per capita (current US$)/Pair/USA",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Pair/USA",
+          "Export value index (2000 = 100)/Pair/USA",
+          "Fixed broadband subscriptions (per 100 people)/Pair/USA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Pair/USA",
+          "GNI (current US$)/Pair/USA",
+          "Gross value added at basic prices (GVA) (current US$)/Pair/USA",
+          "Households and NPISHs Final consumption expenditure (current US$)/Pair/USA",
+          "Industry (including construction), value added (current US$)/Pair/USA",
+          "Number of deaths ages 5-9 years/Pair/USA",
+          "Number of infant deaths/Pair/USA",
+          "Population in largest city/Pair/USA",
+          "Prevalence of current tobacco use (% of adults)/Pair/USA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Pair/USA",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Pair/USA",
+          "Renewable internal freshwater resources per capita (cubic meters)/Pair/USA",
+          "Suicide mortality rate (per 100,000 population)/Pair/USA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Pair/USA",
+          "Agricultural land (% of land area)/Latam/VEN",
+          "Consumer price index (2010 = 100)/Latam/VEN",
+          "Current health expenditure per capita (current US$)/Latam/VEN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam/VEN",
+          "Export value index (2000 = 100)/Latam/VEN",
+          "Fixed broadband subscriptions (per 100 people)/Latam/VEN",
+          "GNI (current US$)/Latam/VEN",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/VEN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/VEN",
+          "Industry (including construction), value added (current US$)/Latam/VEN",
+          "Population in largest city/Latam/VEN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/VEN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/VEN",
+          "Suicide mortality rate (per 100,000 population)/Latam/VEN",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/VEN",
+          "Access to electricity (% of population)/Asia/VNM",
+          "Agricultural land (% of land area)/Asia/VNM",
+          "Consumer price index (2010 = 100)/Asia/VNM",
+          "Current health expenditure (% of GDP)/Asia/VNM",
+          "Current health expenditure per capita (current US$)/Asia/VNM",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/VNM",
+          "Export value index (2000 = 100)/Asia/VNM",
+          "Fixed broadband subscriptions (per 100 people)/Asia/VNM",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/VNM",
+          "GNI (current US$)/Asia/VNM",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/VNM",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/VNM",
+          "Industry (including construction), value added (current US$)/Asia/VNM",
+          "Number of deaths ages 5-9 years/Asia/VNM",
+          "Number of infant deaths/Asia/VNM",
+          "Population in largest city/Asia/VNM",
+          "Prevalence of current tobacco use (% of adults)/Asia/VNM",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia/VNM",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia/VNM",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/VNM",
+          "Suicide mortality rate (per 100,000 population)/Asia/VNM",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/VNM",
+          "Access to electricity (% of population)/Persian Gulf/YEM",
+          "Agricultural land (% of land area)/Persian Gulf/YEM",
+          "Consumer price index (2010 = 100)/Persian Gulf/YEM",
+          "Current health expenditure (% of GDP)/Persian Gulf/YEM",
+          "Current health expenditure per capita (current US$)/Persian Gulf/YEM",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf/YEM",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/YEM",
+          "GNI (current US$)/Persian Gulf/YEM",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/YEM",
+          "Industry (including construction), value added (current US$)/Persian Gulf/YEM",
+          "Number of deaths ages 5-9 years/Persian Gulf/YEM",
+          "Population in largest city/Persian Gulf/YEM",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/YEM",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/YEM",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/YEM",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf/YEM",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf/YEM",
+          "Access to electricity (% of population)/South Africa/ZAF",
+          "CO2 emissions (kg per PPP $ of GDP)/South Africa/ZAF",
+          "Consumer price index (2010 = 100)/South Africa/ZAF",
+          "Current health expenditure per capita (current US$)/South Africa/ZAF",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa/ZAF",
+          "Export value index (2000 = 100)/South Africa/ZAF",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/ZAF",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/ZAF",
+          "GNI (current US$)/South Africa/ZAF",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/ZAF",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/ZAF",
+          "Industry (including construction), value added (current US$)/South Africa/ZAF",
+          "Population in largest city/South Africa/ZAF",
+          "Prevalence of current tobacco use (% of adults)/South Africa/ZAF",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/ZAF",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa/ZAF",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/ZAF",
+          "Access to electricity (% of population)/Asia",
+          "Agricultural land (% of land area)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Gross value added at basic prices (GVA) (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Suicide mortality rate (per 100,000 population)/Asia",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "Suicide mortality rate (per 100,000 population)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "Access to electricity (% of population)/Latam",
+          "Agricultural land (% of land area)/Latam",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure (% of GDP)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam",
+          "Industry (including construction), value added (current US$)/Latam",
+          "Number of deaths ages 5-9 years/Latam",
+          "Number of infant deaths/Latam",
+          "Population in largest city/Latam",
+          "Prevalence of current tobacco use (% of adults)/Latam",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam",
+          "Suicide mortality rate (per 100,000 population)/Latam",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam",
+          "Access to electricity (% of population)/North Africa",
+          "Agricultural land (% of land area)/North Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa",
+          "Consumer price index (2010 = 100)/North Africa",
+          "Current health expenditure per capita (current US$)/North Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa",
+          "Export value index (2000 = 100)/North Africa",
+          "Fixed broadband subscriptions (per 100 people)/North Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa",
+          "GNI (current US$)/North Africa",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa",
+          "Industry (including construction), value added (current US$)/North Africa",
+          "Number of deaths ages 5-9 years/North Africa",
+          "Number of infant deaths/North Africa",
+          "Population in largest city/North Africa",
+          "Prevalence of current tobacco use (% of adults)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Suicide mortality rate (per 100,000 population)/North Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa",
+          "Access to electricity (% of population)/Pair",
+          "Agricultural land (% of land area)/Pair",
+          "CO2 emissions (kg per PPP $ of GDP)/Pair",
+          "Consumer price index (2010 = 100)/Pair",
+          "Current health expenditure (% of GDP)/Pair",
+          "Current health expenditure per capita (current US$)/Pair",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Pair",
+          "Export value index (2000 = 100)/Pair",
+          "Fixed broadband subscriptions (per 100 people)/Pair",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Pair",
+          "GNI (current US$)/Pair",
+          "Gross value added at basic prices (GVA) (current US$)/Pair",
+          "Households and NPISHs Final consumption expenditure (current US$)/Pair",
+          "Industry (including construction), value added (current US$)/Pair",
+          "Number of deaths ages 5-9 years/Pair",
+          "Number of infant deaths/Pair",
+          "Population in largest city/Pair",
+          "Prevalence of current tobacco use (% of adults)/Pair",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Pair",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Pair",
+          "Renewable internal freshwater resources per capita (cubic meters)/Pair",
+          "Suicide mortality rate (per 100,000 population)/Pair",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Pair",
+          "Access to electricity (% of population)/Persian Gulf",
+          "Agricultural land (% of land area)/Persian Gulf",
+          "CO2 emissions (kg per PPP $ of GDP)/Persian Gulf",
+          "Consumer price index (2010 = 100)/Persian Gulf",
+          "Current health expenditure (% of GDP)/Persian Gulf",
+          "Current health expenditure per capita (current US$)/Persian Gulf",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Industry (including construction), value added (current US$)/Persian Gulf",
+          "Number of deaths ages 5-9 years/Persian Gulf",
+          "Number of infant deaths/Persian Gulf",
+          "Population in largest city/Persian Gulf",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf",
+          "Access to electricity (% of population)/South Africa",
+          "Agricultural land (% of land area)/South Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure (% of GDP)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa",
+          "Export value index (2000 = 100)/South Africa",
+          "Fixed broadband subscriptions (per 100 people)/South Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of deaths ages 5-9 years/South Africa",
+          "Number of infant deaths/South Africa",
+          "Population in largest city/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Suicide mortality rate (per 100,000 population)/South Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa",
+          "Access to electricity (% of population)",
+          "Agricultural land (% of land area)",
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+          "Consumer price index (2010 = 100)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure per capita (current US$)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Export value index (2000 = 100)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GNI (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Number of deaths ages 5-9 years",
+          "Number of infant deaths",
+          "Population in largest city",
+          "Prevalence of current tobacco use (% of adults)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
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+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Suicide mortality rate (per 100,000 population)/North Africa",
+          "Access to electricity (% of population)/North Africa",
+          "Agricultural land (% of land area)/North Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa",
+          "Consumer price index (2010 = 100)/North Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa",
+          "Fixed broadband subscriptions (per 100 people)/North Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa",
+          "GNI (current US$)/North Africa",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa",
+          "Industry (including construction), value added (current US$)/North Africa",
+          "Number of deaths ages 5-9 years/North Africa",
+          "Number of infant deaths/North Africa",
+          "Population in largest city/North Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Suicide mortality rate (per 100,000 population)/North Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "Access to electricity (% of population)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "Export value index (2000 = 100)/South Africa",
+          "Fixed broadband subscriptions (per 100 people)/South Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of deaths ages 5-9 years/South Africa",
+          "Number of infant deaths/South Africa",
+          "Population in largest city/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "Access to electricity (% of population)/Asia",
+          "Agricultural land (% of land area)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Gross value added at basic prices (GVA) (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Suicide mortality rate (per 100,000 population)/Asia",
+          "Access to electricity (% of population)/Asia",
+          "Agricultural land (% of land area)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Gross value added at basic prices (GVA) (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Suicide mortality rate (per 100,000 population)/Asia",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia",
+          "Access to electricity (% of population)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Industry (including construction), value added (current US$)/Persian Gulf",
+          "Number of infant deaths/Persian Gulf",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf",
+          "Agricultural land (% of land area)/North Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa",
+          "Consumer price index (2010 = 100)/North Africa",
+          "Current health expenditure per capita (current US$)/North Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa",
+          "Fixed broadband subscriptions (per 100 people)/North Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa",
+          "GNI (current US$)/North Africa",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa",
+          "Industry (including construction), value added (current US$)/North Africa",
+          "Number of deaths ages 5-9 years/North Africa",
+          "Number of infant deaths/North Africa",
+          "Population in largest city/North Africa",
+          "Prevalence of current tobacco use (% of adults)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa",
+          "Agricultural land (% of land area)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Gross value added at basic prices (GVA) (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia",
+          "Access to electricity (% of population)/South Africa",
+          "Agricultural land (% of land area)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "Fixed broadband subscriptions (per 100 people)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of infant deaths/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Access to electricity (% of population)/North Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa",
+          "Consumer price index (2010 = 100)/North Africa",
+          "Current health expenditure per capita (current US$)/North Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa",
+          "Export value index (2000 = 100)/North Africa",
+          "Fixed broadband subscriptions (per 100 people)/North Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa",
+          "GNI (current US$)/North Africa",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa",
+          "Industry (including construction), value added (current US$)/North Africa",
+          "Number of deaths ages 5-9 years/North Africa",
+          "Number of infant deaths/North Africa",
+          "Population in largest city/North Africa",
+          "Prevalence of current tobacco use (% of adults)/North Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Suicide mortality rate (per 100,000 population)/North Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa",
+          "Access to electricity (% of population)/Latam",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam",
+          "Industry (including construction), value added (current US$)/Latam",
+          "Number of deaths ages 5-9 years/Latam",
+          "Number of infant deaths/Latam",
+          "Population in largest city/Latam",
+          "Prevalence of current tobacco use (% of adults)/Latam",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam",
+          "Suicide mortality rate (per 100,000 population)/Latam",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam",
+          "Access to electricity (% of population)/South Africa",
+          "Agricultural land (% of land area)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure (% of GDP)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa",
+          "Export value index (2000 = 100)/South Africa",
+          "Fixed broadband subscriptions (per 100 people)/South Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of deaths ages 5-9 years/South Africa",
+          "Number of infant deaths/South Africa",
+          "Population in largest city/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa",
+          "Access to electricity (% of population)/South Africa",
+          "Agricultural land (% of land area)/South Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of deaths ages 5-9 years/South Africa",
+          "Population in largest city/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Suicide mortality rate (per 100,000 population)/South Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Persian Gulf",
+          "Consumer price index (2010 = 100)/Persian Gulf",
+          "Current health expenditure per capita (current US$)/Persian Gulf",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Industry (including construction), value added (current US$)/Persian Gulf",
+          "Number of deaths ages 5-9 years/Persian Gulf",
+          "Number of infant deaths/Persian Gulf",
+          "Population in largest city/Persian Gulf",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf",
+          "Access to electricity (% of population)/Latam",
+          "Agricultural land (% of land area)/Latam",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam",
+          "Industry (including construction), value added (current US$)/Latam",
+          "Number of deaths ages 5-9 years/Latam",
+          "Number of infant deaths/Latam",
+          "Population in largest city/Latam",
+          "Prevalence of current tobacco use (% of adults)/Latam",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam",
+          "Suicide mortality rate (per 100,000 population)/Latam",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam",
+          "Access to electricity (% of population)/Latam",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure (% of GDP)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam",
+          "Industry (including construction), value added (current US$)/Latam",
+          "Number of deaths ages 5-9 years/Latam",
+          "Number of infant deaths/Latam",
+          "Population in largest city/Latam",
+          "Prevalence of current tobacco use (% of adults)/Latam",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam",
+          "Access to electricity (% of population)/Asia",
+          "Agricultural land (% of land area)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Suicide mortality rate (per 100,000 population)/Asia",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "Suicide mortality rate (per 100,000 population)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "Consumer price index (2010 = 100)/Persian Gulf",
+          "Current health expenditure per capita (current US$)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Number of deaths ages 5-9 years/Persian Gulf",
+          "Population in largest city/Persian Gulf",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf",
+          "Consumer price index (2010 = 100)/Persian Gulf",
+          "Current health expenditure per capita (current US$)/Persian Gulf",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Industry (including construction), value added (current US$)/Persian Gulf",
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estimate)\",\"Ratio of female to male labor force participation rate (%) (national estimate)\",\"Renewable internal freshwater resources per capita (cubic meters)\",\"Suicide mortality rate (per 100,000 population)\",\"Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)\",\"Access to electricity (% of population)\",\"Agricultural land (% of land area)\",\"CO2 emissions (kg per PPP $ of GDP)\",\"Consumer price index (2010 = 100)\",\"Current health expenditure (% of GDP)\",\"Current health expenditure per capita (current US$)\",\"Employment in industry (% of total employment) (modeled ILO estimate)\",\"Export value index (2000 = 100)\",\"Fixed broadband subscriptions (per 100 people)\",\"GDP per unit of energy use (PPP $ per kg of oil equivalent)\",\"GNI (current US$)\",\"Gross value added at basic prices (GVA) (current US$)\",\"Households and NPISHs Final consumption expenditure (current US$)\",\"Industry (including construction), value added (current US$)\",\"Number of deaths ages 5-9 years\",\"Number of infant deaths\",\"Population in largest city\",\"Prevalence of current tobacco use (% of adults)\",\"Ratio of female to male labor force participation rate (%) (modeled ILO estimate)\",\"Ratio of female to male labor force participation rate (%) (national estimate)\",\"Renewable internal freshwater resources per capita (cubic meters)\",\"Suicide mortality rate (per 100,000 population)\",\"Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of 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-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "Exports-G&S"
-        },
-        "width": 700
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique_tri = demo2['Indicator'].unique()\n",
-    "tri = widgets.SelectMultiple(\n",
-    "    options = unique_tri.tolist(),\n",
-    "    value = ['Exports-G&S'],\n",
-    "    description='Indicator',\n",
-    "    disabled=False,\n",
-    "    layout = Layout(width='50%', height='80px')\n",
-    ")\n",
-    "\n",
-    "def graf1(tri):\n",
-    "    dat=demo2.loc[demo2.loc[:, 'Indicator'].isin(np.array(tri))]\n",
-    "    a=px.choropleth(dat, locations=\"Country\", locationmode='ISO-3', \n",
-    "                     color=\"R^2 Pearson\", hover_name=\"Country\",hover_data = [dat.Type, dat.Behaviour],projection=\"natural earth\",\n",
-    "                     color_continuous_scale='Reds', width=700, height=500, title= dat.Indicator.unique().tolist()[0])\n",
-    "    print(tri)\n",
-    "    a.show()\n",
-    "widgets.interactive(graf1, tri=tri)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To wrap up, we can create the second widget that is exactly the same as the previous multiple selection widget. The purpose of this widget is to enable us to choose which Continent we want to visualize. Below is the code implementation of this widget."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
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-      "text/plain": [
-       "interactive(children=(SelectMultiple(description='Continent', index=(2,), layout=Layout(height='80px', width='…"
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+       "                            \n",
+       "var gd = document.getElementById('0747c577-2694-4f05-90a3-aa89ef9cc438');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#TREEMAP: Graph for seeing the correlation worldwide.\n",
+    "\n",
+    "#By indicator.\n",
+    "\n",
+    "fig2=px.treemap(selected_primary,path=['Indicator','Continent','Country'],values='R^2 Spearman',color='R^2 Spearman',color_continuous_scale='RdBu_r')\n",
+    "\n",
+    "fig2.show()\n",
+    "\n",
+    "#By continent\n",
+    "\n",
+    "fig2d=px.treemap(selected_primary,path=['Continent','Country','Group'],values='R^2 Spearman',color='R^2 Spearman',color_continuous_scale='RdBu_r')\n",
+    "fig2d.show()\n",
+    "\n",
+    "#By group of interest\n",
+    "\n",
+    "fig2dd=px.treemap(selected_primary,path=['Group','Continent','Country'],values='R^2 Spearman',color='R^2 Spearman',color_continuous_scale='RdBu_r')\n",
+    "\n",
+    "fig2dd.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "lastdf=final.dropna()\n",
+    "\n",
+    "lastdf.isna().sum()\n",
+    "\n",
+    "from ipywidgets import widgets, HBox\n",
+    "\n",
+    "out = widgets.Output()\n",
+    "\n",
+    "def output_treemap(path):\n",
+    "\n",
+    "    figA = px.treemap(lastdf, path=path, values='R^2 Spearman',\n",
+    "\n",
+    "                  color='R^2 Spearman',\n",
+    "\n",
+    "                  color_continuous_scale='RdBu_r')\n",
+    "\n",
+    "    figA.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
+    "\n",
+    "    out.clear_output(wait=True)\n",
+    "\n",
+    "    with out:\n",
+    "\n",
+    "        figA.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_1_dropdown = widgets.Dropdown(\n",
+    "\n",
+    "    options=['Continent', 'Country','Group','Indicator'],\n",
+    "\n",
+    "    value='Continent',\n",
+    "\n",
+    "    description='Path 1',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "path_2_dropdown = widgets.Dropdown(\n",
+    "\n",
+    "    options=['Continent', 'Country','Group','Indicator'],\n",
+    "\n",
+    "    value='Country',\n",
+    "\n",
+    "    description='Path 2',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "path_3_dropdown=widgets.Dropdown(\n",
+    "\n",
+    "    options=['Continent', 'Country','Group','Indicator'],\n",
+    "\n",
+    "    value='Group',\n",
+    "\n",
+    "    description='Path 3',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "path_4_dropdown=widgets.Dropdown(\n",
+    "\n",
+    "    options=['Continent', 'Country','Group','Indicator'],\n",
+    "\n",
+    "    value='Indicator',\n",
+    "\n",
+    "    description='Path 4',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "ok_button = widgets.Button(\n",
+    "\n",
+    "    description='Ready to go',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    "    button_style='info', # 'success', 'info', 'warning', 'danger' or ''\n",
+    "\n",
+    "    icon='check' # (FontAwesome names without the `fa-` prefix)\n",
+    "\n",
+    ")\n",
+    "\n",
+    "ok_button.on_click(lambda _: output_treemap([px.Constant(\"World\"), path_1_dropdown.value, path_2_dropdown.value,path_3_dropdown.value,path_4_dropdown.value]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "feee0f4cb986435db09003ffbb625805",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Dropdown(description='Path 1', options=('Continent', 'Country', 'Group', 'Indicator'), value='C…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20e40d9a00f846989c56c5c2bc8271e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(HBox([path_1_dropdown, path_2_dropdown, path_3_dropdown,path_4_dropdown,ok_button]), out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spurious correlations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, there is an interesting phenomenon, in some cases there are correlations that have a high coefficient and also an adequate graphics, but they do not make sense in the analysis, these are called **spurious correlations**. Here are some examples:\n",
+    "\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/chart%20(2).jpeg\" width=\"800\" height=\"400\" />\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/chart%20(1).jpeg\" width=\"800\" height=\"400\" />\n",
+    "\n",
+    "\n",
+    "Therefore, we have to be carefull with our results because correlation does not imply causation, it may have happened by chance that both variables are really similar.\n",
+    "So, after some thought and experimenting, we have developed a method that we think, it will allow us to find out if the correlation has happened by chance or if there is really a correlation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This method, consists of the following:\n",
+    "\n",
+    "\n",
+    "Firstly we have classified the indicators by a group, which can be one of the following: *A&D*, *Agriculture*, *Demography*, *Economy*, *Employment*, *Environment*, *Equality*, *Exports*, *Health*, *Mortality* or *Principal*. Moreover inside each group we have also assigned each varible a level, *primary* or *secondary*, depending on their level of relevance. For example we have consider more relevant the *Population in the largest city* over the *Rural population*, thus the first will be *primary* and the latter *secondary*, while both are part of the *Demography* group. \n",
+    "\n",
+    "With this set, we can expose our hypothesis:\n",
+    "\n",
+    "\"It is assumed that the correlation in the primary indicators can be caused by randomness, however if this correlation also appears in the secondary indicators for at least X% of the countries that appears in the primaries (Pareto's rule), we can suppose that there is no randomness affecting each group. Furthermore, the first assumption has to happen in Y% of the secondary indicators to avoid any fortuity.\" \n",
+    "\n",
+    "This hypothesis can be used in a global level, all the countries, or in the different regions. \n",
+    "\n",
+    "For example if, X and Y =80% a primary indicator is repeated 20 times the secondary indicators must have repeated 18 times. And if there are 10 secondary indicators, it has to happen for, at least, 8 indicators.\n",
+    "\n",
+    "Finally, we will finish with two possible errors of 20%, which combined (20%*20%), leaves us with a 4% of margin of error, which is lower than the wildly spread of 5%.\n",
+    "\n",
+    "**This will only work if the data has been collected by independent sources and uses different methods to collect it. Therefore, this step has only been developed for this data (WDI), which we have checked that comes from different sources and is gathered differently.**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c0d4097f473414f859204345da95d46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.8, continuous_update=False, description='% of primary indicators:', max=1.0, step=0.05)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "352b09b697b2410da7213568d23a6e38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.8, continuous_update=False, description='% of secondary indicators:', max=1.0, step=0.05)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "limita=widgets.FloatSlider( value=0.8, min=0, max=1.0, step=0.05, description='% of primary indicators:', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "limitb=widgets.FloatSlider( value=0.8, min=0, max=1.0, step=0.05, description='% of secondary indicators:', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "display(limita,limitb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The margin of error in this combination is: 3.9999999999999982\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=(1-limita.value)*(1-limitb.value)*100\n",
+    "print('The margin of error in this combination is:',a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have selected both percentages and we agree with the margin of error, we can proceed to put into action our method. Firstly, we filter the primary indicators and get the minimun of times thatthe secondary have to be repeated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Min</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A&amp;D</th>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Agriculture</th>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Demography</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Economy</th>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Employment</th>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Environment</th>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equality</th>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exports</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Health</th>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Internet</th>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mortality</th>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Principal</th>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Min\n",
+       "Group            \n",
+       "A&D          13.0\n",
+       "Agriculture  10.0\n",
+       "Demography   24.0\n",
+       "Economy      26.0\n",
+       "Employment   11.0\n",
+       "Environment  13.0\n",
+       "Equality     14.0\n",
+       "Exports      28.0\n",
+       "Health        9.0\n",
+       "Internet     27.0\n",
+       "Mortality    14.0\n",
+       "Principal    23.0"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_p=categories.loc[categories['Level']=='primary']\n",
+    "minprimary=selected_p.groupby('Group').min()\n",
+    "minprimary['Min']=round(minprimary['Number of times repeated']*limita.value)\n",
+    "minprimary.drop(columns=['Indicator','Number of times repeated','Level'], inplace=True)\n",
+    "minprimary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouplist=minprimary.index.to_list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we test if the repetition are accomplished. \n",
+    "\n",
+    "- H_0 data has correlation buy has not happened by randomness.\n",
+    "- H_1 data has correlation due to randomness \n",
+    "\n",
+    "**If Number of times repeated the secondary indicator < Minimun per group, then H_0 denied and H_1 accepted**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Number of times repeated_x</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Level</th>\n",
+       "      <th>Number of times repeated_y</th>\n",
+       "      <th>Min</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.817804</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.759333</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.936963</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>GBR</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.786708</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>HRV</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.924056</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>POL</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4585</th>\n",
+       "      <td>Crop production index (2014-2016 = 100)</td>\n",
+       "      <td>0.851083</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>COL</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Agriculture</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4586</th>\n",
+       "      <td>Crop production index (2014-2016 = 100)</td>\n",
+       "      <td>0.882284</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHL</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Cuadratic</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Agriculture</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4587</th>\n",
+       "      <td>Crop production index (2014-2016 = 100)</td>\n",
+       "      <td>0.967446</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CRI</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Cubic</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Agriculture</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4588</th>\n",
+       "      <td>Crop production index (2014-2016 = 100)</td>\n",
+       "      <td>0.845314</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Agriculture</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4589</th>\n",
+       "      <td>Crop production index (2014-2016 = 100)</td>\n",
+       "      <td>0.998717</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Cubic</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Agriculture</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4590 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Indicator  R^2 Spearman  \\\n",
+       "0     Adolescent fertility rate (births per 1,000 wo...      0.817804   \n",
+       "1     Adolescent fertility rate (births per 1,000 wo...      0.759333   \n",
+       "2     Adolescent fertility rate (births per 1,000 wo...      0.936963   \n",
+       "3     Adolescent fertility rate (births per 1,000 wo...      0.786708   \n",
+       "4     Adolescent fertility rate (births per 1,000 wo...      0.924056   \n",
+       "...                                                 ...           ...   \n",
+       "4585            Crop production index (2014-2016 = 100)      0.851083   \n",
+       "4586            Crop production index (2014-2016 = 100)      0.882284   \n",
+       "4587            Crop production index (2014-2016 = 100)      0.967446   \n",
+       "4588            Crop production index (2014-2016 = 100)      0.845314   \n",
+       "4589            Crop production index (2014-2016 = 100)      0.998717   \n",
+       "\n",
+       "     Behaviour Country           Moved            Type Continent  \\\n",
+       "0     Negative     DEU               1  Does not apply    Europe   \n",
+       "1     Negative     SWE               3  Does not apply    Europe   \n",
+       "2     Negative     GBR              13  Does not apply    Europe   \n",
+       "3     Negative     HRV               8  Does not apply    Europe   \n",
+       "4     Negative     POL              21  Does not apply    Europe   \n",
+       "...        ...     ...             ...             ...       ...   \n",
+       "4585  Positive     COL               8  Does not apply     Latam   \n",
+       "4586  Positive     CHL  Does not apply       Cuadratic     Latam   \n",
+       "4587  Positive     CRI  Does not apply           Cubic     Latam   \n",
+       "4588  Positive     USA  Does not apply          Linear      Pair   \n",
+       "4589  Positive     CHN  Does not apply           Cubic      Pair   \n",
+       "\n",
+       "      Number of times repeated_x        Group      Level  \\\n",
+       "0                             22   Demography  secondary   \n",
+       "1                             22   Demography  secondary   \n",
+       "2                             22   Demography  secondary   \n",
+       "3                             22   Demography  secondary   \n",
+       "4                             22   Demography  secondary   \n",
+       "...                          ...          ...        ...   \n",
+       "4585                          21  Agriculture  secondary   \n",
+       "4586                          21  Agriculture  secondary   \n",
+       "4587                          21  Agriculture  secondary   \n",
+       "4588                          21  Agriculture  secondary   \n",
+       "4589                          21  Agriculture  secondary   \n",
+       "\n",
+       "      Number of times repeated_y   Min  \n",
+       "0                             40  24.0  \n",
+       "1                             40  24.0  \n",
+       "2                             40  24.0  \n",
+       "3                             40  24.0  \n",
+       "4                             40  24.0  \n",
+       "...                          ...   ...  \n",
+       "4585                          27  10.0  \n",
+       "4586                          27  10.0  \n",
+       "4587                          27  10.0  \n",
+       "4588                          27  10.0  \n",
+       "4589                          27  10.0  \n",
+       "\n",
+       "[4590 rows x 12 columns]"
+      ]
      },
+     "execution_count": 52,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "unique_tric = demo2['Continent'].unique()\n",
-    "tric = widgets.SelectMultiple(\n",
-    "    options = unique_tric.tolist(),\n",
-    "    value = ['North Africa'],\n",
-    "    description='Continent',\n",
-    "    disabled=False,\n",
-    "    layout = Layout(width='50%', height='80px')\n",
-    ")\n",
-    "\n",
-    "def graf1(tric):\n",
-    "    dat=demo2.loc[demo2.loc[:, 'Continent'].isin(np.array(tric))]\n",
-    "    a=px.scatter(dat, x=\"R^2 Pearson\", y='Indicator',\n",
-    "                     color=\"R^2 Pearson\", hover_name=\"Country\",hover_data = [dat.Type, dat.Behaviour],\n",
-    "                     color_continuous_scale='Blues', width=700, height=500, title= dat.Continent.unique().tolist()[0])\n",
-    "    a.show()\n",
-    "widgets.interactive(graf1, tric=tric)"
+    "secondary=final.loc[final['Level']=='secondary']\n",
+    "secondary=pd.merge(secondary,minprimary, left_on='Group',right_on='Group')\n",
+    "secondaryp=secondary.loc[:,['Group','Min']]\n",
+    "secondary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Does it have some global casuallity implied?</th>\n",
+       "      <th>% of count (Global)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agriculture</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Demography</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.728195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Economy</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>54.372937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Employment</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Environment</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>73.529412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equality</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>58.928571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exports</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>60.183066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Health</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Internet</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.190083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mortality</th>\n",
+       "      <td>No</td>\n",
+       "      <td>92.337165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Principal</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Does it have some global casuallity implied?  % of count (Global)\n",
+       "Group                                                                        \n",
+       "Agriculture                                           No           100.000000\n",
+       "Demography                                           Yes            37.728195\n",
+       "Economy                                              Yes            54.372937\n",
+       "Employment                                            No           100.000000\n",
+       "Environment                                          Yes            73.529412\n",
+       "Equality                                             Yes            58.928571\n",
+       "Exports                                              Yes            60.183066\n",
+       "Health                                                No           100.000000\n",
+       "Internet                                             Yes            37.190083\n",
+       "Mortality                                             No            92.337165\n",
+       "Principal                                             No           100.000000"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "secondary=final.loc[final['Level']=='secondary']\n",
+    "secondary=pd.merge(secondary,minprimary, left_on='Group',right_on='Group')\n",
+    "secondaryp=secondary.loc[:,['Group','Min']]\n",
+    "Global_Count=secondaryp.groupby('Group').count()\n",
+    "Global_Count.rename(columns={'Min':'Global Count'},inplace=True)\n",
+    "secondary['H_0']=np.where(secondary['Number of times repeated_x']-secondary['Min']>0,'Not Discarded', 'Denied')\n",
+    "seco=secondary.groupby(['H_0','Group']).count()\n",
+    "sec=seco.loc['Not Discarded']\n",
+    "secondarycount=sec.drop(columns=['Indicator','R^2 Spearman','Behaviour','Country','Moved','Type','Continent','Number of times repeated_x','Number of times repeated_y','Level'])\n",
+    "secondarycount.rename(columns={'Min':'Secondary Count'},inplace=True)\n",
+    "continentlist=final['Continent'].unique()\n",
+    "namescontinents=['European', 'North African', 'Asian', 'Pair', 'Persian', 'South African', 'Latino-American']\n",
+    "finalcount=pd.merge(Global_Count,secondarycount, left_on='Group',right_on='Group')\n",
+    "finalcount['Does it have some global casuallity implied?']=np.where(finalcount['Secondary Count']/finalcount['Global Count']>limitb.value,'No', 'Yes')\n",
+    "finalcount['% of count (Global)']=finalcount['Secondary Count']/finalcount['Global Count']*100\n",
+    "finalcount.drop(columns=['Global Count','Secondary Count'],inplace=True)\n",
+    "finalcount"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, if we execute the following loop, it will provide with the variables that follow a normal distribution."
+    "As we can see, in a Global situation for the groups that have a **NO**, we do not need to worry about casualities, however for the rest of the groups correlation can still be a great indicator as a basis for decision making, if we carefully analyze the variables and found some sort of real relationship between them.   "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DEU-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "DEU-Renewable electricity\n",
-      "Statistical=0.806, p=0.000\n",
-      "DEU-Employment-agriculture\n",
-      "Statistical=0.888, p=0.003\n",
-      "DEU-Employment-industry\n",
-      "Statistical=0.906, p=0.009\n",
-      "DEU-Employment-services\n",
-      "Statistical=0.877, p=0.002\n",
-      "DEU-Exports-G&S\n",
-      "Statistical=0.887, p=0.003\n",
-      "DEU-Fertility rate\n",
-      "Statistical=0.893, p=0.004\n",
-      "DEU-Foreign investment\n",
-      "Statistical=0.851, p=0.000\n",
-      "DEU-GDP\n",
-      "Statistical=0.927, p=0.031\n",
-      "DEU-Education GExp\n",
-      "Statistical=0.972, p=0.548\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "DEU-Workers high education\n",
-      "Statistical=0.887, p=0.003\n",
-      "DEU-Literacy rate\n",
-      "Statistical=0.809, p=0.000\n",
-      "DEU-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "DEU-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "DEU-Health services use\n",
-      "Statistical=0.804, p=0.000\n",
-      "DEU-R&D GExp\n",
-      "Statistical=0.879, p=0.002\n",
-      "DEU-Ninis\n",
-      "Statistical=0.819, p=0.000\n",
-      "DEU-Suicide\n",
-      "Statistical=0.892, p=0.004\n",
-      "DEU-International taxes\n",
-      "Statistical=0.824, p=0.000\n",
-      "DEU-Alcohol per capita\n",
-      "Statistical=0.826, p=0.000\n",
-      "FRA-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "FRA-Renewable electricity\n",
-      "Statistical=0.796, p=0.000\n",
-      "FRA-Employment-agriculture\n",
-      "Statistical=0.936, p=0.058\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "FRA-Employment-industry\n",
-      "Statistical=0.904, p=0.008\n",
-      "FRA-Employment-services\n",
-      "Statistical=0.906, p=0.009\n",
-      "FRA-Exports-G&S\n",
-      "Statistical=0.915, p=0.015\n",
-      "FRA-Fertility rate\n",
-      "Statistical=0.876, p=0.002\n",
-      "FRA-Foreign investment\n",
-      "Statistical=0.884, p=0.002\n",
-      "FRA-GDP\n",
-      "Statistical=0.915, p=0.015\n",
-      "FRA-Education GExp\n",
-      "Statistical=0.957, p=0.223\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "FRA-Workers high education\n",
-      "Statistical=0.846, p=0.000\n",
-      "FRA-Literacy rate\n",
-      "Statistical=0.856, p=0.001\n",
-      "FRA-Net migration\n",
-      "Statistical=0.888, p=0.003\n",
-      "FRA-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "FRA-Health services use\n",
-      "Statistical=0.755, p=0.000\n",
-      "FRA-R&D GExp\n",
-      "Statistical=0.896, p=0.005\n",
-      "FRA-Ninis\n",
-      "Statistical=0.840, p=0.000\n",
-      "FRA-Suicide\n",
-      "Statistical=0.902, p=0.007\n",
-      "FRA-International taxes\n",
-      "Statistical=0.754, p=0.000\n",
-      "FRA-Alcohol per capita\n",
-      "Statistical=0.811, p=0.000\n",
-      "SWE-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SWE-Renewable electricity\n",
-      "Statistical=0.832, p=0.000\n",
-      "SWE-Employment-agriculture\n",
-      "Statistical=0.883, p=0.003\n",
-      "SWE-Employment-industry\n",
-      "Statistical=0.898, p=0.006\n",
-      "SWE-Employment-services\n",
-      "Statistical=0.893, p=0.005\n",
-      "SWE-Exports-G&S\n",
-      "Statistical=0.898, p=0.006\n",
-      "SWE-Fertility rate\n",
-      "Statistical=0.850, p=0.001\n",
-      "SWE-Foreign investment\n",
-      "Statistical=0.863, p=0.001\n",
-      "SWE-GDP\n",
-      "Statistical=0.964, p=0.367\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SWE-Education GExp\n",
-      "Statistical=0.976, p=0.700\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SWE-Workers high education\n",
-      "Statistical=0.864, p=0.001\n",
-      "SWE-Literacy rate\n",
-      "Statistical=0.830, p=0.000\n",
-      "SWE-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SWE-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SWE-Health services use\n",
-      "Statistical=0.791, p=0.000\n",
-      "SWE-R&D GExp\n",
-      "Statistical=0.932, p=0.051\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SWE-Ninis\n",
-      "Statistical=0.820, p=0.000\n",
-      "SWE-Suicide\n",
-      "Statistical=0.955, p=0.215\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SWE-International taxes\n",
-      "Statistical=0.832, p=0.000\n",
-      "SWE-Alcohol per capita\n",
-      "Statistical=0.927, p=0.037\n",
-      "GBR-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GBR-Renewable electricity\n",
-      "Statistical=0.759, p=0.000\n",
-      "GBR-Employment-agriculture\n",
-      "Statistical=0.944, p=0.110\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GBR-Employment-industry\n",
-      "Statistical=0.846, p=0.000\n",
-      "GBR-Employment-services\n",
-      "Statistical=0.896, p=0.006\n",
-      "GBR-Exports-G&S\n",
-      "Statistical=0.896, p=0.006\n",
-      "GBR-Fertility rate\n",
-      "Statistical=0.885, p=0.003\n",
-      "GBR-Foreign investment\n",
-      "Statistical=0.881, p=0.003\n",
-      "GBR-GDP\n",
-      "Statistical=0.971, p=0.533\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GBR-Education GExp\n",
-      "Statistical=0.973, p=0.593\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GBR-Workers high education\n",
-      "Statistical=0.882, p=0.003\n",
-      "GBR-Literacy rate\n",
-      "Statistical=0.847, p=0.000\n",
-      "GBR-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GBR-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GBR-Health services use\n",
-      "Statistical=0.917, p=0.020\n",
-      "GBR-R&D GExp\n",
-      "Statistical=0.936, p=0.063\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GBR-Ninis\n",
-      "Statistical=0.773, p=0.000\n",
-      "GBR-Suicide\n",
-      "Statistical=0.941, p=0.088\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GBR-International taxes\n",
-      "Statistical=0.810, p=0.000\n",
-      "GBR-Alcohol per capita\n",
-      "Statistical=0.891, p=0.004\n",
-      "ESP-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ESP-Renewable electricity\n",
-      "Statistical=0.793, p=0.000\n",
-      "ESP-Employment-agriculture\n",
-      "Statistical=0.908, p=0.012\n",
-      "ESP-Employment-industry\n",
-      "Statistical=0.823, p=0.000\n",
-      "ESP-Employment-services\n",
-      "Statistical=0.798, p=0.000\n",
-      "ESP-Exports-G&S\n",
-      "Statistical=0.866, p=0.001\n",
-      "ESP-Fertility rate\n",
-      "Statistical=0.905, p=0.009\n",
-      "ESP-Foreign investment\n",
-      "Statistical=0.897, p=0.006\n",
-      "ESP-GDP\n",
-      "Statistical=0.960, p=0.283\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ESP-Education GExp\n",
-      "Statistical=0.989, p=0.980\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ESP-Workers high education\n",
-      "Statistical=0.850, p=0.001\n",
-      "ESP-Literacy rate\n",
-      "Statistical=0.848, p=0.000\n",
-      "ESP-Net migration\n",
-      "Statistical=0.363, p=0.000\n",
-      "ESP-Mortality-infants\n",
-      "Statistical=0.712, p=0.000\n",
-      "ESP-Health services use\n",
-      "Statistical=0.923, p=0.029\n",
-      "ESP-R&D GExp\n",
-      "Statistical=0.901, p=0.008\n",
-      "ESP-Ninis\n",
-      "Statistical=0.711, p=0.000\n",
-      "ESP-Suicide\n",
-      "Statistical=0.852, p=0.001\n",
-      "ESP-International taxes\n",
-      "Statistical=0.820, p=0.000\n",
-      "ESP-Alcohol per capita\n",
-      "Statistical=0.841, p=0.000\n",
-      "HRV-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "HRV-Renewable electricity\n",
-      "Statistical=0.700, p=0.000\n",
-      "HRV-Employment-agriculture\n",
-      "Statistical=0.829, p=0.000\n",
-      "HRV-Employment-industry\n",
-      "Statistical=0.944, p=0.103\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "HRV-Employment-services\n",
-      "Statistical=0.956, p=0.232\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "HRV-Exports-G&S\n",
-      "Statistical=0.928, p=0.040\n",
-      "HRV-Fertility rate\n",
-      "Statistical=0.911, p=0.013\n",
-      "HRV-Foreign investment\n",
-      "Statistical=0.866, p=0.001\n",
-      "HRV-GDP\n",
-      "Statistical=0.976, p=0.703\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "HRV-Education GExp\n",
-      "Statistical=0.931, p=0.048\n",
-      "HRV-Workers high education\n",
-      "Statistical=0.827, p=0.000\n",
-      "HRV-Literacy rate\n",
-      "Statistical=0.835, p=0.000\n",
-      "HRV-Net migration\n",
-      "Statistical=0.878, p=0.002\n",
-      "HRV-Mortality-infants\n",
-      "Statistical=0.855, p=0.001\n",
-      "HRV-Health services use\n",
-      "Statistical=0.893, p=0.005\n",
-      "HRV-R&D GExp\n",
-      "Statistical=0.817, p=0.000\n",
-      "HRV-Ninis\n",
-      "Statistical=0.822, p=0.000\n",
-      "HRV-Suicide\n",
-      "Statistical=0.910, p=0.013\n",
-      "HRV-International taxes\n",
-      "Statistical=0.716, p=0.000\n",
-      "HRV-Alcohol per capita\n",
-      "Statistical=0.846, p=0.000\n",
-      "POL-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "POL-Renewable electricity\n",
-      "Statistical=0.813, p=0.000\n",
-      "POL-Employment-agriculture\n",
-      "Statistical=0.848, p=0.000\n",
-      "POL-Employment-industry\n",
-      "Statistical=0.935, p=0.060\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "POL-Employment-services\n",
-      "Statistical=0.959, p=0.275\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "POL-Exports-G&S\n",
-      "Statistical=0.884, p=0.003\n",
-      "POL-Fertility rate\n",
-      "Statistical=0.877, p=0.002\n",
-      "POL-Foreign investment\n",
-      "Statistical=0.858, p=0.001\n",
-      "POL-GDP\n",
-      "Statistical=0.904, p=0.009\n",
-      "POL-Education GExp\n",
-      "Statistical=0.974, p=0.628\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "POL-Workers high education\n",
-      "Statistical=0.879, p=0.002\n",
-      "POL-Literacy rate\n",
-      "Statistical=0.836, p=0.000\n",
-      "POL-Net migration\n",
-      "Statistical=0.753, p=0.000\n",
-      "POL-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "POL-Health services use\n",
-      "Statistical=0.797, p=0.000\n",
-      "POL-R&D GExp\n",
-      "Statistical=0.930, p=0.045\n",
-      "POL-Ninis\n",
-      "Statistical=0.805, p=0.000\n",
-      "POL-Suicide\n",
-      "Statistical=0.801, p=0.000\n",
-      "POL-International taxes\n",
-      "Statistical=0.733, p=0.000\n",
-      "POL-Alcohol per capita\n",
-      "Statistical=0.795, p=0.000\n",
-      "GRC-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GRC-Renewable electricity\n",
-      "Statistical=0.808, p=0.000\n",
-      "GRC-Employment-agriculture\n",
-      "Statistical=0.825, p=0.000\n",
-      "GRC-Employment-industry\n",
-      "Statistical=0.837, p=0.000\n",
-      "GRC-Employment-services\n",
-      "Statistical=0.819, p=0.000\n",
-      "GRC-Exports-G&S\n",
-      "Statistical=0.917, p=0.019\n",
-      "GRC-Fertility rate\n",
-      "Statistical=0.903, p=0.008\n",
-      "GRC-Foreign investment\n",
-      "Statistical=0.890, p=0.004\n",
-      "GRC-GDP\n",
-      "Statistical=0.941, p=0.089\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GRC-Education GExp\n",
-      "Statistical=0.944, p=0.105\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GRC-Workers high education\n",
-      "Statistical=0.943, p=0.097\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GRC-Literacy rate\n",
-      "Statistical=0.816, p=0.000\n",
-      "GRC-Net migration\n",
-      "Statistical=0.908, p=0.012\n",
-      "GRC-Mortality-infants\n",
-      "Statistical=0.885, p=0.003\n",
-      "GRC-Health services use\n",
-      "Statistical=0.883, p=0.003\n",
-      "GRC-R&D GExp\n",
-      "Statistical=0.916, p=0.019\n",
-      "GRC-Ninis\n",
-      "Statistical=0.809, p=0.000\n",
-      "GRC-Suicide\n",
-      "Statistical=0.815, p=0.000\n",
-      "GRC-International taxes\n",
-      "Statistical=0.791, p=0.000\n",
-      "GRC-Alcohol per capita\n",
-      "Statistical=0.809, p=0.000\n",
-      "AUT-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AUT-Renewable electricity\n",
-      "Statistical=0.807, p=0.000\n",
-      "AUT-Employment-agriculture\n",
-      "Statistical=0.979, p=0.780\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AUT-Employment-industry\n",
-      "Statistical=0.935, p=0.053\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AUT-Employment-services\n",
-      "Statistical=0.882, p=0.002\n",
-      "AUT-Exports-G&S\n",
-      "Statistical=0.897, p=0.005\n",
-      "AUT-Fertility rate\n",
-      "Statistical=0.791, p=0.000\n",
-      "AUT-Foreign investment\n",
-      "Statistical=0.856, p=0.001\n",
-      "AUT-GDP\n",
-      "Statistical=0.980, p=0.804\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AUT-Education GExp\n",
-      "Statistical=0.793, p=0.000\n",
-      "AUT-Workers high education\n",
-      "Statistical=0.854, p=0.000\n",
-      "AUT-Literacy rate\n",
-      "Statistical=0.806, p=0.000\n",
-      "AUT-Net migration\n",
-      "Statistical=0.650, p=0.000\n",
-      "AUT-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AUT-Health services use\n",
-      "Statistical=0.803, p=0.000\n",
-      "AUT-R&D GExp\n",
-      "Statistical=0.939, p=0.069\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AUT-Ninis\n",
-      "Statistical=0.784, p=0.000\n",
-      "AUT-Suicide\n",
-      "Statistical=0.881, p=0.002\n",
-      "AUT-International taxes\n",
-      "Statistical=0.826, p=0.000\n",
-      "AUT-Alcohol per capita\n",
-      "Statistical=0.838, p=0.000\n",
-      "NLD-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NLD-Renewable electricity\n",
-      "Statistical=0.814, p=0.000\n",
-      "NLD-Employment-agriculture\n",
-      "Statistical=0.945, p=0.115\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NLD-Employment-industry\n",
-      "Statistical=0.935, p=0.059\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NLD-Employment-services\n",
-      "Statistical=0.910, p=0.013\n",
-      "NLD-Exports-G&S\n",
-      "Statistical=0.921, p=0.025\n",
-      "NLD-Fertility rate\n",
-      "Statistical=0.895, p=0.005\n",
-      "NLD-Foreign investment\n",
-      "Statistical=0.874, p=0.002\n",
-      "NLD-GDP\n",
-      "Statistical=0.939, p=0.076\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NLD-Education GExp\n",
-      "Statistical=0.914, p=0.017\n",
-      "NLD-Workers high education\n",
-      "Statistical=0.863, p=0.001\n",
-      "NLD-Literacy rate\n",
-      "Statistical=0.841, p=0.000\n",
-      "NLD-Net migration\n",
-      "Statistical=0.883, p=0.003\n",
-      "NLD-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NLD-Health services use\n",
-      "Statistical=0.903, p=0.008\n",
-      "NLD-R&D GExp\n",
-      "Statistical=0.811, p=0.000\n",
-      "NLD-Ninis\n",
-      "Statistical=0.848, p=0.000\n",
-      "NLD-Suicide\n",
-      "Statistical=0.921, p=0.025\n",
-      "NLD-International taxes\n",
-      "Statistical=0.849, p=0.000\n",
-      "NLD-Alcohol per capita\n",
-      "Statistical=0.787, p=0.000\n",
-      "IRQ-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IRQ-Renewable electricity\n",
-      "Statistical=0.838, p=0.000\n",
-      "IRQ-Employment-agriculture\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IRQ-Employment-industry\n",
-      "Statistical=0.916, p=0.019\n",
-      "IRQ-Employment-services\n",
-      "Statistical=0.904, p=0.009\n",
-      "IRQ-Exports-G&S\n",
-      "Statistical=0.904, p=0.009\n",
-      "IRQ-Fertility rate\n",
-      "Statistical=0.741, p=0.000\n",
-      "IRQ-Foreign investment\n",
-      "Statistical=0.901, p=0.007\n",
-      "IRQ-GDP\n",
-      "Statistical=0.965, p=0.390\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IRQ-Education GExp\n",
-      "Statistical=0.744, p=0.000\n",
-      "IRQ-Workers high education\n",
-      "Statistical=0.869, p=0.001\n",
-      "IRQ-Literacy rate\n",
-      "Statistical=0.815, p=0.000\n",
-      "IRQ-Net migration\n",
-      "Statistical=0.442, p=0.000\n",
-      "IRQ-Mortality-infants\n",
-      "Statistical=0.734, p=0.000\n",
-      "IRQ-Health services use\n",
-      "Statistical=0.862, p=0.001\n",
-      "IRQ-R&D GExp\n",
-      "Statistical=0.961, p=0.314\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IRQ-Ninis\n",
-      "Statistical=0.674, p=0.000\n",
-      "IRQ-Suicide\n",
-      "Statistical=0.661, p=0.000\n",
-      "IRQ-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IRQ-Alcohol per capita\n",
-      "Statistical=0.837, p=0.000\n",
-      "QAT-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "QAT-Renewable electricity\n",
-      "Statistical=0.719, p=0.000\n",
-      "QAT-Employment-agriculture\n",
-      "Statistical=0.876, p=0.002\n",
-      "QAT-Employment-industry\n",
-      "Statistical=0.873, p=0.002\n",
-      "QAT-Employment-services\n",
-      "Statistical=0.860, p=0.001\n",
-      "QAT-Exports-G&S\n",
-      "Statistical=0.858, p=0.001\n",
-      "QAT-Fertility rate\n",
-      "Statistical=0.616, p=0.000\n",
-      "QAT-Foreign investment\n",
-      "Statistical=0.844, p=0.000\n",
-      "QAT-GDP\n",
-      "Statistical=0.879, p=0.002\n",
-      "QAT-Education GExp\n",
-      "Statistical=0.581, p=0.000\n",
-      "QAT-Workers high education\n",
-      "Statistical=0.823, p=0.000\n",
-      "QAT-Literacy rate\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "QAT-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "QAT-Mortality-infants\n",
-      "Statistical=0.686, p=0.000\n",
-      "QAT-Health services use\n",
-      "Statistical=0.922, p=0.026\n",
-      "QAT-R&D GExp\n",
-      "Statistical=0.894, p=0.005\n",
-      "QAT-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "QAT-Suicide\n",
-      "Statistical=0.589, p=0.000\n",
-      "QAT-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "QAT-Alcohol per capita\n",
-      "Statistical=0.844, p=0.000\n",
-      "ARE-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARE-Renewable electricity\n",
-      "Statistical=0.850, p=0.001\n",
-      "ARE-Employment-agriculture\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARE-Employment-industry\n",
-      "Statistical=0.867, p=0.001\n",
-      "ARE-Employment-services\n",
-      "Statistical=0.902, p=0.008\n",
-      "ARE-Exports-G&S\n",
-      "Statistical=0.874, p=0.002\n",
-      "ARE-Fertility rate\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARE-Foreign investment\n",
-      "Statistical=0.780, p=0.000\n",
-      "ARE-GDP\n",
-      "Statistical=0.904, p=0.009\n",
-      "ARE-Education GExp\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARE-Workers high education\n",
-      "Statistical=0.855, p=0.001\n",
-      "ARE-Literacy rate\n",
-      "Statistical=0.742, p=0.000\n",
-      "ARE-Net migration\n",
-      "Statistical=0.580, p=0.000\n",
-      "ARE-Mortality-infants\n",
-      "Statistical=0.761, p=0.000\n",
-      "ARE-Health services use\n",
-      "Statistical=0.920, p=0.024\n",
-      "ARE-R&D GExp\n",
-      "Statistical=0.893, p=0.005\n",
-      "ARE-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARE-Suicide\n",
-      "Statistical=0.575, p=0.000\n",
-      "ARE-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARE-Alcohol per capita\n",
-      "Statistical=0.772, p=0.000\n",
-      "SAU-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SAU-Renewable electricity\n",
-      "Statistical=0.903, p=0.009\n",
-      "SAU-Employment-agriculture\n",
-      "Statistical=0.911, p=0.014\n",
-      "SAU-Employment-industry\n",
-      "Statistical=0.936, p=0.063\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SAU-Employment-services\n",
-      "Statistical=0.915, p=0.018\n",
-      "SAU-Exports-G&S\n",
-      "Statistical=0.918, p=0.021\n",
-      "SAU-Fertility rate\n",
-      "Statistical=0.909, p=0.012\n",
-      "SAU-Foreign investment\n",
-      "Statistical=0.880, p=0.002\n",
-      "SAU-GDP\n",
-      "Statistical=0.922, p=0.027\n",
-      "SAU-Education GExp\n",
-      "Statistical=0.887, p=0.003\n",
-      "SAU-Workers high education\n",
-      "Statistical=0.838, p=0.000\n",
-      "SAU-Literacy rate\n",
-      "Statistical=0.808, p=0.000\n",
-      "SAU-Net migration\n",
-      "Statistical=0.664, p=0.000\n",
-      "SAU-Mortality-infants\n",
-      "Statistical=0.919, p=0.022\n",
-      "SAU-Health services use\n",
-      "Statistical=0.913, p=0.016\n",
-      "SAU-R&D GExp\n",
-      "Statistical=0.930, p=0.043\n",
-      "SAU-Ninis\n",
-      "Statistical=0.749, p=0.000\n",
-      "SAU-Suicide\n",
-      "Statistical=0.640, p=0.000\n",
-      "SAU-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SAU-Alcohol per capita\n",
-      "Statistical=0.778, p=0.000\n",
-      "AZE-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AZE-Renewable electricity\n",
-      "Statistical=0.927, p=0.032\n",
-      "AZE-Employment-agriculture\n",
-      "Statistical=0.841, p=0.000\n",
-      "AZE-Employment-industry\n",
-      "Statistical=0.933, p=0.047\n",
-      "AZE-Employment-services\n",
-      "Statistical=0.906, p=0.009\n",
-      "AZE-Exports-G&S\n",
-      "Statistical=0.843, p=0.000\n",
-      "AZE-Fertility rate\n",
-      "Statistical=0.800, p=0.000\n",
-      "AZE-Foreign investment\n",
-      "Statistical=0.851, p=0.000\n",
-      "AZE-GDP\n",
-      "Statistical=0.913, p=0.014\n",
-      "AZE-Education GExp\n",
-      "Statistical=0.933, p=0.048\n",
-      "AZE-Workers high education\n",
-      "Statistical=0.854, p=0.001\n",
-      "AZE-Literacy rate\n",
-      "Statistical=0.808, p=0.000\n",
-      "AZE-Net migration\n",
-      "Statistical=0.833, p=0.000\n",
-      "AZE-Mortality-infants\n",
-      "Statistical=0.687, p=0.000\n",
-      "AZE-Health services use\n",
-      "Statistical=0.874, p=0.001\n",
-      "AZE-R&D GExp\n",
-      "Statistical=0.802, p=0.000\n",
-      "AZE-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AZE-Suicide\n",
-      "Statistical=0.860, p=0.001\n",
-      "AZE-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "AZE-Alcohol per capita\n",
-      "Statistical=0.866, p=0.001\n",
-      "YEM-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "YEM-Renewable electricity\n",
-      "Statistical=0.740, p=0.000\n",
-      "YEM-Employment-agriculture\n",
-      "Statistical=0.730, p=0.000\n",
-      "YEM-Employment-industry\n",
-      "Statistical=0.820, p=0.000\n",
-      "YEM-Employment-services\n",
-      "Statistical=0.942, p=0.091\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "YEM-Exports-G&S\n",
-      "Statistical=0.880, p=0.002\n",
-      "YEM-Fertility rate\n",
-      "Statistical=0.615, p=0.000\n",
-      "YEM-Foreign investment\n",
-      "Statistical=0.529, p=0.000\n",
-      "YEM-GDP\n",
-      "Statistical=0.931, p=0.046\n",
-      "YEM-Education GExp\n",
-      "Statistical=0.707, p=0.000\n",
-      "YEM-Workers high education\n",
-      "Statistical=0.900, p=0.007\n",
-      "YEM-Literacy rate\n",
-      "Statistical=0.857, p=0.001\n",
-      "YEM-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "YEM-Mortality-infants\n",
-      "Statistical=0.722, p=0.000\n",
-      "YEM-Health services use\n",
-      "Statistical=0.848, p=0.000\n",
-      "YEM-R&D GExp\n",
-      "Statistical=0.897, p=0.006\n",
-      "YEM-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "YEM-Suicide\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "YEM-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "YEM-Alcohol per capita\n",
-      "Statistical=0.863, p=0.001\n",
-      "OMN-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "OMN-Renewable electricity\n",
-      "Statistical=0.706, p=0.000\n",
-      "OMN-Employment-agriculture\n",
-      "Statistical=0.945, p=0.116\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "OMN-Employment-industry\n",
-      "Statistical=0.935, p=0.062\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "OMN-Employment-services\n",
-      "Statistical=0.840, p=0.000\n",
-      "OMN-Exports-G&S\n",
-      "Statistical=0.846, p=0.000\n",
-      "OMN-Fertility rate\n",
-      "Statistical=0.882, p=0.003\n",
-      "OMN-Foreign investment\n",
-      "Statistical=0.880, p=0.002\n",
-      "OMN-GDP\n",
-      "Statistical=0.733, p=0.000\n",
-      "OMN-Education GExp\n",
-      "Statistical=0.820, p=0.000\n",
-      "OMN-Workers high education\n",
-      "Statistical=0.828, p=0.000\n",
-      "OMN-Literacy rate\n",
-      "Statistical=0.798, p=0.000\n",
-      "OMN-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "OMN-Mortality-infants\n",
-      "Statistical=0.788, p=0.000\n",
-      "OMN-Health services use\n",
-      "Statistical=0.815, p=0.000\n",
-      "OMN-R&D GExp\n",
-      "Statistical=0.939, p=0.076\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "OMN-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "OMN-Suicide\n",
-      "Statistical=0.601, p=0.000\n",
-      "OMN-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "OMN-Alcohol per capita\n",
-      "Statistical=0.852, p=0.001\n",
-      "DZA-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "DZA-Renewable electricity\n",
-      "Statistical=0.845, p=0.000\n",
-      "DZA-Employment-agriculture\n",
-      "Statistical=0.725, p=0.000\n",
-      "DZA-Employment-industry\n",
-      "Statistical=0.827, p=0.000\n",
-      "DZA-Employment-services\n",
-      "Statistical=0.782, p=0.000\n",
-      "DZA-Exports-G&S\n",
-      "Statistical=0.904, p=0.008\n",
-      "DZA-Fertility rate\n",
-      "Statistical=0.781, p=0.000\n",
-      "DZA-Foreign investment\n",
-      "Statistical=0.896, p=0.005\n",
-      "DZA-GDP\n",
-      "Statistical=0.922, p=0.023\n",
-      "DZA-Education GExp\n",
-      "Statistical=0.968, p=0.446\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "DZA-Workers high education\n",
-      "Statistical=0.863, p=0.001\n",
-      "DZA-Literacy rate\n",
-      "Statistical=0.851, p=0.000\n",
-      "DZA-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "DZA-Mortality-infants\n",
-      "Statistical=0.808, p=0.000\n",
-      "DZA-Health services use\n",
-      "Statistical=0.783, p=0.000\n",
-      "DZA-R&D GExp\n",
-      "Statistical=0.891, p=0.004\n",
-      "DZA-Ninis\n",
-      "Statistical=0.662, p=0.000\n",
-      "DZA-Suicide\n",
-      "Statistical=0.863, p=0.001\n",
-      "DZA-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "DZA-Alcohol per capita\n",
-      "Statistical=0.813, p=0.000\n",
-      "EGY-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "EGY-Renewable electricity\n",
-      "Statistical=0.827, p=0.000\n",
-      "EGY-Employment-agriculture\n",
-      "Statistical=0.658, p=0.000\n",
-      "EGY-Employment-industry\n",
-      "Statistical=0.944, p=0.108\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "EGY-Employment-services\n",
-      "Statistical=0.919, p=0.022\n",
-      "EGY-Exports-G&S\n",
-      "Statistical=0.952, p=0.181\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "EGY-Fertility rate\n",
-      "Statistical=0.919, p=0.022\n",
-      "EGY-Foreign investment\n",
-      "Statistical=0.855, p=0.001\n",
-      "EGY-GDP\n",
-      "Statistical=0.881, p=0.002\n",
-      "EGY-Education GExp\n",
-      "Statistical=0.874, p=0.002\n",
-      "EGY-Workers high education\n",
-      "Statistical=0.868, p=0.001\n",
-      "EGY-Literacy rate\n",
-      "Statistical=0.855, p=0.001\n",
-      "EGY-Net migration\n",
-      "Statistical=0.880, p=0.002\n",
-      "EGY-Mortality-infants\n",
-      "Statistical=0.784, p=0.000\n",
-      "EGY-Health services use\n",
-      "Statistical=0.827, p=0.000\n",
-      "EGY-R&D GExp\n",
-      "Statistical=0.926, p=0.034\n",
-      "EGY-Ninis\n",
-      "Statistical=0.814, p=0.000\n",
-      "EGY-Suicide\n",
-      "Statistical=0.763, p=0.000\n",
-      "EGY-International taxes\n",
-      "Statistical=0.804, p=0.000\n",
-      "EGY-Alcohol per capita\n",
-      "Statistical=0.823, p=0.000\n",
-      "LBY-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-Renewable electricity\n",
-      "Statistical=0.887, p=0.003\n",
-      "LBY-Employment-agriculture\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-Employment-industry\n",
-      "Statistical=0.917, p=0.020\n",
-      "LBY-Employment-services\n",
-      "Statistical=0.911, p=0.014\n",
-      "LBY-Exports-G&S\n",
-      "Statistical=0.869, p=0.001\n",
-      "LBY-Fertility rate\n",
-      "Statistical=0.865, p=0.001\n",
-      "LBY-Foreign investment\n",
-      "Statistical=0.831, p=0.000\n",
-      "LBY-GDP\n",
-      "Statistical=0.813, p=0.000\n",
-      "LBY-Education GExp\n",
-      "Statistical=0.975, p=0.673\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-Workers high education\n",
-      "Statistical=0.849, p=0.000\n",
-      "LBY-Literacy rate\n",
-      "Statistical=0.826, p=0.000\n",
-      "LBY-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-Mortality-infants\n",
-      "Statistical=0.722, p=0.000\n",
-      "LBY-Health services use\n",
-      "Statistical=0.954, p=0.195\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-R&D GExp\n",
-      "Statistical=0.946, p=0.122\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-Suicide\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBY-Alcohol per capita\n",
-      "Statistical=0.927, p=0.037\n",
-      "ISR-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ISR-Renewable electricity\n",
-      "Statistical=0.814, p=0.000\n",
-      "ISR-Employment-agriculture\n",
-      "Statistical=0.830, p=0.000\n",
-      "ISR-Employment-industry\n",
-      "Statistical=0.892, p=0.004\n",
-      "ISR-Employment-services\n",
-      "Statistical=0.850, p=0.000\n",
-      "ISR-Exports-G&S\n",
-      "Statistical=0.861, p=0.001\n",
-      "ISR-Fertility rate\n",
-      "Statistical=0.890, p=0.003\n",
-      "ISR-Foreign investment\n",
-      "Statistical=0.913, p=0.014\n",
-      "ISR-GDP\n",
-      "Statistical=0.962, p=0.307\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ISR-Education GExp\n",
-      "Statistical=0.855, p=0.001\n",
-      "ISR-Workers high education\n",
-      "Statistical=0.845, p=0.000\n",
-      "ISR-Literacy rate\n",
-      "Statistical=0.656, p=0.000\n",
-      "ISR-Net migration\n",
-      "Statistical=0.915, p=0.015\n",
-      "ISR-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ISR-Health services use\n",
-      "Statistical=0.909, p=0.010\n",
-      "ISR-R&D GExp\n",
-      "Statistical=0.898, p=0.005\n",
-      "ISR-Ninis\n",
-      "Statistical=0.537, p=0.000\n",
-      "ISR-Suicide\n",
-      "Statistical=0.818, p=0.000\n",
-      "ISR-International taxes\n",
-      "Statistical=0.680, p=0.000\n",
-      "ISR-Alcohol per capita\n",
-      "Statistical=0.844, p=0.000\n",
-      "TUR-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "TUR-Renewable electricity\n",
-      "Statistical=0.866, p=0.001\n",
-      "TUR-Employment-agriculture\n",
-      "Statistical=0.754, p=0.000\n",
-      "TUR-Employment-industry\n",
-      "Statistical=0.916, p=0.016\n",
-      "TUR-Employment-services\n",
-      "Statistical=0.895, p=0.005\n",
-      "TUR-Exports-G&S\n",
-      "Statistical=0.941, p=0.082\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "TUR-Fertility rate\n",
-      "Statistical=0.908, p=0.010\n",
-      "TUR-Foreign investment\n",
-      "Statistical=0.877, p=0.002\n",
-      "TUR-GDP\n",
-      "Statistical=0.878, p=0.002\n",
-      "TUR-Education GExp\n",
-      "Statistical=0.859, p=0.001\n",
-      "TUR-Workers high education\n",
-      "Statistical=0.861, p=0.001\n",
-      "TUR-Literacy rate\n",
-      "Statistical=0.845, p=0.000\n",
-      "TUR-Net migration\n",
-      "Statistical=0.755, p=0.000\n",
-      "TUR-Mortality-infants\n",
-      "Statistical=0.906, p=0.009\n",
-      "TUR-Health services use\n",
-      "Statistical=0.904, p=0.008\n",
-      "TUR-R&D GExp\n",
-      "Statistical=0.735, p=0.000\n",
-      "TUR-Ninis\n",
-      "Statistical=0.806, p=0.000\n",
-      "TUR-Suicide\n",
-      "Statistical=0.844, p=0.000\n",
-      "TUR-International taxes\n",
-      "Statistical=0.690, p=0.000\n",
-      "TUR-Alcohol per capita\n",
-      "Statistical=0.775, p=0.000\n",
-      "MAR-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MAR-Renewable electricity\n",
-      "Statistical=0.775, p=0.000\n",
-      "MAR-Employment-agriculture\n",
-      "Statistical=0.865, p=0.001\n",
-      "MAR-Employment-industry\n",
-      "Statistical=0.868, p=0.001\n",
-      "MAR-Employment-services\n",
-      "Statistical=0.878, p=0.002\n",
-      "MAR-Exports-G&S\n",
-      "Statistical=0.893, p=0.005\n",
-      "MAR-Fertility rate\n",
-      "Statistical=0.863, p=0.001\n",
-      "MAR-Foreign investment\n",
-      "Statistical=0.875, p=0.002\n",
-      "MAR-GDP\n",
-      "Statistical=0.799, p=0.000\n",
-      "MAR-Education GExp\n",
-      "Statistical=0.903, p=0.009\n",
-      "MAR-Workers high education\n",
-      "Statistical=0.861, p=0.001\n",
-      "MAR-Literacy rate\n",
-      "Statistical=0.856, p=0.001\n",
-      "MAR-Net migration\n",
-      "Statistical=0.931, p=0.046\n",
-      "MAR-Mortality-infants\n",
-      "Statistical=0.858, p=0.001\n",
-      "MAR-Health services use\n",
-      "Statistical=0.940, p=0.085\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MAR-R&D GExp\n",
-      "Statistical=0.919, p=0.023\n",
-      "MAR-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MAR-Suicide\n",
-      "Statistical=0.786, p=0.000\n",
-      "MAR-International taxes\n",
-      "Statistical=0.784, p=0.000\n",
-      "MAR-Alcohol per capita\n",
-      "Statistical=0.754, p=0.000\n",
-      "SEN-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SEN-Renewable electricity\n",
-      "Statistical=0.860, p=0.001\n",
-      "SEN-Employment-agriculture\n",
-      "Statistical=0.938, p=0.064\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SEN-Employment-industry\n",
-      "Statistical=0.892, p=0.004\n",
-      "SEN-Employment-services\n",
-      "Statistical=0.937, p=0.062\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SEN-Exports-G&S\n",
-      "Statistical=0.904, p=0.008\n",
-      "SEN-Fertility rate\n",
-      "Statistical=0.818, p=0.000\n",
-      "SEN-Foreign investment\n",
-      "Statistical=0.865, p=0.001\n",
-      "SEN-GDP\n",
-      "Statistical=0.942, p=0.084\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SEN-Education GExp\n",
-      "Statistical=0.884, p=0.002\n",
-      "SEN-Workers high education\n",
-      "Statistical=0.879, p=0.002\n",
-      "SEN-Literacy rate\n",
-      "Statistical=0.853, p=0.000\n",
-      "SEN-Net migration\n",
-      "Statistical=0.453, p=0.000\n",
-      "SEN-Mortality-infants\n",
-      "Statistical=0.792, p=0.000\n",
-      "SEN-Health services use\n",
-      "Statistical=0.935, p=0.054\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "SEN-R&D GExp\n",
-      "Statistical=0.815, p=0.000\n",
-      "SEN-Ninis\n",
-      "Statistical=0.546, p=0.000\n",
-      "SEN-Suicide\n",
-      "Statistical=0.689, p=0.000\n",
-      "SEN-International taxes\n",
-      "Statistical=0.895, p=0.005\n",
-      "SEN-Alcohol per capita\n",
-      "Statistical=0.850, p=0.000\n",
-      "ZAF-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ZAF-Renewable electricity\n",
-      "Statistical=0.839, p=0.000\n",
-      "ZAF-Employment-agriculture\n",
-      "Statistical=0.840, p=0.000\n",
-      "ZAF-Employment-industry\n",
-      "Statistical=0.832, p=0.000\n",
-      "ZAF-Employment-services\n",
-      "Statistical=0.910, p=0.011\n",
-      "ZAF-Exports-G&S\n",
-      "Statistical=0.871, p=0.001\n",
-      "ZAF-Fertility rate\n",
-      "Statistical=0.866, p=0.001\n",
-      "ZAF-Foreign investment\n",
-      "Statistical=0.861, p=0.001\n",
-      "ZAF-GDP\n",
-      "Statistical=0.834, p=0.000\n",
-      "ZAF-Education GExp\n",
-      "Statistical=0.968, p=0.458\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ZAF-Workers high education\n",
-      "Statistical=0.886, p=0.003\n",
-      "ZAF-Literacy rate\n",
-      "Statistical=0.855, p=0.001\n",
-      "ZAF-Net migration\n",
-      "Statistical=0.858, p=0.001\n",
-      "ZAF-Mortality-infants\n",
-      "Statistical=0.768, p=0.000\n",
-      "ZAF-Health services use\n",
-      "Statistical=0.863, p=0.001\n",
-      "ZAF-R&D GExp\n",
-      "Statistical=0.957, p=0.232\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ZAF-Ninis\n",
-      "Statistical=0.789, p=0.000\n",
-      "ZAF-Suicide\n",
-      "Statistical=0.885, p=0.003\n",
-      "ZAF-International taxes\n",
-      "Statistical=0.803, p=0.000\n",
-      "ZAF-Alcohol per capita\n",
-      "Statistical=0.846, p=0.000\n",
-      "LBR-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBR-Renewable electricity\n",
-      "Statistical=0.852, p=0.001\n",
-      "LBR-Employment-agriculture\n",
-      "Statistical=0.816, p=0.000\n",
-      "LBR-Employment-industry\n",
-      "Statistical=0.925, p=0.032\n",
-      "LBR-Employment-services\n",
-      "Statistical=0.877, p=0.002\n",
-      "LBR-Exports-G&S\n",
-      "Statistical=0.917, p=0.020\n",
-      "LBR-Fertility rate\n",
-      "Statistical=0.825, p=0.000\n",
-      "LBR-Foreign investment\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBR-GDP\n",
-      "Statistical=0.940, p=0.082\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBR-Education GExp\n",
-      "Statistical=0.542, p=0.000\n",
-      "LBR-Workers high education\n",
-      "Statistical=0.750, p=0.000\n",
-      "LBR-Literacy rate\n",
-      "Statistical=0.862, p=0.001\n",
-      "LBR-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBR-Mortality-infants\n",
-      "Statistical=0.705, p=0.000\n",
-      "LBR-Health services use\n",
-      "Statistical=0.848, p=0.000\n",
-      "LBR-R&D GExp\n",
-      "Statistical=0.946, p=0.120\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBR-Ninis\n",
-      "Statistical=0.671, p=0.000\n",
-      "LBR-Suicide\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBR-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "LBR-Alcohol per capita\n",
-      "Statistical=0.849, p=0.000\n",
-      "MOZ-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MOZ-Renewable electricity\n",
-      "Statistical=0.799, p=0.000\n",
-      "MOZ-Employment-agriculture\n",
-      "Statistical=0.864, p=0.001\n",
-      "MOZ-Employment-industry\n",
-      "Statistical=0.900, p=0.007\n",
-      "MOZ-Employment-services\n",
-      "Statistical=0.843, p=0.000\n",
-      "MOZ-Exports-G&S\n",
-      "Statistical=0.920, p=0.023\n",
-      "MOZ-Fertility rate\n",
-      "Statistical=0.758, p=0.000\n",
-      "MOZ-Foreign investment\n",
-      "Statistical=0.885, p=0.003\n",
-      "MOZ-GDP\n",
-      "Statistical=0.943, p=0.098\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MOZ-Education GExp\n",
-      "Statistical=0.718, p=0.000\n",
-      "MOZ-Workers high education\n",
-      "Statistical=0.912, p=0.015\n",
-      "MOZ-Literacy rate\n",
-      "Statistical=0.850, p=0.000\n",
-      "MOZ-Net migration\n",
-      "Statistical=0.633, p=0.000\n",
-      "MOZ-Mortality-infants\n",
-      "Statistical=0.901, p=0.008\n",
-      "MOZ-Health services use\n",
-      "Statistical=0.870, p=0.001\n",
-      "MOZ-R&D GExp\n",
-      "Statistical=0.873, p=0.002\n",
-      "MOZ-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MOZ-Suicide\n",
-      "Statistical=0.831, p=0.000\n",
-      "MOZ-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MOZ-Alcohol per capita\n",
-      "Statistical=0.788, p=0.000\n",
-      "CMR-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CMR-Renewable electricity\n",
-      "Statistical=0.932, p=0.046\n",
-      "CMR-Employment-agriculture\n",
-      "Statistical=0.844, p=0.000\n",
-      "CMR-Employment-industry\n",
-      "Statistical=0.845, p=0.000\n",
-      "CMR-Employment-services\n",
-      "Statistical=0.818, p=0.000\n",
-      "CMR-Exports-G&S\n",
-      "Statistical=0.858, p=0.001\n",
-      "CMR-Fertility rate\n",
-      "Statistical=0.921, p=0.022\n",
-      "CMR-Foreign investment\n",
-      "Statistical=0.871, p=0.001\n",
-      "CMR-GDP\n",
-      "Statistical=0.967, p=0.427\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CMR-Education GExp\n",
-      "Statistical=0.869, p=0.001\n",
-      "CMR-Workers high education\n",
-      "Statistical=0.877, p=0.002\n",
-      "CMR-Literacy rate\n",
-      "Statistical=0.827, p=0.000\n",
-      "CMR-Net migration\n",
-      "Statistical=0.908, p=0.010\n",
-      "CMR-Mortality-infants\n",
-      "Statistical=0.756, p=0.000\n",
-      "CMR-Health services use\n",
-      "Statistical=0.855, p=0.001\n",
-      "CMR-R&D GExp\n",
-      "Statistical=0.815, p=0.000\n",
-      "CMR-Ninis\n",
-      "Statistical=0.643, p=0.000\n",
-      "CMR-Suicide\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CMR-International taxes\n",
-      "Statistical=0.703, p=0.000\n",
-      "CMR-Alcohol per capita\n",
-      "Statistical=0.815, p=0.000\n",
-      "NGA-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NGA-Renewable electricity\n",
-      "Statistical=0.741, p=0.000\n",
-      "NGA-Employment-agriculture\n",
-      "Statistical=0.446, p=0.000\n",
-      "NGA-Employment-industry\n",
-      "Statistical=0.879, p=0.002\n",
-      "NGA-Employment-services\n",
-      "Statistical=0.941, p=0.082\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NGA-Exports-G&S\n",
-      "Statistical=0.883, p=0.002\n",
-      "NGA-Fertility rate\n",
-      "Statistical=0.945, p=0.105\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NGA-Foreign investment\n",
-      "Statistical=0.898, p=0.005\n",
-      "NGA-GDP\n",
-      "Statistical=0.962, p=0.305\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NGA-Education GExp\n",
-      "Statistical=0.885, p=0.003\n",
-      "NGA-Workers high education\n",
-      "Statistical=0.857, p=0.001\n",
-      "NGA-Literacy rate\n",
-      "Statistical=0.835, p=0.000\n",
-      "NGA-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NGA-Mortality-infants\n",
-      "Statistical=0.868, p=0.001\n",
-      "NGA-Health services use\n",
-      "Statistical=0.956, p=0.214\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NGA-R&D GExp\n",
-      "Statistical=0.749, p=0.000\n",
-      "NGA-Ninis\n",
-      "Statistical=0.743, p=0.000\n",
-      "NGA-Suicide\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NGA-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "NGA-Alcohol per capita\n",
-      "Statistical=0.771, p=0.000\n",
-      "GHA-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GHA-Renewable electricity\n",
-      "Statistical=0.865, p=0.001\n",
-      "GHA-Employment-agriculture\n",
-      "Statistical=0.827, p=0.000\n",
-      "GHA-Employment-industry\n",
-      "Statistical=0.745, p=0.000\n",
-      "GHA-Employment-services\n",
-      "Statistical=0.935, p=0.058\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GHA-Exports-G&S\n",
-      "Statistical=0.783, p=0.000\n",
-      "GHA-Fertility rate\n",
-      "Statistical=0.892, p=0.005\n",
-      "GHA-Foreign investment\n",
-      "Statistical=0.823, p=0.000\n",
-      "GHA-GDP\n",
-      "Statistical=0.958, p=0.265\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GHA-Education GExp\n",
-      "Statistical=0.759, p=0.000\n",
-      "GHA-Workers high education\n",
-      "Statistical=0.801, p=0.000\n",
-      "GHA-Literacy rate\n",
-      "Statistical=0.839, p=0.000\n",
-      "GHA-Net migration\n",
-      "Statistical=0.770, p=0.000\n",
-      "GHA-Mortality-infants\n",
-      "Statistical=0.841, p=0.000\n",
-      "GHA-Health services use\n",
-      "Statistical=0.909, p=0.012\n",
-      "GHA-R&D GExp\n",
-      "Statistical=0.960, p=0.288\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GHA-Ninis\n",
-      "Statistical=0.947, p=0.129\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GHA-Suicide\n",
-      "Statistical=0.656, p=0.000\n",
-      "GHA-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "GHA-Alcohol per capita\n",
-      "Statistical=0.850, p=0.001\n",
-      "BGD-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BGD-Renewable electricity\n",
-      "Statistical=0.808, p=0.000\n",
-      "BGD-Employment-agriculture\n",
-      "Statistical=0.978, p=0.734\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BGD-Employment-industry\n",
-      "Statistical=0.885, p=0.003\n",
-      "BGD-Employment-services\n",
-      "Statistical=0.899, p=0.006\n",
-      "BGD-Exports-G&S\n",
-      "Statistical=0.895, p=0.005\n",
-      "BGD-Fertility rate\n",
-      "Statistical=0.827, p=0.000\n",
-      "BGD-Foreign investment\n",
-      "Statistical=0.855, p=0.001\n",
-      "BGD-GDP\n",
-      "Statistical=0.904, p=0.008\n",
-      "BGD-Education GExp\n",
-      "Statistical=0.862, p=0.001\n",
-      "BGD-Workers high education\n",
-      "Statistical=0.814, p=0.000\n",
-      "BGD-Literacy rate\n",
-      "Statistical=0.853, p=0.000\n",
-      "BGD-Net migration\n",
-      "Statistical=0.761, p=0.000\n",
-      "BGD-Mortality-infants\n",
-      "Statistical=0.894, p=0.004\n",
-      "BGD-Health services use\n",
-      "Statistical=0.917, p=0.017\n",
-      "BGD-R&D GExp\n",
-      "Statistical=0.922, p=0.023\n",
-      "BGD-Ninis\n",
-      "Statistical=0.788, p=0.000\n",
-      "BGD-Suicide\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BGD-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BGD-Alcohol per capita\n",
-      "Statistical=0.836, p=0.000\n",
-      "IND-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IND-Renewable electricity\n",
-      "Statistical=0.743, p=0.000\n",
-      "IND-Employment-agriculture\n",
-      "Statistical=0.892, p=0.005\n",
-      "IND-Employment-industry\n",
-      "Statistical=0.892, p=0.005\n",
-      "IND-Employment-services\n",
-      "Statistical=0.843, p=0.000\n",
-      "IND-Exports-G&S\n",
-      "Statistical=0.922, p=0.027\n",
-      "IND-Fertility rate\n",
-      "Statistical=0.848, p=0.000\n",
-      "IND-Foreign investment\n",
-      "Statistical=0.832, p=0.000\n",
-      "IND-GDP\n",
-      "Statistical=0.930, p=0.044\n",
-      "IND-Education GExp\n",
-      "Statistical=0.829, p=0.000\n",
-      "IND-Workers high education\n",
-      "Statistical=0.858, p=0.001\n",
-      "IND-Literacy rate\n",
-      "Statistical=0.854, p=0.001\n",
-      "IND-Net migration\n",
-      "Statistical=0.884, p=0.003\n",
-      "IND-Mortality-infants\n",
-      "Statistical=0.784, p=0.000\n",
-      "IND-Health services use\n",
-      "Statistical=0.939, p=0.078\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IND-R&D GExp\n",
-      "Statistical=0.793, p=0.000\n",
-      "IND-Ninis\n",
-      "Statistical=0.894, p=0.005\n",
-      "IND-Suicide\n",
-      "Statistical=0.909, p=0.012\n",
-      "IND-International taxes\n",
-      "Statistical=0.781, p=0.000\n",
-      "IND-Alcohol per capita\n",
-      "Statistical=0.855, p=0.001\n",
-      "VNM-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VNM-Renewable electricity\n",
-      "Statistical=0.774, p=0.000\n",
-      "VNM-Employment-agriculture\n",
-      "Statistical=0.695, p=0.000\n",
-      "VNM-Employment-industry\n",
-      "Statistical=0.907, p=0.010\n",
-      "VNM-Employment-services\n",
-      "Statistical=0.905, p=0.008\n",
-      "VNM-Exports-G&S\n",
-      "Statistical=0.905, p=0.008\n",
-      "VNM-Fertility rate\n",
-      "Statistical=0.855, p=0.001\n",
-      "VNM-Foreign investment\n",
-      "Statistical=0.806, p=0.000\n",
-      "VNM-GDP\n",
-      "Statistical=0.727, p=0.000\n",
-      "VNM-Education GExp\n",
-      "Statistical=0.814, p=0.000\n",
-      "VNM-Workers high education\n",
-      "Statistical=0.855, p=0.001\n",
-      "VNM-Literacy rate\n",
-      "Statistical=0.853, p=0.000\n",
-      "VNM-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VNM-Mortality-infants\n",
-      "Statistical=0.846, p=0.000\n",
-      "VNM-Health services use\n",
-      "Statistical=0.694, p=0.000\n",
-      "VNM-R&D GExp\n",
-      "Statistical=0.881, p=0.002\n",
-      "VNM-Ninis\n",
-      "Statistical=0.688, p=0.000\n",
-      "VNM-Suicide\n",
-      "Statistical=0.631, p=0.000\n",
-      "VNM-International taxes\n",
-      "Statistical=0.772, p=0.000\n",
-      "VNM-Alcohol per capita\n",
-      "Statistical=0.780, p=0.000\n",
-      "THA-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "THA-Renewable electricity\n",
-      "Statistical=0.791, p=0.000\n",
-      "THA-Employment-agriculture\n",
-      "Statistical=0.980, p=0.810\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "THA-Employment-industry\n",
-      "Statistical=0.929, p=0.040\n",
-      "THA-Employment-services\n",
-      "Statistical=0.945, p=0.117\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "THA-Exports-G&S\n",
-      "Statistical=0.928, p=0.039\n",
-      "THA-Fertility rate\n",
-      "Statistical=0.863, p=0.001\n",
-      "THA-Foreign investment\n",
-      "Statistical=0.879, p=0.002\n",
-      "THA-GDP\n",
-      "Statistical=0.790, p=0.000\n",
-      "THA-Education GExp\n",
-      "Statistical=0.935, p=0.059\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "THA-Workers high education\n",
-      "Statistical=0.883, p=0.003\n",
-      "THA-Literacy rate\n",
-      "Statistical=0.849, p=0.000\n",
-      "THA-Net migration\n",
-      "Statistical=0.956, p=0.231\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "THA-Mortality-infants\n",
-      "Statistical=0.823, p=0.000\n",
-      "THA-Health services use\n",
-      "Statistical=0.905, p=0.010\n",
-      "THA-R&D GExp\n",
-      "Statistical=0.774, p=0.000\n",
-      "THA-Ninis\n",
-      "Statistical=0.787, p=0.000\n",
-      "THA-Suicide\n",
-      "Statistical=0.825, p=0.000\n",
-      "THA-International taxes\n",
-      "Statistical=0.806, p=0.000\n",
-      "THA-Alcohol per capita\n",
-      "Statistical=0.788, p=0.000\n",
-      "IDN-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IDN-Renewable electricity\n",
-      "Statistical=0.810, p=0.000\n",
-      "IDN-Employment-agriculture\n",
-      "Statistical=0.885, p=0.003\n",
-      "IDN-Employment-industry\n",
-      "Statistical=0.946, p=0.108\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IDN-Employment-services\n",
-      "Statistical=0.924, p=0.027\n",
-      "IDN-Exports-G&S\n",
-      "Statistical=0.950, p=0.147\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IDN-Fertility rate\n",
-      "Statistical=0.888, p=0.003\n",
-      "IDN-Foreign investment\n",
-      "Statistical=0.877, p=0.002\n",
-      "IDN-GDP\n",
-      "Statistical=0.919, p=0.020\n",
-      "IDN-Education GExp\n",
-      "Statistical=0.922, p=0.024\n",
-      "IDN-Workers high education\n",
-      "Statistical=0.830, p=0.000\n",
-      "IDN-Literacy rate\n",
-      "Statistical=0.853, p=0.000\n",
-      "IDN-Net migration\n",
-      "Statistical=0.879, p=0.002\n",
-      "IDN-Mortality-infants\n",
-      "Statistical=0.785, p=0.000\n",
-      "IDN-Health services use\n",
-      "Statistical=0.951, p=0.149\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "IDN-R&D GExp\n",
-      "Statistical=0.893, p=0.004\n",
-      "IDN-Ninis\n",
-      "Statistical=0.835, p=0.000\n",
-      "IDN-Suicide\n",
-      "Statistical=0.678, p=0.000\n",
-      "IDN-International taxes\n",
-      "Statistical=0.874, p=0.001\n",
-      "IDN-Alcohol per capita\n",
-      "Statistical=0.804, p=0.000\n",
-      "PHL-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-Renewable electricity\n",
-      "Statistical=0.845, p=0.000\n",
-      "PHL-Employment-agriculture\n",
-      "Statistical=0.960, p=0.271\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-Employment-industry\n",
-      "Statistical=0.946, p=0.112\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-Employment-services\n",
-      "Statistical=0.900, p=0.006\n",
-      "PHL-Exports-G&S\n",
-      "Statistical=0.945, p=0.106\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-Fertility rate\n",
-      "Statistical=0.880, p=0.002\n",
-      "PHL-Foreign investment\n",
-      "Statistical=0.943, p=0.089\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-GDP\n",
-      "Statistical=0.943, p=0.089\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-Education GExp\n",
-      "Statistical=0.981, p=0.832\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-Workers high education\n",
-      "Statistical=0.849, p=0.000\n",
-      "PHL-Literacy rate\n",
-      "Statistical=0.805, p=0.000\n",
-      "PHL-Net migration\n",
-      "Statistical=0.856, p=0.001\n",
-      "PHL-Mortality-infants\n",
-      "Statistical=0.914, p=0.014\n",
-      "PHL-Health services use\n",
-      "Statistical=0.982, p=0.854\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-R&D GExp\n",
-      "Statistical=0.949, p=0.136\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PHL-Ninis\n",
-      "Statistical=0.617, p=0.000\n",
-      "PHL-Suicide\n",
-      "Statistical=0.874, p=0.001\n",
-      "PHL-International taxes\n",
-      "Statistical=0.846, p=0.000\n",
-      "PHL-Alcohol per capita\n",
-      "Statistical=0.891, p=0.004\n",
-      "KOR-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "KOR-Renewable electricity\n",
-      "Statistical=0.778, p=0.000\n",
-      "KOR-Employment-agriculture\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "KOR-Employment-industry\n",
-      "Statistical=0.910, p=0.011\n",
-      "KOR-Employment-services\n",
-      "Statistical=0.819, p=0.000\n",
-      "KOR-Exports-G&S\n",
-      "Statistical=0.864, p=0.001\n",
-      "KOR-Fertility rate\n",
-      "Statistical=0.891, p=0.004\n",
-      "KOR-Foreign investment\n",
-      "Statistical=0.870, p=0.001\n",
-      "KOR-GDP\n",
-      "Statistical=0.953, p=0.176\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "KOR-Education GExp\n",
-      "Statistical=0.904, p=0.008\n",
-      "KOR-Workers high education\n",
-      "Statistical=0.914, p=0.014\n",
-      "KOR-Literacy rate\n",
-      "Statistical=0.731, p=0.000\n",
-      "KOR-Net migration\n",
-      "Statistical=0.963, p=0.335\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "KOR-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "KOR-Health services use\n",
-      "Statistical=0.841, p=0.000\n",
-      "KOR-R&D GExp\n",
-      "Statistical=0.798, p=0.000\n",
-      "KOR-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "KOR-Suicide\n",
-      "Statistical=0.828, p=0.000\n",
-      "KOR-International taxes\n",
-      "Statistical=0.746, p=0.000\n",
-      "KOR-Alcohol per capita\n",
-      "Statistical=0.847, p=0.000\n",
-      "MEX-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Renewable electricity\n",
-      "Statistical=0.734, p=0.000\n",
-      "MEX-Employment-agriculture\n",
-      "Statistical=0.964, p=0.361\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Employment-industry\n",
-      "Statistical=0.852, p=0.001\n",
-      "MEX-Employment-services\n",
-      "Statistical=0.960, p=0.291\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Exports-G&S\n",
-      "Statistical=0.872, p=0.002\n",
-      "MEX-Fertility rate\n",
-      "Statistical=0.951, p=0.169\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Foreign investment\n",
-      "Statistical=0.927, p=0.036\n",
-      "MEX-GDP\n",
-      "Statistical=0.929, p=0.042\n",
-      "MEX-Education GExp\n",
-      "Statistical=0.967, p=0.445\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Workers high education\n",
-      "Statistical=0.928, p=0.039\n",
-      "MEX-Literacy rate\n",
-      "Statistical=0.856, p=0.001\n",
-      "MEX-Net migration\n",
-      "Statistical=0.926, p=0.035\n",
-      "MEX-Mortality-infants\n",
-      "Statistical=0.931, p=0.046\n",
-      "MEX-Health services use\n",
-      "Statistical=0.927, p=0.036\n",
-      "MEX-R&D GExp\n",
-      "Statistical=0.780, p=0.000\n",
-      "MEX-Ninis\n",
-      "Statistical=0.751, p=0.000\n",
-      "MEX-Suicide\n",
-      "Statistical=0.928, p=0.038\n",
-      "MEX-International taxes\n",
-      "Statistical=0.862, p=0.001\n",
-      "MEX-Alcohol per capita\n",
-      "Statistical=0.846, p=0.000\n",
-      "BRA-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BRA-Renewable electricity\n",
-      "Statistical=0.824, p=0.000\n",
-      "BRA-Employment-agriculture\n",
-      "Statistical=0.875, p=0.002\n",
-      "BRA-Employment-industry\n",
-      "Statistical=0.904, p=0.008\n",
-      "BRA-Employment-services\n",
-      "Statistical=0.890, p=0.003\n",
-      "BRA-Exports-G&S\n",
-      "Statistical=0.932, p=0.045\n",
-      "BRA-Fertility rate\n",
-      "Statistical=0.846, p=0.000\n",
-      "BRA-Foreign investment\n",
-      "Statistical=0.866, p=0.001\n",
-      "BRA-GDP\n",
-      "Statistical=0.861, p=0.001\n",
-      "BRA-Education GExp\n",
-      "Statistical=0.937, p=0.062\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BRA-Workers high education\n",
-      "Statistical=0.907, p=0.009\n",
-      "BRA-Literacy rate\n",
-      "Statistical=0.856, p=0.001\n",
-      "BRA-Net migration\n",
-      "Statistical=0.672, p=0.000\n",
-      "BRA-Mortality-infants\n",
-      "Statistical=0.785, p=0.000\n",
-      "BRA-Health services use\n",
-      "Statistical=0.861, p=0.001\n",
-      "BRA-R&D GExp\n",
-      "Statistical=0.679, p=0.000\n",
-      "BRA-Ninis\n",
-      "Statistical=0.834, p=0.000\n",
-      "BRA-Suicide\n",
-      "Statistical=0.906, p=0.009\n",
-      "BRA-International taxes\n",
-      "Statistical=0.797, p=0.000\n",
-      "BRA-Alcohol per capita\n",
-      "Statistical=0.842, p=0.000\n",
-      "ARG-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARG-Renewable electricity\n",
-      "Statistical=0.767, p=0.000\n",
-      "ARG-Employment-agriculture\n",
-      "Statistical=0.879, p=0.002\n",
-      "ARG-Employment-industry\n",
-      "Statistical=0.956, p=0.227\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARG-Employment-services\n",
-      "Statistical=0.915, p=0.017\n",
-      "ARG-Exports-G&S\n",
-      "Statistical=0.932, p=0.048\n",
-      "ARG-Fertility rate\n",
-      "Statistical=0.864, p=0.001\n",
-      "ARG-Foreign investment\n",
-      "Statistical=0.907, p=0.011\n",
-      "ARG-GDP\n",
-      "Statistical=0.880, p=0.002\n",
-      "ARG-Education GExp\n",
-      "Statistical=0.898, p=0.006\n",
-      "ARG-Workers high education\n",
-      "Statistical=0.931, p=0.047\n",
-      "ARG-Literacy rate\n",
-      "Statistical=0.827, p=0.000\n",
-      "ARG-Net migration\n",
-      "Statistical=0.776, p=0.000\n",
-      "ARG-Mortality-infants\n",
-      "Statistical=0.745, p=0.000\n",
-      "ARG-Health services use\n",
-      "Statistical=0.954, p=0.204\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARG-R&D GExp\n",
-      "Statistical=0.737, p=0.000\n",
-      "ARG-Ninis\n",
-      "Statistical=0.772, p=0.000\n",
-      "ARG-Suicide\n",
-      "Statistical=0.843, p=0.000\n",
-      "ARG-International taxes\n",
-      "Statistical=0.871, p=0.001\n",
-      "ARG-Alcohol per capita\n",
-      "Statistical=0.909, p=0.012\n",
-      "PER-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PER-Renewable electricity\n",
-      "Statistical=0.918, p=0.018\n",
-      "PER-Employment-agriculture\n",
-      "Statistical=0.866, p=0.001\n",
-      "PER-Employment-industry\n",
-      "Statistical=0.811, p=0.000\n",
-      "PER-Employment-services\n",
-      "Statistical=0.951, p=0.154\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PER-Exports-G&S\n",
-      "Statistical=0.904, p=0.008\n",
-      "PER-Fertility rate\n",
-      "Statistical=0.861, p=0.001\n",
-      "PER-Foreign investment\n",
-      "Statistical=0.848, p=0.000\n",
-      "PER-GDP\n",
-      "Statistical=0.905, p=0.008\n",
-      "PER-Education GExp\n",
-      "Statistical=0.915, p=0.015\n",
-      "PER-Workers high education\n",
-      "Statistical=0.845, p=0.000\n",
-      "PER-Literacy rate\n",
-      "Statistical=0.858, p=0.001\n",
-      "PER-Net migration\n",
-      "Statistical=0.936, p=0.058\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PER-Mortality-infants\n",
-      "Statistical=0.772, p=0.000\n",
-      "PER-Health services use\n",
-      "Statistical=0.865, p=0.001\n",
-      "PER-R&D GExp\n",
-      "Statistical=0.919, p=0.019\n",
-      "PER-Ninis\n",
-      "Statistical=0.854, p=0.001\n",
-      "PER-Suicide\n",
-      "Statistical=0.873, p=0.001\n",
-      "PER-International taxes\n",
-      "Statistical=0.770, p=0.000\n",
-      "PER-Alcohol per capita\n",
-      "Statistical=0.924, p=0.026\n",
-      "VEN-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Renewable electricity\n",
-      "Statistical=0.683, p=0.000\n",
-      "VEN-Employment-agriculture\n",
-      "Statistical=0.407, p=0.000\n",
-      "VEN-Employment-industry\n",
-      "Statistical=0.909, p=0.012\n",
-      "VEN-Employment-services\n",
-      "Statistical=0.959, p=0.272\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Exports-G&S\n",
-      "Statistical=0.958, p=0.263\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Fertility rate\n",
-      "Statistical=0.946, p=0.119\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Foreign investment\n",
-      "Statistical=0.874, p=0.002\n",
-      "VEN-GDP\n",
-      "Statistical=0.936, p=0.066\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Education GExp\n",
-      "Statistical=0.941, p=0.087\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Workers high education\n",
-      "Statistical=0.818, p=0.000\n",
-      "VEN-Literacy rate\n",
-      "Statistical=0.781, p=0.000\n",
-      "VEN-Net migration\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Mortality-infants\n",
-      "Statistical=0.834, p=0.000\n",
-      "VEN-Health services use\n",
-      "Statistical=0.914, p=0.016\n",
-      "VEN-R&D GExp\n",
-      "Statistical=0.775, p=0.000\n",
-      "VEN-Ninis\n",
-      "Statistical=0.759, p=0.000\n",
-      "VEN-Suicide\n",
-      "Statistical=0.716, p=0.000\n",
-      "VEN-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Alcohol per capita\n",
-      "Statistical=0.826, p=0.000\n",
-      "COL-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "COL-Renewable electricity\n",
-      "Statistical=0.900, p=0.006\n",
-      "COL-Employment-agriculture\n",
-      "Statistical=0.898, p=0.006\n",
-      "COL-Employment-industry\n",
-      "Statistical=0.883, p=0.002\n",
-      "COL-Employment-services\n",
-      "Statistical=0.889, p=0.003\n",
-      "COL-Exports-G&S\n",
-      "Statistical=0.925, p=0.028\n",
-      "COL-Fertility rate\n",
-      "Statistical=0.851, p=0.000\n",
-      "COL-Foreign investment\n",
-      "Statistical=0.886, p=0.003\n",
-      "COL-GDP\n",
-      "Statistical=0.901, p=0.007\n",
-      "COL-Education GExp\n",
-      "Statistical=0.907, p=0.010\n",
-      "COL-Workers high education\n",
-      "Statistical=0.880, p=0.002\n",
-      "COL-Literacy rate\n",
-      "Statistical=0.855, p=0.001\n",
-      "COL-Net migration\n",
-      "Statistical=0.826, p=0.000\n",
-      "COL-Mortality-infants\n",
-      "Statistical=0.927, p=0.033\n",
-      "COL-Health services use\n",
-      "Statistical=0.921, p=0.023\n",
-      "COL-R&D GExp\n",
-      "Statistical=0.797, p=0.000\n",
-      "COL-Ninis\n",
-      "Statistical=0.827, p=0.000\n",
-      "COL-Suicide\n",
-      "Statistical=0.907, p=0.009\n",
-      "COL-International taxes\n",
-      "Statistical=0.798, p=0.000\n",
-      "COL-Alcohol per capita\n",
-      "Statistical=0.832, p=0.000\n",
-      "CHL-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Renewable electricity\n",
-      "Statistical=0.771, p=0.000\n",
-      "CHL-Employment-agriculture\n",
-      "Statistical=0.933, p=0.053\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Employment-industry\n",
-      "Statistical=0.925, p=0.033\n",
-      "CHL-Employment-services\n",
-      "Statistical=0.843, p=0.000\n",
-      "CHL-Exports-G&S\n",
-      "Statistical=0.921, p=0.025\n",
-      "CHL-Fertility rate\n",
-      "Statistical=0.924, p=0.031\n",
-      "CHL-Foreign investment\n",
-      "Statistical=0.847, p=0.000\n",
-      "CHL-GDP\n",
-      "Statistical=0.912, p=0.015\n",
-      "CHL-Education GExp\n",
-      "Statistical=0.963, p=0.348\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Workers high education\n",
-      "Statistical=0.865, p=0.001\n",
-      "CHL-Literacy rate\n",
-      "Statistical=0.835, p=0.000\n",
-      "CHL-Net migration\n",
-      "Statistical=0.867, p=0.001\n",
-      "CHL-Mortality-infants\n",
-      "Statistical=0.851, p=0.001\n",
-      "CHL-Health services use\n",
-      "Statistical=0.848, p=0.000\n",
-      "CHL-R&D GExp\n",
-      "Statistical=0.833, p=0.000\n",
-      "CHL-Ninis\n",
-      "Statistical=0.984, p=0.910\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Suicide\n",
-      "Statistical=0.728, p=0.000\n",
-      "CHL-International taxes\n",
-      "Statistical=0.903, p=0.009\n",
-      "CHL-Alcohol per capita\n",
-      "Statistical=0.925, p=0.032\n",
-      "PAN-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PAN-Renewable electricity\n",
-      "Statistical=0.759, p=0.000\n",
-      "PAN-Employment-agriculture\n",
-      "Statistical=0.910, p=0.011\n",
-      "PAN-Employment-industry\n",
-      "Statistical=0.932, p=0.043\n",
-      "PAN-Employment-services\n",
-      "Statistical=0.949, p=0.134\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PAN-Exports-G&S\n",
-      "Statistical=0.924, p=0.027\n",
-      "PAN-Fertility rate\n",
-      "Statistical=0.846, p=0.000\n",
-      "PAN-Foreign investment\n",
-      "Statistical=0.858, p=0.001\n",
-      "PAN-GDP\n",
-      "Statistical=0.970, p=0.510\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PAN-Education GExp\n",
-      "Statistical=0.866, p=0.001\n",
-      "PAN-Workers high education\n",
-      "Statistical=0.839, p=0.000\n",
-      "PAN-Literacy rate\n",
-      "Statistical=0.850, p=0.000\n",
-      "PAN-Net migration\n",
-      "Statistical=0.541, p=0.000\n",
-      "PAN-Mortality-infants\n",
-      "Statistical=0.942, p=0.086\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PAN-Health services use\n",
-      "Statistical=0.922, p=0.023\n",
-      "PAN-R&D GExp\n",
-      "Statistical=0.863, p=0.001\n",
-      "PAN-Ninis\n",
-      "Statistical=0.874, p=0.001\n",
-      "PAN-Suicide\n",
-      "Statistical=0.911, p=0.012\n",
-      "PAN-International taxes\n",
-      "Statistical=0.928, p=0.033\n",
-      "PAN-Alcohol per capita\n",
-      "Statistical=0.812, p=0.000\n",
-      "CRI-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Renewable electricity\n",
-      "Statistical=0.764, p=0.000\n",
-      "CRI-Employment-agriculture\n",
-      "Statistical=0.941, p=0.086\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Employment-industry\n",
-      "Statistical=0.904, p=0.009\n",
-      "CRI-Employment-services\n",
-      "Statistical=0.951, p=0.163\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Exports-G&S\n",
-      "Statistical=0.938, p=0.071\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Fertility rate\n",
-      "Statistical=0.913, p=0.016\n",
-      "CRI-Foreign investment\n",
-      "Statistical=0.927, p=0.037\n",
-      "CRI-GDP\n",
-      "Statistical=0.856, p=0.001\n",
-      "CRI-Education GExp\n",
-      "Statistical=0.888, p=0.004\n",
-      "CRI-Workers high education\n",
-      "Statistical=0.861, p=0.001\n",
-      "CRI-Literacy rate\n",
-      "Statistical=0.852, p=0.001\n",
-      "CRI-Net migration\n",
-      "Statistical=0.821, p=0.000\n",
-      "CRI-Mortality-infants\n",
-      "Statistical=0.816, p=0.000\n",
-      "CRI-Health services use\n",
-      "Statistical=0.915, p=0.017\n",
-      "CRI-R&D GExp\n",
-      "Statistical=0.838, p=0.000\n",
-      "CRI-Ninis\n",
-      "Statistical=0.830, p=0.000\n",
-      "CRI-Suicide\n",
-      "Statistical=0.929, p=0.042\n",
-      "CRI-International taxes\n",
-      "Statistical=0.927, p=0.037\n",
-      "CRI-Alcohol per capita\n",
-      "Statistical=0.914, p=0.017\n",
-      "USA-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "USA-Renewable electricity\n",
-      "Statistical=0.831, p=0.000\n",
-      "USA-Employment-agriculture\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "USA-Employment-industry\n",
-      "Statistical=0.853, p=0.000\n",
-      "USA-Employment-services\n",
-      "Statistical=0.836, p=0.000\n",
-      "USA-Exports-G&S\n",
-      "Statistical=0.833, p=0.000\n",
-      "USA-Fertility rate\n",
-      "Statistical=0.901, p=0.007\n",
-      "USA-Foreign investment\n",
-      "Statistical=0.901, p=0.006\n",
-      "USA-GDP\n",
-      "Statistical=0.890, p=0.004\n",
-      "USA-Education GExp\n",
-      "Statistical=0.953, p=0.176\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "USA-Workers high education\n",
-      "Statistical=0.943, p=0.092\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "USA-Literacy rate\n",
-      "Statistical=0.690, p=0.000\n",
-      "USA-Net migration\n",
-      "Statistical=0.838, p=0.000\n",
-      "USA-Mortality-infants\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "USA-Health services use\n",
-      "Statistical=0.937, p=0.062\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "USA-R&D GExp\n",
-      "Statistical=0.939, p=0.071\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "USA-Ninis\n",
-      "Statistical=0.939, p=0.068\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "USA-Suicide\n",
-      "Statistical=0.884, p=0.002\n",
-      "USA-International taxes\n",
-      "Statistical=0.764, p=0.000\n",
-      "USA-Alcohol per capita\n",
-      "Statistical=0.835, p=0.000\n",
-      "CHN-Exports-Commercial services\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHN-Renewable electricity\n",
-      "Statistical=0.717, p=0.000\n",
-      "CHN-Employment-agriculture\n",
-      "Statistical=0.929, p=0.042\n",
-      "CHN-Employment-industry\n",
-      "Statistical=0.923, p=0.028\n",
-      "CHN-Employment-services\n",
-      "Statistical=0.878, p=0.002\n",
-      "CHN-Exports-G&S\n",
-      "Statistical=0.955, p=0.209\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHN-Fertility rate\n",
-      "Statistical=0.897, p=0.006\n",
-      "CHN-Foreign investment\n",
-      "Statistical=0.839, p=0.000\n",
-      "CHN-GDP\n",
-      "Statistical=0.876, p=0.002\n",
-      "CHN-Education GExp\n",
-      "Statistical=0.933, p=0.054\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHN-Workers high education\n",
-      "Statistical=0.824, p=0.000\n",
-      "CHN-Literacy rate\n",
-      "Statistical=0.846, p=0.000\n",
-      "CHN-Net migration\n",
-      "Statistical=0.718, p=0.000\n",
-      "CHN-Mortality-infants\n",
-      "Statistical=0.858, p=0.001\n",
-      "CHN-Health services use\n",
-      "Statistical=0.896, p=0.006\n",
-      "CHN-R&D GExp\n",
-      "Statistical=0.874, p=0.002\n",
-      "CHN-Ninis\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHN-Suicide\n",
-      "Statistical=0.867, p=0.001\n",
-      "CHN-International taxes\n",
-      "Statistical=0.704, p=0.000\n",
-      "CHN-Alcohol per capita\n",
-      "Statistical=0.822, p=0.000\n"
-     ]
+     "data": {
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Does it have some global casuallity implied?</th>\n",
+       "      <th>% of count (Global)</th>\n",
+       "      <th>Does it have some European casuallity implied?</th>\n",
+       "      <th>% of count (European)</th>\n",
+       "      <th>Does it have some North African casuallity implied?</th>\n",
+       "      <th>% of count (North African)</th>\n",
+       "      <th>Does it have some Asian casuallity implied?</th>\n",
+       "      <th>% of count (Asian)</th>\n",
+       "      <th>Does it have some Pair casuallity implied?</th>\n",
+       "      <th>% of count (Pair)</th>\n",
+       "      <th>Does it have some Persian casuallity implied?</th>\n",
+       "      <th>% of count (Persian)</th>\n",
+       "      <th>Does it have some South African casuallity implied?</th>\n",
+       "      <th>% of count (South African)</th>\n",
+       "      <th>Does it have some Latino-American casuallity implied?</th>\n",
+       "      <th>% of count (Latino-American)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agriculture</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Demography</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.728195</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>36.250000</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>39.682540</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>35.294118</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>40.298507</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.037037</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>36.842105</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>40.540541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Economy</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>54.372937</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>61.643836</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>54.362416</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>53.676471</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>53.488372</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>49.321267</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>53.488372</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>54.385965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Employment</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Environment</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>73.529412</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>62.184874</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>76.543210</td>\n",
+       "      <td>No</td>\n",
+       "      <td>81.333333</td>\n",
+       "      <td>No</td>\n",
+       "      <td>81.081081</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>72.727273</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>76.923077</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>65.789474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equality</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>58.928571</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>77.777778</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>55.555556</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>63.636364</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>33.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exports</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>60.183066</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>59.047619</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>56.140351</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>68.421053</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>62.025316</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>59.523810</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>58.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Health</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Internet</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.190083</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>47.619048</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>35.294118</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>26.315789</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>42.857143</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>36.842105</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.500000</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>28.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mortality</th>\n",
+       "      <td>No</td>\n",
+       "      <td>92.337165</td>\n",
+       "      <td>No</td>\n",
+       "      <td>96.296296</td>\n",
+       "      <td>No</td>\n",
+       "      <td>92.753623</td>\n",
+       "      <td>No</td>\n",
+       "      <td>90.476190</td>\n",
+       "      <td>No</td>\n",
+       "      <td>92.753623</td>\n",
+       "      <td>No</td>\n",
+       "      <td>90.217391</td>\n",
+       "      <td>No</td>\n",
+       "      <td>91.578947</td>\n",
+       "      <td>No</td>\n",
+       "      <td>88.461538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Principal</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Does it have some global casuallity implied?  % of count (Global)  \\\n",
+       "Group                                                                           \n",
+       "Agriculture                                           No           100.000000   \n",
+       "Demography                                           Yes            37.728195   \n",
+       "Economy                                              Yes            54.372937   \n",
+       "Employment                                            No           100.000000   \n",
+       "Environment                                          Yes            73.529412   \n",
+       "Equality                                             Yes            58.928571   \n",
+       "Exports                                              Yes            60.183066   \n",
+       "Health                                                No           100.000000   \n",
+       "Internet                                             Yes            37.190083   \n",
+       "Mortality                                             No            92.337165   \n",
+       "Principal                                             No           100.000000   \n",
+       "\n",
+       "            Does it have some European casuallity implied?  \\\n",
+       "Group                                                        \n",
+       "Agriculture                                             No   \n",
+       "Demography                                             Yes   \n",
+       "Economy                                                Yes   \n",
+       "Employment                                              No   \n",
+       "Environment                                            Yes   \n",
+       "Equality                                               Yes   \n",
+       "Exports                                                Yes   \n",
+       "Health                                                  No   \n",
+       "Internet                                               Yes   \n",
+       "Mortality                                               No   \n",
+       "Principal                                               No   \n",
+       "\n",
+       "             % of count (European)  \\\n",
+       "Group                                \n",
+       "Agriculture             100.000000   \n",
+       "Demography               36.250000   \n",
+       "Economy                  61.643836   \n",
+       "Employment              100.000000   \n",
+       "Environment              62.184874   \n",
+       "Equality                 77.777778   \n",
+       "Exports                  59.047619   \n",
+       "Health                  100.000000   \n",
+       "Internet                 47.619048   \n",
+       "Mortality                96.296296   \n",
+       "Principal               100.000000   \n",
+       "\n",
+       "            Does it have some North African casuallity implied?  \\\n",
+       "Group                                                             \n",
+       "Agriculture                                                 No    \n",
+       "Demography                                                 Yes    \n",
+       "Economy                                                    Yes    \n",
+       "Employment                                                  No    \n",
+       "Environment                                                Yes    \n",
+       "Equality                                                   Yes    \n",
+       "Exports                                                    Yes    \n",
+       "Health                                                      No    \n",
+       "Internet                                                   Yes    \n",
+       "Mortality                                                   No    \n",
+       "Principal                                                   No    \n",
+       "\n",
+       "             % of count (North African)  \\\n",
+       "Group                                     \n",
+       "Agriculture                  100.000000   \n",
+       "Demography                    39.682540   \n",
+       "Economy                       54.362416   \n",
+       "Employment                   100.000000   \n",
+       "Environment                   76.543210   \n",
+       "Equality                      75.000000   \n",
+       "Exports                       56.140351   \n",
+       "Health                       100.000000   \n",
+       "Internet                      35.294118   \n",
+       "Mortality                     92.753623   \n",
+       "Principal                    100.000000   \n",
+       "\n",
+       "            Does it have some Asian casuallity implied?  % of count (Asian)  \\\n",
+       "Group                                                                         \n",
+       "Agriculture                                          No          100.000000   \n",
+       "Demography                                          Yes           35.294118   \n",
+       "Economy                                             Yes           53.676471   \n",
+       "Employment                                           No          100.000000   \n",
+       "Environment                                          No           81.333333   \n",
+       "Equality                                            Yes           55.555556   \n",
+       "Exports                                             Yes           68.421053   \n",
+       "Health                                               No          100.000000   \n",
+       "Internet                                            Yes           26.315789   \n",
+       "Mortality                                            No           90.476190   \n",
+       "Principal                                            No          100.000000   \n",
+       "\n",
+       "            Does it have some Pair casuallity implied?  % of count (Pair)  \\\n",
+       "Group                                                                       \n",
+       "Agriculture                                         No         100.000000   \n",
+       "Demography                                         Yes          40.298507   \n",
+       "Economy                                            Yes          53.488372   \n",
+       "Employment                                          No         100.000000   \n",
+       "Environment                                         No          81.081081   \n",
+       "Equality                                           Yes          50.000000   \n",
+       "Exports                                            Yes          60.000000   \n",
+       "Health                                              No         100.000000   \n",
+       "Internet                                           Yes          42.857143   \n",
+       "Mortality                                           No          92.753623   \n",
+       "Principal                                           No         100.000000   \n",
+       "\n",
+       "            Does it have some Persian casuallity implied?  \\\n",
+       "Group                                                       \n",
+       "Agriculture                                            No   \n",
+       "Demography                                            Yes   \n",
+       "Economy                                               Yes   \n",
+       "Employment                                             No   \n",
+       "Environment                                           Yes   \n",
+       "Equality                                              Yes   \n",
+       "Exports                                               Yes   \n",
+       "Health                                                 No   \n",
+       "Internet                                              Yes   \n",
+       "Mortality                                              No   \n",
+       "Principal                                              No   \n",
+       "\n",
+       "             % of count (Persian)  \\\n",
+       "Group                               \n",
+       "Agriculture            100.000000   \n",
+       "Demography              37.037037   \n",
+       "Economy                 49.321267   \n",
+       "Employment             100.000000   \n",
+       "Environment             72.727273   \n",
+       "Equality                50.000000   \n",
+       "Exports                 62.025316   \n",
+       "Health                 100.000000   \n",
+       "Internet                36.842105   \n",
+       "Mortality               90.217391   \n",
+       "Principal              100.000000   \n",
+       "\n",
+       "            Does it have some South African casuallity implied?  \\\n",
+       "Group                                                             \n",
+       "Agriculture                                                 No    \n",
+       "Demography                                                 Yes    \n",
+       "Economy                                                    Yes    \n",
+       "Employment                                                  No    \n",
+       "Environment                                                Yes    \n",
+       "Equality                                                   Yes    \n",
+       "Exports                                                    Yes    \n",
+       "Health                                                      No    \n",
+       "Internet                                                   Yes    \n",
+       "Mortality                                                   No    \n",
+       "Principal                                                   No    \n",
+       "\n",
+       "             % of count (South African)  \\\n",
+       "Group                                     \n",
+       "Agriculture                  100.000000   \n",
+       "Demography                    36.842105   \n",
+       "Economy                       53.488372   \n",
+       "Employment                   100.000000   \n",
+       "Environment                   76.923077   \n",
+       "Equality                      63.636364   \n",
+       "Exports                       59.523810   \n",
+       "Health                       100.000000   \n",
+       "Internet                      37.500000   \n",
+       "Mortality                     91.578947   \n",
+       "Principal                    100.000000   \n",
+       "\n",
+       "            Does it have some Latino-American casuallity implied?  \\\n",
+       "Group                                                               \n",
+       "Agriculture                                                 No      \n",
+       "Demography                                                 Yes      \n",
+       "Economy                                                    Yes      \n",
+       "Employment                                                  No      \n",
+       "Environment                                                Yes      \n",
+       "Equality                                                   Yes      \n",
+       "Exports                                                    Yes      \n",
+       "Health                                                      No      \n",
+       "Internet                                                   Yes      \n",
+       "Mortality                                                   No      \n",
+       "Principal                                                   No      \n",
+       "\n",
+       "             % of count (Latino-American)  \n",
+       "Group                                      \n",
+       "Agriculture                    100.000000  \n",
+       "Demography                      40.540541  \n",
+       "Economy                         54.385965  \n",
+       "Employment                     100.000000  \n",
+       "Environment                     65.789474  \n",
+       "Equality                        33.333333  \n",
+       "Exports                         58.333333  \n",
+       "Health                         100.000000  \n",
+       "Internet                        28.571429  \n",
+       "Mortality                       88.461538  \n",
+       "Principal                      100.000000  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for i in range(0,len(clist)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == clist[i]]\n",
-    "    for e in range(2,len(columns)):\n",
-    "        data=dat.iloc[:, e]\n",
-    "        stat, p = shapiro(data)\n",
-    "        print(clist[i] +\"-\"+ columns[e])\n",
-    "        print('Statistical=%.3f, p=%.3f' % (stat, p))\n",
-    "        alpha = 0.05\n",
-    "        if p > alpha:\n",
-    "            print('Data is NORMAL ( H0 not denied )')\n",
-    "        else:\n",
-    "            pass"
+    "for i in range(0,len(continentlist)):\n",
+    "    apfinal=final.loc[final['Continent']==continentlist[i]]\n",
+    "    \n",
+    "    selected_p=categories.loc[categories['Level']=='primary']\n",
+    "    minprimary=selected_p.groupby('Group').min()\n",
+    "    minprimary['Min']=round(minprimary['Number of times repeated']*limita.value)\n",
+    "    minprimary.drop(columns=['Indicator','Number of times repeated','Level'], inplace=True)\n",
+    "\n",
+    "    grouplist=minprimary.index.to_list()\n",
+    "\n",
+    "    secondary=apfinal.loc[apfinal['Level']=='secondary']\n",
+    "    secondary=pd.merge(secondary,minprimary, left_on='Group',right_on='Group')\n",
+    "\n",
+    "    secondaryp=secondary.loc[:,['Group','Min']]\n",
+    "    Global_Count=secondaryp.groupby('Group').count()\n",
+    "    Global_Count.rename(columns={'Min':'Global Count'},inplace=True)\n",
+    "\n",
+    "    secondary['H_0']=np.where(secondary['Number of times repeated_x']-secondary['Min']>0,'Not Discarded', 'Denied')\n",
+    "    seco=secondary.groupby(['H_0','Group']).count()\n",
+    "    sec=seco.loc['Not Discarded']\n",
+    "    secondarycount=sec.drop(columns=['Indicator','R^2 Spearman','Behaviour','Country','Moved','Type','Continent','Number of times repeated_x','Number of times repeated_y','Level'])\n",
+    "    secondarycount.rename(columns={'Min':'Secondary Count'},inplace=True)\n",
+    "\n",
+    "    apfinalcount=pd.merge(Global_Count,secondarycount, left_on='Group',right_on='Group')\n",
+    "    apfinalcount['Does it have some '+namescontinents[i]+' casuallity implied?']=np.where(apfinalcount['Secondary Count']/apfinalcount['Global Count']>limitb.value,'No', 'Yes')\n",
+    "    apfinalcount['% of count ('+namescontinents[i]+')']=apfinalcount['Secondary Count']/apfinalcount['Global Count']*100\n",
+    "    apfinalcount.drop(columns=['Global Count','Secondary Count'],inplace=True)\n",
+    "    finalcount=pd.merge(finalcount,apfinalcount, left_on='Group',right_on='Group')\n",
+    "\n",
+    "finalcount"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can observe into much more detail the differnt regions that we defined before, and for groups that have a **NO**, we do not need to worry about casualities. Meanwhile, for the rest of the groups correlation can still be a great indicator as a basis for decision making, if we carefully analyze the variables and found some sort of real relationship between them.   "
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4 ('.venv': poetry)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -9927,11 +35792,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.12"
   },
   "vscode": {
    "interpreter": {
-    "hash": "ad72b07258a3fa24452fee21e868a537ead700a3a6ac2a1adaf5006160c747dc"
+    "hash": "e21cf16a31979fe9d2d6e9786ebc932e404e707404f15260e38504afc6c53159"
    }
   }
  },
diff --git a/WDI-Correlation and visualization of results.ipynb b/WDI-Correlation and visualization of results.ipynb
new file mode 100644
index 0000000..8d8f7f5
--- /dev/null
+++ b/WDI-Correlation and visualization of results.ipynb	
@@ -0,0 +1,206377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Correlation study"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import libraries and functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import glob\n",
+    "import os\n",
+    "from zipfile import ZipFile\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "import functools as ft\n",
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import Layout\n",
+    "from ipywidgets import interact, interact_manual\n",
+    "import plotly.express as px\n",
+    "from scipy import stats\n",
+    "from scipy.stats import shapiro\n",
+    "from scipy.stats import pearsonr\n",
+    "from scipy.stats import spearmanr\n",
+    "from pandas.api.types import is_numeric_dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the correlation study, we are going to check if the indicators are related in a relevantly way to GDP(current US$), through both Pearson and Spearman correlations and their respective p-values. Thus, our hypotheses are as follow:\n",
+    "\n",
+    "- H_0: the indicator and the GDP are uncorrelated.​\n",
+    "- H_1: the indicator and the GDP are correlated.​\n",
+    "\n",
+    "**If p-value < α then reject H_0 and accept H_1.​**\n",
+    "\n",
+    "P-value: is the probability of obtaining test results at least as extreme as the result actually observed. (Marging of the error)\n",
+    "\n",
+    "​Confidence level (α): probability that a population parameter will fall between a set of values for a certain proportion of times. α = 1 - p-value.\n",
+    "\n",
+    "​Significance level: probability of the study rejecting the null hypothesis when it is actually true.​ \n",
+    "\n",
+    "At the end we want to follow a process that checks if with Pearson and Spearman the correlation is relevant, as the image below ilustrates. \n",
+    "\n",
+    "![](https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/Spearman%20and%20pearson%20process.JPG)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Categorization4= pd.read_csv (os.getcwd()+'/Data/'+'Categorization.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "indicators_list=Categorization4['Indicator'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns=indicators_list+['Country','Year','Continent']\n",
+    "clist=Categorization4['Country'].unique()\n",
+    "common=['Unnamed: 0','Country','Year']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The correlation between two variables does not always need to be a linear one, since data of these variables can follow a different distribution that better adjusts. Therefore, we have computed four types (linear, quadratic, cubic and logarithmic) for each indicator in each country and we chose the one with the highest correlation. In the following cell, we have defined a function that will allow us to calculate the different posibilities of relations: cuadratic, cubic and logaritmic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def multcolumn(frame):\n",
+    "    for u in range(0, len(columns)-3):\n",
+    "        name=columns[u]+'¨l'\n",
+    "        name2=columns[u]+'¨^2'\n",
+    "        name3=columns[u]+'¨^3'\n",
+    "        namelog=columns[u]+'¨log'\n",
+    "        frame.loc[:,name2] = frame[columns[u]]**2\n",
+    "        frame.loc[:,name3] = frame[columns[u]]**3\n",
+    "        frame.loc[:,namelog] = np.log(frame[columns[u]])\n",
+    "        frame.rename(columns={columns[u]:name}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Moreover, we want to know the correlation between all the variables, so to acomplish this, we have created the following loop, which will help us create a new dataframe where we will have: the *Indicator*, the *Type* of relation, the value of the *R^2*, its *Behaviour*, the *Country* and the *Continent*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= pd.read_csv (os.getcwd()+'/Data/'+'GoldenDataFrame.csv')\n",
+    "df_study=df[[c for c in df.columns if c in columns]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "multcolumn(df_study)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Firstly we are going to create two lists for the variables, which their level of confidence for each correlation, so later on, we can calculate only the correlations of those variables. Which will be filtered by the values that we want from the following sliders. The predetermined values for each case are 0.05 for the values and 0.75 for them to be a relevant correlations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b20e00c8aeca422da078f4eaca6d3986",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.05, continuous_update=False, description='Level of confidence (Pearson):', max=1.0, step=0…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4500dbbc0fff401197dd1817a20a276b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.75, continuous_update=False, description='Relevant correlation (Pearson):', max=1.0, step=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "987be8a539424a90b91f2e4a5abfc97d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.05, continuous_update=False, description='Level of confidence (Spearman):', max=1.0, step=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7a8261513664525a5402f32530114c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.75, continuous_update=False, description='Relevant correlation (Spearman):', max=1.0, step…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "apv=widgets.FloatSlider(value=0.05,min=0,max=1.0,step=0.025,description='Level of confidence (Pearson):',disabled=False,continuous_update=False,orientation='horizontal',readout=True)\n",
+    "cpv=widgets.FloatSlider( value=0.05, min=0, max=1.0, step=0.05, description='Level of confidence (Spearman):', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "bcor=widgets.FloatSlider( value=0.75, min=0, max=1.0, step=0.05, description='Relevant correlation (Pearson):', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "dcor=widgets.FloatSlider( value=0.75, min=0, max=1.0, step=0.05, description='Relevant correlation (Spearman):', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "display(apv,bcor,cpv,dcor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following block, we check the hypotheses set before and extract only the variables that deny H_0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dat=df_study.loc[df_study.loc[:, 'Country'] == clist[0]]\n",
+    "listacorpe=[]\n",
+    "listacorsp=[]\n",
+    "clmns=dat.columns.values.tolist()\n",
+    "dat.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "for c in range(0, len(clmns)):\n",
+    "    if dat[clmns[c]].isna().sum()>=1:\n",
+    "        del(dat[clmns[c]])\n",
+    "pilares=dat.columns.values.tolist()\n",
+    "for u in range(0,len(pilares)):\n",
+    "    if is_numeric_dtype(dat[pilares[u]]):\n",
+    "        correlation, pvalue=pearsonr(dat[pilares[u]], dat['GDP (current US$)¨l'])\n",
+    "        if pvalue<=apv.value:\n",
+    "            listacorpe.append(pilares[u])\n",
+    "        else:\n",
+    "            pass\n",
+    "        correlation, pvalue=spearmanr(dat[pilares[u]], dat['GDP (current US$)¨l'])\n",
+    "        if pvalue<=cpv.value:\n",
+    "            listacorsp.append(pilares[u])\n",
+    "        else:\n",
+    "            pass\n",
+    "    else:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Secondly, we need to calculate the correlation table for each country, therefore we use the basic function `corr()` which provides either the Pearson correlation table or the Spearman correlation table, as well as a filter for the countries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dat=df_study.loc[df_study.loc[:, 'Country'] == clist[0]]\n",
+    "\n",
+    "datp=dat[dat.columns[dat.columns.isin(listacorpe)]]\n",
+    "corp=datp.corr('pearson')\n",
+    "\n",
+    "datsp=dat[dat.columns[dat.columns.isin(listacorsp)]]\n",
+    "cors=datsp.corr('spearman')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we calculate the coefficient of determination which is the correlation squared."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corp.loc[:,'R^2 Pearson'] = corp['GDP (current US$)¨l']**2\n",
+    "\n",
+    "cors.loc[:,'R^2 Spearman'] = cors['GDP (current US$)¨l']**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Moreover, we are going to create new columns to know which *Indicator* are we talking about, and the *Type* of correlation that is being analyzed (linear, cuadratic, cubic or logarithmic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corp.loc[:,'Indicator']=corp.index\n",
+    "corp[['Indicator','Type']]=corp.Indicator.str.split('¨',1, expand=True)\n",
+    "\n",
+    "cors.loc[:,'Indicator']=cors.index\n",
+    "cors[['Indicator','Type']]=cors.Indicator.str.split('¨',1, expand=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can apply the filter we have consider that is enough, selected on the slider. If is not varied it is R^2>=0.75 to filter the correlations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpcolumn=corp[['Indicator','R^2 Pearson','Type','GDP (current US$)¨l']]\n",
+    "corpcolumn=corpcolumn.loc[corpcolumn.loc[:, 'R^2 Pearson'] >=bcor.value]\n",
+    "\n",
+    "corscolumn=cors[['Indicator','R^2 Spearman','Type','GDP (current US$)¨l']]\n",
+    "corscolumn=corscolumn.loc[corscolumn.loc[:, 'R^2 Spearman'] >= dcor.value]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Furthermore, we add all the columns that we have created into a data frame, thanks to the following cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idp=corpcolumn.groupby('Indicator')['R^2 Pearson'].transform(max)==corpcolumn['R^2 Pearson']\n",
+    "corpcolumn[idp]\n",
+    "maxp_df=pd.DataFrame(corpcolumn[idp])\n",
+    "\n",
+    "ids=corscolumn.groupby('Indicator')['R^2 Spearman'].transform(max)==corscolumn['R^2 Spearman']\n",
+    "corscolumn[ids]\n",
+    "maxs_df=pd.DataFrame(corscolumn[ids])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we conmute the values, by expressions. For example if the correlation is positive, we want in the new column called *Behaviour* the word Positive. Or for the *Type* column if the greatest correlation is cuadratic we want to put, Cuadratic. We also add the country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maxp_df['Behaviour']=np.where(maxp_df['GDP (current US$)¨l']>0, 'Positive', 'Negative')\n",
+    "maxp_df['Type']=maxp_df['Type'].replace(['l','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
+    "maxp_df['Country']= clist[0]\n",
+    "\n",
+    "maxs_df['Behaviour']=np.where(maxs_df['GDP (current US$)¨l']>0, 'Positive', 'Negative')\n",
+    "maxs_df['Type']=maxs_df['Type'].replace(['l','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
+    "maxs_df['Country']= clist[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition, we also drop the columns which do not add any value, as *GDP*, *Year*, and *Unnamed:0*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maxp_df.drop(\"GDP (current US$)¨l\",axis=1,inplace=True)\n",
+    "maxp_df=maxp_df.reset_index(drop=True)\n",
+    "maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='Year'].index)\n",
+    "maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='GDP (current US$)'].index)\n",
+    "maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='Unnamed: 0'].index)\n",
+    "\n",
+    "maxs_df.drop(\"GDP (current US$)¨l\",axis=1,inplace=True)\n",
+    "maxs_df=maxs_df.reset_index(drop=True)\n",
+    "maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Year'].index)\n",
+    "maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='GDP (current US$)'].index)\n",
+    "maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Unnamed: 0'].index)\n",
+    "maxs_df=maxs_df.sort_values(by = 'R^2 Spearman',ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And finally we sort the values in descending order by the column *R^2 Pearson*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maxp_df_deu=maxp_df.sort_values(by = 'R^2 Pearson',ascending = False)\n",
+    "pearsondf= maxp_df_deu\n",
+    "spearmandf=maxs_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, we can do it with all the countries and create just one dataframe, where we match both data frames, the Pearson and the Spearman, only where there is a case in both sides. Meaning if there is a relevant correlation for Country x in Variable y for Pearson, it will only appear if there is also a relevant correlation for Country x in Variable y for Spearman."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>R^2 Pearson</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adjusted net national income (current US$)</td>\n",
+       "      <td>0.996519</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.999141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Gross value added at basic prices (GVA) (curre...</td>\n",
+       "      <td>0.996519</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.999851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>GNI (current US$)</td>\n",
+       "      <td>0.996337</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.999362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Gross national expenditure (current US$)</td>\n",
+       "      <td>0.990490</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.997181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Final consumption expenditure (current US$)</td>\n",
+       "      <td>0.988302</td>\n",
+       "      <td>Linear</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>0.996540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5965</th>\n",
+       "      <td>Prevalence of anemia among women of reproducti...</td>\n",
+       "      <td>0.793298</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.752969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5966</th>\n",
+       "      <td>Logistics performance index: Ability to track ...</td>\n",
+       "      <td>0.788049</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.926947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5967</th>\n",
+       "      <td>Logistics performance index: Competence and qu...</td>\n",
+       "      <td>0.788049</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.890297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5968</th>\n",
+       "      <td>Out-of-pocket expenditure (% of current health...</td>\n",
+       "      <td>0.782573</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.929883</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5969</th>\n",
+       "      <td>Domestic general government health expenditure...</td>\n",
+       "      <td>0.758910</td>\n",
+       "      <td>Cubic</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>0.797637</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5970 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Indicator  R^2 Spearman  \\\n",
+       "0            Adjusted net national income (current US$)      0.996519   \n",
+       "1     Gross value added at basic prices (GVA) (curre...      0.996519   \n",
+       "2                                     GNI (current US$)      0.996337   \n",
+       "3              Gross national expenditure (current US$)      0.990490   \n",
+       "4           Final consumption expenditure (current US$)      0.988302   \n",
+       "...                                                 ...           ...   \n",
+       "5965  Prevalence of anemia among women of reproducti...      0.793298   \n",
+       "5966  Logistics performance index: Ability to track ...      0.788049   \n",
+       "5967  Logistics performance index: Competence and qu...      0.788049   \n",
+       "5968  Out-of-pocket expenditure (% of current health...      0.782573   \n",
+       "5969  Domestic general government health expenditure...      0.758910   \n",
+       "\n",
+       "             Type Behaviour Country  R^2 Pearson  \n",
+       "0          Linear  Positive     DEU     0.999141  \n",
+       "1          Linear  Positive     DEU     0.999851  \n",
+       "2          Linear  Positive     DEU     0.999362  \n",
+       "3          Linear  Positive     DEU     0.997181  \n",
+       "4          Linear  Positive     DEU     0.996540  \n",
+       "...           ...       ...     ...          ...  \n",
+       "5965  Logarithmic  Negative     CHN     0.752969  \n",
+       "5966  Logarithmic  Positive     CHN     0.926947  \n",
+       "5967  Logarithmic  Positive     CHN     0.890297  \n",
+       "5968  Logarithmic  Negative     CHN     0.929883  \n",
+       "5969        Cubic  Positive     CHN     0.797637  \n",
+       "\n",
+       "[5970 rows x 6 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pearsondf\n",
+    "spearmandf\n",
+    "for i in range(1,len(clist)):\n",
+    "    dat=df_study.loc[df_study.loc[:, 'Country'] == clist[i]]\n",
+    "    listacorpe=[]\n",
+    "    listacorsp=[]\n",
+    "    clmns=dat.columns.values.tolist()\n",
+    "    dat.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "    for c in range(0, len(clmns)):\n",
+    "        if dat[clmns[c]].isna().sum()>=1:\n",
+    "            del(dat[clmns[c]])\n",
+    "    pilares=dat.columns.values.tolist()\n",
+    "    for u in range(0,len(pilares)):\n",
+    "        if is_numeric_dtype(dat[pilares[u]]):\n",
+    "            correlation, pvalue=pearsonr(dat[pilares[u]], dat['GDP (current US$)¨l'])\n",
+    "            if pvalue<=apv.value:\n",
+    "                listacorpe.append(pilares[u])\n",
+    "            else:\n",
+    "                pass\n",
+    "            correlation, pvalue=spearmanr(dat[pilares[u]], dat['GDP (current US$)¨l'])\n",
+    "            if pvalue<=cpv.value:\n",
+    "                listacorsp.append(pilares[u])\n",
+    "            else:\n",
+    "                pass\n",
+    "        else:\n",
+    "            pass\n",
+    "    \n",
+    "    dat=df_study.loc[df_study.loc[:, 'Country'] == clist[i]]\n",
+    "\n",
+    "    datp=dat[dat.columns[dat.columns.isin(listacorpe)]]\n",
+    "    corp=datp.corr('pearson')\n",
+    "\n",
+    "    datsp=dat[dat.columns[dat.columns.isin(listacorsp)]]\n",
+    "    cors=datsp.corr('spearman')\n",
+    "\n",
+    "\n",
+    "    corp.loc[:,'R^2 Pearson'] = corp['GDP (current US$)¨l']**2\n",
+    "\n",
+    "    cors.loc[:,'R^2 Spearman'] = cors['GDP (current US$)¨l']**2\n",
+    "\n",
+    "\n",
+    "    corp.loc[:,'Indicator']=corp.index\n",
+    "    corp[['Indicator','Type']]=corp.Indicator.str.split('¨',1, expand=True)\n",
+    "\n",
+    "    cors.loc[:,'Indicator']=cors.index\n",
+    "    cors[['Indicator','Type']]=cors.Indicator.str.split('¨',1, expand=True)\n",
+    "\n",
+    "\n",
+    "    corpcolumn=corp[['Indicator','R^2 Pearson','Type','GDP (current US$)¨l']]\n",
+    "    corpcolumn=corpcolumn.loc[corpcolumn.loc[:, 'R^2 Pearson'] >= bcor.value]\n",
+    "    \n",
+    "    corscolumn=cors[['Indicator','R^2 Spearman','Type','GDP (current US$)¨l']]\n",
+    "    corscolumn=corscolumn.loc[corscolumn.loc[:, 'R^2 Spearman'] >= dcor.value]\n",
+    "\n",
+    "\n",
+    "    idp=corpcolumn.groupby('Indicator')['R^2 Pearson'].transform(max)==corpcolumn['R^2 Pearson']\n",
+    "    corpcolumn[idp]\n",
+    "    maxp_df=pd.DataFrame(corpcolumn[idp])\n",
+    "\n",
+    "    ids=corscolumn.groupby('Indicator')['R^2 Spearman'].transform(max)==corscolumn['R^2 Spearman']\n",
+    "    corscolumn[ids]\n",
+    "    maxs_df=pd.DataFrame(corscolumn[ids])\n",
+    "\n",
+    "\n",
+    "    maxp_df['Behaviour']=np.where(maxp_df['GDP (current US$)¨l']>0, 'Positive', 'Negative')\n",
+    "    maxp_df['Type']=maxp_df['Type'].replace(['l','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
+    "    maxp_df['Country']= clist[i]\n",
+    "\n",
+    "    maxs_df['Behaviour']=np.where(maxs_df['GDP (current US$)¨l']>0, 'Positive', 'Negative')\n",
+    "    maxs_df['Type']=maxs_df['Type'].replace(['l','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
+    "    maxs_df['Country']= clist[i]\n",
+    "\n",
+    "\n",
+    "    maxp_df.drop(\"GDP (current US$)¨l\",axis=1,inplace=True)\n",
+    "    maxp_df=maxp_df.reset_index(drop=True)\n",
+    "    maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='Year'].index)\n",
+    "    maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='GDP (current US$)'].index)\n",
+    "    maxp_df = maxp_df.drop(maxp_df[maxp_df['Indicator']=='Unnamed: 0'].index)\n",
+    "\n",
+    "    maxs_df.drop(\"GDP (current US$)¨l\",axis=1,inplace=True)\n",
+    "    maxs_df=maxs_df.reset_index(drop=True)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Year'].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='GDP (current US$)'].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Unnamed: 0'].index)\n",
+    "    maxs_df=maxs_df.sort_values(by = 'R^2 Spearman',ascending = False)\n",
+    "\n",
+    "\n",
+    "    maxp_df=maxp_df.sort_values(by = 'R^2 Pearson',ascending = False)\n",
+    "    pearsondf=pd.concat((pearsondf, maxp_df), axis = 0)\n",
+    "    spearmandf=pd.concat((spearmandf, maxs_df), axis = 0)\n",
+    "\n",
+    "corrtable=spearmandf.merge(pearsondf, left_on=('Indicator', 'Country','Type','Behaviour'), right_on=('Indicator', 'Country','Type','Behaviour'))\n",
+    "display(corrtable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, a table has been created showing the number of times a variable has a high relationship in our 48 countries. These that appear many times will be interesting for us to draw conclusions. Then, we will checck if they are primary or seconday variable type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columnssf=corrtable.Indicator.to_list()\n",
+    "columnsf=np.unique(columnssf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Number of times repeated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>GDP per capita (current US$)</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Industry (including construction), value added...</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>GNI (current US$)</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>Households and NPISHs Final consumption expend...</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>Final consumption expenditure (current US$)</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>Net official development assistance and offici...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>Net capital account (BoP, current US$)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>Natural gas rents (% of GDP)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Arms exports (SIPRI trend indicator values)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>Gross fixed capital formation (% of GDP)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>405 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             Indicator  \\\n",
+       "127                       GDP per capita (current US$)   \n",
+       "178  Industry (including construction), value added...   \n",
+       "132                                  GNI (current US$)   \n",
+       "152  Households and NPISHs Final consumption expend...   \n",
+       "108        Final consumption expenditure (current US$)   \n",
+       "..                                                 ...   \n",
+       "250  Net official development assistance and offici...   \n",
+       "248             Net capital account (BoP, current US$)   \n",
+       "246                       Natural gas rents (% of GDP)   \n",
+       "29         Arms exports (SIPRI trend indicator values)   \n",
+       "142           Gross fixed capital formation (% of GDP)   \n",
+       "\n",
+       "     Number of times repeated  \n",
+       "127                        46  \n",
+       "178                        46  \n",
+       "132                        46  \n",
+       "152                        45  \n",
+       "108                        45  \n",
+       "..                        ...  \n",
+       "250                         1  \n",
+       "248                         1  \n",
+       "246                         1  \n",
+       "29                          1  \n",
+       "142                         1  \n",
+       "\n",
+       "[405 rows x 2 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "\n",
+    "df_indicators = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Indicator','Number of times repeated'])\n",
+    "df_indicators=df_indicators.sort_values(by = 'Number of times repeated',ascending = False)\n",
+    "df_indicators\n",
+    "display(df_indicators)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#To get list of all number of times repeated.\n",
+    "#from IPython.display import HTML\n",
+    "\n",
+    "#HTML(df_indicators.to_html(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While for the temporal diferences, we want to know which works better in each case, the process to be followed is described in the following picture.\n",
+    "\n",
+    "![](https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/Temporal%20process.JPG)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= pd.read_csv (os.getcwd()+'/Data/'+'GoldenDataFrame.csv')\n",
+    "df_study=df[[c for c in df.columns if c in columns]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moveddf=pd.DataFrame()\n",
+    "dat=df_study.loc[df_study.loc[:, 'Country'] == clist[0]]\n",
+    "clmns=dat.columns.values.tolist()\n",
+    "dat.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "tempdiffs=['GDP (current US$)+1','GDP (current US$)+2','GDP (current US$)+3','GDP (current US$)+5','GDP (current US$)+8','GDP (current US$)+13','GDP (current US$)+21']\n",
+    "cors=dat.corr('spearman')\n",
+    "for f in range(0, len(tempdiffs)):\n",
+    "    cors.loc[:,'R^2 Spearman'] = cors[tempdiffs[f]]**2\n",
+    "    cors.loc[:,'Indicator']=cors.index\n",
+    "    corscolumn=cors[['Indicator','R^2 Spearman',tempdiffs[f]]]\n",
+    "    corscolumn=corscolumn.loc[corscolumn.loc[:, 'R^2 Spearman'] >= dcor.value]\n",
+    "    ids=corscolumn.groupby('Indicator')['R^2 Spearman'].transform(max)==corscolumn['R^2 Spearman']\n",
+    "    corscolumn[ids]\n",
+    "    maxs_df=pd.DataFrame(corscolumn[ids])\n",
+    "    maxs_df['Behaviour']=np.where(maxs_df[tempdiffs[f]]>0, 'Positive', 'Negative')\n",
+    "    maxs_df['Country']= clist[0]\n",
+    "    maxs_df[['Variable','Moved']]=tempdiffs[f].split('+')\n",
+    "    maxs_df.drop(tempdiffs[f],axis=1,inplace=True)\n",
+    "    maxs_df.drop(columns='Variable',inplace=True)\n",
+    "    maxs_df=maxs_df.reset_index(drop=True)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Year'].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='GDP (current US$)'].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator'].isin(tempdiffs)].index)\n",
+    "    maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Unnamed: 0'].index)\n",
+    "    maxs_df=maxs_df.sort_values(by = 'R^2 Spearman',ascending = False)\n",
+    "    moveddf=pd.concat((moveddf,maxs_df),axis=0)\n",
+    "ids=moveddf.groupby('Indicator')['R^2 Spearman'].transform(max)==moveddf['R^2 Spearman']\n",
+    "moveddf[ids]\n",
+    "moveddf=pd.DataFrame(moveddf[ids])\n",
+    "temporaldf=moveddf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adjusted savings: education expenditure (curre...</td>\n",
+       "      <td>0.824514</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>General government final consumption expenditu...</td>\n",
+       "      <td>0.822917</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Individuals using the Internet (% of population)</td>\n",
+       "      <td>0.822917</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>Surface area (sq. km)</td>\n",
+       "      <td>0.821642</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.817804</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>Pump price for diesel fuel (US$ per liter)</td>\n",
+       "      <td>0.787248</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>Pump price for gasoline (US$ per liter)</td>\n",
+       "      <td>0.787248</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Chemicals (% of value added in manufacturing)</td>\n",
+       "      <td>0.781385</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>Proportion of population pushed below the $1.9...</td>\n",
+       "      <td>0.769754</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Depth of credit information index (0=low to 8=...</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9700 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             Indicator  R^2 Spearman  \\\n",
+       "4    Adjusted savings: education expenditure (curre...      0.824514   \n",
+       "35   General government final consumption expenditu...      0.822917   \n",
+       "45    Individuals using the Internet (% of population)      0.822917   \n",
+       "78                               Surface area (sq. km)      0.821642   \n",
+       "5    Adolescent fertility rate (births per 1,000 wo...      0.817804   \n",
+       "..                                                 ...           ...   \n",
+       "167         Pump price for diesel fuel (US$ per liter)      0.787248   \n",
+       "168            Pump price for gasoline (US$ per liter)      0.787248   \n",
+       "30       Chemicals (% of value added in manufacturing)      0.781385   \n",
+       "162  Proportion of population pushed below the $1.9...      0.769754   \n",
+       "38   Depth of credit information index (0=low to 8=...      0.750000   \n",
+       "\n",
+       "    Behaviour Country Moved  \n",
+       "4    Positive     DEU     1  \n",
+       "35   Positive     DEU     1  \n",
+       "45   Positive     DEU     1  \n",
+       "78   Positive     DEU     1  \n",
+       "5    Negative     DEU     1  \n",
+       "..        ...     ...   ...  \n",
+       "167  Negative     CHN    21  \n",
+       "168  Negative     CHN    21  \n",
+       "30   Negative     CHN    21  \n",
+       "162  Positive     CHN    21  \n",
+       "38   Positive     CHN    21  \n",
+       "\n",
+       "[9700 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for i in range(1,len(clist)):\n",
+    "    dat=df_study.loc[df_study.loc[:, 'Country'] == clist[i]]\n",
+    "    clmns=dat.columns.values.tolist()\n",
+    "    dat.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "    tempdiffs=['GDP (current US$)+1','GDP (current US$)+2','GDP (current US$)+3','GDP (current US$)+5','GDP (current US$)+8','GDP (current US$)+13','GDP (current US$)+21']\n",
+    "    cors=dat.corr('spearman')\n",
+    "    moveddf=pd.DataFrame()\n",
+    "    for f in range(0, len(tempdiffs)):\n",
+    "        cors.loc[:,'R^2 Spearman'] = cors[tempdiffs[f]]**2\n",
+    "        cors.loc[:,'Indicator']=cors.index\n",
+    "        corscolumn=cors[['Indicator','R^2 Spearman',tempdiffs[f]]]\n",
+    "        corscolumn=corscolumn.loc[corscolumn.loc[:, 'R^2 Spearman'] >= dcor.value]\n",
+    "        ids=corscolumn.groupby('Indicator')['R^2 Spearman'].transform(max)==corscolumn['R^2 Spearman']\n",
+    "        corscolumn[ids]\n",
+    "        maxs_df=pd.DataFrame(corscolumn[ids])\n",
+    "        maxs_df['Behaviour']=np.where(maxs_df[tempdiffs[f]]>0, 'Positive', 'Negative')\n",
+    "        maxs_df['Country']= clist[i]\n",
+    "        maxs_df[['Variable','Moved']]=tempdiffs[f].split('+')\n",
+    "        maxs_df.drop(tempdiffs[f],axis=1,inplace=True)\n",
+    "        maxs_df.drop(columns='Variable',inplace=True)\n",
+    "        maxs_df=maxs_df.reset_index(drop=True)\n",
+    "        maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Year'].index)\n",
+    "        maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator'].isin(tempdiffs)].index)\n",
+    "        maxs_df = maxs_df.drop(maxs_df[maxs_df['Indicator']=='Unnamed: 0'].index)\n",
+    "        maxs_df=maxs_df.sort_values(by = 'R^2 Spearman',ascending = False)\n",
+    "        moveddf=pd.concat((moveddf,maxs_df),axis=0)\n",
+    "    ids=moveddf.groupby('Indicator')['R^2 Spearman'].transform(max)==moveddf['R^2 Spearman']\n",
+    "    moveddf[ids]\n",
+    "    moveddf=pd.DataFrame(moveddf[ids])\n",
+    "    temporaldf=pd.concat((temporaldf,moveddf),axis=0)\n",
+    "temporaldf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adjusted savings: education expenditure (curre...</td>\n",
+       "      <td>0.824514</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>General government final consumption expenditu...</td>\n",
+       "      <td>0.822917</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Individuals using the Internet (% of population)</td>\n",
+       "      <td>0.822917</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>Surface area (sq. km)</td>\n",
+       "      <td>0.821642</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.817804</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5965</th>\n",
+       "      <td>Prevalence of anemia among women of reproducti...</td>\n",
+       "      <td>0.793298</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5966</th>\n",
+       "      <td>Logistics performance index: Ability to track ...</td>\n",
+       "      <td>0.788049</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5967</th>\n",
+       "      <td>Logistics performance index: Competence and qu...</td>\n",
+       "      <td>0.788049</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5968</th>\n",
+       "      <td>Out-of-pocket expenditure (% of current health...</td>\n",
+       "      <td>0.782573</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Logarithmic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5969</th>\n",
+       "      <td>Domestic general government health expenditure...</td>\n",
+       "      <td>0.758910</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CHN</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Cubic</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15670 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Indicator  R^2 Spearman  \\\n",
+       "4     Adjusted savings: education expenditure (curre...      0.824514   \n",
+       "35    General government final consumption expenditu...      0.822917   \n",
+       "45     Individuals using the Internet (% of population)      0.822917   \n",
+       "78                                Surface area (sq. km)      0.821642   \n",
+       "5     Adolescent fertility rate (births per 1,000 wo...      0.817804   \n",
+       "...                                                 ...           ...   \n",
+       "5965  Prevalence of anemia among women of reproducti...      0.793298   \n",
+       "5966  Logistics performance index: Ability to track ...      0.788049   \n",
+       "5967  Logistics performance index: Competence and qu...      0.788049   \n",
+       "5968  Out-of-pocket expenditure (% of current health...      0.782573   \n",
+       "5969  Domestic general government health expenditure...      0.758910   \n",
+       "\n",
+       "     Behaviour Country           Moved            Type  \n",
+       "4     Positive     DEU               1  Does not apply  \n",
+       "35    Positive     DEU               1  Does not apply  \n",
+       "45    Positive     DEU               1  Does not apply  \n",
+       "78    Positive     DEU               1  Does not apply  \n",
+       "5     Negative     DEU               1  Does not apply  \n",
+       "...        ...     ...             ...             ...  \n",
+       "5965  Negative     CHN  Does not apply     Logarithmic  \n",
+       "5966  Positive     CHN  Does not apply     Logarithmic  \n",
+       "5967  Positive     CHN  Does not apply     Logarithmic  \n",
+       "5968  Negative     CHN  Does not apply     Logarithmic  \n",
+       "5969  Positive     CHN  Does not apply           Cubic  \n",
+       "\n",
+       "[15670 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alist=['Indicator','R^2 Spearman','Behaviour','Country','Type']\n",
+    "forcomparassion=corrtable[alist]\n",
+    "\n",
+    "quarterfinal=pd.concat((temporaldf,forcomparassion),axis=0)\n",
+    "quarterfinal.fillna('Does not apply',inplace=True)\n",
+    "quarterfinal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quarterfinal.to_csv(os.getcwd()+'/Data/Quarterfinal.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quarterfinal= pd.read_csv (os.getcwd()+'/Data/'+'Quarterfinal.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#To understand better the data, we categorize it (Area label and Primary/Secondary).\n",
+    "categories= pd.read_excel (os.getcwd()+'/Data/'+'dfindicators - Copy.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categories.rename(columns={'Type':'Group'}, inplace=True)\n",
+    "quarterfinal.drop(columns=('Unnamed: 0'), inplace=True)\n",
+    "clist=quarterfinal['Country'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To sum up, we compare both of the highest correlations and decide which is best, by comparing the R^2. So the process of searching for correlations is as follows.\n",
+    "\n",
+    "![](https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/Final%20table%20process.JPG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Number of times repeated_x</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Level</th>\n",
+       "      <th>Number of times repeated_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.817804</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.759333</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.936963</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>GBR</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.786708</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>HRV</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
+       "      <td>0.924056</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>POL</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Demography</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6640</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.839228</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>PER</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6641</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.792184</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>VEN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6642</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.840648</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>COL</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6643</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.963636</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>PAN</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6644</th>\n",
+       "      <td>Completeness of birth registration (%)</td>\n",
+       "      <td>0.789474</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>CRI</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>Latam</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Demoraphy</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6645 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              Indicator  R^2 Spearman  \\\n",
+       "0     Adolescent fertility rate (births per 1,000 wo...      0.817804   \n",
+       "1     Adolescent fertility rate (births per 1,000 wo...      0.759333   \n",
+       "2     Adolescent fertility rate (births per 1,000 wo...      0.936963   \n",
+       "3     Adolescent fertility rate (births per 1,000 wo...      0.786708   \n",
+       "4     Adolescent fertility rate (births per 1,000 wo...      0.924056   \n",
+       "...                                                 ...           ...   \n",
+       "6640             Completeness of birth registration (%)      0.839228   \n",
+       "6641             Completeness of birth registration (%)      0.792184   \n",
+       "6642             Completeness of birth registration (%)      0.840648   \n",
+       "6643             Completeness of birth registration (%)      0.963636   \n",
+       "6644             Completeness of birth registration (%)      0.789474   \n",
+       "\n",
+       "     Behaviour Country Moved            Type Continent  \\\n",
+       "0     Negative     DEU     1  Does not apply    Europe   \n",
+       "1     Negative     SWE     3  Does not apply    Europe   \n",
+       "2     Negative     GBR    13  Does not apply    Europe   \n",
+       "3     Negative     HRV     8  Does not apply    Europe   \n",
+       "4     Negative     POL    21  Does not apply    Europe   \n",
+       "...        ...     ...   ...             ...       ...   \n",
+       "6640  Positive     PER     5  Does not apply     Latam   \n",
+       "6641  Negative     VEN     1  Does not apply     Latam   \n",
+       "6642  Positive     COL     2  Does not apply     Latam   \n",
+       "6643  Positive     PAN    21  Does not apply     Latam   \n",
+       "6644  Positive     CRI    13  Does not apply     Latam   \n",
+       "\n",
+       "      Number of times repeated_x       Group      Level  \\\n",
+       "0                             22  Demography  secondary   \n",
+       "1                             22  Demography  secondary   \n",
+       "2                             22  Demography  secondary   \n",
+       "3                             22  Demography  secondary   \n",
+       "4                             22  Demography  secondary   \n",
+       "...                          ...         ...        ...   \n",
+       "6640                          11   Demoraphy  secondary   \n",
+       "6641                          11   Demoraphy  secondary   \n",
+       "6642                          11   Demoraphy  secondary   \n",
+       "6643                          11   Demoraphy  secondary   \n",
+       "6644                          11   Demoraphy  secondary   \n",
+       "\n",
+       "      Number of times repeated_y  \n",
+       "0                             40  \n",
+       "1                             40  \n",
+       "2                             40  \n",
+       "3                             40  \n",
+       "4                             40  \n",
+       "...                          ...  \n",
+       "6640                          21  \n",
+       "6641                          21  \n",
+       "6642                          21  \n",
+       "6643                          21  \n",
+       "6644                          21  \n",
+       "\n",
+       "[6645 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final=pd.DataFrame()\n",
+    "for i in range(0,len(clist)):\n",
+    "    dat=quarterfinal.loc[quarterfinal.loc[:, 'Country'] == clist[i]]\n",
+    "    ids=dat.groupby('Indicator')['R^2 Spearman'].transform(max)==dat['R^2 Spearman']\n",
+    "    dat[ids]\n",
+    "    semifinal=pd.DataFrame(dat[ids])\n",
+    "    final=pd.concat((final,semifinal), axis=0)\n",
+    "final_indicators_list=categories.Indicator.unique()\n",
+    "final['Continent']=final['Country'].map(all_countries)\n",
+    "final=final.loc[final.loc[:, 'Indicator'].isin(np.array(final_indicators_list))]\n",
+    "final=pd.merge(final,categories, left_on='Indicator',right_on='Indicator')\n",
+    "\n",
+    "columnssf=final.Indicator.to_list()\n",
+    "columnsf=np.unique(columnssf)\n",
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "final_indicators = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Indicator','Number of times repeated'])\n",
+    "\n",
+    "final=pd.merge(final,final_indicators, left_on='Indicator',right_on='Indicator')\n",
+    "final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The former table final collects the indicators that have shown a high correlation with the GDP (according to the criteria established, see above). It provides detail of each case specifying the numerical value of the correlation (R^2 Spearman); the alignment with the GDP (Behaviour); the country and continent where it applies; if there is any temporal displacement; the group of interest it belongs to and the number of times repeated.\n",
+    "Moreover, if we focus over the column of \"Number of times repeated\". It reflects the number of times a high correlation of each indicator is repeated over the whole sample (aggregating all countries).\n",
+    "\n",
+    "In addition we have used the `itables` library to if wanted any one can search through the table as they wish."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"module\">\n",
+       "  window.__itables_render = async (table_id, dt_args) => {\n",
+       "    await initializeDataTables();\n",
+       "    $(table_id).DataTable(dt_args);\n",
+       "  };\n",
+       "\n",
+       "  async function initializeDataTables() {\n",
+       "    if ($.prototype.DataTable) {\n",
+       "      return;\n",
+       "    }\n",
+       "    const dt = (await import(\"data:text/javascript;base64,/*! DataTables 1.12.1
 * ©2008-2022 SpryMedia Ltd - datatables.net/license
 */

// ESM export of DataTables
export default function ($, root) {
	if (! root) {
		root = window;
	}

	if (! $) {
		$ = window.jQuery;
	}

	
	var DataTable = function ( selector, options )
	{
		// When creating with `new`, create a new DataTable, returning the API instance
		if (this instanceof DataTable) {
			return $(selector).DataTable(options);
		}
		else {
			// Argument switching
			options = selector;
		}
	
		/**
		 * Perform a jQuery selector action on the table's TR elements (from the tbody) and
		 * return the resulting jQuery object.
		 *  @param {string|node|jQuery} sSelector jQuery selector or node collection to act on
		 *  @param {object} [oOpts] Optional parameters for modifying the rows to be included
		 *  @param {string} [oOpts.filter=none] Select TR elements that meet the current filter
		 *    criterion ("applied") or all TR elements (i.e. no filter).
		 *  @param {string} [oOpts.order=current] Order of the TR elements in the processed array.
		 *    Can be either 'current', whereby the current sorting of the table is used, or
		 *    'original' whereby the original order the data was read into the table is used.
		 *  @param {string} [oOpts.page=all] Limit the selection to the currently displayed page
		 *    ("current") or not ("all"). If 'current' is given, then order is assumed to be
		 *    'current' and filter is 'applied', regardless of what they might be given as.
		 *  @returns {object} jQuery object, filtered by the given selector.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Highlight every second row
		 *      oTable.$('tr:odd').css('backgroundColor', 'blue');
		 *    } );
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Filter to rows with 'Webkit' in them, add a background colour and then
		 *      // remove the filter, thus highlighting the 'Webkit' rows only.
		 *      oTable.fnFilter('Webkit');
		 *      oTable.$('tr', {"search": "applied"}).css('backgroundColor', 'blue');
		 *      oTable.fnFilter('');
		 *    } );
		 */
		this.$ = function ( sSelector, oOpts )
		{
			return this.api(true).$( sSelector, oOpts );
		};
		
		
		/**
		 * Almost identical to $ in operation, but in this case returns the data for the matched
		 * rows - as such, the jQuery selector used should match TR row nodes or TD/TH cell nodes
		 * rather than any descendants, so the data can be obtained for the row/cell. If matching
		 * rows are found, the data returned is the original data array/object that was used to
		 * create the row (or a generated array if from a DOM source).
		 *
		 * This method is often useful in-combination with $ where both functions are given the
		 * same parameters and the array indexes will match identically.
		 *  @param {string|node|jQuery} sSelector jQuery selector or node collection to act on
		 *  @param {object} [oOpts] Optional parameters for modifying the rows to be included
		 *  @param {string} [oOpts.filter=none] Select elements that meet the current filter
		 *    criterion ("applied") or all elements (i.e. no filter).
		 *  @param {string} [oOpts.order=current] Order of the data in the processed array.
		 *    Can be either 'current', whereby the current sorting of the table is used, or
		 *    'original' whereby the original order the data was read into the table is used.
		 *  @param {string} [oOpts.page=all] Limit the selection to the currently displayed page
		 *    ("current") or not ("all"). If 'current' is given, then order is assumed to be
		 *    'current' and filter is 'applied', regardless of what they might be given as.
		 *  @returns {array} Data for the matched elements. If any elements, as a result of the
		 *    selector, were not TR, TD or TH elements in the DataTable, they will have a null
		 *    entry in the array.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Get the data from the first row in the table
		 *      var data = oTable._('tr:first');
		 *
		 *      // Do something useful with the data
		 *      alert( "First cell is: "+data[0] );
		 *    } );
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Filter to 'Webkit' and get all data for
		 *      oTable.fnFilter('Webkit');
		 *      var data = oTable._('tr', {"search": "applied"});
		 *
		 *      // Do something with the data
		 *      alert( data.length+" rows matched the search" );
		 *    } );
		 */
		this._ = function ( sSelector, oOpts )
		{
			return this.api(true).rows( sSelector, oOpts ).data();
		};
		
		
		/**
		 * Create a DataTables Api instance, with the currently selected tables for
		 * the Api's context.
		 * @param {boolean} [traditional=false] Set the API instance's context to be
		 *   only the table referred to by the `DataTable.ext.iApiIndex` option, as was
		 *   used in the API presented by DataTables 1.9- (i.e. the traditional mode),
		 *   or if all tables captured in the jQuery object should be used.
		 * @return {DataTables.Api}
		 */
		this.api = function ( traditional )
		{
			return traditional ?
				new _Api(
					_fnSettingsFromNode( this[ _ext.iApiIndex ] )
				) :
				new _Api( this );
		};
		
		
		/**
		 * Add a single new row or multiple rows of data to the table. Please note
		 * that this is suitable for client-side processing only - if you are using
		 * server-side processing (i.e. "bServerSide": true), then to add data, you
		 * must add it to the data source, i.e. the server-side, through an Ajax call.
		 *  @param {array|object} data The data to be added to the table. This can be:
		 *    <ul>
		 *      <li>1D array of data - add a single row with the data provided</li>
		 *      <li>2D array of arrays - add multiple rows in a single call</li>
		 *      <li>object - data object when using <i>mData</i></li>
		 *      <li>array of objects - multiple data objects when using <i>mData</i></li>
		 *    </ul>
		 *  @param {bool} [redraw=true] redraw the table or not
		 *  @returns {array} An array of integers, representing the list of indexes in
		 *    <i>aoData</i> ({@link DataTable.models.oSettings}) that have been added to
		 *    the table.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    // Global var for counter
		 *    var giCount = 2;
		 *
		 *    $(document).ready(function() {
		 *      $('#example').dataTable();
		 *    } );
		 *
		 *    function fnClickAddRow() {
		 *      $('#example').dataTable().fnAddData( [
		 *        giCount+".1",
		 *        giCount+".2",
		 *        giCount+".3",
		 *        giCount+".4" ]
		 *      );
		 *
		 *      giCount++;
		 *    }
		 */
		this.fnAddData = function( data, redraw )
		{
			var api = this.api( true );
		
			/* Check if we want to add multiple rows or not */
			var rows = Array.isArray(data) && ( Array.isArray(data[0]) || $.isPlainObject(data[0]) ) ?
				api.rows.add( data ) :
				api.row.add( data );
		
			if ( redraw === undefined || redraw ) {
				api.draw();
			}
		
			return rows.flatten().toArray();
		};
		
		
		/**
		 * This function will make DataTables recalculate the column sizes, based on the data
		 * contained in the table and the sizes applied to the columns (in the DOM, CSS or
		 * through the sWidth parameter). This can be useful when the width of the table's
		 * parent element changes (for example a window resize).
		 *  @param {boolean} [bRedraw=true] Redraw the table or not, you will typically want to
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable( {
		 *        "sScrollY": "200px",
		 *        "bPaginate": false
		 *      } );
		 *
		 *      $(window).on('resize', function () {
		 *        oTable.fnAdjustColumnSizing();
		 *      } );
		 *    } );
		 */
		this.fnAdjustColumnSizing = function ( bRedraw )
		{
			var api = this.api( true ).columns.adjust();
			var settings = api.settings()[0];
			var scroll = settings.oScroll;
		
			if ( bRedraw === undefined || bRedraw ) {
				api.draw( false );
			}
			else if ( scroll.sX !== "" || scroll.sY !== "" ) {
				/* If not redrawing, but scrolling, we want to apply the new column sizes anyway */
				_fnScrollDraw( settings );
			}
		};
		
		
		/**
		 * Quickly and simply clear a table
		 *  @param {bool} [bRedraw=true] redraw the table or not
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Immediately 'nuke' the current rows (perhaps waiting for an Ajax callback...)
		 *      oTable.fnClearTable();
		 *    } );
		 */
		this.fnClearTable = function( bRedraw )
		{
			var api = this.api( true ).clear();
		
			if ( bRedraw === undefined || bRedraw ) {
				api.draw();
			}
		};
		
		
		/**
		 * The exact opposite of 'opening' a row, this function will close any rows which
		 * are currently 'open'.
		 *  @param {node} nTr the table row to 'close'
		 *  @returns {int} 0 on success, or 1 if failed (can't find the row)
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable;
		 *
		 *      // 'open' an information row when a row is clicked on
		 *      $('#example tbody tr').click( function () {
		 *        if ( oTable.fnIsOpen(this) ) {
		 *          oTable.fnClose( this );
		 *        } else {
		 *          oTable.fnOpen( this, "Temporary row opened", "info_row" );
		 *        }
		 *      } );
		 *
		 *      oTable = $('#example').dataTable();
		 *    } );
		 */
		this.fnClose = function( nTr )
		{
			this.api( true ).row( nTr ).child.hide();
		};
		
		
		/**
		 * Remove a row for the table
		 *  @param {mixed} target The index of the row from aoData to be deleted, or
		 *    the TR element you want to delete
		 *  @param {function|null} [callBack] Callback function
		 *  @param {bool} [redraw=true] Redraw the table or not
		 *  @returns {array} The row that was deleted
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Immediately remove the first row
		 *      oTable.fnDeleteRow( 0 );
		 *    } );
		 */
		this.fnDeleteRow = function( target, callback, redraw )
		{
			var api = this.api( true );
			var rows = api.rows( target );
			var settings = rows.settings()[0];
			var data = settings.aoData[ rows[0][0] ];
		
			rows.remove();
		
			if ( callback ) {
				callback.call( this, settings, data );
			}
		
			if ( redraw === undefined || redraw ) {
				api.draw();
			}
		
			return data;
		};
		
		
		/**
		 * Restore the table to it's original state in the DOM by removing all of DataTables
		 * enhancements, alterations to the DOM structure of the table and event listeners.
		 *  @param {boolean} [remove=false] Completely remove the table from the DOM
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      // This example is fairly pointless in reality, but shows how fnDestroy can be used
		 *      var oTable = $('#example').dataTable();
		 *      oTable.fnDestroy();
		 *    } );
		 */
		this.fnDestroy = function ( remove )
		{
			this.api( true ).destroy( remove );
		};
		
		
		/**
		 * Redraw the table
		 *  @param {bool} [complete=true] Re-filter and resort (if enabled) the table before the draw.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Re-draw the table - you wouldn't want to do it here, but it's an example :-)
		 *      oTable.fnDraw();
		 *    } );
		 */
		this.fnDraw = function( complete )
		{
			// Note that this isn't an exact match to the old call to _fnDraw - it takes
			// into account the new data, but can hold position.
			this.api( true ).draw( complete );
		};
		
		
		/**
		 * Filter the input based on data
		 *  @param {string} sInput String to filter the table on
		 *  @param {int|null} [iColumn] Column to limit filtering to
		 *  @param {bool} [bRegex=false] Treat as regular expression or not
		 *  @param {bool} [bSmart=true] Perform smart filtering or not
		 *  @param {bool} [bShowGlobal=true] Show the input global filter in it's input box(es)
		 *  @param {bool} [bCaseInsensitive=true] Do case-insensitive matching (true) or not (false)
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Sometime later - filter...
		 *      oTable.fnFilter( 'test string' );
		 *    } );
		 */
		this.fnFilter = function( sInput, iColumn, bRegex, bSmart, bShowGlobal, bCaseInsensitive )
		{
			var api = this.api( true );
		
			if ( iColumn === null || iColumn === undefined ) {
				api.search( sInput, bRegex, bSmart, bCaseInsensitive );
			}
			else {
				api.column( iColumn ).search( sInput, bRegex, bSmart, bCaseInsensitive );
			}
		
			api.draw();
		};
		
		
		/**
		 * Get the data for the whole table, an individual row or an individual cell based on the
		 * provided parameters.
		 *  @param {int|node} [src] A TR row node, TD/TH cell node or an integer. If given as
		 *    a TR node then the data source for the whole row will be returned. If given as a
		 *    TD/TH cell node then iCol will be automatically calculated and the data for the
		 *    cell returned. If given as an integer, then this is treated as the aoData internal
		 *    data index for the row (see fnGetPosition) and the data for that row used.
		 *  @param {int} [col] Optional column index that you want the data of.
		 *  @returns {array|object|string} If mRow is undefined, then the data for all rows is
		 *    returned. If mRow is defined, just data for that row, and is iCol is
		 *    defined, only data for the designated cell is returned.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    // Row data
		 *    $(document).ready(function() {
		 *      oTable = $('#example').dataTable();
		 *
		 *      oTable.$('tr').click( function () {
		 *        var data = oTable.fnGetData( this );
		 *        // ... do something with the array / object of data for the row
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Individual cell data
		 *    $(document).ready(function() {
		 *      oTable = $('#example').dataTable();
		 *
		 *      oTable.$('td').click( function () {
		 *        var sData = oTable.fnGetData( this );
		 *        alert( 'The cell clicked on had the value of '+sData );
		 *      } );
		 *    } );
		 */
		this.fnGetData = function( src, col )
		{
			var api = this.api( true );
		
			if ( src !== undefined ) {
				var type = src.nodeName ? src.nodeName.toLowerCase() : '';
		
				return col !== undefined || type == 'td' || type == 'th' ?
					api.cell( src, col ).data() :
					api.row( src ).data() || null;
			}
		
			return api.data().toArray();
		};
		
		
		/**
		 * Get an array of the TR nodes that are used in the table's body. Note that you will
		 * typically want to use the '$' API method in preference to this as it is more
		 * flexible.
		 *  @param {int} [iRow] Optional row index for the TR element you want
		 *  @returns {array|node} If iRow is undefined, returns an array of all TR elements
		 *    in the table's body, or iRow is defined, just the TR element requested.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Get the nodes from the table
		 *      var nNodes = oTable.fnGetNodes( );
		 *    } );
		 */
		this.fnGetNodes = function( iRow )
		{
			var api = this.api( true );
		
			return iRow !== undefined ?
				api.row( iRow ).node() :
				api.rows().nodes().flatten().toArray();
		};
		
		
		/**
		 * Get the array indexes of a particular cell from it's DOM element
		 * and column index including hidden columns
		 *  @param {node} node this can either be a TR, TD or TH in the table's body
		 *  @returns {int} If nNode is given as a TR, then a single index is returned, or
		 *    if given as a cell, an array of [row index, column index (visible),
		 *    column index (all)] is given.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      $('#example tbody td').click( function () {
		 *        // Get the position of the current data from the node
		 *        var aPos = oTable.fnGetPosition( this );
		 *
		 *        // Get the data array for this row
		 *        var aData = oTable.fnGetData( aPos[0] );
		 *
		 *        // Update the data array and return the value
		 *        aData[ aPos[1] ] = 'clicked';
		 *        this.innerHTML = 'clicked';
		 *      } );
		 *
		 *      // Init DataTables
		 *      oTable = $('#example').dataTable();
		 *    } );
		 */
		this.fnGetPosition = function( node )
		{
			var api = this.api( true );
			var nodeName = node.nodeName.toUpperCase();
		
			if ( nodeName == 'TR' ) {
				return api.row( node ).index();
			}
			else if ( nodeName == 'TD' || nodeName == 'TH' ) {
				var cell = api.cell( node ).index();
		
				return [
					cell.row,
					cell.columnVisible,
					cell.column
				];
			}
			return null;
		};
		
		
		/**
		 * Check to see if a row is 'open' or not.
		 *  @param {node} nTr the table row to check
		 *  @returns {boolean} true if the row is currently open, false otherwise
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable;
		 *
		 *      // 'open' an information row when a row is clicked on
		 *      $('#example tbody tr').click( function () {
		 *        if ( oTable.fnIsOpen(this) ) {
		 *          oTable.fnClose( this );
		 *        } else {
		 *          oTable.fnOpen( this, "Temporary row opened", "info_row" );
		 *        }
		 *      } );
		 *
		 *      oTable = $('#example').dataTable();
		 *    } );
		 */
		this.fnIsOpen = function( nTr )
		{
			return this.api( true ).row( nTr ).child.isShown();
		};
		
		
		/**
		 * This function will place a new row directly after a row which is currently
		 * on display on the page, with the HTML contents that is passed into the
		 * function. This can be used, for example, to ask for confirmation that a
		 * particular record should be deleted.
		 *  @param {node} nTr The table row to 'open'
		 *  @param {string|node|jQuery} mHtml The HTML to put into the row
		 *  @param {string} sClass Class to give the new TD cell
		 *  @returns {node} The row opened. Note that if the table row passed in as the
		 *    first parameter, is not found in the table, this method will silently
		 *    return.
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable;
		 *
		 *      // 'open' an information row when a row is clicked on
		 *      $('#example tbody tr').click( function () {
		 *        if ( oTable.fnIsOpen(this) ) {
		 *          oTable.fnClose( this );
		 *        } else {
		 *          oTable.fnOpen( this, "Temporary row opened", "info_row" );
		 *        }
		 *      } );
		 *
		 *      oTable = $('#example').dataTable();
		 *    } );
		 */
		this.fnOpen = function( nTr, mHtml, sClass )
		{
			return this.api( true )
				.row( nTr )
				.child( mHtml, sClass )
				.show()
				.child()[0];
		};
		
		
		/**
		 * Change the pagination - provides the internal logic for pagination in a simple API
		 * function. With this function you can have a DataTables table go to the next,
		 * previous, first or last pages.
		 *  @param {string|int} mAction Paging action to take: "first", "previous", "next" or "last"
		 *    or page number to jump to (integer), note that page 0 is the first page.
		 *  @param {bool} [bRedraw=true] Redraw the table or not
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *      oTable.fnPageChange( 'next' );
		 *    } );
		 */
		this.fnPageChange = function ( mAction, bRedraw )
		{
			var api = this.api( true ).page( mAction );
		
			if ( bRedraw === undefined || bRedraw ) {
				api.draw(false);
			}
		};
		
		
		/**
		 * Show a particular column
		 *  @param {int} iCol The column whose display should be changed
		 *  @param {bool} bShow Show (true) or hide (false) the column
		 *  @param {bool} [bRedraw=true] Redraw the table or not
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Hide the second column after initialisation
		 *      oTable.fnSetColumnVis( 1, false );
		 *    } );
		 */
		this.fnSetColumnVis = function ( iCol, bShow, bRedraw )
		{
			var api = this.api( true ).column( iCol ).visible( bShow );
		
			if ( bRedraw === undefined || bRedraw ) {
				api.columns.adjust().draw();
			}
		};
		
		
		/**
		 * Get the settings for a particular table for external manipulation
		 *  @returns {object} DataTables settings object. See
		 *    {@link DataTable.models.oSettings}
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *      var oSettings = oTable.fnSettings();
		 *
		 *      // Show an example parameter from the settings
		 *      alert( oSettings._iDisplayStart );
		 *    } );
		 */
		this.fnSettings = function()
		{
			return _fnSettingsFromNode( this[_ext.iApiIndex] );
		};
		
		
		/**
		 * Sort the table by a particular column
		 *  @param {int} iCol the data index to sort on. Note that this will not match the
		 *    'display index' if you have hidden data entries
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Sort immediately with columns 0 and 1
		 *      oTable.fnSort( [ [0,'asc'], [1,'asc'] ] );
		 *    } );
		 */
		this.fnSort = function( aaSort )
		{
			this.api( true ).order( aaSort ).draw();
		};
		
		
		/**
		 * Attach a sort listener to an element for a given column
		 *  @param {node} nNode the element to attach the sort listener to
		 *  @param {int} iColumn the column that a click on this node will sort on
		 *  @param {function} [fnCallback] callback function when sort is run
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *
		 *      // Sort on column 1, when 'sorter' is clicked on
		 *      oTable.fnSortListener( document.getElementById('sorter'), 1 );
		 *    } );
		 */
		this.fnSortListener = function( nNode, iColumn, fnCallback )
		{
			this.api( true ).order.listener( nNode, iColumn, fnCallback );
		};
		
		
		/**
		 * Update a table cell or row - this method will accept either a single value to
		 * update the cell with, an array of values with one element for each column or
		 * an object in the same format as the original data source. The function is
		 * self-referencing in order to make the multi column updates easier.
		 *  @param {object|array|string} mData Data to update the cell/row with
		 *  @param {node|int} mRow TR element you want to update or the aoData index
		 *  @param {int} [iColumn] The column to update, give as null or undefined to
		 *    update a whole row.
		 *  @param {bool} [bRedraw=true] Redraw the table or not
		 *  @param {bool} [bAction=true] Perform pre-draw actions or not
		 *  @returns {int} 0 on success, 1 on error
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *      oTable.fnUpdate( 'Example update', 0, 0 ); // Single cell
		 *      oTable.fnUpdate( ['a', 'b', 'c', 'd', 'e'], $('tbody tr')[0] ); // Row
		 *    } );
		 */
		this.fnUpdate = function( mData, mRow, iColumn, bRedraw, bAction )
		{
			var api = this.api( true );
		
			if ( iColumn === undefined || iColumn === null ) {
				api.row( mRow ).data( mData );
			}
			else {
				api.cell( mRow, iColumn ).data( mData );
			}
		
			if ( bAction === undefined || bAction ) {
				api.columns.adjust();
			}
		
			if ( bRedraw === undefined || bRedraw ) {
				api.draw();
			}
			return 0;
		};
		
		
		/**
		 * Provide a common method for plug-ins to check the version of DataTables being used, in order
		 * to ensure compatibility.
		 *  @param {string} sVersion Version string to check for, in the format "X.Y.Z". Note that the
		 *    formats "X" and "X.Y" are also acceptable.
		 *  @returns {boolean} true if this version of DataTables is greater or equal to the required
		 *    version, or false if this version of DataTales is not suitable
		 *  @method
		 *  @dtopt API
		 *  @deprecated Since v1.10
		 *
		 *  @example
		 *    $(document).ready(function() {
		 *      var oTable = $('#example').dataTable();
		 *      alert( oTable.fnVersionCheck( '1.9.0' ) );
		 *    } );
		 */
		this.fnVersionCheck = _ext.fnVersionCheck;
		
	
		var _that = this;
		var emptyInit = options === undefined;
		var len = this.length;
	
		if ( emptyInit ) {
			options = {};
		}
	
		this.oApi = this.internal = _ext.internal;
	
		// Extend with old style plug-in API methods
		for ( var fn in DataTable.ext.internal ) {
			if ( fn ) {
				this[fn] = _fnExternApiFunc(fn);
			}
		}
	
		this.each(function() {
			// For each initialisation we want to give it a clean initialisation
			// object that can be bashed around
			var o = {};
			var oInit = len > 1 ? // optimisation for single table case
				_fnExtend( o, options, true ) :
				options;
	
			/*global oInit,_that,emptyInit*/
			var i=0, iLen, j, jLen, k, kLen;
			var sId = this.getAttribute( 'id' );
			var bInitHandedOff = false;
			var defaults = DataTable.defaults;
			var $this = $(this);
			
			
			/* Sanity check */
			if ( this.nodeName.toLowerCase() != 'table' )
			{
				_fnLog( null, 0, 'Non-table node initialisation ('+this.nodeName+')', 2 );
				return;
			}
			
			/* Backwards compatibility for the defaults */
			_fnCompatOpts( defaults );
			_fnCompatCols( defaults.column );
			
			/* Convert the camel-case defaults to Hungarian */
			_fnCamelToHungarian( defaults, defaults, true );
			_fnCamelToHungarian( defaults.column, defaults.column, true );
			
			/* Setting up the initialisation object */
			_fnCamelToHungarian( defaults, $.extend( oInit, $this.data() ), true );
			
			
			
			/* Check to see if we are re-initialising a table */
			var allSettings = DataTable.settings;
			for ( i=0, iLen=allSettings.length ; i<iLen ; i++ )
			{
				var s = allSettings[i];
			
				/* Base check on table node */
				if (
					s.nTable == this ||
					(s.nTHead && s.nTHead.parentNode == this) ||
					(s.nTFoot && s.nTFoot.parentNode == this)
				) {
					var bRetrieve = oInit.bRetrieve !== undefined ? oInit.bRetrieve : defaults.bRetrieve;
					var bDestroy = oInit.bDestroy !== undefined ? oInit.bDestroy : defaults.bDestroy;
			
					if ( emptyInit || bRetrieve )
					{
						return s.oInstance;
					}
					else if ( bDestroy )
					{
						s.oInstance.fnDestroy();
						break;
					}
					else
					{
						_fnLog( s, 0, 'Cannot reinitialise DataTable', 3 );
						return;
					}
				}
			
				/* If the element we are initialising has the same ID as a table which was previously
				 * initialised, but the table nodes don't match (from before) then we destroy the old
				 * instance by simply deleting it. This is under the assumption that the table has been
				 * destroyed by other methods. Anyone using non-id selectors will need to do this manually
				 */
				if ( s.sTableId == this.id )
				{
					allSettings.splice( i, 1 );
					break;
				}
			}
			
			/* Ensure the table has an ID - required for accessibility */
			if ( sId === null || sId === "" )
			{
				sId = "DataTables_Table_"+(DataTable.ext._unique++);
				this.id = sId;
			}
			
			/* Create the settings object for this table and set some of the default parameters */
			var oSettings = $.extend( true, {}, DataTable.models.oSettings, {
				"sDestroyWidth": $this[0].style.width,
				"sInstance":     sId,
				"sTableId":      sId
			} );
			oSettings.nTable = this;
			oSettings.oApi   = _that.internal;
			oSettings.oInit  = oInit;
			
			allSettings.push( oSettings );
			
			// Need to add the instance after the instance after the settings object has been added
			// to the settings array, so we can self reference the table instance if more than one
			oSettings.oInstance = (_that.length===1) ? _that : $this.dataTable();
			
			// Backwards compatibility, before we apply all the defaults
			_fnCompatOpts( oInit );
			_fnLanguageCompat( oInit.oLanguage );
			
			// If the length menu is given, but the init display length is not, use the length menu
			if ( oInit.aLengthMenu && ! oInit.iDisplayLength )
			{
				oInit.iDisplayLength = Array.isArray( oInit.aLengthMenu[0] ) ?
					oInit.aLengthMenu[0][0] : oInit.aLengthMenu[0];
			}
			
			// Apply the defaults and init options to make a single init object will all
			// options defined from defaults and instance options.
			oInit = _fnExtend( $.extend( true, {}, defaults ), oInit );
			
			
			// Map the initialisation options onto the settings object
			_fnMap( oSettings.oFeatures, oInit, [
				"bPaginate",
				"bLengthChange",
				"bFilter",
				"bSort",
				"bSortMulti",
				"bInfo",
				"bProcessing",
				"bAutoWidth",
				"bSortClasses",
				"bServerSide",
				"bDeferRender"
			] );
			_fnMap( oSettings, oInit, [
				"asStripeClasses",
				"ajax",
				"fnServerData",
				"fnFormatNumber",
				"sServerMethod",
				"aaSorting",
				"aaSortingFixed",
				"aLengthMenu",
				"sPaginationType",
				"sAjaxSource",
				"sAjaxDataProp",
				"iStateDuration",
				"sDom",
				"bSortCellsTop",
				"iTabIndex",
				"fnStateLoadCallback",
				"fnStateSaveCallback",
				"renderer",
				"searchDelay",
				"rowId",
				[ "iCookieDuration", "iStateDuration" ], // backwards compat
				[ "oSearch", "oPreviousSearch" ],
				[ "aoSearchCols", "aoPreSearchCols" ],
				[ "iDisplayLength", "_iDisplayLength" ]
			] );
			_fnMap( oSettings.oScroll, oInit, [
				[ "sScrollX", "sX" ],
				[ "sScrollXInner", "sXInner" ],
				[ "sScrollY", "sY" ],
				[ "bScrollCollapse", "bCollapse" ]
			] );
			_fnMap( oSettings.oLanguage, oInit, "fnInfoCallback" );
			
			/* Callback functions which are array driven */
			_fnCallbackReg( oSettings, 'aoDrawCallback',       oInit.fnDrawCallback,      'user' );
			_fnCallbackReg( oSettings, 'aoServerParams',       oInit.fnServerParams,      'user' );
			_fnCallbackReg( oSettings, 'aoStateSaveParams',    oInit.fnStateSaveParams,   'user' );
			_fnCallbackReg( oSettings, 'aoStateLoadParams',    oInit.fnStateLoadParams,   'user' );
			_fnCallbackReg( oSettings, 'aoStateLoaded',        oInit.fnStateLoaded,       'user' );
			_fnCallbackReg( oSettings, 'aoRowCallback',        oInit.fnRowCallback,       'user' );
			_fnCallbackReg( oSettings, 'aoRowCreatedCallback', oInit.fnCreatedRow,        'user' );
			_fnCallbackReg( oSettings, 'aoHeaderCallback',     oInit.fnHeaderCallback,    'user' );
			_fnCallbackReg( oSettings, 'aoFooterCallback',     oInit.fnFooterCallback,    'user' );
			_fnCallbackReg( oSettings, 'aoInitComplete',       oInit.fnInitComplete,      'user' );
			_fnCallbackReg( oSettings, 'aoPreDrawCallback',    oInit.fnPreDrawCallback,   'user' );
			
			oSettings.rowIdFn = _fnGetObjectDataFn( oInit.rowId );
			
			/* Browser support detection */
			_fnBrowserDetect( oSettings );
			
			var oClasses = oSettings.oClasses;
			
			$.extend( oClasses, DataTable.ext.classes, oInit.oClasses );
			$this.addClass( oClasses.sTable );
			
			
			if ( oSettings.iInitDisplayStart === undefined )
			{
				/* Display start point, taking into account the save saving */
				oSettings.iInitDisplayStart = oInit.iDisplayStart;
				oSettings._iDisplayStart = oInit.iDisplayStart;
			}
			
			if ( oInit.iDeferLoading !== null )
			{
				oSettings.bDeferLoading = true;
				var tmp = Array.isArray( oInit.iDeferLoading );
				oSettings._iRecordsDisplay = tmp ? oInit.iDeferLoading[0] : oInit.iDeferLoading;
				oSettings._iRecordsTotal = tmp ? oInit.iDeferLoading[1] : oInit.iDeferLoading;
			}
			
			/* Language definitions */
			var oLanguage = oSettings.oLanguage;
			$.extend( true, oLanguage, oInit.oLanguage );
			
			if ( oLanguage.sUrl )
			{
				/* Get the language definitions from a file - because this Ajax call makes the language
				 * get async to the remainder of this function we use bInitHandedOff to indicate that
				 * _fnInitialise will be fired by the returned Ajax handler, rather than the constructor
				 */
				$.ajax( {
					dataType: 'json',
					url: oLanguage.sUrl,
					success: function ( json ) {
						_fnCamelToHungarian( defaults.oLanguage, json );
						_fnLanguageCompat( json );
						$.extend( true, oLanguage, json, oSettings.oInit.oLanguage );
			
						_fnCallbackFire( oSettings, null, 'i18n', [oSettings]);
						_fnInitialise( oSettings );
					},
					error: function () {
						// Error occurred loading language file, continue on as best we can
						_fnInitialise( oSettings );
					}
				} );
				bInitHandedOff = true;
			}
			else {
				_fnCallbackFire( oSettings, null, 'i18n', [oSettings]);
			}
			
			/*
			 * Stripes
			 */
			if ( oInit.asStripeClasses === null )
			{
				oSettings.asStripeClasses =[
					oClasses.sStripeOdd,
					oClasses.sStripeEven
				];
			}
			
			/* Remove row stripe classes if they are already on the table row */
			var stripeClasses = oSettings.asStripeClasses;
			var rowOne = $this.children('tbody').find('tr').eq(0);
			if ( $.inArray( true, $.map( stripeClasses, function(el, i) {
				return rowOne.hasClass(el);
			} ) ) !== -1 ) {
				$('tbody tr', this).removeClass( stripeClasses.join(' ') );
				oSettings.asDestroyStripes = stripeClasses.slice();
			}
			
			/*
			 * Columns
			 * See if we should load columns automatically or use defined ones
			 */
			var anThs = [];
			var aoColumnsInit;
			var nThead = this.getElementsByTagName('thead');
			if ( nThead.length !== 0 )
			{
				_fnDetectHeader( oSettings.aoHeader, nThead[0] );
				anThs = _fnGetUniqueThs( oSettings );
			}
			
			/* If not given a column array, generate one with nulls */
			if ( oInit.aoColumns === null )
			{
				aoColumnsInit = [];
				for ( i=0, iLen=anThs.length ; i<iLen ; i++ )
				{
					aoColumnsInit.push( null );
				}
			}
			else
			{
				aoColumnsInit = oInit.aoColumns;
			}
			
			/* Add the columns */
			for ( i=0, iLen=aoColumnsInit.length ; i<iLen ; i++ )
			{
				_fnAddColumn( oSettings, anThs ? anThs[i] : null );
			}
			
			/* Apply the column definitions */
			_fnApplyColumnDefs( oSettings, oInit.aoColumnDefs, aoColumnsInit, function (iCol, oDef) {
				_fnColumnOptions( oSettings, iCol, oDef );
			} );
			
			/* HTML5 attribute detection - build an mData object automatically if the
			 * attributes are found
			 */
			if ( rowOne.length ) {
				var a = function ( cell, name ) {
					return cell.getAttribute( 'data-'+name ) !== null ? name : null;
				};
			
				$( rowOne[0] ).children('th, td').each( function (i, cell) {
					var col = oSettings.aoColumns[i];
			
					if ( col.mData === i ) {
						var sort = a( cell, 'sort' ) || a( cell, 'order' );
						var filter = a( cell, 'filter' ) || a( cell, 'search' );
			
						if ( sort !== null || filter !== null ) {
							col.mData = {
								_:      i+'.display',
								sort:   sort !== null   ? i+'.@data-'+sort   : undefined,
								type:   sort !== null   ? i+'.@data-'+sort   : undefined,
								filter: filter !== null ? i+'.@data-'+filter : undefined
							};
			
							_fnColumnOptions( oSettings, i );
						}
					}
				} );
			}
			
			var features = oSettings.oFeatures;
			var loadedInit = function () {
				/*
				 * Sorting
				 * @todo For modularisation (1.11) this needs to do into a sort start up handler
				 */
			
				// If aaSorting is not defined, then we use the first indicator in asSorting
				// in case that has been altered, so the default sort reflects that option
				if ( oInit.aaSorting === undefined ) {
					var sorting = oSettings.aaSorting;
					for ( i=0, iLen=sorting.length ; i<iLen ; i++ ) {
						sorting[i][1] = oSettings.aoColumns[ i ].asSorting[0];
					}
				}
			
				/* Do a first pass on the sorting classes (allows any size changes to be taken into
				 * account, and also will apply sorting disabled classes if disabled
				 */
				_fnSortingClasses( oSettings );
			
				if ( features.bSort ) {
					_fnCallbackReg( oSettings, 'aoDrawCallback', function () {
						if ( oSettings.bSorted ) {
							var aSort = _fnSortFlatten( oSettings );
							var sortedColumns = {};
			
							$.each( aSort, function (i, val) {
								sortedColumns[ val.src ] = val.dir;
							} );
			
							_fnCallbackFire( oSettings, null, 'order', [oSettings, aSort, sortedColumns] );
							_fnSortAria( oSettings );
						}
					} );
				}
			
				_fnCallbackReg( oSettings, 'aoDrawCallback', function () {
					if ( oSettings.bSorted || _fnDataSource( oSettings ) === 'ssp' || features.bDeferRender ) {
						_fnSortingClasses( oSettings );
					}
				}, 'sc' );
			
			
				/*
				 * Final init
				 * Cache the header, body and footer as required, creating them if needed
				 */
			
				// Work around for Webkit bug 83867 - store the caption-side before removing from doc
				var captions = $this.children('caption').each( function () {
					this._captionSide = $(this).css('caption-side');
				} );
			
				var thead = $this.children('thead');
				if ( thead.length === 0 ) {
					thead = $('<thead/>').appendTo($this);
				}
				oSettings.nTHead = thead[0];
			
				var tbody = $this.children('tbody');
				if ( tbody.length === 0 ) {
					tbody = $('<tbody/>').insertAfter(thead);
				}
				oSettings.nTBody = tbody[0];
			
				var tfoot = $this.children('tfoot');
				if ( tfoot.length === 0 && captions.length > 0 && (oSettings.oScroll.sX !== "" || oSettings.oScroll.sY !== "") ) {
					// If we are a scrolling table, and no footer has been given, then we need to create
					// a tfoot element for the caption element to be appended to
					tfoot = $('<tfoot/>').appendTo($this);
				}
			
				if ( tfoot.length === 0 || tfoot.children().length === 0 ) {
					$this.addClass( oClasses.sNoFooter );
				}
				else if ( tfoot.length > 0 ) {
					oSettings.nTFoot = tfoot[0];
					_fnDetectHeader( oSettings.aoFooter, oSettings.nTFoot );
				}
			
				/* Check if there is data passing into the constructor */
				if ( oInit.aaData ) {
					for ( i=0 ; i<oInit.aaData.length ; i++ ) {
						_fnAddData( oSettings, oInit.aaData[ i ] );
					}
				}
				else if ( oSettings.bDeferLoading || _fnDataSource( oSettings ) == 'dom' ) {
					/* Grab the data from the page - only do this when deferred loading or no Ajax
					 * source since there is no point in reading the DOM data if we are then going
					 * to replace it with Ajax data
					 */
					_fnAddTr( oSettings, $(oSettings.nTBody).children('tr') );
				}
			
				/* Copy the data index array */
				oSettings.aiDisplay = oSettings.aiDisplayMaster.slice();
			
				/* Initialisation complete - table can be drawn */
				oSettings.bInitialised = true;
			
				/* Check if we need to initialise the table (it might not have been handed off to the
				 * language processor)
				 */
				if ( bInitHandedOff === false ) {
					_fnInitialise( oSettings );
				}
			};
			
			/* Must be done after everything which can be overridden by the state saving! */
			_fnCallbackReg( oSettings, 'aoDrawCallback', _fnSaveState, 'state_save' );
			
			if ( oInit.bStateSave )
			{
				features.bStateSave = true;
				_fnLoadState( oSettings, oInit, loadedInit );
			}
			else {
				loadedInit();
			}
			
		} );
		_that = null;
		return this;
	};
	
	
	/*
	 * It is useful to have variables which are scoped locally so only the
	 * DataTables functions can access them and they don't leak into global space.
	 * At the same time these functions are often useful over multiple files in the
	 * core and API, so we list, or at least document, all variables which are used
	 * by DataTables as private variables here. This also ensures that there is no
	 * clashing of variable names and that they can easily referenced for reuse.
	 */
	
	
	// Defined else where
	//  _selector_run
	//  _selector_opts
	//  _selector_first
	//  _selector_row_indexes
	
	var _ext; // DataTable.ext
	var _Api; // DataTable.Api
	var _api_register; // DataTable.Api.register
	var _api_registerPlural; // DataTable.Api.registerPlural
	
	var _re_dic = {};
	var _re_new_lines = /[\r\n\u2028]/g;
	var _re_html = /<.*?>/g;
	
	// This is not strict ISO8601 - Date.parse() is quite lax, although
	// implementations differ between browsers.
	var _re_date = /^\d{2,4}[\.\/\-]\d{1,2}[\.\/\-]\d{1,2}([T ]{1}\d{1,2}[:\.]\d{2}([\.:]\d{2})?)?$/;
	
	// Escape regular expression special characters
	var _re_escape_regex = new RegExp( '(\\' + [ '/', '.', '*', '+', '?', '|', '(', ')', '[', ']', '{', '}', '\\', '$', '^', '-' ].join('|\\') + ')', 'g' );
	
	// http://en.wikipedia.org/wiki/Foreign_exchange_market
	// - \u20BD - Russian ruble.
	// - \u20a9 - South Korean Won
	// - \u20BA - Turkish Lira
	// - \u20B9 - Indian Rupee
	// - R - Brazil (R$) and South Africa
	// - fr - Swiss Franc
	// - kr - Swedish krona, Norwegian krone and Danish krone
	// - \u2009 is thin space and \u202F is narrow no-break space, both used in many
	// - Ƀ - Bitcoin
	// - Ξ - Ethereum
	//   standards as thousands separators.
	var _re_formatted_numeric = /['\u00A0,$£€¥%\u2009\u202F\u20BD\u20a9\u20BArfkɃΞ]/gi;
	
	
	var _empty = function ( d ) {
		return !d || d === true || d === '-' ? true : false;
	};
	
	
	var _intVal = function ( s ) {
		var integer = parseInt( s, 10 );
		return !isNaN(integer) && isFinite(s) ? integer : null;
	};
	
	// Convert from a formatted number with characters other than `.` as the
	// decimal place, to a Javascript number
	var _numToDecimal = function ( num, decimalPoint ) {
		// Cache created regular expressions for speed as this function is called often
		if ( ! _re_dic[ decimalPoint ] ) {
			_re_dic[ decimalPoint ] = new RegExp( _fnEscapeRegex( decimalPoint ), 'g' );
		}
		return typeof num === 'string' && decimalPoint !== '.' ?
			num.replace( /\./g, '' ).replace( _re_dic[ decimalPoint ], '.' ) :
			num;
	};
	
	
	var _isNumber = function ( d, decimalPoint, formatted ) {
		var strType = typeof d === 'string';
	
		// If empty return immediately so there must be a number if it is a
		// formatted string (this stops the string "k", or "kr", etc being detected
		// as a formatted number for currency
		if ( _empty( d ) ) {
			return true;
		}
	
		if ( decimalPoint && strType ) {
			d = _numToDecimal( d, decimalPoint );
		}
	
		if ( formatted && strType ) {
			d = d.replace( _re_formatted_numeric, '' );
		}
	
		return !isNaN( parseFloat(d) ) && isFinite( d );
	};
	
	
	// A string without HTML in it can be considered to be HTML still
	var _isHtml = function ( d ) {
		return _empty( d ) || typeof d === 'string';
	};
	
	
	var _htmlNumeric = function ( d, decimalPoint, formatted ) {
		if ( _empty( d ) ) {
			return true;
		}
	
		var html = _isHtml( d );
		return ! html ?
			null :
			_isNumber( _stripHtml( d ), decimalPoint, formatted ) ?
				true :
				null;
	};
	
	
	var _pluck = function ( a, prop, prop2 ) {
		var out = [];
		var i=0, ien=a.length;
	
		// Could have the test in the loop for slightly smaller code, but speed
		// is essential here
		if ( prop2 !== undefined ) {
			for ( ; i<ien ; i++ ) {
				if ( a[i] && a[i][ prop ] ) {
					out.push( a[i][ prop ][ prop2 ] );
				}
			}
		}
		else {
			for ( ; i<ien ; i++ ) {
				if ( a[i] ) {
					out.push( a[i][ prop ] );
				}
			}
		}
	
		return out;
	};
	
	
	// Basically the same as _pluck, but rather than looping over `a` we use `order`
	// as the indexes to pick from `a`
	var _pluck_order = function ( a, order, prop, prop2 )
	{
		var out = [];
		var i=0, ien=order.length;
	
		// Could have the test in the loop for slightly smaller code, but speed
		// is essential here
		if ( prop2 !== undefined ) {
			for ( ; i<ien ; i++ ) {
				if ( a[ order[i] ][ prop ] ) {
					out.push( a[ order[i] ][ prop ][ prop2 ] );
				}
			}
		}
		else {
			for ( ; i<ien ; i++ ) {
				out.push( a[ order[i] ][ prop ] );
			}
		}
	
		return out;
	};
	
	
	var _range = function ( len, start )
	{
		var out = [];
		var end;
	
		if ( start === undefined ) {
			start = 0;
			end = len;
		}
		else {
			end = start;
			start = len;
		}
	
		for ( var i=start ; i<end ; i++ ) {
			out.push( i );
		}
	
		return out;
	};
	
	
	var _removeEmpty = function ( a )
	{
		var out = [];
	
		for ( var i=0, ien=a.length ; i<ien ; i++ ) {
			if ( a[i] ) { // careful - will remove all falsy values!
				out.push( a[i] );
			}
		}
	
		return out;
	};
	
	
	var _stripHtml = function ( d ) {
		return d.replace( _re_html, '' );
	};
	
	
	/**
	 * Determine if all values in the array are unique. This means we can short
	 * cut the _unique method at the cost of a single loop. A sorted array is used
	 * to easily check the values.
	 *
	 * @param  {array} src Source array
	 * @return {boolean} true if all unique, false otherwise
	 * @ignore
	 */
	var _areAllUnique = function ( src ) {
		if ( src.length < 2 ) {
			return true;
		}
	
		var sorted = src.slice().sort();
		var last = sorted[0];
	
		for ( var i=1, ien=sorted.length ; i<ien ; i++ ) {
			if ( sorted[i] === last ) {
				return false;
			}
	
			last = sorted[i];
		}
	
		return true;
	};
	
	
	/**
	 * Find the unique elements in a source array.
	 *
	 * @param  {array} src Source array
	 * @return {array} Array of unique items
	 * @ignore
	 */
	var _unique = function ( src )
	{
		if ( _areAllUnique( src ) ) {
			return src.slice();
		}
	
		// A faster unique method is to use object keys to identify used values,
		// but this doesn't work with arrays or objects, which we must also
		// consider. See jsperf.com/compare-array-unique-versions/4 for more
		// information.
		var
			out = [],
			val,
			i, ien=src.length,
			j, k=0;
	
		again: for ( i=0 ; i<ien ; i++ ) {
			val = src[i];
	
			for ( j=0 ; j<k ; j++ ) {
				if ( out[j] === val ) {
					continue again;
				}
			}
	
			out.push( val );
			k++;
		}
	
		return out;
	};
	
	// Surprisingly this is faster than [].concat.apply
	// https://jsperf.com/flatten-an-array-loop-vs-reduce/2
	var _flatten = function (out, val) {
		if (Array.isArray(val)) {
			for (var i=0 ; i<val.length ; i++) {
				_flatten(out, val[i]);
			}
		}
		else {
			out.push(val);
		}
	  
		return out;
	}
	
	var _includes = function (search, start) {
		if (start === undefined) {
			start = 0;
		}
	
		return this.indexOf(search, start) !== -1;	
	};
	
	// Array.isArray polyfill.
	// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/isArray
	if (! Array.isArray) {
	    Array.isArray = function(arg) {
	        return Object.prototype.toString.call(arg) === '[object Array]';
	    };
	}
	
	if (! Array.prototype.includes) {
		Array.prototype.includes = _includes;
	}
	
	// .trim() polyfill
	// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/String/trim
	if (!String.prototype.trim) {
	  String.prototype.trim = function () {
	    return this.replace(/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, '');
	  };
	}
	
	if (! String.prototype.includes) {
		String.prototype.includes = _includes;
	}
	
	/**
	 * DataTables utility methods
	 * 
	 * This namespace provides helper methods that DataTables uses internally to
	 * create a DataTable, but which are not exclusively used only for DataTables.
	 * These methods can be used by extension authors to save the duplication of
	 * code.
	 *
	 *  @namespace
	 */
	DataTable.util = {
		/**
		 * Throttle the calls to a function. Arguments and context are maintained
		 * for the throttled function.
		 *
		 * @param {function} fn Function to be called
		 * @param {integer} freq Call frequency in mS
		 * @return {function} Wrapped function
		 */
		throttle: function ( fn, freq ) {
			var
				frequency = freq !== undefined ? freq : 200,
				last,
				timer;
	
			return function () {
				var
					that = this,
					now  = +new Date(),
					args = arguments;
	
				if ( last && now < last + frequency ) {
					clearTimeout( timer );
	
					timer = setTimeout( function () {
						last = undefined;
						fn.apply( that, args );
					}, frequency );
				}
				else {
					last = now;
					fn.apply( that, args );
				}
			};
		},
	
	
		/**
		 * Escape a string such that it can be used in a regular expression
		 *
		 *  @param {string} val string to escape
		 *  @returns {string} escaped string
		 */
		escapeRegex: function ( val ) {
			return val.replace( _re_escape_regex, '\\$1' );
		},
	
		/**
		 * Create a function that will write to a nested object or array
		 * @param {*} source JSON notation string
		 * @returns Write function
		 */
		set: function ( source ) {
			if ( $.isPlainObject( source ) ) {
				/* Unlike get, only the underscore (global) option is used for for
				 * setting data since we don't know the type here. This is why an object
				 * option is not documented for `mData` (which is read/write), but it is
				 * for `mRender` which is read only.
				 */
				return DataTable.util.set( source._ );
			}
			else if ( source === null ) {
				// Nothing to do when the data source is null
				return function () {};
			}
			else if ( typeof source === 'function' ) {
				return function (data, val, meta) {
					source( data, 'set', val, meta );
				};
			}
			else if ( typeof source === 'string' && (source.indexOf('.') !== -1 ||
					  source.indexOf('[') !== -1 || source.indexOf('(') !== -1) )
			{
				// Like the get, we need to get data from a nested object
				var setData = function (data, val, src) {
					var a = _fnSplitObjNotation( src ), b;
					var aLast = a[a.length-1];
					var arrayNotation, funcNotation, o, innerSrc;
		
					for ( var i=0, iLen=a.length-1 ; i<iLen ; i++ ) {
						// Protect against prototype pollution
						if (a[i] === '__proto__' || a[i] === 'constructor') {
							throw new Error('Cannot set prototype values');
						}
		
						// Check if we are dealing with an array notation request
						arrayNotation = a[i].match(__reArray);
						funcNotation = a[i].match(__reFn);
		
						if ( arrayNotation ) {
							a[i] = a[i].replace(__reArray, '');
							data[ a[i] ] = [];
		
							// Get the remainder of the nested object to set so we can recurse
							b = a.slice();
							b.splice( 0, i+1 );
							innerSrc = b.join('.');
		
							// Traverse each entry in the array setting the properties requested
							if ( Array.isArray( val ) ) {
								for ( var j=0, jLen=val.length ; j<jLen ; j++ ) {
									o = {};
									setData( o, val[j], innerSrc );
									data[ a[i] ].push( o );
								}
							}
							else {
								// We've been asked to save data to an array, but it
								// isn't array data to be saved. Best that can be done
								// is to just save the value.
								data[ a[i] ] = val;
							}
		
							// The inner call to setData has already traversed through the remainder
							// of the source and has set the data, thus we can exit here
							return;
						}
						else if ( funcNotation ) {
							// Function call
							a[i] = a[i].replace(__reFn, '');
							data = data[ a[i] ]( val );
						}
		
						// If the nested object doesn't currently exist - since we are
						// trying to set the value - create it
						if ( data[ a[i] ] === null || data[ a[i] ] === undefined ) {
							data[ a[i] ] = {};
						}
						data = data[ a[i] ];
					}
		
					// Last item in the input - i.e, the actual set
					if ( aLast.match(__reFn ) ) {
						// Function call
						data = data[ aLast.replace(__reFn, '') ]( val );
					}
					else {
						// If array notation is used, we just want to strip it and use the property name
						// and assign the value. If it isn't used, then we get the result we want anyway
						data[ aLast.replace(__reArray, '') ] = val;
					}
				};
		
				return function (data, val) { // meta is also passed in, but not used
					return setData( data, val, source );
				};
			}
			else {
				// Array or flat object mapping
				return function (data, val) { // meta is also passed in, but not used
					data[source] = val;
				};
			}
		},
	
		/**
		 * Create a function that will read nested objects from arrays, based on JSON notation
		 * @param {*} source JSON notation string
		 * @returns Value read
		 */
		get: function ( source ) {
			if ( $.isPlainObject( source ) ) {
				// Build an object of get functions, and wrap them in a single call
				var o = {};
				$.each( source, function (key, val) {
					if ( val ) {
						o[key] = DataTable.util.get( val );
					}
				} );
		
				return function (data, type, row, meta) {
					var t = o[type] || o._;
					return t !== undefined ?
						t(data, type, row, meta) :
						data;
				};
			}
			else if ( source === null ) {
				// Give an empty string for rendering / sorting etc
				return function (data) { // type, row and meta also passed, but not used
					return data;
				};
			}
			else if ( typeof source === 'function' ) {
				return function (data, type, row, meta) {
					return source( data, type, row, meta );
				};
			}
			else if ( typeof source === 'string' && (source.indexOf('.') !== -1 ||
					  source.indexOf('[') !== -1 || source.indexOf('(') !== -1) )
			{
				/* If there is a . in the source string then the data source is in a
				 * nested object so we loop over the data for each level to get the next
				 * level down. On each loop we test for undefined, and if found immediately
				 * return. This allows entire objects to be missing and sDefaultContent to
				 * be used if defined, rather than throwing an error
				 */
				var fetchData = function (data, type, src) {
					var arrayNotation, funcNotation, out, innerSrc;
		
					if ( src !== "" ) {
						var a = _fnSplitObjNotation( src );
		
						for ( var i=0, iLen=a.length ; i<iLen ; i++ ) {
							// Check if we are dealing with special notation
							arrayNotation = a[i].match(__reArray);
							funcNotation = a[i].match(__reFn);
		
							if ( arrayNotation ) {
								// Array notation
								a[i] = a[i].replace(__reArray, '');
		
								// Condition allows simply [] to be passed in
								if ( a[i] !== "" ) {
									data = data[ a[i] ];
								}
								out = [];
		
								// Get the remainder of the nested object to get
								a.splice( 0, i+1 );
								innerSrc = a.join('.');
		
								// Traverse each entry in the array getting the properties requested
								if ( Array.isArray( data ) ) {
									for ( var j=0, jLen=data.length ; j<jLen ; j++ ) {
										out.push( fetchData( data[j], type, innerSrc ) );
									}
								}
		
								// If a string is given in between the array notation indicators, that
								// is used to join the strings together, otherwise an array is returned
								var join = arrayNotation[0].substring(1, arrayNotation[0].length-1);
								data = (join==="") ? out : out.join(join);
		
								// The inner call to fetchData has already traversed through the remainder
								// of the source requested, so we exit from the loop
								break;
							}
							else if ( funcNotation ) {
								// Function call
								a[i] = a[i].replace(__reFn, '');
								data = data[ a[i] ]();
								continue;
							}
		
							if ( data === null || data[ a[i] ] === undefined ) {
								return undefined;
							}
	
							data = data[ a[i] ];
						}
					}
		
					return data;
				};
		
				return function (data, type) { // row and meta also passed, but not used
					return fetchData( data, type, source );
				};
			}
			else {
				// Array or flat object mapping
				return function (data, type) { // row and meta also passed, but not used
					return data[source];
				};
			}
		}
	};
	
	
	
	/**
	 * Create a mapping object that allows camel case parameters to be looked up
	 * for their Hungarian counterparts. The mapping is stored in a private
	 * parameter called `_hungarianMap` which can be accessed on the source object.
	 *  @param {object} o
	 *  @memberof DataTable#oApi
	 */
	function _fnHungarianMap ( o )
	{
		var
			hungarian = 'a aa ai ao as b fn i m o s ',
			match,
			newKey,
			map = {};
	
		$.each( o, function (key, val) {
			match = key.match(/^([^A-Z]+?)([A-Z])/);
	
			if ( match && hungarian.indexOf(match[1]+' ') !== -1 )
			{
				newKey = key.replace( match[0], match[2].toLowerCase() );
				map[ newKey ] = key;
	
				if ( match[1] === 'o' )
				{
					_fnHungarianMap( o[key] );
				}
			}
		} );
	
		o._hungarianMap = map;
	}
	
	
	/**
	 * Convert from camel case parameters to Hungarian, based on a Hungarian map
	 * created by _fnHungarianMap.
	 *  @param {object} src The model object which holds all parameters that can be
	 *    mapped.
	 *  @param {object} user The object to convert from camel case to Hungarian.
	 *  @param {boolean} force When set to `true`, properties which already have a
	 *    Hungarian value in the `user` object will be overwritten. Otherwise they
	 *    won't be.
	 *  @memberof DataTable#oApi
	 */
	function _fnCamelToHungarian ( src, user, force )
	{
		if ( ! src._hungarianMap ) {
			_fnHungarianMap( src );
		}
	
		var hungarianKey;
	
		$.each( user, function (key, val) {
			hungarianKey = src._hungarianMap[ key ];
	
			if ( hungarianKey !== undefined && (force || user[hungarianKey] === undefined) )
			{
				// For objects, we need to buzz down into the object to copy parameters
				if ( hungarianKey.charAt(0) === 'o' )
				{
					// Copy the camelCase options over to the hungarian
					if ( ! user[ hungarianKey ] ) {
						user[ hungarianKey ] = {};
					}
					$.extend( true, user[hungarianKey], user[key] );
	
					_fnCamelToHungarian( src[hungarianKey], user[hungarianKey], force );
				}
				else {
					user[hungarianKey] = user[ key ];
				}
			}
		} );
	}
	
	
	/**
	 * Language compatibility - when certain options are given, and others aren't, we
	 * need to duplicate the values over, in order to provide backwards compatibility
	 * with older language files.
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnLanguageCompat( lang )
	{
		// Note the use of the Hungarian notation for the parameters in this method as
		// this is called after the mapping of camelCase to Hungarian
		var defaults = DataTable.defaults.oLanguage;
	
		// Default mapping
		var defaultDecimal = defaults.sDecimal;
		if ( defaultDecimal ) {
			_addNumericSort( defaultDecimal );
		}
	
		if ( lang ) {
			var zeroRecords = lang.sZeroRecords;
	
			// Backwards compatibility - if there is no sEmptyTable given, then use the same as
			// sZeroRecords - assuming that is given.
			if ( ! lang.sEmptyTable && zeroRecords &&
				defaults.sEmptyTable === "No data available in table" )
			{
				_fnMap( lang, lang, 'sZeroRecords', 'sEmptyTable' );
			}
	
			// Likewise with loading records
			if ( ! lang.sLoadingRecords && zeroRecords &&
				defaults.sLoadingRecords === "Loading..." )
			{
				_fnMap( lang, lang, 'sZeroRecords', 'sLoadingRecords' );
			}
	
			// Old parameter name of the thousands separator mapped onto the new
			if ( lang.sInfoThousands ) {
				lang.sThousands = lang.sInfoThousands;
			}
	
			var decimal = lang.sDecimal;
			if ( decimal && defaultDecimal !== decimal ) {
				_addNumericSort( decimal );
			}
		}
	}
	
	
	/**
	 * Map one parameter onto another
	 *  @param {object} o Object to map
	 *  @param {*} knew The new parameter name
	 *  @param {*} old The old parameter name
	 */
	var _fnCompatMap = function ( o, knew, old ) {
		if ( o[ knew ] !== undefined ) {
			o[ old ] = o[ knew ];
		}
	};
	
	
	/**
	 * Provide backwards compatibility for the main DT options. Note that the new
	 * options are mapped onto the old parameters, so this is an external interface
	 * change only.
	 *  @param {object} init Object to map
	 */
	function _fnCompatOpts ( init )
	{
		_fnCompatMap( init, 'ordering',      'bSort' );
		_fnCompatMap( init, 'orderMulti',    'bSortMulti' );
		_fnCompatMap( init, 'orderClasses',  'bSortClasses' );
		_fnCompatMap( init, 'orderCellsTop', 'bSortCellsTop' );
		_fnCompatMap( init, 'order',         'aaSorting' );
		_fnCompatMap( init, 'orderFixed',    'aaSortingFixed' );
		_fnCompatMap( init, 'paging',        'bPaginate' );
		_fnCompatMap( init, 'pagingType',    'sPaginationType' );
		_fnCompatMap( init, 'pageLength',    'iDisplayLength' );
		_fnCompatMap( init, 'searching',     'bFilter' );
	
		// Boolean initialisation of x-scrolling
		if ( typeof init.sScrollX === 'boolean' ) {
			init.sScrollX = init.sScrollX ? '100%' : '';
		}
		if ( typeof init.scrollX === 'boolean' ) {
			init.scrollX = init.scrollX ? '100%' : '';
		}
	
		// Column search objects are in an array, so it needs to be converted
		// element by element
		var searchCols = init.aoSearchCols;
	
		if ( searchCols ) {
			for ( var i=0, ien=searchCols.length ; i<ien ; i++ ) {
				if ( searchCols[i] ) {
					_fnCamelToHungarian( DataTable.models.oSearch, searchCols[i] );
				}
			}
		}
	}
	
	
	/**
	 * Provide backwards compatibility for column options. Note that the new options
	 * are mapped onto the old parameters, so this is an external interface change
	 * only.
	 *  @param {object} init Object to map
	 */
	function _fnCompatCols ( init )
	{
		_fnCompatMap( init, 'orderable',     'bSortable' );
		_fnCompatMap( init, 'orderData',     'aDataSort' );
		_fnCompatMap( init, 'orderSequence', 'asSorting' );
		_fnCompatMap( init, 'orderDataType', 'sortDataType' );
	
		// orderData can be given as an integer
		var dataSort = init.aDataSort;
		if ( typeof dataSort === 'number' && ! Array.isArray( dataSort ) ) {
			init.aDataSort = [ dataSort ];
		}
	}
	
	
	/**
	 * Browser feature detection for capabilities, quirks
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnBrowserDetect( settings )
	{
		// We don't need to do this every time DataTables is constructed, the values
		// calculated are specific to the browser and OS configuration which we
		// don't expect to change between initialisations
		if ( ! DataTable.__browser ) {
			var browser = {};
			DataTable.__browser = browser;
	
			// Scrolling feature / quirks detection
			var n = $('<div/>')
				.css( {
					position: 'fixed',
					top: 0,
					left: $(window).scrollLeft()*-1, // allow for scrolling
					height: 1,
					width: 1,
					overflow: 'hidden'
				} )
				.append(
					$('<div/>')
						.css( {
							position: 'absolute',
							top: 1,
							left: 1,
							width: 100,
							overflow: 'scroll'
						} )
						.append(
							$('<div/>')
								.css( {
									width: '100%',
									height: 10
								} )
						)
				)
				.appendTo( 'body' );
	
			var outer = n.children();
			var inner = outer.children();
	
			// Numbers below, in order, are:
			// inner.offsetWidth, inner.clientWidth, outer.offsetWidth, outer.clientWidth
			//
			// IE6 XP:                           100 100 100  83
			// IE7 Vista:                        100 100 100  83
			// IE 8+ Windows:                     83  83 100  83
			// Evergreen Windows:                 83  83 100  83
			// Evergreen Mac with scrollbars:     85  85 100  85
			// Evergreen Mac without scrollbars: 100 100 100 100
	
			// Get scrollbar width
			browser.barWidth = outer[0].offsetWidth - outer[0].clientWidth;
	
			// IE6/7 will oversize a width 100% element inside a scrolling element, to
			// include the width of the scrollbar, while other browsers ensure the inner
			// element is contained without forcing scrolling
			browser.bScrollOversize = inner[0].offsetWidth === 100 && outer[0].clientWidth !== 100;
	
			// In rtl text layout, some browsers (most, but not all) will place the
			// scrollbar on the left, rather than the right.
			browser.bScrollbarLeft = Math.round( inner.offset().left ) !== 1;
	
			// IE8- don't provide height and width for getBoundingClientRect
			browser.bBounding = n[0].getBoundingClientRect().width ? true : false;
	
			n.remove();
		}
	
		$.extend( settings.oBrowser, DataTable.__browser );
		settings.oScroll.iBarWidth = DataTable.__browser.barWidth;
	}
	
	
	/**
	 * Array.prototype reduce[Right] method, used for browsers which don't support
	 * JS 1.6. Done this way to reduce code size, since we iterate either way
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnReduce ( that, fn, init, start, end, inc )
	{
		var
			i = start,
			value,
			isSet = false;
	
		if ( init !== undefined ) {
			value = init;
			isSet = true;
		}
	
		while ( i !== end ) {
			if ( ! that.hasOwnProperty(i) ) {
				continue;
			}
	
			value = isSet ?
				fn( value, that[i], i, that ) :
				that[i];
	
			isSet = true;
			i += inc;
		}
	
		return value;
	}
	
	/**
	 * Add a column to the list used for the table with default values
	 *  @param {object} oSettings dataTables settings object
	 *  @param {node} nTh The th element for this column
	 *  @memberof DataTable#oApi
	 */
	function _fnAddColumn( oSettings, nTh )
	{
		// Add column to aoColumns array
		var oDefaults = DataTable.defaults.column;
		var iCol = oSettings.aoColumns.length;
		var oCol = $.extend( {}, DataTable.models.oColumn, oDefaults, {
			"nTh": nTh ? nTh : document.createElement('th'),
			"sTitle":    oDefaults.sTitle    ? oDefaults.sTitle    : nTh ? nTh.innerHTML : '',
			"aDataSort": oDefaults.aDataSort ? oDefaults.aDataSort : [iCol],
			"mData": oDefaults.mData ? oDefaults.mData : iCol,
			idx: iCol
		} );
		oSettings.aoColumns.push( oCol );
	
		// Add search object for column specific search. Note that the `searchCols[ iCol ]`
		// passed into extend can be undefined. This allows the user to give a default
		// with only some of the parameters defined, and also not give a default
		var searchCols = oSettings.aoPreSearchCols;
		searchCols[ iCol ] = $.extend( {}, DataTable.models.oSearch, searchCols[ iCol ] );
	
		// Use the default column options function to initialise classes etc
		_fnColumnOptions( oSettings, iCol, $(nTh).data() );
	}
	
	
	/**
	 * Apply options for a column
	 *  @param {object} oSettings dataTables settings object
	 *  @param {int} iCol column index to consider
	 *  @param {object} oOptions object with sType, bVisible and bSearchable etc
	 *  @memberof DataTable#oApi
	 */
	function _fnColumnOptions( oSettings, iCol, oOptions )
	{
		var oCol = oSettings.aoColumns[ iCol ];
		var oClasses = oSettings.oClasses;
		var th = $(oCol.nTh);
	
		// Try to get width information from the DOM. We can't get it from CSS
		// as we'd need to parse the CSS stylesheet. `width` option can override
		if ( ! oCol.sWidthOrig ) {
			// Width attribute
			oCol.sWidthOrig = th.attr('width') || null;
	
			// Style attribute
			var t = (th.attr('style') || '').match(/width:\s*(\d+[pxem%]+)/);
			if ( t ) {
				oCol.sWidthOrig = t[1];
			}
		}
	
		/* User specified column options */
		if ( oOptions !== undefined && oOptions !== null )
		{
			// Backwards compatibility
			_fnCompatCols( oOptions );
	
			// Map camel case parameters to their Hungarian counterparts
			_fnCamelToHungarian( DataTable.defaults.column, oOptions, true );
	
			/* Backwards compatibility for mDataProp */
			if ( oOptions.mDataProp !== undefined && !oOptions.mData )
			{
				oOptions.mData = oOptions.mDataProp;
			}
	
			if ( oOptions.sType )
			{
				oCol._sManualType = oOptions.sType;
			}
	
			// `class` is a reserved word in Javascript, so we need to provide
			// the ability to use a valid name for the camel case input
			if ( oOptions.className && ! oOptions.sClass )
			{
				oOptions.sClass = oOptions.className;
			}
			if ( oOptions.sClass ) {
				th.addClass( oOptions.sClass );
			}
	
			var origClass = oCol.sClass;
	
			$.extend( oCol, oOptions );
			_fnMap( oCol, oOptions, "sWidth", "sWidthOrig" );
	
			// Merge class from previously defined classes with this one, rather than just
			// overwriting it in the extend above
			if (origClass !== oCol.sClass) {
				oCol.sClass = origClass + ' ' + oCol.sClass;
			}
	
			/* iDataSort to be applied (backwards compatibility), but aDataSort will take
			 * priority if defined
			 */
			if ( oOptions.iDataSort !== undefined )
			{
				oCol.aDataSort = [ oOptions.iDataSort ];
			}
			_fnMap( oCol, oOptions, "aDataSort" );
		}
	
		/* Cache the data get and set functions for speed */
		var mDataSrc = oCol.mData;
		var mData = _fnGetObjectDataFn( mDataSrc );
		var mRender = oCol.mRender ? _fnGetObjectDataFn( oCol.mRender ) : null;
	
		var attrTest = function( src ) {
			return typeof src === 'string' && src.indexOf('@') !== -1;
		};
		oCol._bAttrSrc = $.isPlainObject( mDataSrc ) && (
			attrTest(mDataSrc.sort) || attrTest(mDataSrc.type) || attrTest(mDataSrc.filter)
		);
		oCol._setter = null;
	
		oCol.fnGetData = function (rowData, type, meta) {
			var innerData = mData( rowData, type, undefined, meta );
	
			return mRender && type ?
				mRender( innerData, type, rowData, meta ) :
				innerData;
		};
		oCol.fnSetData = function ( rowData, val, meta ) {
			return _fnSetObjectDataFn( mDataSrc )( rowData, val, meta );
		};
	
		// Indicate if DataTables should read DOM data as an object or array
		// Used in _fnGetRowElements
		if ( typeof mDataSrc !== 'number' ) {
			oSettings._rowReadObject = true;
		}
	
		/* Feature sorting overrides column specific when off */
		if ( !oSettings.oFeatures.bSort )
		{
			oCol.bSortable = false;
			th.addClass( oClasses.sSortableNone ); // Have to add class here as order event isn't called
		}
	
		/* Check that the class assignment is correct for sorting */
		var bAsc = $.inArray('asc', oCol.asSorting) !== -1;
		var bDesc = $.inArray('desc', oCol.asSorting) !== -1;
		if ( !oCol.bSortable || (!bAsc && !bDesc) )
		{
			oCol.sSortingClass = oClasses.sSortableNone;
			oCol.sSortingClassJUI = "";
		}
		else if ( bAsc && !bDesc )
		{
			oCol.sSortingClass = oClasses.sSortableAsc;
			oCol.sSortingClassJUI = oClasses.sSortJUIAscAllowed;
		}
		else if ( !bAsc && bDesc )
		{
			oCol.sSortingClass = oClasses.sSortableDesc;
			oCol.sSortingClassJUI = oClasses.sSortJUIDescAllowed;
		}
		else
		{
			oCol.sSortingClass = oClasses.sSortable;
			oCol.sSortingClassJUI = oClasses.sSortJUI;
		}
	}
	
	
	/**
	 * Adjust the table column widths for new data. Note: you would probably want to
	 * do a redraw after calling this function!
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnAdjustColumnSizing ( settings )
	{
		/* Not interested in doing column width calculation if auto-width is disabled */
		if ( settings.oFeatures.bAutoWidth !== false )
		{
			var columns = settings.aoColumns;
	
			_fnCalculateColumnWidths( settings );
			for ( var i=0 , iLen=columns.length ; i<iLen ; i++ )
			{
				columns[i].nTh.style.width = columns[i].sWidth;
			}
		}
	
		var scroll = settings.oScroll;
		if ( scroll.sY !== '' || scroll.sX !== '')
		{
			_fnScrollDraw( settings );
		}
	
		_fnCallbackFire( settings, null, 'column-sizing', [settings] );
	}
	
	
	/**
	 * Convert the index of a visible column to the index in the data array (take account
	 * of hidden columns)
	 *  @param {object} oSettings dataTables settings object
	 *  @param {int} iMatch Visible column index to lookup
	 *  @returns {int} i the data index
	 *  @memberof DataTable#oApi
	 */
	function _fnVisibleToColumnIndex( oSettings, iMatch )
	{
		var aiVis = _fnGetColumns( oSettings, 'bVisible' );
	
		return typeof aiVis[iMatch] === 'number' ?
			aiVis[iMatch] :
			null;
	}
	
	
	/**
	 * Convert the index of an index in the data array and convert it to the visible
	 *   column index (take account of hidden columns)
	 *  @param {int} iMatch Column index to lookup
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {int} i the data index
	 *  @memberof DataTable#oApi
	 */
	function _fnColumnIndexToVisible( oSettings, iMatch )
	{
		var aiVis = _fnGetColumns( oSettings, 'bVisible' );
		var iPos = $.inArray( iMatch, aiVis );
	
		return iPos !== -1 ? iPos : null;
	}
	
	
	/**
	 * Get the number of visible columns
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {int} i the number of visible columns
	 *  @memberof DataTable#oApi
	 */
	function _fnVisbleColumns( oSettings )
	{
		var vis = 0;
	
		// No reduce in IE8, use a loop for now
		$.each( oSettings.aoColumns, function ( i, col ) {
			if ( col.bVisible && $(col.nTh).css('display') !== 'none' ) {
				vis++;
			}
		} );
	
		return vis;
	}
	
	
	/**
	 * Get an array of column indexes that match a given property
	 *  @param {object} oSettings dataTables settings object
	 *  @param {string} sParam Parameter in aoColumns to look for - typically
	 *    bVisible or bSearchable
	 *  @returns {array} Array of indexes with matched properties
	 *  @memberof DataTable#oApi
	 */
	function _fnGetColumns( oSettings, sParam )
	{
		var a = [];
	
		$.map( oSettings.aoColumns, function(val, i) {
			if ( val[sParam] ) {
				a.push( i );
			}
		} );
	
		return a;
	}
	
	
	/**
	 * Calculate the 'type' of a column
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnColumnTypes ( settings )
	{
		var columns = settings.aoColumns;
		var data = settings.aoData;
		var types = DataTable.ext.type.detect;
		var i, ien, j, jen, k, ken;
		var col, cell, detectedType, cache;
	
		// For each column, spin over the 
		for ( i=0, ien=columns.length ; i<ien ; i++ ) {
			col = columns[i];
			cache = [];
	
			if ( ! col.sType && col._sManualType ) {
				col.sType = col._sManualType;
			}
			else if ( ! col.sType ) {
				for ( j=0, jen=types.length ; j<jen ; j++ ) {
					for ( k=0, ken=data.length ; k<ken ; k++ ) {
						// Use a cache array so we only need to get the type data
						// from the formatter once (when using multiple detectors)
						if ( cache[k] === undefined ) {
							cache[k] = _fnGetCellData( settings, k, i, 'type' );
						}
	
						detectedType = types[j]( cache[k], settings );
	
						// If null, then this type can't apply to this column, so
						// rather than testing all cells, break out. There is an
						// exception for the last type which is `html`. We need to
						// scan all rows since it is possible to mix string and HTML
						// types
						if ( ! detectedType && j !== types.length-1 ) {
							break;
						}
	
						// Only a single match is needed for html type since it is
						// bottom of the pile and very similar to string - but it
						// must not be empty
						if ( detectedType === 'html' && ! _empty(cache[k]) ) {
							break;
						}
					}
	
					// Type is valid for all data points in the column - use this
					// type
					if ( detectedType ) {
						col.sType = detectedType;
						break;
					}
				}
	
				// Fall back - if no type was detected, always use string
				if ( ! col.sType ) {
					col.sType = 'string';
				}
			}
		}
	}
	
	
	/**
	 * Take the column definitions and static columns arrays and calculate how
	 * they relate to column indexes. The callback function will then apply the
	 * definition found for a column to a suitable configuration object.
	 *  @param {object} oSettings dataTables settings object
	 *  @param {array} aoColDefs The aoColumnDefs array that is to be applied
	 *  @param {array} aoCols The aoColumns array that defines columns individually
	 *  @param {function} fn Callback function - takes two parameters, the calculated
	 *    column index and the definition for that column.
	 *  @memberof DataTable#oApi
	 */
	function _fnApplyColumnDefs( oSettings, aoColDefs, aoCols, fn )
	{
		var i, iLen, j, jLen, k, kLen, def;
		var columns = oSettings.aoColumns;
	
		// Column definitions with aTargets
		if ( aoColDefs )
		{
			/* Loop over the definitions array - loop in reverse so first instance has priority */
			for ( i=aoColDefs.length-1 ; i>=0 ; i-- )
			{
				def = aoColDefs[i];
	
				/* Each definition can target multiple columns, as it is an array */
				var aTargets = def.target !== undefined
					? def.target
					: def.targets !== undefined
						? def.targets
						: def.aTargets;
	
				if ( ! Array.isArray( aTargets ) )
				{
					aTargets = [ aTargets ];
				}
	
				for ( j=0, jLen=aTargets.length ; j<jLen ; j++ )
				{
					if ( typeof aTargets[j] === 'number' && aTargets[j] >= 0 )
					{
						/* Add columns that we don't yet know about */
						while( columns.length <= aTargets[j] )
						{
							_fnAddColumn( oSettings );
						}
	
						/* Integer, basic index */
						fn( aTargets[j], def );
					}
					else if ( typeof aTargets[j] === 'number' && aTargets[j] < 0 )
					{
						/* Negative integer, right to left column counting */
						fn( columns.length+aTargets[j], def );
					}
					else if ( typeof aTargets[j] === 'string' )
					{
						/* Class name matching on TH element */
						for ( k=0, kLen=columns.length ; k<kLen ; k++ )
						{
							if ( aTargets[j] == "_all" ||
							     $(columns[k].nTh).hasClass( aTargets[j] ) )
							{
								fn( k, def );
							}
						}
					}
				}
			}
		}
	
		// Statically defined columns array
		if ( aoCols )
		{
			for ( i=0, iLen=aoCols.length ; i<iLen ; i++ )
			{
				fn( i, aoCols[i] );
			}
		}
	}
	
	/**
	 * Add a data array to the table, creating DOM node etc. This is the parallel to
	 * _fnGatherData, but for adding rows from a Javascript source, rather than a
	 * DOM source.
	 *  @param {object} oSettings dataTables settings object
	 *  @param {array} aData data array to be added
	 *  @param {node} [nTr] TR element to add to the table - optional. If not given,
	 *    DataTables will create a row automatically
	 *  @param {array} [anTds] Array of TD|TH elements for the row - must be given
	 *    if nTr is.
	 *  @returns {int} >=0 if successful (index of new aoData entry), -1 if failed
	 *  @memberof DataTable#oApi
	 */
	function _fnAddData ( oSettings, aDataIn, nTr, anTds )
	{
		/* Create the object for storing information about this new row */
		var iRow = oSettings.aoData.length;
		var oData = $.extend( true, {}, DataTable.models.oRow, {
			src: nTr ? 'dom' : 'data',
			idx: iRow
		} );
	
		oData._aData = aDataIn;
		oSettings.aoData.push( oData );
	
		/* Create the cells */
		var nTd, sThisType;
		var columns = oSettings.aoColumns;
	
		// Invalidate the column types as the new data needs to be revalidated
		for ( var i=0, iLen=columns.length ; i<iLen ; i++ )
		{
			columns[i].sType = null;
		}
	
		/* Add to the display array */
		oSettings.aiDisplayMaster.push( iRow );
	
		var id = oSettings.rowIdFn( aDataIn );
		if ( id !== undefined ) {
			oSettings.aIds[ id ] = oData;
		}
	
		/* Create the DOM information, or register it if already present */
		if ( nTr || ! oSettings.oFeatures.bDeferRender )
		{
			_fnCreateTr( oSettings, iRow, nTr, anTds );
		}
	
		return iRow;
	}
	
	
	/**
	 * Add one or more TR elements to the table. Generally we'd expect to
	 * use this for reading data from a DOM sourced table, but it could be
	 * used for an TR element. Note that if a TR is given, it is used (i.e.
	 * it is not cloned).
	 *  @param {object} settings dataTables settings object
	 *  @param {array|node|jQuery} trs The TR element(s) to add to the table
	 *  @returns {array} Array of indexes for the added rows
	 *  @memberof DataTable#oApi
	 */
	function _fnAddTr( settings, trs )
	{
		var row;
	
		// Allow an individual node to be passed in
		if ( ! (trs instanceof $) ) {
			trs = $(trs);
		}
	
		return trs.map( function (i, el) {
			row = _fnGetRowElements( settings, el );
			return _fnAddData( settings, row.data, el, row.cells );
		} );
	}
	
	
	/**
	 * Take a TR element and convert it to an index in aoData
	 *  @param {object} oSettings dataTables settings object
	 *  @param {node} n the TR element to find
	 *  @returns {int} index if the node is found, null if not
	 *  @memberof DataTable#oApi
	 */
	function _fnNodeToDataIndex( oSettings, n )
	{
		return (n._DT_RowIndex!==undefined) ? n._DT_RowIndex : null;
	}
	
	
	/**
	 * Take a TD element and convert it into a column data index (not the visible index)
	 *  @param {object} oSettings dataTables settings object
	 *  @param {int} iRow The row number the TD/TH can be found in
	 *  @param {node} n The TD/TH element to find
	 *  @returns {int} index if the node is found, -1 if not
	 *  @memberof DataTable#oApi
	 */
	function _fnNodeToColumnIndex( oSettings, iRow, n )
	{
		return $.inArray( n, oSettings.aoData[ iRow ].anCells );
	}
	
	
	/**
	 * Get the data for a given cell from the internal cache, taking into account data mapping
	 *  @param {object} settings dataTables settings object
	 *  @param {int} rowIdx aoData row id
	 *  @param {int} colIdx Column index
	 *  @param {string} type data get type ('display', 'type' 'filter|search' 'sort|order')
	 *  @returns {*} Cell data
	 *  @memberof DataTable#oApi
	 */
	function _fnGetCellData( settings, rowIdx, colIdx, type )
	{
		if (type === 'search') {
			type = 'filter';
		}
		else if (type === 'order') {
			type = 'sort';
		}
	
		var draw           = settings.iDraw;
		var col            = settings.aoColumns[colIdx];
		var rowData        = settings.aoData[rowIdx]._aData;
		var defaultContent = col.sDefaultContent;
		var cellData       = col.fnGetData( rowData, type, {
			settings: settings,
			row:      rowIdx,
			col:      colIdx
		} );
	
		if ( cellData === undefined ) {
			if ( settings.iDrawError != draw && defaultContent === null ) {
				_fnLog( settings, 0, "Requested unknown parameter "+
					(typeof col.mData=='function' ? '{function}' : "'"+col.mData+"'")+
					" for row "+rowIdx+", column "+colIdx, 4 );
				settings.iDrawError = draw;
			}
			return defaultContent;
		}
	
		// When the data source is null and a specific data type is requested (i.e.
		// not the original data), we can use default column data
		if ( (cellData === rowData || cellData === null) && defaultContent !== null && type !== undefined ) {
			cellData = defaultContent;
		}
		else if ( typeof cellData === 'function' ) {
			// If the data source is a function, then we run it and use the return,
			// executing in the scope of the data object (for instances)
			return cellData.call( rowData );
		}
	
		if ( cellData === null && type === 'display' ) {
			return '';
		}
	
		if ( type === 'filter' ) {
			var fomatters = DataTable.ext.type.search;
	
			if ( fomatters[ col.sType ] ) {
				cellData = fomatters[ col.sType ]( cellData );
			}
		}
	
		return cellData;
	}
	
	
	/**
	 * Set the value for a specific cell, into the internal data cache
	 *  @param {object} settings dataTables settings object
	 *  @param {int} rowIdx aoData row id
	 *  @param {int} colIdx Column index
	 *  @param {*} val Value to set
	 *  @memberof DataTable#oApi
	 */
	function _fnSetCellData( settings, rowIdx, colIdx, val )
	{
		var col     = settings.aoColumns[colIdx];
		var rowData = settings.aoData[rowIdx]._aData;
	
		col.fnSetData( rowData, val, {
			settings: settings,
			row:      rowIdx,
			col:      colIdx
		}  );
	}
	
	
	// Private variable that is used to match action syntax in the data property object
	var __reArray = /\[.*?\]$/;
	var __reFn = /\(\)$/;
	
	/**
	 * Split string on periods, taking into account escaped periods
	 * @param  {string} str String to split
	 * @return {array} Split string
	 */
	function _fnSplitObjNotation( str )
	{
		return $.map( str.match(/(\\.|[^\.])+/g) || [''], function ( s ) {
			return s.replace(/\\\./g, '.');
		} );
	}
	
	
	/**
	 * Return a function that can be used to get data from a source object, taking
	 * into account the ability to use nested objects as a source
	 *  @param {string|int|function} mSource The data source for the object
	 *  @returns {function} Data get function
	 *  @memberof DataTable#oApi
	 */
	var _fnGetObjectDataFn = DataTable.util.get;
	
	
	/**
	 * Return a function that can be used to set data from a source object, taking
	 * into account the ability to use nested objects as a source
	 *  @param {string|int|function} mSource The data source for the object
	 *  @returns {function} Data set function
	 *  @memberof DataTable#oApi
	 */
	var _fnSetObjectDataFn = DataTable.util.set;
	
	
	/**
	 * Return an array with the full table data
	 *  @param {object} oSettings dataTables settings object
	 *  @returns array {array} aData Master data array
	 *  @memberof DataTable#oApi
	 */
	function _fnGetDataMaster ( settings )
	{
		return _pluck( settings.aoData, '_aData' );
	}
	
	
	/**
	 * Nuke the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnClearTable( settings )
	{
		settings.aoData.length = 0;
		settings.aiDisplayMaster.length = 0;
		settings.aiDisplay.length = 0;
		settings.aIds = {};
	}
	
	
	 /**
	 * Take an array of integers (index array) and remove a target integer (value - not
	 * the key!)
	 *  @param {array} a Index array to target
	 *  @param {int} iTarget value to find
	 *  @memberof DataTable#oApi
	 */
	function _fnDeleteIndex( a, iTarget, splice )
	{
		var iTargetIndex = -1;
	
		for ( var i=0, iLen=a.length ; i<iLen ; i++ )
		{
			if ( a[i] == iTarget )
			{
				iTargetIndex = i;
			}
			else if ( a[i] > iTarget )
			{
				a[i]--;
			}
		}
	
		if ( iTargetIndex != -1 && splice === undefined )
		{
			a.splice( iTargetIndex, 1 );
		}
	}
	
	
	/**
	 * Mark cached data as invalid such that a re-read of the data will occur when
	 * the cached data is next requested. Also update from the data source object.
	 *
	 * @param {object} settings DataTables settings object
	 * @param {int}    rowIdx   Row index to invalidate
	 * @param {string} [src]    Source to invalidate from: undefined, 'auto', 'dom'
	 *     or 'data'
	 * @param {int}    [colIdx] Column index to invalidate. If undefined the whole
	 *     row will be invalidated
	 * @memberof DataTable#oApi
	 *
	 * @todo For the modularisation of v1.11 this will need to become a callback, so
	 *   the sort and filter methods can subscribe to it. That will required
	 *   initialisation options for sorting, which is why it is not already baked in
	 */
	function _fnInvalidate( settings, rowIdx, src, colIdx )
	{
		var row = settings.aoData[ rowIdx ];
		var i, ien;
		var cellWrite = function ( cell, col ) {
			// This is very frustrating, but in IE if you just write directly
			// to innerHTML, and elements that are overwritten are GC'ed,
			// even if there is a reference to them elsewhere
			while ( cell.childNodes.length ) {
				cell.removeChild( cell.firstChild );
			}
	
			cell.innerHTML = _fnGetCellData( settings, rowIdx, col, 'display' );
		};
	
		// Are we reading last data from DOM or the data object?
		if ( src === 'dom' || ((! src || src === 'auto') && row.src === 'dom') ) {
			// Read the data from the DOM
			row._aData = _fnGetRowElements(
					settings, row, colIdx, colIdx === undefined ? undefined : row._aData
				)
				.data;
		}
		else {
			// Reading from data object, update the DOM
			var cells = row.anCells;
	
			if ( cells ) {
				if ( colIdx !== undefined ) {
					cellWrite( cells[colIdx], colIdx );
				}
				else {
					for ( i=0, ien=cells.length ; i<ien ; i++ ) {
						cellWrite( cells[i], i );
					}
				}
			}
		}
	
		// For both row and cell invalidation, the cached data for sorting and
		// filtering is nulled out
		row._aSortData = null;
		row._aFilterData = null;
	
		// Invalidate the type for a specific column (if given) or all columns since
		// the data might have changed
		var cols = settings.aoColumns;
		if ( colIdx !== undefined ) {
			cols[ colIdx ].sType = null;
		}
		else {
			for ( i=0, ien=cols.length ; i<ien ; i++ ) {
				cols[i].sType = null;
			}
	
			// Update DataTables special `DT_*` attributes for the row
			_fnRowAttributes( settings, row );
		}
	}
	
	
	/**
	 * Build a data source object from an HTML row, reading the contents of the
	 * cells that are in the row.
	 *
	 * @param {object} settings DataTables settings object
	 * @param {node|object} TR element from which to read data or existing row
	 *   object from which to re-read the data from the cells
	 * @param {int} [colIdx] Optional column index
	 * @param {array|object} [d] Data source object. If `colIdx` is given then this
	 *   parameter should also be given and will be used to write the data into.
	 *   Only the column in question will be written
	 * @returns {object} Object with two parameters: `data` the data read, in
	 *   document order, and `cells` and array of nodes (they can be useful to the
	 *   caller, so rather than needing a second traversal to get them, just return
	 *   them from here).
	 * @memberof DataTable#oApi
	 */
	function _fnGetRowElements( settings, row, colIdx, d )
	{
		var
			tds = [],
			td = row.firstChild,
			name, col, o, i=0, contents,
			columns = settings.aoColumns,
			objectRead = settings._rowReadObject;
	
		// Allow the data object to be passed in, or construct
		d = d !== undefined ?
			d :
			objectRead ?
				{} :
				[];
	
		var attr = function ( str, td  ) {
			if ( typeof str === 'string' ) {
				var idx = str.indexOf('@');
	
				if ( idx !== -1 ) {
					var attr = str.substring( idx+1 );
					var setter = _fnSetObjectDataFn( str );
					setter( d, td.getAttribute( attr ) );
				}
			}
		};
	
		// Read data from a cell and store into the data object
		var cellProcess = function ( cell ) {
			if ( colIdx === undefined || colIdx === i ) {
				col = columns[i];
				contents = (cell.innerHTML).trim();
	
				if ( col && col._bAttrSrc ) {
					var setter = _fnSetObjectDataFn( col.mData._ );
					setter( d, contents );
	
					attr( col.mData.sort, cell );
					attr( col.mData.type, cell );
					attr( col.mData.filter, cell );
				}
				else {
					// Depending on the `data` option for the columns the data can
					// be read to either an object or an array.
					if ( objectRead ) {
						if ( ! col._setter ) {
							// Cache the setter function
							col._setter = _fnSetObjectDataFn( col.mData );
						}
						col._setter( d, contents );
					}
					else {
						d[i] = contents;
					}
				}
			}
	
			i++;
		};
	
		if ( td ) {
			// `tr` element was passed in
			while ( td ) {
				name = td.nodeName.toUpperCase();
	
				if ( name == "TD" || name == "TH" ) {
					cellProcess( td );
					tds.push( td );
				}
	
				td = td.nextSibling;
			}
		}
		else {
			// Existing row object passed in
			tds = row.anCells;
	
			for ( var j=0, jen=tds.length ; j<jen ; j++ ) {
				cellProcess( tds[j] );
			}
		}
	
		// Read the ID from the DOM if present
		var rowNode = row.firstChild ? row : row.nTr;
	
		if ( rowNode ) {
			var id = rowNode.getAttribute( 'id' );
	
			if ( id ) {
				_fnSetObjectDataFn( settings.rowId )( d, id );
			}
		}
	
		return {
			data: d,
			cells: tds
		};
	}
	/**
	 * Create a new TR element (and it's TD children) for a row
	 *  @param {object} oSettings dataTables settings object
	 *  @param {int} iRow Row to consider
	 *  @param {node} [nTrIn] TR element to add to the table - optional. If not given,
	 *    DataTables will create a row automatically
	 *  @param {array} [anTds] Array of TD|TH elements for the row - must be given
	 *    if nTr is.
	 *  @memberof DataTable#oApi
	 */
	function _fnCreateTr ( oSettings, iRow, nTrIn, anTds )
	{
		var
			row = oSettings.aoData[iRow],
			rowData = row._aData,
			cells = [],
			nTr, nTd, oCol,
			i, iLen, create;
	
		if ( row.nTr === null )
		{
			nTr = nTrIn || document.createElement('tr');
	
			row.nTr = nTr;
			row.anCells = cells;
	
			/* Use a private property on the node to allow reserve mapping from the node
			 * to the aoData array for fast look up
			 */
			nTr._DT_RowIndex = iRow;
	
			/* Special parameters can be given by the data source to be used on the row */
			_fnRowAttributes( oSettings, row );
	
			/* Process each column */
			for ( i=0, iLen=oSettings.aoColumns.length ; i<iLen ; i++ )
			{
				oCol = oSettings.aoColumns[i];
				create = nTrIn ? false : true;
	
				nTd = create ? document.createElement( oCol.sCellType ) : anTds[i];
				nTd._DT_CellIndex = {
					row: iRow,
					column: i
				};
				
				cells.push( nTd );
	
				// Need to create the HTML if new, or if a rendering function is defined
				if ( create || ((oCol.mRender || oCol.mData !== i) &&
					 (!$.isPlainObject(oCol.mData) || oCol.mData._ !== i+'.display')
				)) {
					nTd.innerHTML = _fnGetCellData( oSettings, iRow, i, 'display' );
				}
	
				/* Add user defined class */
				if ( oCol.sClass )
				{
					nTd.className += ' '+oCol.sClass;
				}
	
				// Visibility - add or remove as required
				if ( oCol.bVisible && ! nTrIn )
				{
					nTr.appendChild( nTd );
				}
				else if ( ! oCol.bVisible && nTrIn )
				{
					nTd.parentNode.removeChild( nTd );
				}
	
				if ( oCol.fnCreatedCell )
				{
					oCol.fnCreatedCell.call( oSettings.oInstance,
						nTd, _fnGetCellData( oSettings, iRow, i ), rowData, iRow, i
					);
				}
			}
	
			_fnCallbackFire( oSettings, 'aoRowCreatedCallback', null, [nTr, rowData, iRow, cells] );
		}
	}
	
	
	/**
	 * Add attributes to a row based on the special `DT_*` parameters in a data
	 * source object.
	 *  @param {object} settings DataTables settings object
	 *  @param {object} DataTables row object for the row to be modified
	 *  @memberof DataTable#oApi
	 */
	function _fnRowAttributes( settings, row )
	{
		var tr = row.nTr;
		var data = row._aData;
	
		if ( tr ) {
			var id = settings.rowIdFn( data );
	
			if ( id ) {
				tr.id = id;
			}
	
			if ( data.DT_RowClass ) {
				// Remove any classes added by DT_RowClass before
				var a = data.DT_RowClass.split(' ');
				row.__rowc = row.__rowc ?
					_unique( row.__rowc.concat( a ) ) :
					a;
	
				$(tr)
					.removeClass( row.__rowc.join(' ') )
					.addClass( data.DT_RowClass );
			}
	
			if ( data.DT_RowAttr ) {
				$(tr).attr( data.DT_RowAttr );
			}
	
			if ( data.DT_RowData ) {
				$(tr).data( data.DT_RowData );
			}
		}
	}
	
	
	/**
	 * Create the HTML header for the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnBuildHead( oSettings )
	{
		var i, ien, cell, row, column;
		var thead = oSettings.nTHead;
		var tfoot = oSettings.nTFoot;
		var createHeader = $('th, td', thead).length === 0;
		var classes = oSettings.oClasses;
		var columns = oSettings.aoColumns;
	
		if ( createHeader ) {
			row = $('<tr/>').appendTo( thead );
		}
	
		for ( i=0, ien=columns.length ; i<ien ; i++ ) {
			column = columns[i];
			cell = $( column.nTh ).addClass( column.sClass );
	
			if ( createHeader ) {
				cell.appendTo( row );
			}
	
			// 1.11 move into sorting
			if ( oSettings.oFeatures.bSort ) {
				cell.addClass( column.sSortingClass );
	
				if ( column.bSortable !== false ) {
					cell
						.attr( 'tabindex', oSettings.iTabIndex )
						.attr( 'aria-controls', oSettings.sTableId );
	
					_fnSortAttachListener( oSettings, column.nTh, i );
				}
			}
	
			if ( column.sTitle != cell[0].innerHTML ) {
				cell.html( column.sTitle );
			}
	
			_fnRenderer( oSettings, 'header' )(
				oSettings, cell, column, classes
			);
		}
	
		if ( createHeader ) {
			_fnDetectHeader( oSettings.aoHeader, thead );
		}
	
		/* Deal with the footer - add classes if required */
		$(thead).children('tr').children('th, td').addClass( classes.sHeaderTH );
		$(tfoot).children('tr').children('th, td').addClass( classes.sFooterTH );
	
		// Cache the footer cells. Note that we only take the cells from the first
		// row in the footer. If there is more than one row the user wants to
		// interact with, they need to use the table().foot() method. Note also this
		// allows cells to be used for multiple columns using colspan
		if ( tfoot !== null ) {
			var cells = oSettings.aoFooter[0];
	
			for ( i=0, ien=cells.length ; i<ien ; i++ ) {
				column = columns[i];
				column.nTf = cells[i].cell;
	
				if ( column.sClass ) {
					$(column.nTf).addClass( column.sClass );
				}
			}
		}
	}
	
	
	/**
	 * Draw the header (or footer) element based on the column visibility states. The
	 * methodology here is to use the layout array from _fnDetectHeader, modified for
	 * the instantaneous column visibility, to construct the new layout. The grid is
	 * traversed over cell at a time in a rows x columns grid fashion, although each
	 * cell insert can cover multiple elements in the grid - which is tracks using the
	 * aApplied array. Cell inserts in the grid will only occur where there isn't
	 * already a cell in that position.
	 *  @param {object} oSettings dataTables settings object
	 *  @param array {objects} aoSource Layout array from _fnDetectHeader
	 *  @param {boolean} [bIncludeHidden=false] If true then include the hidden columns in the calc,
	 *  @memberof DataTable#oApi
	 */
	function _fnDrawHead( oSettings, aoSource, bIncludeHidden )
	{
		var i, iLen, j, jLen, k, kLen, n, nLocalTr;
		var aoLocal = [];
		var aApplied = [];
		var iColumns = oSettings.aoColumns.length;
		var iRowspan, iColspan;
	
		if ( ! aoSource )
		{
			return;
		}
	
		if (  bIncludeHidden === undefined )
		{
			bIncludeHidden = false;
		}
	
		/* Make a copy of the master layout array, but without the visible columns in it */
		for ( i=0, iLen=aoSource.length ; i<iLen ; i++ )
		{
			aoLocal[i] = aoSource[i].slice();
			aoLocal[i].nTr = aoSource[i].nTr;
	
			/* Remove any columns which are currently hidden */
			for ( j=iColumns-1 ; j>=0 ; j-- )
			{
				if ( !oSettings.aoColumns[j].bVisible && !bIncludeHidden )
				{
					aoLocal[i].splice( j, 1 );
				}
			}
	
			/* Prep the applied array - it needs an element for each row */
			aApplied.push( [] );
		}
	
		for ( i=0, iLen=aoLocal.length ; i<iLen ; i++ )
		{
			nLocalTr = aoLocal[i].nTr;
	
			/* All cells are going to be replaced, so empty out the row */
			if ( nLocalTr )
			{
				while( (n = nLocalTr.firstChild) )
				{
					nLocalTr.removeChild( n );
				}
			}
	
			for ( j=0, jLen=aoLocal[i].length ; j<jLen ; j++ )
			{
				iRowspan = 1;
				iColspan = 1;
	
				/* Check to see if there is already a cell (row/colspan) covering our target
				 * insert point. If there is, then there is nothing to do.
				 */
				if ( aApplied[i][j] === undefined )
				{
					nLocalTr.appendChild( aoLocal[i][j].cell );
					aApplied[i][j] = 1;
	
					/* Expand the cell to cover as many rows as needed */
					while ( aoLocal[i+iRowspan] !== undefined &&
					        aoLocal[i][j].cell == aoLocal[i+iRowspan][j].cell )
					{
						aApplied[i+iRowspan][j] = 1;
						iRowspan++;
					}
	
					/* Expand the cell to cover as many columns as needed */
					while ( aoLocal[i][j+iColspan] !== undefined &&
					        aoLocal[i][j].cell == aoLocal[i][j+iColspan].cell )
					{
						/* Must update the applied array over the rows for the columns */
						for ( k=0 ; k<iRowspan ; k++ )
						{
							aApplied[i+k][j+iColspan] = 1;
						}
						iColspan++;
					}
	
					/* Do the actual expansion in the DOM */
					$(aoLocal[i][j].cell)
						.attr('rowspan', iRowspan)
						.attr('colspan', iColspan);
				}
			}
		}
	}
	
	
	/**
	 * Insert the required TR nodes into the table for display
	 *  @param {object} oSettings dataTables settings object
	 *  @param ajaxComplete true after ajax call to complete rendering
	 *  @memberof DataTable#oApi
	 */
	function _fnDraw( oSettings, ajaxComplete )
	{
		// Allow for state saving and a custom start position
		_fnStart( oSettings );
	
		/* Provide a pre-callback function which can be used to cancel the draw is false is returned */
		var aPreDraw = _fnCallbackFire( oSettings, 'aoPreDrawCallback', 'preDraw', [oSettings] );
		if ( $.inArray( false, aPreDraw ) !== -1 )
		{
			_fnProcessingDisplay( oSettings, false );
			return;
		}
	
		var anRows = [];
		var iRowCount = 0;
		var asStripeClasses = oSettings.asStripeClasses;
		var iStripes = asStripeClasses.length;
		var oLang = oSettings.oLanguage;
		var bServerSide = _fnDataSource( oSettings ) == 'ssp';
		var aiDisplay = oSettings.aiDisplay;
		var iDisplayStart = oSettings._iDisplayStart;
		var iDisplayEnd = oSettings.fnDisplayEnd();
	
		oSettings.bDrawing = true;
	
		/* Server-side processing draw intercept */
		if ( oSettings.bDeferLoading )
		{
			oSettings.bDeferLoading = false;
			oSettings.iDraw++;
			_fnProcessingDisplay( oSettings, false );
		}
		else if ( !bServerSide )
		{
			oSettings.iDraw++;
		}
		else if ( !oSettings.bDestroying && !ajaxComplete)
		{
			_fnAjaxUpdate( oSettings );
			return;
		}
	
		if ( aiDisplay.length !== 0 )
		{
			var iStart = bServerSide ? 0 : iDisplayStart;
			var iEnd = bServerSide ? oSettings.aoData.length : iDisplayEnd;
	
			for ( var j=iStart ; j<iEnd ; j++ )
			{
				var iDataIndex = aiDisplay[j];
				var aoData = oSettings.aoData[ iDataIndex ];
				if ( aoData.nTr === null )
				{
					_fnCreateTr( oSettings, iDataIndex );
				}
	
				var nRow = aoData.nTr;
	
				/* Remove the old striping classes and then add the new one */
				if ( iStripes !== 0 )
				{
					var sStripe = asStripeClasses[ iRowCount % iStripes ];
					if ( aoData._sRowStripe != sStripe )
					{
						$(nRow).removeClass( aoData._sRowStripe ).addClass( sStripe );
						aoData._sRowStripe = sStripe;
					}
				}
	
				// Row callback functions - might want to manipulate the row
				// iRowCount and j are not currently documented. Are they at all
				// useful?
				_fnCallbackFire( oSettings, 'aoRowCallback', null,
					[nRow, aoData._aData, iRowCount, j, iDataIndex] );
	
				anRows.push( nRow );
				iRowCount++;
			}
		}
		else
		{
			/* Table is empty - create a row with an empty message in it */
			var sZero = oLang.sZeroRecords;
			if ( oSettings.iDraw == 1 &&  _fnDataSource( oSettings ) == 'ajax' )
			{
				sZero = oLang.sLoadingRecords;
			}
			else if ( oLang.sEmptyTable && oSettings.fnRecordsTotal() === 0 )
			{
				sZero = oLang.sEmptyTable;
			}
	
			anRows[ 0 ] = $( '<tr/>', { 'class': iStripes ? asStripeClasses[0] : '' } )
				.append( $('<td />', {
					'valign':  'top',
					'colSpan': _fnVisbleColumns( oSettings ),
					'class':   oSettings.oClasses.sRowEmpty
				} ).html( sZero ) )[0];
		}
	
		/* Header and footer callbacks */
		_fnCallbackFire( oSettings, 'aoHeaderCallback', 'header', [ $(oSettings.nTHead).children('tr')[0],
			_fnGetDataMaster( oSettings ), iDisplayStart, iDisplayEnd, aiDisplay ] );
	
		_fnCallbackFire( oSettings, 'aoFooterCallback', 'footer', [ $(oSettings.nTFoot).children('tr')[0],
			_fnGetDataMaster( oSettings ), iDisplayStart, iDisplayEnd, aiDisplay ] );
	
		var body = $(oSettings.nTBody);
	
		body.children().detach();
		body.append( $(anRows) );
	
		/* Call all required callback functions for the end of a draw */
		_fnCallbackFire( oSettings, 'aoDrawCallback', 'draw', [oSettings] );
	
		/* Draw is complete, sorting and filtering must be as well */
		oSettings.bSorted = false;
		oSettings.bFiltered = false;
		oSettings.bDrawing = false;
	}
	
	
	/**
	 * Redraw the table - taking account of the various features which are enabled
	 *  @param {object} oSettings dataTables settings object
	 *  @param {boolean} [holdPosition] Keep the current paging position. By default
	 *    the paging is reset to the first page
	 *  @memberof DataTable#oApi
	 */
	function _fnReDraw( settings, holdPosition )
	{
		var
			features = settings.oFeatures,
			sort     = features.bSort,
			filter   = features.bFilter;
	
		if ( sort ) {
			_fnSort( settings );
		}
	
		if ( filter ) {
			_fnFilterComplete( settings, settings.oPreviousSearch );
		}
		else {
			// No filtering, so we want to just use the display master
			settings.aiDisplay = settings.aiDisplayMaster.slice();
		}
	
		if ( holdPosition !== true ) {
			settings._iDisplayStart = 0;
		}
	
		// Let any modules know about the draw hold position state (used by
		// scrolling internally)
		settings._drawHold = holdPosition;
	
		_fnDraw( settings );
	
		settings._drawHold = false;
	}
	
	
	/**
	 * Add the options to the page HTML for the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnAddOptionsHtml ( oSettings )
	{
		var classes = oSettings.oClasses;
		var table = $(oSettings.nTable);
		var holding = $('<div/>').insertBefore( table ); // Holding element for speed
		var features = oSettings.oFeatures;
	
		// All DataTables are wrapped in a div
		var insert = $('<div/>', {
			id:      oSettings.sTableId+'_wrapper',
			'class': classes.sWrapper + (oSettings.nTFoot ? '' : ' '+classes.sNoFooter)
		} );
	
		oSettings.nHolding = holding[0];
		oSettings.nTableWrapper = insert[0];
		oSettings.nTableReinsertBefore = oSettings.nTable.nextSibling;
	
		/* Loop over the user set positioning and place the elements as needed */
		var aDom = oSettings.sDom.split('');
		var featureNode, cOption, nNewNode, cNext, sAttr, j;
		for ( var i=0 ; i<aDom.length ; i++ )
		{
			featureNode = null;
			cOption = aDom[i];
	
			if ( cOption == '<' )
			{
				/* New container div */
				nNewNode = $('<div/>')[0];
	
				/* Check to see if we should append an id and/or a class name to the container */
				cNext = aDom[i+1];
				if ( cNext == "'" || cNext == '"' )
				{
					sAttr = "";
					j = 2;
					while ( aDom[i+j] != cNext )
					{
						sAttr += aDom[i+j];
						j++;
					}
	
					/* Replace jQuery UI constants @todo depreciated */
					if ( sAttr == "H" )
					{
						sAttr = classes.sJUIHeader;
					}
					else if ( sAttr == "F" )
					{
						sAttr = classes.sJUIFooter;
					}
	
					/* The attribute can be in the format of "#id.class", "#id" or "class" This logic
					 * breaks the string into parts and applies them as needed
					 */
					if ( sAttr.indexOf('.') != -1 )
					{
						var aSplit = sAttr.split('.');
						nNewNode.id = aSplit[0].substr(1, aSplit[0].length-1);
						nNewNode.className = aSplit[1];
					}
					else if ( sAttr.charAt(0) == "#" )
					{
						nNewNode.id = sAttr.substr(1, sAttr.length-1);
					}
					else
					{
						nNewNode.className = sAttr;
					}
	
					i += j; /* Move along the position array */
				}
	
				insert.append( nNewNode );
				insert = $(nNewNode);
			}
			else if ( cOption == '>' )
			{
				/* End container div */
				insert = insert.parent();
			}
			// @todo Move options into their own plugins?
			else if ( cOption == 'l' && features.bPaginate && features.bLengthChange )
			{
				/* Length */
				featureNode = _fnFeatureHtmlLength( oSettings );
			}
			else if ( cOption == 'f' && features.bFilter )
			{
				/* Filter */
				featureNode = _fnFeatureHtmlFilter( oSettings );
			}
			else if ( cOption == 'r' && features.bProcessing )
			{
				/* pRocessing */
				featureNode = _fnFeatureHtmlProcessing( oSettings );
			}
			else if ( cOption == 't' )
			{
				/* Table */
				featureNode = _fnFeatureHtmlTable( oSettings );
			}
			else if ( cOption ==  'i' && features.bInfo )
			{
				/* Info */
				featureNode = _fnFeatureHtmlInfo( oSettings );
			}
			else if ( cOption == 'p' && features.bPaginate )
			{
				/* Pagination */
				featureNode = _fnFeatureHtmlPaginate( oSettings );
			}
			else if ( DataTable.ext.feature.length !== 0 )
			{
				/* Plug-in features */
				var aoFeatures = DataTable.ext.feature;
				for ( var k=0, kLen=aoFeatures.length ; k<kLen ; k++ )
				{
					if ( cOption == aoFeatures[k].cFeature )
					{
						featureNode = aoFeatures[k].fnInit( oSettings );
						break;
					}
				}
			}
	
			/* Add to the 2D features array */
			if ( featureNode )
			{
				var aanFeatures = oSettings.aanFeatures;
	
				if ( ! aanFeatures[cOption] )
				{
					aanFeatures[cOption] = [];
				}
	
				aanFeatures[cOption].push( featureNode );
				insert.append( featureNode );
			}
		}
	
		/* Built our DOM structure - replace the holding div with what we want */
		holding.replaceWith( insert );
		oSettings.nHolding = null;
	}
	
	
	/**
	 * Use the DOM source to create up an array of header cells. The idea here is to
	 * create a layout grid (array) of rows x columns, which contains a reference
	 * to the cell that that point in the grid (regardless of col/rowspan), such that
	 * any column / row could be removed and the new grid constructed
	 *  @param array {object} aLayout Array to store the calculated layout in
	 *  @param {node} nThead The header/footer element for the table
	 *  @memberof DataTable#oApi
	 */
	function _fnDetectHeader ( aLayout, nThead )
	{
		var nTrs = $(nThead).children('tr');
		var nTr, nCell;
		var i, k, l, iLen, jLen, iColShifted, iColumn, iColspan, iRowspan;
		var bUnique;
		var fnShiftCol = function ( a, i, j ) {
			var k = a[i];
	                while ( k[j] ) {
				j++;
			}
			return j;
		};
	
		aLayout.splice( 0, aLayout.length );
	
		/* We know how many rows there are in the layout - so prep it */
		for ( i=0, iLen=nTrs.length ; i<iLen ; i++ )
		{
			aLayout.push( [] );
		}
	
		/* Calculate a layout array */
		for ( i=0, iLen=nTrs.length ; i<iLen ; i++ )
		{
			nTr = nTrs[i];
			iColumn = 0;
	
			/* For every cell in the row... */
			nCell = nTr.firstChild;
			while ( nCell ) {
				if ( nCell.nodeName.toUpperCase() == "TD" ||
				     nCell.nodeName.toUpperCase() == "TH" )
				{
					/* Get the col and rowspan attributes from the DOM and sanitise them */
					iColspan = nCell.getAttribute('colspan') * 1;
					iRowspan = nCell.getAttribute('rowspan') * 1;
					iColspan = (!iColspan || iColspan===0 || iColspan===1) ? 1 : iColspan;
					iRowspan = (!iRowspan || iRowspan===0 || iRowspan===1) ? 1 : iRowspan;
	
					/* There might be colspan cells already in this row, so shift our target
					 * accordingly
					 */
					iColShifted = fnShiftCol( aLayout, i, iColumn );
	
					/* Cache calculation for unique columns */
					bUnique = iColspan === 1 ? true : false;
	
					/* If there is col / rowspan, copy the information into the layout grid */
					for ( l=0 ; l<iColspan ; l++ )
					{
						for ( k=0 ; k<iRowspan ; k++ )
						{
							aLayout[i+k][iColShifted+l] = {
								"cell": nCell,
								"unique": bUnique
							};
							aLayout[i+k].nTr = nTr;
						}
					}
				}
				nCell = nCell.nextSibling;
			}
		}
	}
	
	
	/**
	 * Get an array of unique th elements, one for each column
	 *  @param {object} oSettings dataTables settings object
	 *  @param {node} nHeader automatically detect the layout from this node - optional
	 *  @param {array} aLayout thead/tfoot layout from _fnDetectHeader - optional
	 *  @returns array {node} aReturn list of unique th's
	 *  @memberof DataTable#oApi
	 */
	function _fnGetUniqueThs ( oSettings, nHeader, aLayout )
	{
		var aReturn = [];
		if ( !aLayout )
		{
			aLayout = oSettings.aoHeader;
			if ( nHeader )
			{
				aLayout = [];
				_fnDetectHeader( aLayout, nHeader );
			}
		}
	
		for ( var i=0, iLen=aLayout.length ; i<iLen ; i++ )
		{
			for ( var j=0, jLen=aLayout[i].length ; j<jLen ; j++ )
			{
				if ( aLayout[i][j].unique &&
					 (!aReturn[j] || !oSettings.bSortCellsTop) )
				{
					aReturn[j] = aLayout[i][j].cell;
				}
			}
		}
	
		return aReturn;
	}
	
	/**
	 * Set the start position for draw
	 *  @param {object} oSettings dataTables settings object
	 */
	function _fnStart( oSettings )
	{
		var bServerSide = _fnDataSource( oSettings ) == 'ssp';
		var iInitDisplayStart = oSettings.iInitDisplayStart;
	
		// Check and see if we have an initial draw position from state saving
		if ( iInitDisplayStart !== undefined && iInitDisplayStart !== -1 )
		{
			oSettings._iDisplayStart = bServerSide ?
				iInitDisplayStart :
				iInitDisplayStart >= oSettings.fnRecordsDisplay() ?
					0 :
					iInitDisplayStart;
	
			oSettings.iInitDisplayStart = -1;
		}
	}
	
	/**
	 * Create an Ajax call based on the table's settings, taking into account that
	 * parameters can have multiple forms, and backwards compatibility.
	 *
	 * @param {object} oSettings dataTables settings object
	 * @param {array} data Data to send to the server, required by
	 *     DataTables - may be augmented by developer callbacks
	 * @param {function} fn Callback function to run when data is obtained
	 */
	function _fnBuildAjax( oSettings, data, fn )
	{
		// Compatibility with 1.9-, allow fnServerData and event to manipulate
		_fnCallbackFire( oSettings, 'aoServerParams', 'serverParams', [data] );
	
		// Convert to object based for 1.10+ if using the old array scheme which can
		// come from server-side processing or serverParams
		if ( data && Array.isArray(data) ) {
			var tmp = {};
			var rbracket = /(.*?)\[\]$/;
	
			$.each( data, function (key, val) {
				var match = val.name.match(rbracket);
	
				if ( match ) {
					// Support for arrays
					var name = match[0];
	
					if ( ! tmp[ name ] ) {
						tmp[ name ] = [];
					}
					tmp[ name ].push( val.value );
				}
				else {
					tmp[val.name] = val.value;
				}
			} );
			data = tmp;
		}
	
		var ajaxData;
		var ajax = oSettings.ajax;
		var instance = oSettings.oInstance;
		var callback = function ( json ) {
			var status = oSettings.jqXHR
				? oSettings.jqXHR.status
				: null;
	
			if ( json === null || (typeof status === 'number' && status == 204 ) ) {
				json = {};
				_fnAjaxDataSrc( oSettings, json, [] );
			}
	
			var error = json.error || json.sError;
			if ( error ) {
				_fnLog( oSettings, 0, error );
			}
	
			oSettings.json = json;
	
			_fnCallbackFire( oSettings, null, 'xhr', [oSettings, json, oSettings.jqXHR] );
			fn( json );
		};
	
		if ( $.isPlainObject( ajax ) && ajax.data )
		{
			ajaxData = ajax.data;
	
			var newData = typeof ajaxData === 'function' ?
				ajaxData( data, oSettings ) :  // fn can manipulate data or return
				ajaxData;                      // an object object or array to merge
	
			// If the function returned something, use that alone
			data = typeof ajaxData === 'function' && newData ?
				newData :
				$.extend( true, data, newData );
	
			// Remove the data property as we've resolved it already and don't want
			// jQuery to do it again (it is restored at the end of the function)
			delete ajax.data;
		}
	
		var baseAjax = {
			"data": data,
			"success": callback,
			"dataType": "json",
			"cache": false,
			"type": oSettings.sServerMethod,
			"error": function (xhr, error, thrown) {
				var ret = _fnCallbackFire( oSettings, null, 'xhr', [oSettings, null, oSettings.jqXHR] );
	
				if ( $.inArray( true, ret ) === -1 ) {
					if ( error == "parsererror" ) {
						_fnLog( oSettings, 0, 'Invalid JSON response', 1 );
					}
					else if ( xhr.readyState === 4 ) {
						_fnLog( oSettings, 0, 'Ajax error', 7 );
					}
				}
	
				_fnProcessingDisplay( oSettings, false );
			}
		};
	
		// Store the data submitted for the API
		oSettings.oAjaxData = data;
	
		// Allow plug-ins and external processes to modify the data
		_fnCallbackFire( oSettings, null, 'preXhr', [oSettings, data] );
	
		if ( oSettings.fnServerData )
		{
			// DataTables 1.9- compatibility
			oSettings.fnServerData.call( instance,
				oSettings.sAjaxSource,
				$.map( data, function (val, key) { // Need to convert back to 1.9 trad format
					return { name: key, value: val };
				} ),
				callback,
				oSettings
			);
		}
		else if ( oSettings.sAjaxSource || typeof ajax === 'string' )
		{
			// DataTables 1.9- compatibility
			oSettings.jqXHR = $.ajax( $.extend( baseAjax, {
				url: ajax || oSettings.sAjaxSource
			} ) );
		}
		else if ( typeof ajax === 'function' )
		{
			// Is a function - let the caller define what needs to be done
			oSettings.jqXHR = ajax.call( instance, data, callback, oSettings );
		}
		else
		{
			// Object to extend the base settings
			oSettings.jqXHR = $.ajax( $.extend( baseAjax, ajax ) );
	
			// Restore for next time around
			ajax.data = ajaxData;
		}
	}
	
	
	/**
	 * Update the table using an Ajax call
	 *  @param {object} settings dataTables settings object
	 *  @returns {boolean} Block the table drawing or not
	 *  @memberof DataTable#oApi
	 */
	function _fnAjaxUpdate( settings )
	{
		settings.iDraw++;
		_fnProcessingDisplay( settings, true );
	
		_fnBuildAjax(
			settings,
			_fnAjaxParameters( settings ),
			function(json) {
				_fnAjaxUpdateDraw( settings, json );
			}
		);
	}
	
	
	/**
	 * Build up the parameters in an object needed for a server-side processing
	 * request. Note that this is basically done twice, is different ways - a modern
	 * method which is used by default in DataTables 1.10 which uses objects and
	 * arrays, or the 1.9- method with is name / value pairs. 1.9 method is used if
	 * the sAjaxSource option is used in the initialisation, or the legacyAjax
	 * option is set.
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {bool} block the table drawing or not
	 *  @memberof DataTable#oApi
	 */
	function _fnAjaxParameters( settings )
	{
		var
			columns = settings.aoColumns,
			columnCount = columns.length,
			features = settings.oFeatures,
			preSearch = settings.oPreviousSearch,
			preColSearch = settings.aoPreSearchCols,
			i, data = [], dataProp, column, columnSearch,
			sort = _fnSortFlatten( settings ),
			displayStart = settings._iDisplayStart,
			displayLength = features.bPaginate !== false ?
				settings._iDisplayLength :
				-1;
	
		var param = function ( name, value ) {
			data.push( { 'name': name, 'value': value } );
		};
	
		// DataTables 1.9- compatible method
		param( 'sEcho',          settings.iDraw );
		param( 'iColumns',       columnCount );
		param( 'sColumns',       _pluck( columns, 'sName' ).join(',') );
		param( 'iDisplayStart',  displayStart );
		param( 'iDisplayLength', displayLength );
	
		// DataTables 1.10+ method
		var d = {
			draw:    settings.iDraw,
			columns: [],
			order:   [],
			start:   displayStart,
			length:  displayLength,
			search:  {
				value: preSearch.sSearch,
				regex: preSearch.bRegex
			}
		};
	
		for ( i=0 ; i<columnCount ; i++ ) {
			column = columns[i];
			columnSearch = preColSearch[i];
			dataProp = typeof column.mData=="function" ? 'function' : column.mData ;
	
			d.columns.push( {
				data:       dataProp,
				name:       column.sName,
				searchable: column.bSearchable,
				orderable:  column.bSortable,
				search:     {
					value: columnSearch.sSearch,
					regex: columnSearch.bRegex
				}
			} );
	
			param( "mDataProp_"+i, dataProp );
	
			if ( features.bFilter ) {
				param( 'sSearch_'+i,     columnSearch.sSearch );
				param( 'bRegex_'+i,      columnSearch.bRegex );
				param( 'bSearchable_'+i, column.bSearchable );
			}
	
			if ( features.bSort ) {
				param( 'bSortable_'+i, column.bSortable );
			}
		}
	
		if ( features.bFilter ) {
			param( 'sSearch', preSearch.sSearch );
			param( 'bRegex', preSearch.bRegex );
		}
	
		if ( features.bSort ) {
			$.each( sort, function ( i, val ) {
				d.order.push( { column: val.col, dir: val.dir } );
	
				param( 'iSortCol_'+i, val.col );
				param( 'sSortDir_'+i, val.dir );
			} );
	
			param( 'iSortingCols', sort.length );
		}
	
		// If the legacy.ajax parameter is null, then we automatically decide which
		// form to use, based on sAjaxSource
		var legacy = DataTable.ext.legacy.ajax;
		if ( legacy === null ) {
			return settings.sAjaxSource ? data : d;
		}
	
		// Otherwise, if legacy has been specified then we use that to decide on the
		// form
		return legacy ? data : d;
	}
	
	
	/**
	 * Data the data from the server (nuking the old) and redraw the table
	 *  @param {object} oSettings dataTables settings object
	 *  @param {object} json json data return from the server.
	 *  @param {string} json.sEcho Tracking flag for DataTables to match requests
	 *  @param {int} json.iTotalRecords Number of records in the data set, not accounting for filtering
	 *  @param {int} json.iTotalDisplayRecords Number of records in the data set, accounting for filtering
	 *  @param {array} json.aaData The data to display on this page
	 *  @param {string} [json.sColumns] Column ordering (sName, comma separated)
	 *  @memberof DataTable#oApi
	 */
	function _fnAjaxUpdateDraw ( settings, json )
	{
		// v1.10 uses camelCase variables, while 1.9 uses Hungarian notation.
		// Support both
		var compat = function ( old, modern ) {
			return json[old] !== undefined ? json[old] : json[modern];
		};
	
		var data = _fnAjaxDataSrc( settings, json );
		var draw            = compat( 'sEcho',                'draw' );
		var recordsTotal    = compat( 'iTotalRecords',        'recordsTotal' );
		var recordsFiltered = compat( 'iTotalDisplayRecords', 'recordsFiltered' );
	
		if ( draw !== undefined ) {
			// Protect against out of sequence returns
			if ( draw*1 < settings.iDraw ) {
				return;
			}
			settings.iDraw = draw * 1;
		}
	
		// No data in returned object, so rather than an array, we show an empty table
		if ( ! data ) {
			data = [];
		}
	
		_fnClearTable( settings );
		settings._iRecordsTotal   = parseInt(recordsTotal, 10);
		settings._iRecordsDisplay = parseInt(recordsFiltered, 10);
	
		for ( var i=0, ien=data.length ; i<ien ; i++ ) {
			_fnAddData( settings, data[i] );
		}
		settings.aiDisplay = settings.aiDisplayMaster.slice();
	
		_fnDraw( settings, true );
	
		if ( ! settings._bInitComplete ) {
			_fnInitComplete( settings, json );
		}
	
		_fnProcessingDisplay( settings, false );
	}
	
	
	/**
	 * Get the data from the JSON data source to use for drawing a table. Using
	 * `_fnGetObjectDataFn` allows the data to be sourced from a property of the
	 * source object, or from a processing function.
	 *  @param {object} oSettings dataTables settings object
	 *  @param  {object} json Data source object / array from the server
	 *  @return {array} Array of data to use
	 */
	 function _fnAjaxDataSrc ( oSettings, json, write )
	 {
		var dataSrc = $.isPlainObject( oSettings.ajax ) && oSettings.ajax.dataSrc !== undefined ?
			oSettings.ajax.dataSrc :
			oSettings.sAjaxDataProp; // Compatibility with 1.9-.
	
		if ( ! write ) {
			if ( dataSrc === 'data' ) {
				// If the default, then we still want to support the old style, and safely ignore
				// it if possible
				return json.aaData || json[dataSrc];
			}
	
			return dataSrc !== "" ?
				_fnGetObjectDataFn( dataSrc )( json ) :
				json;
		}
	
		// set
		_fnSetObjectDataFn( dataSrc )( json, write );
	}
	
	/**
	 * Generate the node required for filtering text
	 *  @returns {node} Filter control element
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlFilter ( settings )
	{
		var classes = settings.oClasses;
		var tableId = settings.sTableId;
		var language = settings.oLanguage;
		var previousSearch = settings.oPreviousSearch;
		var features = settings.aanFeatures;
		var input = '<input type="search" class="'+classes.sFilterInput+'"/>';
	
		var str = language.sSearch;
		str = str.match(/_INPUT_/) ?
			str.replace('_INPUT_', input) :
			str+input;
	
		var filter = $('<div/>', {
				'id': ! features.f ? tableId+'_filter' : null,
				'class': classes.sFilter
			} )
			.append( $('<label/>' ).append( str ) );
	
		var searchFn = function(event) {
			/* Update all other filter input elements for the new display */
			var n = features.f;
			var val = !this.value ? "" : this.value; // mental IE8 fix :-(
			if(previousSearch.return && event.key !== "Enter") {
				return;
			}
			/* Now do the filter */
			if ( val != previousSearch.sSearch ) {
				_fnFilterComplete( settings, {
					"sSearch": val,
					"bRegex": previousSearch.bRegex,
					"bSmart": previousSearch.bSmart ,
					"bCaseInsensitive": previousSearch.bCaseInsensitive,
					"return": previousSearch.return
				} );
	
				// Need to redraw, without resorting
				settings._iDisplayStart = 0;
				_fnDraw( settings );
			}
		};
	
		var searchDelay = settings.searchDelay !== null ?
			settings.searchDelay :
			_fnDataSource( settings ) === 'ssp' ?
				400 :
				0;
	
		var jqFilter = $('input', filter)
			.val( previousSearch.sSearch )
			.attr( 'placeholder', language.sSearchPlaceholder )
			.on(
				'keyup.DT search.DT input.DT paste.DT cut.DT',
				searchDelay ?
					_fnThrottle( searchFn, searchDelay ) :
					searchFn
			)
			.on( 'mouseup', function(e) {
				// Edge fix! Edge 17 does not trigger anything other than mouse events when clicking
				// on the clear icon (Edge bug 17584515). This is safe in other browsers as `searchFn`
				// checks the value to see if it has changed. In other browsers it won't have.
				setTimeout( function () {
					searchFn.call(jqFilter[0], e);
				}, 10);
			} )
			.on( 'keypress.DT', function(e) {
				/* Prevent form submission */
				if ( e.keyCode == 13 ) {
					return false;
				}
			} )
			.attr('aria-controls', tableId);
	
		// Update the input elements whenever the table is filtered
		$(settings.nTable).on( 'search.dt.DT', function ( ev, s ) {
			if ( settings === s ) {
				// IE9 throws an 'unknown error' if document.activeElement is used
				// inside an iframe or frame...
				try {
					if ( jqFilter[0] !== document.activeElement ) {
						jqFilter.val( previousSearch.sSearch );
					}
				}
				catch ( e ) {}
			}
		} );
	
		return filter[0];
	}
	
	
	/**
	 * Filter the table using both the global filter and column based filtering
	 *  @param {object} oSettings dataTables settings object
	 *  @param {object} oSearch search information
	 *  @param {int} [iForce] force a research of the master array (1) or not (undefined or 0)
	 *  @memberof DataTable#oApi
	 */
	function _fnFilterComplete ( oSettings, oInput, iForce )
	{
		var oPrevSearch = oSettings.oPreviousSearch;
		var aoPrevSearch = oSettings.aoPreSearchCols;
		var fnSaveFilter = function ( oFilter ) {
			/* Save the filtering values */
			oPrevSearch.sSearch = oFilter.sSearch;
			oPrevSearch.bRegex = oFilter.bRegex;
			oPrevSearch.bSmart = oFilter.bSmart;
			oPrevSearch.bCaseInsensitive = oFilter.bCaseInsensitive;
			oPrevSearch.return = oFilter.return;
		};
		var fnRegex = function ( o ) {
			// Backwards compatibility with the bEscapeRegex option
			return o.bEscapeRegex !== undefined ? !o.bEscapeRegex : o.bRegex;
		};
	
		// Resolve any column types that are unknown due to addition or invalidation
		// @todo As per sort - can this be moved into an event handler?
		_fnColumnTypes( oSettings );
	
		/* In server-side processing all filtering is done by the server, so no point hanging around here */
		if ( _fnDataSource( oSettings ) != 'ssp' )
		{
			/* Global filter */
			_fnFilter( oSettings, oInput.sSearch, iForce, fnRegex(oInput), oInput.bSmart, oInput.bCaseInsensitive, oInput.return );
			fnSaveFilter( oInput );
	
			/* Now do the individual column filter */
			for ( var i=0 ; i<aoPrevSearch.length ; i++ )
			{
				_fnFilterColumn( oSettings, aoPrevSearch[i].sSearch, i, fnRegex(aoPrevSearch[i]),
					aoPrevSearch[i].bSmart, aoPrevSearch[i].bCaseInsensitive );
			}
	
			/* Custom filtering */
			_fnFilterCustom( oSettings );
		}
		else
		{
			fnSaveFilter( oInput );
		}
	
		/* Tell the draw function we have been filtering */
		oSettings.bFiltered = true;
		_fnCallbackFire( oSettings, null, 'search', [oSettings] );
	}
	
	
	/**
	 * Apply custom filtering functions
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnFilterCustom( settings )
	{
		var filters = DataTable.ext.search;
		var displayRows = settings.aiDisplay;
		var row, rowIdx;
	
		for ( var i=0, ien=filters.length ; i<ien ; i++ ) {
			var rows = [];
	
			// Loop over each row and see if it should be included
			for ( var j=0, jen=displayRows.length ; j<jen ; j++ ) {
				rowIdx = displayRows[ j ];
				row = settings.aoData[ rowIdx ];
	
				if ( filters[i]( settings, row._aFilterData, rowIdx, row._aData, j ) ) {
					rows.push( rowIdx );
				}
			}
	
			// So the array reference doesn't break set the results into the
			// existing array
			displayRows.length = 0;
			$.merge( displayRows, rows );
		}
	}
	
	
	/**
	 * Filter the table on a per-column basis
	 *  @param {object} oSettings dataTables settings object
	 *  @param {string} sInput string to filter on
	 *  @param {int} iColumn column to filter
	 *  @param {bool} bRegex treat search string as a regular expression or not
	 *  @param {bool} bSmart use smart filtering or not
	 *  @param {bool} bCaseInsensitive Do case insensitive matching or not
	 *  @memberof DataTable#oApi
	 */
	function _fnFilterColumn ( settings, searchStr, colIdx, regex, smart, caseInsensitive )
	{
		if ( searchStr === '' ) {
			return;
		}
	
		var data;
		var out = [];
		var display = settings.aiDisplay;
		var rpSearch = _fnFilterCreateSearch( searchStr, regex, smart, caseInsensitive );
	
		for ( var i=0 ; i<display.length ; i++ ) {
			data = settings.aoData[ display[i] ]._aFilterData[ colIdx ];
	
			if ( rpSearch.test( data ) ) {
				out.push( display[i] );
			}
		}
	
		settings.aiDisplay = out;
	}
	
	
	/**
	 * Filter the data table based on user input and draw the table
	 *  @param {object} settings dataTables settings object
	 *  @param {string} input string to filter on
	 *  @param {int} force optional - force a research of the master array (1) or not (undefined or 0)
	 *  @param {bool} regex treat as a regular expression or not
	 *  @param {bool} smart perform smart filtering or not
	 *  @param {bool} caseInsensitive Do case insensitive matching or not
	 *  @memberof DataTable#oApi
	 */
	function _fnFilter( settings, input, force, regex, smart, caseInsensitive )
	{
		var rpSearch = _fnFilterCreateSearch( input, regex, smart, caseInsensitive );
		var prevSearch = settings.oPreviousSearch.sSearch;
		var displayMaster = settings.aiDisplayMaster;
		var display, invalidated, i;
		var filtered = [];
	
		// Need to take account of custom filtering functions - always filter
		if ( DataTable.ext.search.length !== 0 ) {
			force = true;
		}
	
		// Check if any of the rows were invalidated
		invalidated = _fnFilterData( settings );
	
		// If the input is blank - we just want the full data set
		if ( input.length <= 0 ) {
			settings.aiDisplay = displayMaster.slice();
		}
		else {
			// New search - start from the master array
			if ( invalidated ||
				 force ||
				 regex ||
				 prevSearch.length > input.length ||
				 input.indexOf(prevSearch) !== 0 ||
				 settings.bSorted // On resort, the display master needs to be
				                  // re-filtered since indexes will have changed
			) {
				settings.aiDisplay = displayMaster.slice();
			}
	
			// Search the display array
			display = settings.aiDisplay;
	
			for ( i=0 ; i<display.length ; i++ ) {
				if ( rpSearch.test( settings.aoData[ display[i] ]._sFilterRow ) ) {
					filtered.push( display[i] );
				}
			}
	
			settings.aiDisplay = filtered;
		}
	}
	
	
	/**
	 * Build a regular expression object suitable for searching a table
	 *  @param {string} sSearch string to search for
	 *  @param {bool} bRegex treat as a regular expression or not
	 *  @param {bool} bSmart perform smart filtering or not
	 *  @param {bool} bCaseInsensitive Do case insensitive matching or not
	 *  @returns {RegExp} constructed object
	 *  @memberof DataTable#oApi
	 */
	function _fnFilterCreateSearch( search, regex, smart, caseInsensitive )
	{
		search = regex ?
			search :
			_fnEscapeRegex( search );
		
		if ( smart ) {
			/* For smart filtering we want to allow the search to work regardless of
			 * word order. We also want double quoted text to be preserved, so word
			 * order is important - a la google. So this is what we want to
			 * generate:
			 * 
			 * ^(?=.*?\bone\b)(?=.*?\btwo three\b)(?=.*?\bfour\b).*$
			 */
			var a = $.map( search.match( /"[^"]+"|[^ ]+/g ) || [''], function ( word ) {
				if ( word.charAt(0) === '"' ) {
					var m = word.match( /^"(.*)"$/ );
					word = m ? m[1] : word;
				}
	
				return word.replace('"', '');
			} );
	
			search = '^(?=.*?'+a.join( ')(?=.*?' )+').*$';
		}
	
		return new RegExp( search, caseInsensitive ? 'i' : '' );
	}
	
	
	/**
	 * Escape a string such that it can be used in a regular expression
	 *  @param {string} sVal string to escape
	 *  @returns {string} escaped string
	 *  @memberof DataTable#oApi
	 */
	var _fnEscapeRegex = DataTable.util.escapeRegex;
	
	var __filter_div = $('<div>')[0];
	var __filter_div_textContent = __filter_div.textContent !== undefined;
	
	// Update the filtering data for each row if needed (by invalidation or first run)
	function _fnFilterData ( settings )
	{
		var columns = settings.aoColumns;
		var column;
		var i, j, ien, jen, filterData, cellData, row;
		var wasInvalidated = false;
	
		for ( i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
			row = settings.aoData[i];
	
			if ( ! row._aFilterData ) {
				filterData = [];
	
				for ( j=0, jen=columns.length ; j<jen ; j++ ) {
					column = columns[j];
	
					if ( column.bSearchable ) {
						cellData = _fnGetCellData( settings, i, j, 'filter' );
	
						// Search in DataTables 1.10 is string based. In 1.11 this
						// should be altered to also allow strict type checking.
						if ( cellData === null ) {
							cellData = '';
						}
	
						if ( typeof cellData !== 'string' && cellData.toString ) {
							cellData = cellData.toString();
						}
					}
					else {
						cellData = '';
					}
	
					// If it looks like there is an HTML entity in the string,
					// attempt to decode it so sorting works as expected. Note that
					// we could use a single line of jQuery to do this, but the DOM
					// method used here is much faster http://jsperf.com/html-decode
					if ( cellData.indexOf && cellData.indexOf('&') !== -1 ) {
						__filter_div.innerHTML = cellData;
						cellData = __filter_div_textContent ?
							__filter_div.textContent :
							__filter_div.innerText;
					}
	
					if ( cellData.replace ) {
						cellData = cellData.replace(/[\r\n\u2028]/g, '');
					}
	
					filterData.push( cellData );
				}
	
				row._aFilterData = filterData;
				row._sFilterRow = filterData.join('  ');
				wasInvalidated = true;
			}
		}
	
		return wasInvalidated;
	}
	
	
	/**
	 * Convert from the internal Hungarian notation to camelCase for external
	 * interaction
	 *  @param {object} obj Object to convert
	 *  @returns {object} Inverted object
	 *  @memberof DataTable#oApi
	 */
	function _fnSearchToCamel ( obj )
	{
		return {
			search:          obj.sSearch,
			smart:           obj.bSmart,
			regex:           obj.bRegex,
			caseInsensitive: obj.bCaseInsensitive
		};
	}
	
	
	
	/**
	 * Convert from camelCase notation to the internal Hungarian. We could use the
	 * Hungarian convert function here, but this is cleaner
	 *  @param {object} obj Object to convert
	 *  @returns {object} Inverted object
	 *  @memberof DataTable#oApi
	 */
	function _fnSearchToHung ( obj )
	{
		return {
			sSearch:          obj.search,
			bSmart:           obj.smart,
			bRegex:           obj.regex,
			bCaseInsensitive: obj.caseInsensitive
		};
	}
	
	/**
	 * Generate the node required for the info display
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {node} Information element
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlInfo ( settings )
	{
		var
			tid = settings.sTableId,
			nodes = settings.aanFeatures.i,
			n = $('<div/>', {
				'class': settings.oClasses.sInfo,
				'id': ! nodes ? tid+'_info' : null
			} );
	
		if ( ! nodes ) {
			// Update display on each draw
			settings.aoDrawCallback.push( {
				"fn": _fnUpdateInfo,
				"sName": "information"
			} );
	
			n
				.attr( 'role', 'status' )
				.attr( 'aria-live', 'polite' );
	
			// Table is described by our info div
			$(settings.nTable).attr( 'aria-describedby', tid+'_info' );
		}
	
		return n[0];
	}
	
	
	/**
	 * Update the information elements in the display
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnUpdateInfo ( settings )
	{
		/* Show information about the table */
		var nodes = settings.aanFeatures.i;
		if ( nodes.length === 0 ) {
			return;
		}
	
		var
			lang  = settings.oLanguage,
			start = settings._iDisplayStart+1,
			end   = settings.fnDisplayEnd(),
			max   = settings.fnRecordsTotal(),
			total = settings.fnRecordsDisplay(),
			out   = total ?
				lang.sInfo :
				lang.sInfoEmpty;
	
		if ( total !== max ) {
			/* Record set after filtering */
			out += ' ' + lang.sInfoFiltered;
		}
	
		// Convert the macros
		out += lang.sInfoPostFix;
		out = _fnInfoMacros( settings, out );
	
		var callback = lang.fnInfoCallback;
		if ( callback !== null ) {
			out = callback.call( settings.oInstance,
				settings, start, end, max, total, out
			);
		}
	
		$(nodes).html( out );
	}
	
	
	function _fnInfoMacros ( settings, str )
	{
		// When infinite scrolling, we are always starting at 1. _iDisplayStart is used only
		// internally
		var
			formatter  = settings.fnFormatNumber,
			start      = settings._iDisplayStart+1,
			len        = settings._iDisplayLength,
			vis        = settings.fnRecordsDisplay(),
			all        = len === -1;
	
		return str.
			replace(/_START_/g, formatter.call( settings, start ) ).
			replace(/_END_/g,   formatter.call( settings, settings.fnDisplayEnd() ) ).
			replace(/_MAX_/g,   formatter.call( settings, settings.fnRecordsTotal() ) ).
			replace(/_TOTAL_/g, formatter.call( settings, vis ) ).
			replace(/_PAGE_/g,  formatter.call( settings, all ? 1 : Math.ceil( start / len ) ) ).
			replace(/_PAGES_/g, formatter.call( settings, all ? 1 : Math.ceil( vis / len ) ) );
	}
	
	
	
	/**
	 * Draw the table for the first time, adding all required features
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnInitialise ( settings )
	{
		var i, iLen, iAjaxStart=settings.iInitDisplayStart;
		var columns = settings.aoColumns, column;
		var features = settings.oFeatures;
		var deferLoading = settings.bDeferLoading; // value modified by the draw
	
		/* Ensure that the table data is fully initialised */
		if ( ! settings.bInitialised ) {
			setTimeout( function(){ _fnInitialise( settings ); }, 200 );
			return;
		}
	
		/* Show the display HTML options */
		_fnAddOptionsHtml( settings );
	
		/* Build and draw the header / footer for the table */
		_fnBuildHead( settings );
		_fnDrawHead( settings, settings.aoHeader );
		_fnDrawHead( settings, settings.aoFooter );
	
		/* Okay to show that something is going on now */
		_fnProcessingDisplay( settings, true );
	
		/* Calculate sizes for columns */
		if ( features.bAutoWidth ) {
			_fnCalculateColumnWidths( settings );
		}
	
		for ( i=0, iLen=columns.length ; i<iLen ; i++ ) {
			column = columns[i];
	
			if ( column.sWidth ) {
				column.nTh.style.width = _fnStringToCss( column.sWidth );
			}
		}
	
		_fnCallbackFire( settings, null, 'preInit', [settings] );
	
		// If there is default sorting required - let's do it. The sort function
		// will do the drawing for us. Otherwise we draw the table regardless of the
		// Ajax source - this allows the table to look initialised for Ajax sourcing
		// data (show 'loading' message possibly)
		_fnReDraw( settings );
	
		// Server-side processing init complete is done by _fnAjaxUpdateDraw
		var dataSrc = _fnDataSource( settings );
		if ( dataSrc != 'ssp' || deferLoading ) {
			// if there is an ajax source load the data
			if ( dataSrc == 'ajax' ) {
				_fnBuildAjax( settings, [], function(json) {
					var aData = _fnAjaxDataSrc( settings, json );
	
					// Got the data - add it to the table
					for ( i=0 ; i<aData.length ; i++ ) {
						_fnAddData( settings, aData[i] );
					}
	
					// Reset the init display for cookie saving. We've already done
					// a filter, and therefore cleared it before. So we need to make
					// it appear 'fresh'
					settings.iInitDisplayStart = iAjaxStart;
	
					_fnReDraw( settings );
	
					_fnProcessingDisplay( settings, false );
					_fnInitComplete( settings, json );
				}, settings );
			}
			else {
				_fnProcessingDisplay( settings, false );
				_fnInitComplete( settings );
			}
		}
	}
	
	
	/**
	 * Draw the table for the first time, adding all required features
	 *  @param {object} oSettings dataTables settings object
	 *  @param {object} [json] JSON from the server that completed the table, if using Ajax source
	 *    with client-side processing (optional)
	 *  @memberof DataTable#oApi
	 */
	function _fnInitComplete ( settings, json )
	{
		settings._bInitComplete = true;
	
		// When data was added after the initialisation (data or Ajax) we need to
		// calculate the column sizing
		if ( json || settings.oInit.aaData ) {
			_fnAdjustColumnSizing( settings );
		}
	
		_fnCallbackFire( settings, null, 'plugin-init', [settings, json] );
		_fnCallbackFire( settings, 'aoInitComplete', 'init', [settings, json] );
	}
	
	
	function _fnLengthChange ( settings, val )
	{
		var len = parseInt( val, 10 );
		settings._iDisplayLength = len;
	
		_fnLengthOverflow( settings );
	
		// Fire length change event
		_fnCallbackFire( settings, null, 'length', [settings, len] );
	}
	
	
	/**
	 * Generate the node required for user display length changing
	 *  @param {object} settings dataTables settings object
	 *  @returns {node} Display length feature node
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlLength ( settings )
	{
		var
			classes  = settings.oClasses,
			tableId  = settings.sTableId,
			menu     = settings.aLengthMenu,
			d2       = Array.isArray( menu[0] ),
			lengths  = d2 ? menu[0] : menu,
			language = d2 ? menu[1] : menu;
	
		var select = $('<select/>', {
			'name':          tableId+'_length',
			'aria-controls': tableId,
			'class':         classes.sLengthSelect
		} );
	
		for ( var i=0, ien=lengths.length ; i<ien ; i++ ) {
			select[0][ i ] = new Option(
				typeof language[i] === 'number' ?
					settings.fnFormatNumber( language[i] ) :
					language[i],
				lengths[i]
			);
		}
	
		var div = $('<div><label/></div>').addClass( classes.sLength );
		if ( ! settings.aanFeatures.l ) {
			div[0].id = tableId+'_length';
		}
	
		div.children().append(
			settings.oLanguage.sLengthMenu.replace( '_MENU_', select[0].outerHTML )
		);
	
		// Can't use `select` variable as user might provide their own and the
		// reference is broken by the use of outerHTML
		$('select', div)
			.val( settings._iDisplayLength )
			.on( 'change.DT', function(e) {
				_fnLengthChange( settings, $(this).val() );
				_fnDraw( settings );
			} );
	
		// Update node value whenever anything changes the table's length
		$(settings.nTable).on( 'length.dt.DT', function (e, s, len) {
			if ( settings === s ) {
				$('select', div).val( len );
			}
		} );
	
		return div[0];
	}
	
	
	
	/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
	 * Note that most of the paging logic is done in
	 * DataTable.ext.pager
	 */
	
	/**
	 * Generate the node required for default pagination
	 *  @param {object} oSettings dataTables settings object
	 *  @returns {node} Pagination feature node
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlPaginate ( settings )
	{
		var
			type   = settings.sPaginationType,
			plugin = DataTable.ext.pager[ type ],
			modern = typeof plugin === 'function',
			redraw = function( settings ) {
				_fnDraw( settings );
			},
			node = $('<div/>').addClass( settings.oClasses.sPaging + type )[0],
			features = settings.aanFeatures;
	
		if ( ! modern ) {
			plugin.fnInit( settings, node, redraw );
		}
	
		/* Add a draw callback for the pagination on first instance, to update the paging display */
		if ( ! features.p )
		{
			node.id = settings.sTableId+'_paginate';
	
			settings.aoDrawCallback.push( {
				"fn": function( settings ) {
					if ( modern ) {
						var
							start      = settings._iDisplayStart,
							len        = settings._iDisplayLength,
							visRecords = settings.fnRecordsDisplay(),
							all        = len === -1,
							page = all ? 0 : Math.ceil( start / len ),
							pages = all ? 1 : Math.ceil( visRecords / len ),
							buttons = plugin(page, pages),
							i, ien;
	
						for ( i=0, ien=features.p.length ; i<ien ; i++ ) {
							_fnRenderer( settings, 'pageButton' )(
								settings, features.p[i], i, buttons, page, pages
							);
						}
					}
					else {
						plugin.fnUpdate( settings, redraw );
					}
				},
				"sName": "pagination"
			} );
		}
	
		return node;
	}
	
	
	/**
	 * Alter the display settings to change the page
	 *  @param {object} settings DataTables settings object
	 *  @param {string|int} action Paging action to take: "first", "previous",
	 *    "next" or "last" or page number to jump to (integer)
	 *  @param [bool] redraw Automatically draw the update or not
	 *  @returns {bool} true page has changed, false - no change
	 *  @memberof DataTable#oApi
	 */
	function _fnPageChange ( settings, action, redraw )
	{
		var
			start     = settings._iDisplayStart,
			len       = settings._iDisplayLength,
			records   = settings.fnRecordsDisplay();
	
		if ( records === 0 || len === -1 )
		{
			start = 0;
		}
		else if ( typeof action === "number" )
		{
			start = action * len;
	
			if ( start > records )
			{
				start = 0;
			}
		}
		else if ( action == "first" )
		{
			start = 0;
		}
		else if ( action == "previous" )
		{
			start = len >= 0 ?
				start - len :
				0;
	
			if ( start < 0 )
			{
			  start = 0;
			}
		}
		else if ( action == "next" )
		{
			if ( start + len < records )
			{
				start += len;
			}
		}
		else if ( action == "last" )
		{
			start = Math.floor( (records-1) / len) * len;
		}
		else
		{
			_fnLog( settings, 0, "Unknown paging action: "+action, 5 );
		}
	
		var changed = settings._iDisplayStart !== start;
		settings._iDisplayStart = start;
	
		if ( changed ) {
			_fnCallbackFire( settings, null, 'page', [settings] );
	
			if ( redraw ) {
				_fnDraw( settings );
			}
		}
	
		return changed;
	}
	
	
	
	/**
	 * Generate the node required for the processing node
	 *  @param {object} settings dataTables settings object
	 *  @returns {node} Processing element
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlProcessing ( settings )
	{
		return $('<div/>', {
				'id': ! settings.aanFeatures.r ? settings.sTableId+'_processing' : null,
				'class': settings.oClasses.sProcessing
			} )
			.html( settings.oLanguage.sProcessing )
			.append('<div><div></div><div></div><div></div><div></div></div>')
			.insertBefore( settings.nTable )[0];
	}
	
	
	/**
	 * Display or hide the processing indicator
	 *  @param {object} settings dataTables settings object
	 *  @param {bool} show Show the processing indicator (true) or not (false)
	 *  @memberof DataTable#oApi
	 */
	function _fnProcessingDisplay ( settings, show )
	{
		if ( settings.oFeatures.bProcessing ) {
			$(settings.aanFeatures.r).css( 'display', show ? 'block' : 'none' );
		}
	
		_fnCallbackFire( settings, null, 'processing', [settings, show] );
	}
	
	/**
	 * Add any control elements for the table - specifically scrolling
	 *  @param {object} settings dataTables settings object
	 *  @returns {node} Node to add to the DOM
	 *  @memberof DataTable#oApi
	 */
	function _fnFeatureHtmlTable ( settings )
	{
		var table = $(settings.nTable);
	
		// Scrolling from here on in
		var scroll = settings.oScroll;
	
		if ( scroll.sX === '' && scroll.sY === '' ) {
			return settings.nTable;
		}
	
		var scrollX = scroll.sX;
		var scrollY = scroll.sY;
		var classes = settings.oClasses;
		var caption = table.children('caption');
		var captionSide = caption.length ? caption[0]._captionSide : null;
		var headerClone = $( table[0].cloneNode(false) );
		var footerClone = $( table[0].cloneNode(false) );
		var footer = table.children('tfoot');
		var _div = '<div/>';
		var size = function ( s ) {
			return !s ? null : _fnStringToCss( s );
		};
	
		if ( ! footer.length ) {
			footer = null;
		}
	
		/*
		 * The HTML structure that we want to generate in this function is:
		 *  div - scroller
		 *    div - scroll head
		 *      div - scroll head inner
		 *        table - scroll head table
		 *          thead - thead
		 *    div - scroll body
		 *      table - table (master table)
		 *        thead - thead clone for sizing
		 *        tbody - tbody
		 *    div - scroll foot
		 *      div - scroll foot inner
		 *        table - scroll foot table
		 *          tfoot - tfoot
		 */
		var scroller = $( _div, { 'class': classes.sScrollWrapper } )
			.append(
				$(_div, { 'class': classes.sScrollHead } )
					.css( {
						overflow: 'hidden',
						position: 'relative',
						border: 0,
						width: scrollX ? size(scrollX) : '100%'
					} )
					.append(
						$(_div, { 'class': classes.sScrollHeadInner } )
							.css( {
								'box-sizing': 'content-box',
								width: scroll.sXInner || '100%'
							} )
							.append(
								headerClone
									.removeAttr('id')
									.css( 'margin-left', 0 )
									.append( captionSide === 'top' ? caption : null )
									.append(
										table.children('thead')
									)
							)
					)
			)
			.append(
				$(_div, { 'class': classes.sScrollBody } )
					.css( {
						position: 'relative',
						overflow: 'auto',
						width: size( scrollX )
					} )
					.append( table )
			);
	
		if ( footer ) {
			scroller.append(
				$(_div, { 'class': classes.sScrollFoot } )
					.css( {
						overflow: 'hidden',
						border: 0,
						width: scrollX ? size(scrollX) : '100%'
					} )
					.append(
						$(_div, { 'class': classes.sScrollFootInner } )
							.append(
								footerClone
									.removeAttr('id')
									.css( 'margin-left', 0 )
									.append( captionSide === 'bottom' ? caption : null )
									.append(
										table.children('tfoot')
									)
							)
					)
			);
		}
	
		var children = scroller.children();
		var scrollHead = children[0];
		var scrollBody = children[1];
		var scrollFoot = footer ? children[2] : null;
	
		// When the body is scrolled, then we also want to scroll the headers
		if ( scrollX ) {
			$(scrollBody).on( 'scroll.DT', function (e) {
				var scrollLeft = this.scrollLeft;
	
				scrollHead.scrollLeft = scrollLeft;
	
				if ( footer ) {
					scrollFoot.scrollLeft = scrollLeft;
				}
			} );
		}
	
		$(scrollBody).css('max-height', scrollY);
		if (! scroll.bCollapse) {
			$(scrollBody).css('height', scrollY);
		}
	
		settings.nScrollHead = scrollHead;
		settings.nScrollBody = scrollBody;
		settings.nScrollFoot = scrollFoot;
	
		// On redraw - align columns
		settings.aoDrawCallback.push( {
			"fn": _fnScrollDraw,
			"sName": "scrolling"
		} );
	
		return scroller[0];
	}
	
	
	
	/**
	 * Update the header, footer and body tables for resizing - i.e. column
	 * alignment.
	 *
	 * Welcome to the most horrible function DataTables. The process that this
	 * function follows is basically:
	 *   1. Re-create the table inside the scrolling div
	 *   2. Take live measurements from the DOM
	 *   3. Apply the measurements to align the columns
	 *   4. Clean up
	 *
	 *  @param {object} settings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnScrollDraw ( settings )
	{
		// Given that this is such a monster function, a lot of variables are use
		// to try and keep the minimised size as small as possible
		var
			scroll         = settings.oScroll,
			scrollX        = scroll.sX,
			scrollXInner   = scroll.sXInner,
			scrollY        = scroll.sY,
			barWidth       = scroll.iBarWidth,
			divHeader      = $(settings.nScrollHead),
			divHeaderStyle = divHeader[0].style,
			divHeaderInner = divHeader.children('div'),
			divHeaderInnerStyle = divHeaderInner[0].style,
			divHeaderTable = divHeaderInner.children('table'),
			divBodyEl      = settings.nScrollBody,
			divBody        = $(divBodyEl),
			divBodyStyle   = divBodyEl.style,
			divFooter      = $(settings.nScrollFoot),
			divFooterInner = divFooter.children('div'),
			divFooterTable = divFooterInner.children('table'),
			header         = $(settings.nTHead),
			table          = $(settings.nTable),
			tableEl        = table[0],
			tableStyle     = tableEl.style,
			footer         = settings.nTFoot ? $(settings.nTFoot) : null,
			browser        = settings.oBrowser,
			ie67           = browser.bScrollOversize,
			dtHeaderCells  = _pluck( settings.aoColumns, 'nTh' ),
			headerTrgEls, footerTrgEls,
			headerSrcEls, footerSrcEls,
			headerCopy, footerCopy,
			headerWidths=[], footerWidths=[],
			headerContent=[], footerContent=[],
			idx, correction, sanityWidth,
			zeroOut = function(nSizer) {
				var style = nSizer.style;
				style.paddingTop = "0";
				style.paddingBottom = "0";
				style.borderTopWidth = "0";
				style.borderBottomWidth = "0";
				style.height = 0;
			};
	
		// If the scrollbar visibility has changed from the last draw, we need to
		// adjust the column sizes as the table width will have changed to account
		// for the scrollbar
		var scrollBarVis = divBodyEl.scrollHeight > divBodyEl.clientHeight;
		
		if ( settings.scrollBarVis !== scrollBarVis && settings.scrollBarVis !== undefined ) {
			settings.scrollBarVis = scrollBarVis;
			_fnAdjustColumnSizing( settings );
			return; // adjust column sizing will call this function again
		}
		else {
			settings.scrollBarVis = scrollBarVis;
		}
	
		/*
		 * 1. Re-create the table inside the scrolling div
		 */
	
		// Remove the old minimised thead and tfoot elements in the inner table
		table.children('thead, tfoot').remove();
	
		if ( footer ) {
			footerCopy = footer.clone().prependTo( table );
			footerTrgEls = footer.find('tr'); // the original tfoot is in its own table and must be sized
			footerSrcEls = footerCopy.find('tr');
			footerCopy.find('[id]').removeAttr('id');
		}
	
		// Clone the current header and footer elements and then place it into the inner table
		headerCopy = header.clone().prependTo( table );
		headerTrgEls = header.find('tr'); // original header is in its own table
		headerSrcEls = headerCopy.find('tr');
		headerCopy.find('th, td').removeAttr('tabindex');
		headerCopy.find('[id]').removeAttr('id');
	
	
		/*
		 * 2. Take live measurements from the DOM - do not alter the DOM itself!
		 */
	
		// Remove old sizing and apply the calculated column widths
		// Get the unique column headers in the newly created (cloned) header. We want to apply the
		// calculated sizes to this header
		if ( ! scrollX )
		{
			divBodyStyle.width = '100%';
			divHeader[0].style.width = '100%';
		}
	
		$.each( _fnGetUniqueThs( settings, headerCopy ), function ( i, el ) {
			idx = _fnVisibleToColumnIndex( settings, i );
			el.style.width = settings.aoColumns[idx].sWidth;
		} );
	
		if ( footer ) {
			_fnApplyToChildren( function(n) {
				n.style.width = "";
			}, footerSrcEls );
		}
	
		// Size the table as a whole
		sanityWidth = table.outerWidth();
		if ( scrollX === "" ) {
			// No x scrolling
			tableStyle.width = "100%";
	
			// IE7 will make the width of the table when 100% include the scrollbar
			// - which is shouldn't. When there is a scrollbar we need to take this
			// into account.
			if ( ie67 && (table.find('tbody').height() > divBodyEl.offsetHeight ||
				divBody.css('overflow-y') == "scroll")
			) {
				tableStyle.width = _fnStringToCss( table.outerWidth() - barWidth);
			}
	
			// Recalculate the sanity width
			sanityWidth = table.outerWidth();
		}
		else if ( scrollXInner !== "" ) {
			// legacy x scroll inner has been given - use it
			tableStyle.width = _fnStringToCss(scrollXInner);
	
			// Recalculate the sanity width
			sanityWidth = table.outerWidth();
		}
	
		// Hidden header should have zero height, so remove padding and borders. Then
		// set the width based on the real headers
	
		// Apply all styles in one pass
		_fnApplyToChildren( zeroOut, headerSrcEls );
	
		// Read all widths in next pass
		_fnApplyToChildren( function(nSizer) {
			var style = window.getComputedStyle ?
				window.getComputedStyle(nSizer).width :
				_fnStringToCss( $(nSizer).width() );
	
			headerContent.push( nSizer.innerHTML );
			headerWidths.push( style );
		}, headerSrcEls );
	
		// Apply all widths in final pass
		_fnApplyToChildren( function(nToSize, i) {
			nToSize.style.width = headerWidths[i];
		}, headerTrgEls );
	
		$(headerSrcEls).css('height', 0);
	
		/* Same again with the footer if we have one */
		if ( footer )
		{
			_fnApplyToChildren( zeroOut, footerSrcEls );
	
			_fnApplyToChildren( function(nSizer) {
				footerContent.push( nSizer.innerHTML );
				footerWidths.push( _fnStringToCss( $(nSizer).css('width') ) );
			}, footerSrcEls );
	
			_fnApplyToChildren( function(nToSize, i) {
				nToSize.style.width = footerWidths[i];
			}, footerTrgEls );
	
			$(footerSrcEls).height(0);
		}
	
	
		/*
		 * 3. Apply the measurements
		 */
	
		// "Hide" the header and footer that we used for the sizing. We need to keep
		// the content of the cell so that the width applied to the header and body
		// both match, but we want to hide it completely. We want to also fix their
		// width to what they currently are
		_fnApplyToChildren( function(nSizer, i) {
			nSizer.innerHTML = '<div class="dataTables_sizing">'+headerContent[i]+'</div>';
			nSizer.childNodes[0].style.height = "0";
			nSizer.childNodes[0].style.overflow = "hidden";
			nSizer.style.width = headerWidths[i];
		}, headerSrcEls );
	
		if ( footer )
		{
			_fnApplyToChildren( function(nSizer, i) {
				nSizer.innerHTML = '<div class="dataTables_sizing">'+footerContent[i]+'</div>';
				nSizer.childNodes[0].style.height = "0";
				nSizer.childNodes[0].style.overflow = "hidden";
				nSizer.style.width = footerWidths[i];
			}, footerSrcEls );
		}
	
		// Sanity check that the table is of a sensible width. If not then we are going to get
		// misalignment - try to prevent this by not allowing the table to shrink below its min width
		if ( Math.round(table.outerWidth()) < Math.round(sanityWidth) )
		{
			// The min width depends upon if we have a vertical scrollbar visible or not */
			correction = ((divBodyEl.scrollHeight > divBodyEl.offsetHeight ||
				divBody.css('overflow-y') == "scroll")) ?
					sanityWidth+barWidth :
					sanityWidth;
	
			// IE6/7 are a law unto themselves...
			if ( ie67 && (divBodyEl.scrollHeight >
				divBodyEl.offsetHeight || divBody.css('overflow-y') == "scroll")
			) {
				tableStyle.width = _fnStringToCss( correction-barWidth );
			}
	
			// And give the user a warning that we've stopped the table getting too small
			if ( scrollX === "" || scrollXInner !== "" ) {
				_fnLog( settings, 1, 'Possible column misalignment', 6 );
			}
		}
		else
		{
			correction = '100%';
		}
	
		// Apply to the container elements
		divBodyStyle.width = _fnStringToCss( correction );
		divHeaderStyle.width = _fnStringToCss( correction );
	
		if ( footer ) {
			settings.nScrollFoot.style.width = _fnStringToCss( correction );
		}
	
	
		/*
		 * 4. Clean up
		 */
		if ( ! scrollY ) {
			/* IE7< puts a vertical scrollbar in place (when it shouldn't be) due to subtracting
			 * the scrollbar height from the visible display, rather than adding it on. We need to
			 * set the height in order to sort this. Don't want to do it in any other browsers.
			 */
			if ( ie67 ) {
				divBodyStyle.height = _fnStringToCss( tableEl.offsetHeight+barWidth );
			}
		}
	
		/* Finally set the width's of the header and footer tables */
		var iOuterWidth = table.outerWidth();
		divHeaderTable[0].style.width = _fnStringToCss( iOuterWidth );
		divHeaderInnerStyle.width = _fnStringToCss( iOuterWidth );
	
		// Figure out if there are scrollbar present - if so then we need a the header and footer to
		// provide a bit more space to allow "overflow" scrolling (i.e. past the scrollbar)
		var bScrolling = table.height() > divBodyEl.clientHeight || divBody.css('overflow-y') == "scroll";
		var padding = 'padding' + (browser.bScrollbarLeft ? 'Left' : 'Right' );
		divHeaderInnerStyle[ padding ] = bScrolling ? barWidth+"px" : "0px";
	
		if ( footer ) {
			divFooterTable[0].style.width = _fnStringToCss( iOuterWidth );
			divFooterInner[0].style.width = _fnStringToCss( iOuterWidth );
			divFooterInner[0].style[padding] = bScrolling ? barWidth+"px" : "0px";
		}
	
		// Correct DOM ordering for colgroup - comes before the thead
		table.children('colgroup').insertBefore( table.children('thead') );
	
		/* Adjust the position of the header in case we loose the y-scrollbar */
		divBody.trigger('scroll');
	
		// If sorting or filtering has occurred, jump the scrolling back to the top
		// only if we aren't holding the position
		if ( (settings.bSorted || settings.bFiltered) && ! settings._drawHold ) {
			divBodyEl.scrollTop = 0;
		}
	}
	
	
	
	/**
	 * Apply a given function to the display child nodes of an element array (typically
	 * TD children of TR rows
	 *  @param {function} fn Method to apply to the objects
	 *  @param array {nodes} an1 List of elements to look through for display children
	 *  @param array {nodes} an2 Another list (identical structure to the first) - optional
	 *  @memberof DataTable#oApi
	 */
	function _fnApplyToChildren( fn, an1, an2 )
	{
		var index=0, i=0, iLen=an1.length;
		var nNode1, nNode2;
	
		while ( i < iLen ) {
			nNode1 = an1[i].firstChild;
			nNode2 = an2 ? an2[i].firstChild : null;
	
			while ( nNode1 ) {
				if ( nNode1.nodeType === 1 ) {
					if ( an2 ) {
						fn( nNode1, nNode2, index );
					}
					else {
						fn( nNode1, index );
					}
	
					index++;
				}
	
				nNode1 = nNode1.nextSibling;
				nNode2 = an2 ? nNode2.nextSibling : null;
			}
	
			i++;
		}
	}
	
	
	
	var __re_html_remove = /<.*?>/g;
	
	
	/**
	 * Calculate the width of columns for the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnCalculateColumnWidths ( oSettings )
	{
		var
			table = oSettings.nTable,
			columns = oSettings.aoColumns,
			scroll = oSettings.oScroll,
			scrollY = scroll.sY,
			scrollX = scroll.sX,
			scrollXInner = scroll.sXInner,
			columnCount = columns.length,
			visibleColumns = _fnGetColumns( oSettings, 'bVisible' ),
			headerCells = $('th', oSettings.nTHead),
			tableWidthAttr = table.getAttribute('width'), // from DOM element
			tableContainer = table.parentNode,
			userInputs = false,
			i, column, columnIdx, width, outerWidth,
			browser = oSettings.oBrowser,
			ie67 = browser.bScrollOversize;
	
		var styleWidth = table.style.width;
		if ( styleWidth && styleWidth.indexOf('%') !== -1 ) {
			tableWidthAttr = styleWidth;
		}
	
		/* Convert any user input sizes into pixel sizes */
		for ( i=0 ; i<visibleColumns.length ; i++ ) {
			column = columns[ visibleColumns[i] ];
	
			if ( column.sWidth !== null ) {
				column.sWidth = _fnConvertToWidth( column.sWidthOrig, tableContainer );
	
				userInputs = true;
			}
		}
	
		/* If the number of columns in the DOM equals the number that we have to
		 * process in DataTables, then we can use the offsets that are created by
		 * the web- browser. No custom sizes can be set in order for this to happen,
		 * nor scrolling used
		 */
		if ( ie67 || ! userInputs && ! scrollX && ! scrollY &&
		     columnCount == _fnVisbleColumns( oSettings ) &&
		     columnCount == headerCells.length
		) {
			for ( i=0 ; i<columnCount ; i++ ) {
				var colIdx = _fnVisibleToColumnIndex( oSettings, i );
	
				if ( colIdx !== null ) {
					columns[ colIdx ].sWidth = _fnStringToCss( headerCells.eq(i).width() );
				}
			}
		}
		else
		{
			// Otherwise construct a single row, worst case, table with the widest
			// node in the data, assign any user defined widths, then insert it into
			// the DOM and allow the browser to do all the hard work of calculating
			// table widths
			var tmpTable = $(table).clone() // don't use cloneNode - IE8 will remove events on the main table
				.css( 'visibility', 'hidden' )
				.removeAttr( 'id' );
	
			// Clean up the table body
			tmpTable.find('tbody tr').remove();
			var tr = $('<tr/>').appendTo( tmpTable.find('tbody') );
	
			// Clone the table header and footer - we can't use the header / footer
			// from the cloned table, since if scrolling is active, the table's
			// real header and footer are contained in different table tags
			tmpTable.find('thead, tfoot').remove();
			tmpTable
				.append( $(oSettings.nTHead).clone() )
				.append( $(oSettings.nTFoot).clone() );
	
			// Remove any assigned widths from the footer (from scrolling)
			tmpTable.find('tfoot th, tfoot td').css('width', '');
	
			// Apply custom sizing to the cloned header
			headerCells = _fnGetUniqueThs( oSettings, tmpTable.find('thead')[0] );
	
			for ( i=0 ; i<visibleColumns.length ; i++ ) {
				column = columns[ visibleColumns[i] ];
	
				headerCells[i].style.width = column.sWidthOrig !== null && column.sWidthOrig !== '' ?
					_fnStringToCss( column.sWidthOrig ) :
					'';
	
				// For scrollX we need to force the column width otherwise the
				// browser will collapse it. If this width is smaller than the
				// width the column requires, then it will have no effect
				if ( column.sWidthOrig && scrollX ) {
					$( headerCells[i] ).append( $('<div/>').css( {
						width: column.sWidthOrig,
						margin: 0,
						padding: 0,
						border: 0,
						height: 1
					} ) );
				}
			}
	
			// Find the widest cell for each column and put it into the table
			if ( oSettings.aoData.length ) {
				for ( i=0 ; i<visibleColumns.length ; i++ ) {
					columnIdx = visibleColumns[i];
					column = columns[ columnIdx ];
	
					$( _fnGetWidestNode( oSettings, columnIdx ) )
						.clone( false )
						.append( column.sContentPadding )
						.appendTo( tr );
				}
			}
	
			// Tidy the temporary table - remove name attributes so there aren't
			// duplicated in the dom (radio elements for example)
			$('[name]', tmpTable).removeAttr('name');
	
			// Table has been built, attach to the document so we can work with it.
			// A holding element is used, positioned at the top of the container
			// with minimal height, so it has no effect on if the container scrolls
			// or not. Otherwise it might trigger scrolling when it actually isn't
			// needed
			var holder = $('<div/>').css( scrollX || scrollY ?
					{
						position: 'absolute',
						top: 0,
						left: 0,
						height: 1,
						right: 0,
						overflow: 'hidden'
					} :
					{}
				)
				.append( tmpTable )
				.appendTo( tableContainer );
	
			// When scrolling (X or Y) we want to set the width of the table as 
			// appropriate. However, when not scrolling leave the table width as it
			// is. This results in slightly different, but I think correct behaviour
			if ( scrollX && scrollXInner ) {
				tmpTable.width( scrollXInner );
			}
			else if ( scrollX ) {
				tmpTable.css( 'width', 'auto' );
				tmpTable.removeAttr('width');
	
				// If there is no width attribute or style, then allow the table to
				// collapse
				if ( tmpTable.width() < tableContainer.clientWidth && tableWidthAttr ) {
					tmpTable.width( tableContainer.clientWidth );
				}
			}
			else if ( scrollY ) {
				tmpTable.width( tableContainer.clientWidth );
			}
			else if ( tableWidthAttr ) {
				tmpTable.width( tableWidthAttr );
			}
	
			// Get the width of each column in the constructed table - we need to
			// know the inner width (so it can be assigned to the other table's
			// cells) and the outer width so we can calculate the full width of the
			// table. This is safe since DataTables requires a unique cell for each
			// column, but if ever a header can span multiple columns, this will
			// need to be modified.
			var total = 0;
			for ( i=0 ; i<visibleColumns.length ; i++ ) {
				var cell = $(headerCells[i]);
				var border = cell.outerWidth() - cell.width();
	
				// Use getBounding... where possible (not IE8-) because it can give
				// sub-pixel accuracy, which we then want to round up!
				var bounding = browser.bBounding ?
					Math.ceil( headerCells[i].getBoundingClientRect().width ) :
					cell.outerWidth();
	
				// Total is tracked to remove any sub-pixel errors as the outerWidth
				// of the table might not equal the total given here (IE!).
				total += bounding;
	
				// Width for each column to use
				columns[ visibleColumns[i] ].sWidth = _fnStringToCss( bounding - border );
			}
	
			table.style.width = _fnStringToCss( total );
	
			// Finished with the table - ditch it
			holder.remove();
		}
	
		// If there is a width attr, we want to attach an event listener which
		// allows the table sizing to automatically adjust when the window is
		// resized. Use the width attr rather than CSS, since we can't know if the
		// CSS is a relative value or absolute - DOM read is always px.
		if ( tableWidthAttr ) {
			table.style.width = _fnStringToCss( tableWidthAttr );
		}
	
		if ( (tableWidthAttr || scrollX) && ! oSettings._reszEvt ) {
			var bindResize = function () {
				$(window).on('resize.DT-'+oSettings.sInstance, _fnThrottle( function () {
					_fnAdjustColumnSizing( oSettings );
				} ) );
			};
	
			// IE6/7 will crash if we bind a resize event handler on page load.
			// To be removed in 1.11 which drops IE6/7 support
			if ( ie67 ) {
				setTimeout( bindResize, 1000 );
			}
			else {
				bindResize();
			}
	
			oSettings._reszEvt = true;
		}
	}
	
	
	/**
	 * Throttle the calls to a function. Arguments and context are maintained for
	 * the throttled function
	 *  @param {function} fn Function to be called
	 *  @param {int} [freq=200] call frequency in mS
	 *  @returns {function} wrapped function
	 *  @memberof DataTable#oApi
	 */
	var _fnThrottle = DataTable.util.throttle;
	
	
	/**
	 * Convert a CSS unit width to pixels (e.g. 2em)
	 *  @param {string} width width to be converted
	 *  @param {node} parent parent to get the with for (required for relative widths) - optional
	 *  @returns {int} width in pixels
	 *  @memberof DataTable#oApi
	 */
	function _fnConvertToWidth ( width, parent )
	{
		if ( ! width ) {
			return 0;
		}
	
		var n = $('<div/>')
			.css( 'width', _fnStringToCss( width ) )
			.appendTo( parent || document.body );
	
		var val = n[0].offsetWidth;
		n.remove();
	
		return val;
	}
	
	
	/**
	 * Get the widest node
	 *  @param {object} settings dataTables settings object
	 *  @param {int} colIdx column of interest
	 *  @returns {node} widest table node
	 *  @memberof DataTable#oApi
	 */
	function _fnGetWidestNode( settings, colIdx )
	{
		var idx = _fnGetMaxLenString( settings, colIdx );
		if ( idx < 0 ) {
			return null;
		}
	
		var data = settings.aoData[ idx ];
		return ! data.nTr ? // Might not have been created when deferred rendering
			$('<td/>').html( _fnGetCellData( settings, idx, colIdx, 'display' ) )[0] :
			data.anCells[ colIdx ];
	}
	
	
	/**
	 * Get the maximum strlen for each data column
	 *  @param {object} settings dataTables settings object
	 *  @param {int} colIdx column of interest
	 *  @returns {string} max string length for each column
	 *  @memberof DataTable#oApi
	 */
	function _fnGetMaxLenString( settings, colIdx )
	{
		var s, max=-1, maxIdx = -1;
	
		for ( var i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
			s = _fnGetCellData( settings, i, colIdx, 'display' )+'';
			s = s.replace( __re_html_remove, '' );
			s = s.replace( /&nbsp;/g, ' ' );
	
			if ( s.length > max ) {
				max = s.length;
				maxIdx = i;
			}
		}
	
		return maxIdx;
	}
	
	
	/**
	 * Append a CSS unit (only if required) to a string
	 *  @param {string} value to css-ify
	 *  @returns {string} value with css unit
	 *  @memberof DataTable#oApi
	 */
	function _fnStringToCss( s )
	{
		if ( s === null ) {
			return '0px';
		}
	
		if ( typeof s == 'number' ) {
			return s < 0 ?
				'0px' :
				s+'px';
		}
	
		// Check it has a unit character already
		return s.match(/\d$/) ?
			s+'px' :
			s;
	}
	
	
	
	function _fnSortFlatten ( settings )
	{
		var
			i, iLen, k, kLen,
			aSort = [],
			aiOrig = [],
			aoColumns = settings.aoColumns,
			aDataSort, iCol, sType, srcCol,
			fixed = settings.aaSortingFixed,
			fixedObj = $.isPlainObject( fixed ),
			nestedSort = [],
			add = function ( a ) {
				if ( a.length && ! Array.isArray( a[0] ) ) {
					// 1D array
					nestedSort.push( a );
				}
				else {
					// 2D array
					$.merge( nestedSort, a );
				}
			};
	
		// Build the sort array, with pre-fix and post-fix options if they have been
		// specified
		if ( Array.isArray( fixed ) ) {
			add( fixed );
		}
	
		if ( fixedObj && fixed.pre ) {
			add( fixed.pre );
		}
	
		add( settings.aaSorting );
	
		if (fixedObj && fixed.post ) {
			add( fixed.post );
		}
	
		for ( i=0 ; i<nestedSort.length ; i++ )
		{
			srcCol = nestedSort[i][0];
			aDataSort = aoColumns[ srcCol ].aDataSort;
	
			for ( k=0, kLen=aDataSort.length ; k<kLen ; k++ )
			{
				iCol = aDataSort[k];
				sType = aoColumns[ iCol ].sType || 'string';
	
				if ( nestedSort[i]._idx === undefined ) {
					nestedSort[i]._idx = $.inArray( nestedSort[i][1], aoColumns[iCol].asSorting );
				}
	
				aSort.push( {
					src:       srcCol,
					col:       iCol,
					dir:       nestedSort[i][1],
					index:     nestedSort[i]._idx,
					type:      sType,
					formatter: DataTable.ext.type.order[ sType+"-pre" ]
				} );
			}
		}
	
		return aSort;
	}
	
	/**
	 * Change the order of the table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 *  @todo This really needs split up!
	 */
	function _fnSort ( oSettings )
	{
		var
			i, ien, iLen, j, jLen, k, kLen,
			sDataType, nTh,
			aiOrig = [],
			oExtSort = DataTable.ext.type.order,
			aoData = oSettings.aoData,
			aoColumns = oSettings.aoColumns,
			aDataSort, data, iCol, sType, oSort,
			formatters = 0,
			sortCol,
			displayMaster = oSettings.aiDisplayMaster,
			aSort;
	
		// Resolve any column types that are unknown due to addition or invalidation
		// @todo Can this be moved into a 'data-ready' handler which is called when
		//   data is going to be used in the table?
		_fnColumnTypes( oSettings );
	
		aSort = _fnSortFlatten( oSettings );
	
		for ( i=0, ien=aSort.length ; i<ien ; i++ ) {
			sortCol = aSort[i];
	
			// Track if we can use the fast sort algorithm
			if ( sortCol.formatter ) {
				formatters++;
			}
	
			// Load the data needed for the sort, for each cell
			_fnSortData( oSettings, sortCol.col );
		}
	
		/* No sorting required if server-side or no sorting array */
		if ( _fnDataSource( oSettings ) != 'ssp' && aSort.length !== 0 )
		{
			// Create a value - key array of the current row positions such that we can use their
			// current position during the sort, if values match, in order to perform stable sorting
			for ( i=0, iLen=displayMaster.length ; i<iLen ; i++ ) {
				aiOrig[ displayMaster[i] ] = i;
			}
	
			/* Do the sort - here we want multi-column sorting based on a given data source (column)
			 * and sorting function (from oSort) in a certain direction. It's reasonably complex to
			 * follow on it's own, but this is what we want (example two column sorting):
			 *  fnLocalSorting = function(a,b){
			 *    var iTest;
			 *    iTest = oSort['string-asc']('data11', 'data12');
			 *      if (iTest !== 0)
			 *        return iTest;
			 *    iTest = oSort['numeric-desc']('data21', 'data22');
			 *    if (iTest !== 0)
			 *      return iTest;
			 *    return oSort['numeric-asc']( aiOrig[a], aiOrig[b] );
			 *  }
			 * Basically we have a test for each sorting column, if the data in that column is equal,
			 * test the next column. If all columns match, then we use a numeric sort on the row
			 * positions in the original data array to provide a stable sort.
			 *
			 * Note - I know it seems excessive to have two sorting methods, but the first is around
			 * 15% faster, so the second is only maintained for backwards compatibility with sorting
			 * methods which do not have a pre-sort formatting function.
			 */
			if ( formatters === aSort.length ) {
				// All sort types have formatting functions
				displayMaster.sort( function ( a, b ) {
					var
						x, y, k, test, sort,
						len=aSort.length,
						dataA = aoData[a]._aSortData,
						dataB = aoData[b]._aSortData;
	
					for ( k=0 ; k<len ; k++ ) {
						sort = aSort[k];
	
						x = dataA[ sort.col ];
						y = dataB[ sort.col ];
	
						test = x<y ? -1 : x>y ? 1 : 0;
						if ( test !== 0 ) {
							return sort.dir === 'asc' ? test : -test;
						}
					}
	
					x = aiOrig[a];
					y = aiOrig[b];
					return x<y ? -1 : x>y ? 1 : 0;
				} );
			}
			else {
				// Depreciated - remove in 1.11 (providing a plug-in option)
				// Not all sort types have formatting methods, so we have to call their sorting
				// methods.
				displayMaster.sort( function ( a, b ) {
					var
						x, y, k, l, test, sort, fn,
						len=aSort.length,
						dataA = aoData[a]._aSortData,
						dataB = aoData[b]._aSortData;
	
					for ( k=0 ; k<len ; k++ ) {
						sort = aSort[k];
	
						x = dataA[ sort.col ];
						y = dataB[ sort.col ];
	
						fn = oExtSort[ sort.type+"-"+sort.dir ] || oExtSort[ "string-"+sort.dir ];
						test = fn( x, y );
						if ( test !== 0 ) {
							return test;
						}
					}
	
					x = aiOrig[a];
					y = aiOrig[b];
					return x<y ? -1 : x>y ? 1 : 0;
				} );
			}
		}
	
		/* Tell the draw function that we have sorted the data */
		oSettings.bSorted = true;
	}
	
	
	function _fnSortAria ( settings )
	{
		var label;
		var nextSort;
		var columns = settings.aoColumns;
		var aSort = _fnSortFlatten( settings );
		var oAria = settings.oLanguage.oAria;
	
		// ARIA attributes - need to loop all columns, to update all (removing old
		// attributes as needed)
		for ( var i=0, iLen=columns.length ; i<iLen ; i++ )
		{
			var col = columns[i];
			var asSorting = col.asSorting;
			var sTitle = col.ariaTitle || col.sTitle.replace( /<.*?>/g, "" );
			var th = col.nTh;
	
			// IE7 is throwing an error when setting these properties with jQuery's
			// attr() and removeAttr() methods...
			th.removeAttribute('aria-sort');
	
			/* In ARIA only the first sorting column can be marked as sorting - no multi-sort option */
			if ( col.bSortable ) {
				if ( aSort.length > 0 && aSort[0].col == i ) {
					th.setAttribute('aria-sort', aSort[0].dir=="asc" ? "ascending" : "descending" );
					nextSort = asSorting[ aSort[0].index+1 ] || asSorting[0];
				}
				else {
					nextSort = asSorting[0];
				}
	
				label = sTitle + ( nextSort === "asc" ?
					oAria.sSortAscending :
					oAria.sSortDescending
				);
			}
			else {
				label = sTitle;
			}
	
			th.setAttribute('aria-label', label);
		}
	}
	
	
	/**
	 * Function to run on user sort request
	 *  @param {object} settings dataTables settings object
	 *  @param {node} attachTo node to attach the handler to
	 *  @param {int} colIdx column sorting index
	 *  @param {boolean} [append=false] Append the requested sort to the existing
	 *    sort if true (i.e. multi-column sort)
	 *  @param {function} [callback] callback function
	 *  @memberof DataTable#oApi
	 */
	function _fnSortListener ( settings, colIdx, append, callback )
	{
		var col = settings.aoColumns[ colIdx ];
		var sorting = settings.aaSorting;
		var asSorting = col.asSorting;
		var nextSortIdx;
		var next = function ( a, overflow ) {
			var idx = a._idx;
			if ( idx === undefined ) {
				idx = $.inArray( a[1], asSorting );
			}
	
			return idx+1 < asSorting.length ?
				idx+1 :
				overflow ?
					null :
					0;
		};
	
		// Convert to 2D array if needed
		if ( typeof sorting[0] === 'number' ) {
			sorting = settings.aaSorting = [ sorting ];
		}
	
		// If appending the sort then we are multi-column sorting
		if ( append && settings.oFeatures.bSortMulti ) {
			// Are we already doing some kind of sort on this column?
			var sortIdx = $.inArray( colIdx, _pluck(sorting, '0') );
	
			if ( sortIdx !== -1 ) {
				// Yes, modify the sort
				nextSortIdx = next( sorting[sortIdx], true );
	
				if ( nextSortIdx === null && sorting.length === 1 ) {
					nextSortIdx = 0; // can't remove sorting completely
				}
	
				if ( nextSortIdx === null ) {
					sorting.splice( sortIdx, 1 );
				}
				else {
					sorting[sortIdx][1] = asSorting[ nextSortIdx ];
					sorting[sortIdx]._idx = nextSortIdx;
				}
			}
			else {
				// No sort on this column yet
				sorting.push( [ colIdx, asSorting[0], 0 ] );
				sorting[sorting.length-1]._idx = 0;
			}
		}
		else if ( sorting.length && sorting[0][0] == colIdx ) {
			// Single column - already sorting on this column, modify the sort
			nextSortIdx = next( sorting[0] );
	
			sorting.length = 1;
			sorting[0][1] = asSorting[ nextSortIdx ];
			sorting[0]._idx = nextSortIdx;
		}
		else {
			// Single column - sort only on this column
			sorting.length = 0;
			sorting.push( [ colIdx, asSorting[0] ] );
			sorting[0]._idx = 0;
		}
	
		// Run the sort by calling a full redraw
		_fnReDraw( settings );
	
		// callback used for async user interaction
		if ( typeof callback == 'function' ) {
			callback( settings );
		}
	}
	
	
	/**
	 * Attach a sort handler (click) to a node
	 *  @param {object} settings dataTables settings object
	 *  @param {node} attachTo node to attach the handler to
	 *  @param {int} colIdx column sorting index
	 *  @param {function} [callback] callback function
	 *  @memberof DataTable#oApi
	 */
	function _fnSortAttachListener ( settings, attachTo, colIdx, callback )
	{
		var col = settings.aoColumns[ colIdx ];
	
		_fnBindAction( attachTo, {}, function (e) {
			/* If the column is not sortable - don't to anything */
			if ( col.bSortable === false ) {
				return;
			}
	
			// If processing is enabled use a timeout to allow the processing
			// display to be shown - otherwise to it synchronously
			if ( settings.oFeatures.bProcessing ) {
				_fnProcessingDisplay( settings, true );
	
				setTimeout( function() {
					_fnSortListener( settings, colIdx, e.shiftKey, callback );
	
					// In server-side processing, the draw callback will remove the
					// processing display
					if ( _fnDataSource( settings ) !== 'ssp' ) {
						_fnProcessingDisplay( settings, false );
					}
				}, 0 );
			}
			else {
				_fnSortListener( settings, colIdx, e.shiftKey, callback );
			}
		} );
	}
	
	
	/**
	 * Set the sorting classes on table's body, Note: it is safe to call this function
	 * when bSort and bSortClasses are false
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnSortingClasses( settings )
	{
		var oldSort = settings.aLastSort;
		var sortClass = settings.oClasses.sSortColumn;
		var sort = _fnSortFlatten( settings );
		var features = settings.oFeatures;
		var i, ien, colIdx;
	
		if ( features.bSort && features.bSortClasses ) {
			// Remove old sorting classes
			for ( i=0, ien=oldSort.length ; i<ien ; i++ ) {
				colIdx = oldSort[i].src;
	
				// Remove column sorting
				$( _pluck( settings.aoData, 'anCells', colIdx ) )
					.removeClass( sortClass + (i<2 ? i+1 : 3) );
			}
	
			// Add new column sorting
			for ( i=0, ien=sort.length ; i<ien ; i++ ) {
				colIdx = sort[i].src;
	
				$( _pluck( settings.aoData, 'anCells', colIdx ) )
					.addClass( sortClass + (i<2 ? i+1 : 3) );
			}
		}
	
		settings.aLastSort = sort;
	}
	
	
	// Get the data to sort a column, be it from cache, fresh (populating the
	// cache), or from a sort formatter
	function _fnSortData( settings, idx )
	{
		// Custom sorting function - provided by the sort data type
		var column = settings.aoColumns[ idx ];
		var customSort = DataTable.ext.order[ column.sSortDataType ];
		var customData;
	
		if ( customSort ) {
			customData = customSort.call( settings.oInstance, settings, idx,
				_fnColumnIndexToVisible( settings, idx )
			);
		}
	
		// Use / populate cache
		var row, cellData;
		var formatter = DataTable.ext.type.order[ column.sType+"-pre" ];
	
		for ( var i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
			row = settings.aoData[i];
	
			if ( ! row._aSortData ) {
				row._aSortData = [];
			}
	
			if ( ! row._aSortData[idx] || customSort ) {
				cellData = customSort ?
					customData[i] : // If there was a custom sort function, use data from there
					_fnGetCellData( settings, i, idx, 'sort' );
	
				row._aSortData[ idx ] = formatter ?
					formatter( cellData ) :
					cellData;
			}
		}
	}
	
	
	
	/**
	 * Save the state of a table
	 *  @param {object} oSettings dataTables settings object
	 *  @memberof DataTable#oApi
	 */
	function _fnSaveState ( settings )
	{
		if (settings._bLoadingState) {
			return;
		}
	
		/* Store the interesting variables */
		var state = {
			time:    +new Date(),
			start:   settings._iDisplayStart,
			length:  settings._iDisplayLength,
			order:   $.extend( true, [], settings.aaSorting ),
			search:  _fnSearchToCamel( settings.oPreviousSearch ),
			columns: $.map( settings.aoColumns, function ( col, i ) {
				return {
					visible: col.bVisible,
					search: _fnSearchToCamel( settings.aoPreSearchCols[i] )
				};
			} )
		};
	
		settings.oSavedState = state;
		_fnCallbackFire( settings, "aoStateSaveParams", 'stateSaveParams', [settings, state] );
		
		if ( settings.oFeatures.bStateSave && !settings.bDestroying )
		{
			settings.fnStateSaveCallback.call( settings.oInstance, settings, state );
		}	
	}
	
	
	/**
	 * Attempt to load a saved table state
	 *  @param {object} oSettings dataTables settings object
	 *  @param {object} oInit DataTables init object so we can override settings
	 *  @param {function} callback Callback to execute when the state has been loaded
	 *  @memberof DataTable#oApi
	 */
	function _fnLoadState ( settings, oInit, callback )
	{
		if ( ! settings.oFeatures.bStateSave ) {
			callback();
			return;
		}
	
		var loaded = function(state) {
			_fnImplementState(settings, state, callback);
		}
	
		var state = settings.fnStateLoadCallback.call( settings.oInstance, settings, loaded );
	
		if ( state !== undefined ) {
			_fnImplementState( settings, state, callback );
		}
		// otherwise, wait for the loaded callback to be executed
	
		return true;
	}
	
	function _fnImplementState ( settings, s, callback) {
		var i, ien;
		var columns = settings.aoColumns;
		settings._bLoadingState = true;
	
		// When StateRestore was introduced the state could now be implemented at any time
		// Not just initialisation. To do this an api instance is required in some places
		var api = settings._bInitComplete ? new DataTable.Api(settings) : null;
	
		if ( ! s || ! s.time ) {
			settings._bLoadingState = false;
			callback();
			return;
		}
	
		// Allow custom and plug-in manipulation functions to alter the saved data set and
		// cancelling of loading by returning false
		var abStateLoad = _fnCallbackFire( settings, 'aoStateLoadParams', 'stateLoadParams', [settings, s] );
		if ( $.inArray( false, abStateLoad ) !== -1 ) {
			settings._bLoadingState = false;
			callback();
			return;
		}
	
		// Reject old data
		var duration = settings.iStateDuration;
		if ( duration > 0 && s.time < +new Date() - (duration*1000) ) {
			settings._bLoadingState = false;
			callback();
			return;
		}
	
		// Number of columns have changed - all bets are off, no restore of settings
		if ( s.columns && columns.length !== s.columns.length ) {
			settings._bLoadingState = false;
			callback();
			return;
		}
	
		// Store the saved state so it might be accessed at any time
		settings.oLoadedState = $.extend( true, {}, s );
	
		// Page Length
		if ( s.length !== undefined ) {
			// If already initialised just set the value directly so that the select element is also updated
			if (api) {
				api.page.len(s.length)
			}
			else {
				settings._iDisplayLength   = s.length;
			}
		}
	
		// Restore key features - todo - for 1.11 this needs to be done by
		// subscribed events
		if ( s.start !== undefined ) {
			if(api === null) {
				settings._iDisplayStart    = s.start;
				settings.iInitDisplayStart = s.start;
			}
			else {
				_fnPageChange(settings, s.start/settings._iDisplayLength);
			}
		}
	
		// Order
		if ( s.order !== undefined ) {
			settings.aaSorting = [];
			$.each( s.order, function ( i, col ) {
				settings.aaSorting.push( col[0] >= columns.length ?
					[ 0, col[1] ] :
					col
				);
			} );
		}
	
		// Search
		if ( s.search !== undefined ) {
			$.extend( settings.oPreviousSearch, _fnSearchToHung( s.search ) );
		}
	
		// Columns
		if ( s.columns ) {
			for ( i=0, ien=s.columns.length ; i<ien ; i++ ) {
				var col = s.columns[i];
	
				// Visibility
				if ( col.visible !== undefined ) {
					// If the api is defined, the table has been initialised so we need to use it rather than internal settings
					if (api) {
						// Don't redraw the columns on every iteration of this loop, we will do this at the end instead
						api.column(i).visible(col.visible, false);
					}
					else {
						columns[i].bVisible = col.visible;
					}
				}
	
				// Search
				if ( col.search !== undefined ) {
					$.extend( settings.aoPreSearchCols[i], _fnSearchToHung( col.search ) );
				}
			}
			
			// If the api is defined then we need to adjust the columns once the visibility has been changed
			if (api) {
				api.columns.adjust();
			}
		}
	
		settings._bLoadingState = false;
		_fnCallbackFire( settings, 'aoStateLoaded', 'stateLoaded', [settings, s] );
		callback();
	};
	
	
	/**
	 * Return the settings object for a particular table
	 *  @param {node} table table we are using as a dataTable
	 *  @returns {object} Settings object - or null if not found
	 *  @memberof DataTable#oApi
	 */
	function _fnSettingsFromNode ( table )
	{
		var settings = DataTable.settings;
		var idx = $.inArray( table, _pluck( settings, 'nTable' ) );
	
		return idx !== -1 ?
			settings[ idx ] :
			null;
	}
	
	
	/**
	 * Log an error message
	 *  @param {object} settings dataTables settings object
	 *  @param {int} level log error messages, or display them to the user
	 *  @param {string} msg error message
	 *  @param {int} tn Technical note id to get more information about the error.
	 *  @memberof DataTable#oApi
	 */
	function _fnLog( settings, level, msg, tn )
	{
		msg = 'DataTables warning: '+
			(settings ? 'table id='+settings.sTableId+' - ' : '')+msg;
	
		if ( tn ) {
			msg += '. For more information about this error, please see '+
			'http://datatables.net/tn/'+tn;
		}
	
		if ( ! level  ) {
			// Backwards compatibility pre 1.10
			var ext = DataTable.ext;
			var type = ext.sErrMode || ext.errMode;
	
			if ( settings ) {
				_fnCallbackFire( settings, null, 'error', [ settings, tn, msg ] );
			}
	
			if ( type == 'alert' ) {
				alert( msg );
			}
			else if ( type == 'throw' ) {
				throw new Error(msg);
			}
			else if ( typeof type == 'function' ) {
				type( settings, tn, msg );
			}
		}
		else if ( window.console && console.log ) {
			console.log( msg );
		}
	}
	
	
	/**
	 * See if a property is defined on one object, if so assign it to the other object
	 *  @param {object} ret target object
	 *  @param {object} src source object
	 *  @param {string} name property
	 *  @param {string} [mappedName] name to map too - optional, name used if not given
	 *  @memberof DataTable#oApi
	 */
	function _fnMap( ret, src, name, mappedName )
	{
		if ( Array.isArray( name ) ) {
			$.each( name, function (i, val) {
				if ( Array.isArray( val ) ) {
					_fnMap( ret, src, val[0], val[1] );
				}
				else {
					_fnMap( ret, src, val );
				}
			} );
	
			return;
		}
	
		if ( mappedName === undefined ) {
			mappedName = name;
		}
	
		if ( src[name] !== undefined ) {
			ret[mappedName] = src[name];
		}
	}
	
	
	/**
	 * Extend objects - very similar to jQuery.extend, but deep copy objects, and
	 * shallow copy arrays. The reason we need to do this, is that we don't want to
	 * deep copy array init values (such as aaSorting) since the dev wouldn't be
	 * able to override them, but we do want to deep copy arrays.
	 *  @param {object} out Object to extend
	 *  @param {object} extender Object from which the properties will be applied to
	 *      out
	 *  @param {boolean} breakRefs If true, then arrays will be sliced to take an
	 *      independent copy with the exception of the `data` or `aaData` parameters
	 *      if they are present. This is so you can pass in a collection to
	 *      DataTables and have that used as your data source without breaking the
	 *      references
	 *  @returns {object} out Reference, just for convenience - out === the return.
	 *  @memberof DataTable#oApi
	 *  @todo This doesn't take account of arrays inside the deep copied objects.
	 */
	function _fnExtend( out, extender, breakRefs )
	{
		var val;
	
		for ( var prop in extender ) {
			if ( extender.hasOwnProperty(prop) ) {
				val = extender[prop];
	
				if ( $.isPlainObject( val ) ) {
					if ( ! $.isPlainObject( out[prop] ) ) {
						out[prop] = {};
					}
					$.extend( true, out[prop], val );
				}
				else if ( breakRefs && prop !== 'data' && prop !== 'aaData' && Array.isArray(val) ) {
					out[prop] = val.slice();
				}
				else {
					out[prop] = val;
				}
			}
		}
	
		return out;
	}
	
	
	/**
	 * Bind an event handers to allow a click or return key to activate the callback.
	 * This is good for accessibility since a return on the keyboard will have the
	 * same effect as a click, if the element has focus.
	 *  @param {element} n Element to bind the action to
	 *  @param {object} oData Data object to pass to the triggered function
	 *  @param {function} fn Callback function for when the event is triggered
	 *  @memberof DataTable#oApi
	 */
	function _fnBindAction( n, oData, fn )
	{
		$(n)
			.on( 'click.DT', oData, function (e) {
					$(n).trigger('blur'); // Remove focus outline for mouse users
					fn(e);
				} )
			.on( 'keypress.DT', oData, function (e){
					if ( e.which === 13 ) {
						e.preventDefault();
						fn(e);
					}
				} )
			.on( 'selectstart.DT', function () {
					/* Take the brutal approach to cancelling text selection */
					return false;
				} );
	}
	
	
	/**
	 * Register a callback function. Easily allows a callback function to be added to
	 * an array store of callback functions that can then all be called together.
	 *  @param {object} oSettings dataTables settings object
	 *  @param {string} sStore Name of the array storage for the callbacks in oSettings
	 *  @param {function} fn Function to be called back
	 *  @param {string} sName Identifying name for the callback (i.e. a label)
	 *  @memberof DataTable#oApi
	 */
	function _fnCallbackReg( oSettings, sStore, fn, sName )
	{
		if ( fn )
		{
			oSettings[sStore].push( {
				"fn": fn,
				"sName": sName
			} );
		}
	}
	
	
	/**
	 * Fire callback functions and trigger events. Note that the loop over the
	 * callback array store is done backwards! Further note that you do not want to
	 * fire off triggers in time sensitive applications (for example cell creation)
	 * as its slow.
	 *  @param {object} settings dataTables settings object
	 *  @param {string} callbackArr Name of the array storage for the callbacks in
	 *      oSettings
	 *  @param {string} eventName Name of the jQuery custom event to trigger. If
	 *      null no trigger is fired
	 *  @param {array} args Array of arguments to pass to the callback function /
	 *      trigger
	 *  @memberof DataTable#oApi
	 */
	function _fnCallbackFire( settings, callbackArr, eventName, args )
	{
		var ret = [];
	
		if ( callbackArr ) {
			ret = $.map( settings[callbackArr].slice().reverse(), function (val, i) {
				return val.fn.apply( settings.oInstance, args );
			} );
		}
	
		if ( eventName !== null ) {
			var e = $.Event( eventName+'.dt' );
	
			$(settings.nTable).trigger( e, args );
	
			ret.push( e.result );
		}
	
		return ret;
	}
	
	
	function _fnLengthOverflow ( settings )
	{
		var
			start = settings._iDisplayStart,
			end = settings.fnDisplayEnd(),
			len = settings._iDisplayLength;
	
		/* If we have space to show extra rows (backing up from the end point - then do so */
		if ( start >= end )
		{
			start = end - len;
		}
	
		// Keep the start record on the current page
		start -= (start % len);
	
		if ( len === -1 || start < 0 )
		{
			start = 0;
		}
	
		settings._iDisplayStart = start;
	}
	
	
	function _fnRenderer( settings, type )
	{
		var renderer = settings.renderer;
		var host = DataTable.ext.renderer[type];
	
		if ( $.isPlainObject( renderer ) && renderer[type] ) {
			// Specific renderer for this type. If available use it, otherwise use
			// the default.
			return host[renderer[type]] || host._;
		}
		else if ( typeof renderer === 'string' ) {
			// Common renderer - if there is one available for this type use it,
			// otherwise use the default
			return host[renderer] || host._;
		}
	
		// Use the default
		return host._;
	}
	
	
	/**
	 * Detect the data source being used for the table. Used to simplify the code
	 * a little (ajax) and to make it compress a little smaller.
	 *
	 *  @param {object} settings dataTables settings object
	 *  @returns {string} Data source
	 *  @memberof DataTable#oApi
	 */
	function _fnDataSource ( settings )
	{
		if ( settings.oFeatures.bServerSide ) {
			return 'ssp';
		}
		else if ( settings.ajax || settings.sAjaxSource ) {
			return 'ajax';
		}
		return 'dom';
	}
	
	
	
	
	/**
	 * Computed structure of the DataTables API, defined by the options passed to
	 * `DataTable.Api.register()` when building the API.
	 *
	 * The structure is built in order to speed creation and extension of the Api
	 * objects since the extensions are effectively pre-parsed.
	 *
	 * The array is an array of objects with the following structure, where this
	 * base array represents the Api prototype base:
	 *
	 *     [
	 *       {
	 *         name:      'data'                -- string   - Property name
	 *         val:       function () {},       -- function - Api method (or undefined if just an object
	 *         methodExt: [ ... ],              -- array    - Array of Api object definitions to extend the method result
	 *         propExt:   [ ... ]               -- array    - Array of Api object definitions to extend the property
	 *       },
	 *       {
	 *         name:     'row'
	 *         val:       {},
	 *         methodExt: [ ... ],
	 *         propExt:   [
	 *           {
	 *             name:      'data'
	 *             val:       function () {},
	 *             methodExt: [ ... ],
	 *             propExt:   [ ... ]
	 *           },
	 *           ...
	 *         ]
	 *       }
	 *     ]
	 *
	 * @type {Array}
	 * @ignore
	 */
	var __apiStruct = [];
	
	
	/**
	 * `Array.prototype` reference.
	 *
	 * @type object
	 * @ignore
	 */
	var __arrayProto = Array.prototype;
	
	
	/**
	 * Abstraction for `context` parameter of the `Api` constructor to allow it to
	 * take several different forms for ease of use.
	 *
	 * Each of the input parameter types will be converted to a DataTables settings
	 * object where possible.
	 *
	 * @param  {string|node|jQuery|object} mixed DataTable identifier. Can be one
	 *   of:
	 *
	 *   * `string` - jQuery selector. Any DataTables' matching the given selector
	 *     with be found and used.
	 *   * `node` - `TABLE` node which has already been formed into a DataTable.
	 *   * `jQuery` - A jQuery object of `TABLE` nodes.
	 *   * `object` - DataTables settings object
	 *   * `DataTables.Api` - API instance
	 * @return {array|null} Matching DataTables settings objects. `null` or
	 *   `undefined` is returned if no matching DataTable is found.
	 * @ignore
	 */
	var _toSettings = function ( mixed )
	{
		var idx, jq;
		var settings = DataTable.settings;
		var tables = $.map( settings, function (el, i) {
			return el.nTable;
		} );
	
		if ( ! mixed ) {
			return [];
		}
		else if ( mixed.nTable && mixed.oApi ) {
			// DataTables settings object
			return [ mixed ];
		}
		else if ( mixed.nodeName && mixed.nodeName.toLowerCase() === 'table' ) {
			// Table node
			idx = $.inArray( mixed, tables );
			return idx !== -1 ? [ settings[idx] ] : null;
		}
		else if ( mixed && typeof mixed.settings === 'function' ) {
			return mixed.settings().toArray();
		}
		else if ( typeof mixed === 'string' ) {
			// jQuery selector
			jq = $(mixed);
		}
		else if ( mixed instanceof $ ) {
			// jQuery object (also DataTables instance)
			jq = mixed;
		}
	
		if ( jq ) {
			return jq.map( function(i) {
				idx = $.inArray( this, tables );
				return idx !== -1 ? settings[idx] : null;
			} ).toArray();
		}
	};
	
	
	/**
	 * DataTables API class - used to control and interface with  one or more
	 * DataTables enhanced tables.
	 *
	 * The API class is heavily based on jQuery, presenting a chainable interface
	 * that you can use to interact with tables. Each instance of the API class has
	 * a "context" - i.e. the tables that it will operate on. This could be a single
	 * table, all tables on a page or a sub-set thereof.
	 *
	 * Additionally the API is designed to allow you to easily work with the data in
	 * the tables, retrieving and manipulating it as required. This is done by
	 * presenting the API class as an array like interface. The contents of the
	 * array depend upon the actions requested by each method (for example
	 * `rows().nodes()` will return an array of nodes, while `rows().data()` will
	 * return an array of objects or arrays depending upon your table's
	 * configuration). The API object has a number of array like methods (`push`,
	 * `pop`, `reverse` etc) as well as additional helper methods (`each`, `pluck`,
	 * `unique` etc) to assist your working with the data held in a table.
	 *
	 * Most methods (those which return an Api instance) are chainable, which means
	 * the return from a method call also has all of the methods available that the
	 * top level object had. For example, these two calls are equivalent:
	 *
	 *     // Not chained
	 *     api.row.add( {...} );
	 *     api.draw();
	 *
	 *     // Chained
	 *     api.row.add( {...} ).draw();
	 *
	 * @class DataTable.Api
	 * @param {array|object|string|jQuery} context DataTable identifier. This is
	 *   used to define which DataTables enhanced tables this API will operate on.
	 *   Can be one of:
	 *
	 *   * `string` - jQuery selector. Any DataTables' matching the given selector
	 *     with be found and used.
	 *   * `node` - `TABLE` node which has already been formed into a DataTable.
	 *   * `jQuery` - A jQuery object of `TABLE` nodes.
	 *   * `object` - DataTables settings object
	 * @param {array} [data] Data to initialise the Api instance with.
	 *
	 * @example
	 *   // Direct initialisation during DataTables construction
	 *   var api = $('#example').DataTable();
	 *
	 * @example
	 *   // Initialisation using a DataTables jQuery object
	 *   var api = $('#example').dataTable().api();
	 *
	 * @example
	 *   // Initialisation as a constructor
	 *   var api = new $.fn.DataTable.Api( 'table.dataTable' );
	 */
	_Api = function ( context, data )
	{
		if ( ! (this instanceof _Api) ) {
			return new _Api( context, data );
		}
	
		var settings = [];
		var ctxSettings = function ( o ) {
			var a = _toSettings( o );
			if ( a ) {
				settings.push.apply( settings, a );
			}
		};
	
		if ( Array.isArray( context ) ) {
			for ( var i=0, ien=context.length ; i<ien ; i++ ) {
				ctxSettings( context[i] );
			}
		}
		else {
			ctxSettings( context );
		}
	
		// Remove duplicates
		this.context = _unique( settings );
	
		// Initial data
		if ( data ) {
			$.merge( this, data );
		}
	
		// selector
		this.selector = {
			rows: null,
			cols: null,
			opts: null
		};
	
		_Api.extend( this, this, __apiStruct );
	};
	
	DataTable.Api = _Api;
	
	// Don't destroy the existing prototype, just extend it. Required for jQuery 2's
	// isPlainObject.
	$.extend( _Api.prototype, {
		any: function ()
		{
			return this.count() !== 0;
		},
	
	
		concat:  __arrayProto.concat,
	
	
		context: [], // array of table settings objects
	
	
		count: function ()
		{
			return this.flatten().length;
		},
	
	
		each: function ( fn )
		{
			for ( var i=0, ien=this.length ; i<ien; i++ ) {
				fn.call( this, this[i], i, this );
			}
	
			return this;
		},
	
	
		eq: function ( idx )
		{
			var ctx = this.context;
	
			return ctx.length > idx ?
				new _Api( ctx[idx], this[idx] ) :
				null;
		},
	
	
		filter: function ( fn )
		{
			var a = [];
	
			if ( __arrayProto.filter ) {
				a = __arrayProto.filter.call( this, fn, this );
			}
			else {
				// Compatibility for browsers without EMCA-252-5 (JS 1.6)
				for ( var i=0, ien=this.length ; i<ien ; i++ ) {
					if ( fn.call( this, this[i], i, this ) ) {
						a.push( this[i] );
					}
				}
			}
	
			return new _Api( this.context, a );
		},
	
	
		flatten: function ()
		{
			var a = [];
			return new _Api( this.context, a.concat.apply( a, this.toArray() ) );
		},
	
	
		join:    __arrayProto.join,
	
	
		indexOf: __arrayProto.indexOf || function (obj, start)
		{
			for ( var i=(start || 0), ien=this.length ; i<ien ; i++ ) {
				if ( this[i] === obj ) {
					return i;
				}
			}
			return -1;
		},
	
		iterator: function ( flatten, type, fn, alwaysNew ) {
			var
				a = [], ret,
				i, ien, j, jen,
				context = this.context,
				rows, items, item,
				selector = this.selector;
	
			// Argument shifting
			if ( typeof flatten === 'string' ) {
				alwaysNew = fn;
				fn = type;
				type = flatten;
				flatten = false;
			}
	
			for ( i=0, ien=context.length ; i<ien ; i++ ) {
				var apiInst = new _Api( context[i] );
	
				if ( type === 'table' ) {
					ret = fn.call( apiInst, context[i], i );
	
					if ( ret !== undefined ) {
						a.push( ret );
					}
				}
				else if ( type === 'columns' || type === 'rows' ) {
					// this has same length as context - one entry for each table
					ret = fn.call( apiInst, context[i], this[i], i );
	
					if ( ret !== undefined ) {
						a.push( ret );
					}
				}
				else if ( type === 'column' || type === 'column-rows' || type === 'row' || type === 'cell' ) {
					// columns and rows share the same structure.
					// 'this' is an array of column indexes for each context
					items = this[i];
	
					if ( type === 'column-rows' ) {
						rows = _selector_row_indexes( context[i], selector.opts );
					}
	
					for ( j=0, jen=items.length ; j<jen ; j++ ) {
						item = items[j];
	
						if ( type === 'cell' ) {
							ret = fn.call( apiInst, context[i], item.row, item.column, i, j );
						}
						else {
							ret = fn.call( apiInst, context[i], item, i, j, rows );
						}
	
						if ( ret !== undefined ) {
							a.push( ret );
						}
					}
				}
			}
	
			if ( a.length || alwaysNew ) {
				var api = new _Api( context, flatten ? a.concat.apply( [], a ) : a );
				var apiSelector = api.selector;
				apiSelector.rows = selector.rows;
				apiSelector.cols = selector.cols;
				apiSelector.opts = selector.opts;
				return api;
			}
			return this;
		},
	
	
		lastIndexOf: __arrayProto.lastIndexOf || function (obj, start)
		{
			// Bit cheeky...
			return this.indexOf.apply( this.toArray.reverse(), arguments );
		},
	
	
		length:  0,
	
	
		map: function ( fn )
		{
			var a = [];
	
			if ( __arrayProto.map ) {
				a = __arrayProto.map.call( this, fn, this );
			}
			else {
				// Compatibility for browsers without EMCA-252-5 (JS 1.6)
				for ( var i=0, ien=this.length ; i<ien ; i++ ) {
					a.push( fn.call( this, this[i], i ) );
				}
			}
	
			return new _Api( this.context, a );
		},
	
	
		pluck: function ( prop )
		{
			let fn = DataTable.util.get(prop);
	
			return this.map( function ( el ) {
				return fn(el);
			} );
		},
	
		pop:     __arrayProto.pop,
	
	
		push:    __arrayProto.push,
	
	
		// Does not return an API instance
		reduce: __arrayProto.reduce || function ( fn, init )
		{
			return _fnReduce( this, fn, init, 0, this.length, 1 );
		},
	
	
		reduceRight: __arrayProto.reduceRight || function ( fn, init )
		{
			return _fnReduce( this, fn, init, this.length-1, -1, -1 );
		},
	
	
		reverse: __arrayProto.reverse,
	
	
		// Object with rows, columns and opts
		selector: null,
	
	
		shift:   __arrayProto.shift,
	
	
		slice: function () {
			return new _Api( this.context, this );
		},
	
	
		sort:    __arrayProto.sort, // ? name - order?
	
	
		splice:  __arrayProto.splice,
	
	
		toArray: function ()
		{
			return __arrayProto.slice.call( this );
		},
	
	
		to$: function ()
		{
			return $( this );
		},
	
	
		toJQuery: function ()
		{
			return $( this );
		},
	
	
		unique: function ()
		{
			return new _Api( this.context, _unique(this) );
		},
	
	
		unshift: __arrayProto.unshift
	} );
	
	
	_Api.extend = function ( scope, obj, ext )
	{
		// Only extend API instances and static properties of the API
		if ( ! ext.length || ! obj || ( ! (obj instanceof _Api) && ! obj.__dt_wrapper ) ) {
			return;
		}
	
		var
			i, ien,
			struct,
			methodScoping = function ( scope, fn, struc ) {
				return function () {
					var ret = fn.apply( scope, arguments );
	
					// Method extension
					_Api.extend( ret, ret, struc.methodExt );
					return ret;
				};
			};
	
		for ( i=0, ien=ext.length ; i<ien ; i++ ) {
			struct = ext[i];
	
			// Value
			obj[ struct.name ] = struct.type === 'function' ?
				methodScoping( scope, struct.val, struct ) :
				struct.type === 'object' ?
					{} :
					struct.val;
	
			obj[ struct.name ].__dt_wrapper = true;
	
			// Property extension
			_Api.extend( scope, obj[ struct.name ], struct.propExt );
		}
	};
	
	
	// @todo - Is there need for an augment function?
	// _Api.augment = function ( inst, name )
	// {
	// 	// Find src object in the structure from the name
	// 	var parts = name.split('.');
	
	// 	_Api.extend( inst, obj );
	// };
	
	
	//     [
	//       {
	//         name:      'data'                -- string   - Property name
	//         val:       function () {},       -- function - Api method (or undefined if just an object
	//         methodExt: [ ... ],              -- array    - Array of Api object definitions to extend the method result
	//         propExt:   [ ... ]               -- array    - Array of Api object definitions to extend the property
	//       },
	//       {
	//         name:     'row'
	//         val:       {},
	//         methodExt: [ ... ],
	//         propExt:   [
	//           {
	//             name:      'data'
	//             val:       function () {},
	//             methodExt: [ ... ],
	//             propExt:   [ ... ]
	//           },
	//           ...
	//         ]
	//       }
	//     ]
	
	_Api.register = _api_register = function ( name, val )
	{
		if ( Array.isArray( name ) ) {
			for ( var j=0, jen=name.length ; j<jen ; j++ ) {
				_Api.register( name[j], val );
			}
			return;
		}
	
		var
			i, ien,
			heir = name.split('.'),
			struct = __apiStruct,
			key, method;
	
		var find = function ( src, name ) {
			for ( var i=0, ien=src.length ; i<ien ; i++ ) {
				if ( src[i].name === name ) {
					return src[i];
				}
			}
			return null;
		};
	
		for ( i=0, ien=heir.length ; i<ien ; i++ ) {
			method = heir[i].indexOf('()') !== -1;
			key = method ?
				heir[i].replace('()', '') :
				heir[i];
	
			var src = find( struct, key );
			if ( ! src ) {
				src = {
					name:      key,
					val:       {},
					methodExt: [],
					propExt:   [],
					type:      'object'
				};
				struct.push( src );
			}
	
			if ( i === ien-1 ) {
				src.val = val;
				src.type = typeof val === 'function' ?
					'function' :
					$.isPlainObject( val ) ?
						'object' :
						'other';
			}
			else {
				struct = method ?
					src.methodExt :
					src.propExt;
			}
		}
	};
	
	_Api.registerPlural = _api_registerPlural = function ( pluralName, singularName, val ) {
		_Api.register( pluralName, val );
	
		_Api.register( singularName, function () {
			var ret = val.apply( this, arguments );
	
			if ( ret === this ) {
				// Returned item is the API instance that was passed in, return it
				return this;
			}
			else if ( ret instanceof _Api ) {
				// New API instance returned, want the value from the first item
				// in the returned array for the singular result.
				return ret.length ?
					Array.isArray( ret[0] ) ?
						new _Api( ret.context, ret[0] ) : // Array results are 'enhanced'
						ret[0] :
					undefined;
			}
	
			// Non-API return - just fire it back
			return ret;
		} );
	};
	
	
	/**
	 * Selector for HTML tables. Apply the given selector to the give array of
	 * DataTables settings objects.
	 *
	 * @param {string|integer} [selector] jQuery selector string or integer
	 * @param  {array} Array of DataTables settings objects to be filtered
	 * @return {array}
	 * @ignore
	 */
	var __table_selector = function ( selector, a )
	{
		if ( Array.isArray(selector) ) {
			return $.map( selector, function (item) {
				return __table_selector(item, a);
			} );
		}
	
		// Integer is used to pick out a table by index
		if ( typeof selector === 'number' ) {
			return [ a[ selector ] ];
		}
	
		// Perform a jQuery selector on the table nodes
		var nodes = $.map( a, function (el, i) {
			return el.nTable;
		} );
	
		return $(nodes)
			.filter( selector )
			.map( function (i) {
				// Need to translate back from the table node to the settings
				var idx = $.inArray( this, nodes );
				return a[ idx ];
			} )
			.toArray();
	};
	
	
	
	/**
	 * Context selector for the API's context (i.e. the tables the API instance
	 * refers to.
	 *
	 * @name    DataTable.Api#tables
	 * @param {string|integer} [selector] Selector to pick which tables the iterator
	 *   should operate on. If not given, all tables in the current context are
	 *   used. This can be given as a jQuery selector (for example `':gt(0)'`) to
	 *   select multiple tables or as an integer to select a single table.
	 * @returns {DataTable.Api} Returns a new API instance if a selector is given.
	 */
	_api_register( 'tables()', function ( selector ) {
		// A new instance is created if there was a selector specified
		return selector !== undefined && selector !== null ?
			new _Api( __table_selector( selector, this.context ) ) :
			this;
	} );
	
	
	_api_register( 'table()', function ( selector ) {
		var tables = this.tables( selector );
		var ctx = tables.context;
	
		// Truncate to the first matched table
		return ctx.length ?
			new _Api( ctx[0] ) :
			tables;
	} );
	
	
	_api_registerPlural( 'tables().nodes()', 'table().node()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTable;
		}, 1 );
	} );
	
	
	_api_registerPlural( 'tables().body()', 'table().body()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTBody;
		}, 1 );
	} );
	
	
	_api_registerPlural( 'tables().header()', 'table().header()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTHead;
		}, 1 );
	} );
	
	
	_api_registerPlural( 'tables().footer()', 'table().footer()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTFoot;
		}, 1 );
	} );
	
	
	_api_registerPlural( 'tables().containers()', 'table().container()' , function () {
		return this.iterator( 'table', function ( ctx ) {
			return ctx.nTableWrapper;
		}, 1 );
	} );
	
	
	
	/**
	 * Redraw the tables in the current context.
	 */
	_api_register( 'draw()', function ( paging ) {
		return this.iterator( 'table', function ( settings ) {
			if ( paging === 'page' ) {
				_fnDraw( settings );
			}
			else {
				if ( typeof paging === 'string' ) {
					paging = paging === 'full-hold' ?
						false :
						true;
				}
	
				_fnReDraw( settings, paging===false );
			}
		} );
	} );
	
	
	
	/**
	 * Get the current page index.
	 *
	 * @return {integer} Current page index (zero based)
	 *//**
	 * Set the current page.
	 *
	 * Note that if you attempt to show a page which does not exist, DataTables will
	 * not throw an error, but rather reset the paging.
	 *
	 * @param {integer|string} action The paging action to take. This can be one of:
	 *  * `integer` - The page index to jump to
	 *  * `string` - An action to take:
	 *    * `first` - Jump to first page.
	 *    * `next` - Jump to the next page
	 *    * `previous` - Jump to previous page
	 *    * `last` - Jump to the last page.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'page()', function ( action ) {
		if ( action === undefined ) {
			return this.page.info().page; // not an expensive call
		}
	
		// else, have an action to take on all tables
		return this.iterator( 'table', function ( settings ) {
			_fnPageChange( settings, action );
		} );
	} );
	
	
	/**
	 * Paging information for the first table in the current context.
	 *
	 * If you require paging information for another table, use the `table()` method
	 * with a suitable selector.
	 *
	 * @return {object} Object with the following properties set:
	 *  * `page` - Current page index (zero based - i.e. the first page is `0`)
	 *  * `pages` - Total number of pages
	 *  * `start` - Display index for the first record shown on the current page
	 *  * `end` - Display index for the last record shown on the current page
	 *  * `length` - Display length (number of records). Note that generally `start
	 *    + length = end`, but this is not always true, for example if there are
	 *    only 2 records to show on the final page, with a length of 10.
	 *  * `recordsTotal` - Full data set length
	 *  * `recordsDisplay` - Data set length once the current filtering criterion
	 *    are applied.
	 */
	_api_register( 'page.info()', function ( action ) {
		if ( this.context.length === 0 ) {
			return undefined;
		}
	
		var
			settings   = this.context[0],
			start      = settings._iDisplayStart,
			len        = settings.oFeatures.bPaginate ? settings._iDisplayLength : -1,
			visRecords = settings.fnRecordsDisplay(),
			all        = len === -1;
	
		return {
			"page":           all ? 0 : Math.floor( start / len ),
			"pages":          all ? 1 : Math.ceil( visRecords / len ),
			"start":          start,
			"end":            settings.fnDisplayEnd(),
			"length":         len,
			"recordsTotal":   settings.fnRecordsTotal(),
			"recordsDisplay": visRecords,
			"serverSide":     _fnDataSource( settings ) === 'ssp'
		};
	} );
	
	
	/**
	 * Get the current page length.
	 *
	 * @return {integer} Current page length. Note `-1` indicates that all records
	 *   are to be shown.
	 *//**
	 * Set the current page length.
	 *
	 * @param {integer} Page length to set. Use `-1` to show all records.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'page.len()', function ( len ) {
		// Note that we can't call this function 'length()' because `length`
		// is a Javascript property of functions which defines how many arguments
		// the function expects.
		if ( len === undefined ) {
			return this.context.length !== 0 ?
				this.context[0]._iDisplayLength :
				undefined;
		}
	
		// else, set the page length
		return this.iterator( 'table', function ( settings ) {
			_fnLengthChange( settings, len );
		} );
	} );
	
	
	
	var __reload = function ( settings, holdPosition, callback ) {
		// Use the draw event to trigger a callback
		if ( callback ) {
			var api = new _Api( settings );
	
			api.one( 'draw', function () {
				callback( api.ajax.json() );
			} );
		}
	
		if ( _fnDataSource( settings ) == 'ssp' ) {
			_fnReDraw( settings, holdPosition );
		}
		else {
			_fnProcessingDisplay( settings, true );
	
			// Cancel an existing request
			var xhr = settings.jqXHR;
			if ( xhr && xhr.readyState !== 4 ) {
				xhr.abort();
			}
	
			// Trigger xhr
			_fnBuildAjax( settings, [], function( json ) {
				_fnClearTable( settings );
	
				var data = _fnAjaxDataSrc( settings, json );
				for ( var i=0, ien=data.length ; i<ien ; i++ ) {
					_fnAddData( settings, data[i] );
				}
	
				_fnReDraw( settings, holdPosition );
				_fnProcessingDisplay( settings, false );
			} );
		}
	};
	
	
	/**
	 * Get the JSON response from the last Ajax request that DataTables made to the
	 * server. Note that this returns the JSON from the first table in the current
	 * context.
	 *
	 * @return {object} JSON received from the server.
	 */
	_api_register( 'ajax.json()', function () {
		var ctx = this.context;
	
		if ( ctx.length > 0 ) {
			return ctx[0].json;
		}
	
		// else return undefined;
	} );
	
	
	/**
	 * Get the data submitted in the last Ajax request
	 */
	_api_register( 'ajax.params()', function () {
		var ctx = this.context;
	
		if ( ctx.length > 0 ) {
			return ctx[0].oAjaxData;
		}
	
		// else return undefined;
	} );
	
	
	/**
	 * Reload tables from the Ajax data source. Note that this function will
	 * automatically re-draw the table when the remote data has been loaded.
	 *
	 * @param {boolean} [reset=true] Reset (default) or hold the current paging
	 *   position. A full re-sort and re-filter is performed when this method is
	 *   called, which is why the pagination reset is the default action.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'ajax.reload()', function ( callback, resetPaging ) {
		return this.iterator( 'table', function (settings) {
			__reload( settings, resetPaging===false, callback );
		} );
	} );
	
	
	/**
	 * Get the current Ajax URL. Note that this returns the URL from the first
	 * table in the current context.
	 *
	 * @return {string} Current Ajax source URL
	 *//**
	 * Set the Ajax URL. Note that this will set the URL for all tables in the
	 * current context.
	 *
	 * @param {string} url URL to set.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'ajax.url()', function ( url ) {
		var ctx = this.context;
	
		if ( url === undefined ) {
			// get
			if ( ctx.length === 0 ) {
				return undefined;
			}
			ctx = ctx[0];
	
			return ctx.ajax ?
				$.isPlainObject( ctx.ajax ) ?
					ctx.ajax.url :
					ctx.ajax :
				ctx.sAjaxSource;
		}
	
		// set
		return this.iterator( 'table', function ( settings ) {
			if ( $.isPlainObject( settings.ajax ) ) {
				settings.ajax.url = url;
			}
			else {
				settings.ajax = url;
			}
			// No need to consider sAjaxSource here since DataTables gives priority
			// to `ajax` over `sAjaxSource`. So setting `ajax` here, renders any
			// value of `sAjaxSource` redundant.
		} );
	} );
	
	
	/**
	 * Load data from the newly set Ajax URL. Note that this method is only
	 * available when `ajax.url()` is used to set a URL. Additionally, this method
	 * has the same effect as calling `ajax.reload()` but is provided for
	 * convenience when setting a new URL. Like `ajax.reload()` it will
	 * automatically redraw the table once the remote data has been loaded.
	 *
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'ajax.url().load()', function ( callback, resetPaging ) {
		// Same as a reload, but makes sense to present it for easy access after a
		// url change
		return this.iterator( 'table', function ( ctx ) {
			__reload( ctx, resetPaging===false, callback );
		} );
	} );
	
	
	
	
	var _selector_run = function ( type, selector, selectFn, settings, opts )
	{
		var
			out = [], res,
			a, i, ien, j, jen,
			selectorType = typeof selector;
	
		// Can't just check for isArray here, as an API or jQuery instance might be
		// given with their array like look
		if ( ! selector || selectorType === 'string' || selectorType === 'function' || selector.length === undefined ) {
			selector = [ selector ];
		}
	
		for ( i=0, ien=selector.length ; i<ien ; i++ ) {
			// Only split on simple strings - complex expressions will be jQuery selectors
			a = selector[i] && selector[i].split && ! selector[i].match(/[\[\(:]/) ?
				selector[i].split(',') :
				[ selector[i] ];
	
			for ( j=0, jen=a.length ; j<jen ; j++ ) {
				res = selectFn( typeof a[j] === 'string' ? (a[j]).trim() : a[j] );
	
				if ( res && res.length ) {
					out = out.concat( res );
				}
			}
		}
	
		// selector extensions
		var ext = _ext.selector[ type ];
		if ( ext.length ) {
			for ( i=0, ien=ext.length ; i<ien ; i++ ) {
				out = ext[i]( settings, opts, out );
			}
		}
	
		return _unique( out );
	};
	
	
	var _selector_opts = function ( opts )
	{
		if ( ! opts ) {
			opts = {};
		}
	
		// Backwards compatibility for 1.9- which used the terminology filter rather
		// than search
		if ( opts.filter && opts.search === undefined ) {
			opts.search = opts.filter;
		}
	
		return $.extend( {
			search: 'none',
			order: 'current',
			page: 'all'
		}, opts );
	};
	
	
	var _selector_first = function ( inst )
	{
		// Reduce the API instance to the first item found
		for ( var i=0, ien=inst.length ; i<ien ; i++ ) {
			if ( inst[i].length > 0 ) {
				// Assign the first element to the first item in the instance
				// and truncate the instance and context
				inst[0] = inst[i];
				inst[0].length = 1;
				inst.length = 1;
				inst.context = [ inst.context[i] ];
	
				return inst;
			}
		}
	
		// Not found - return an empty instance
		inst.length = 0;
		return inst;
	};
	
	
	var _selector_row_indexes = function ( settings, opts )
	{
		var
			i, ien, tmp, a=[],
			displayFiltered = settings.aiDisplay,
			displayMaster = settings.aiDisplayMaster;
	
		var
			search = opts.search,  // none, applied, removed
			order  = opts.order,   // applied, current, index (original - compatibility with 1.9)
			page   = opts.page;    // all, current
	
		if ( _fnDataSource( settings ) == 'ssp' ) {
			// In server-side processing mode, most options are irrelevant since
			// rows not shown don't exist and the index order is the applied order
			// Removed is a special case - for consistency just return an empty
			// array
			return search === 'removed' ?
				[] :
				_range( 0, displayMaster.length );
		}
		else if ( page == 'current' ) {
			// Current page implies that order=current and filter=applied, since it is
			// fairly senseless otherwise, regardless of what order and search actually
			// are
			for ( i=settings._iDisplayStart, ien=settings.fnDisplayEnd() ; i<ien ; i++ ) {
				a.push( displayFiltered[i] );
			}
		}
		else if ( order == 'current' || order == 'applied' ) {
			if ( search == 'none') {
				a = displayMaster.slice();
			}
			else if ( search == 'applied' ) {
				a = displayFiltered.slice();
			}
			else if ( search == 'removed' ) {
				// O(n+m) solution by creating a hash map
				var displayFilteredMap = {};
	
				for ( var i=0, ien=displayFiltered.length ; i<ien ; i++ ) {
					displayFilteredMap[displayFiltered[i]] = null;
				}
	
				a = $.map( displayMaster, function (el) {
					return ! displayFilteredMap.hasOwnProperty(el) ?
						el :
						null;
				} );
			}
		}
		else if ( order == 'index' || order == 'original' ) {
			for ( i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
				if ( search == 'none' ) {
					a.push( i );
				}
				else { // applied | removed
					tmp = $.inArray( i, displayFiltered );
	
					if ((tmp === -1 && search == 'removed') ||
						(tmp >= 0   && search == 'applied') )
					{
						a.push( i );
					}
				}
			}
		}
	
		return a;
	};
	
	
	/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
	 * Rows
	 *
	 * {}          - no selector - use all available rows
	 * {integer}   - row aoData index
	 * {node}      - TR node
	 * {string}    - jQuery selector to apply to the TR elements
	 * {array}     - jQuery array of nodes, or simply an array of TR nodes
	 *
	 */
	var __row_selector = function ( settings, selector, opts )
	{
		var rows;
		var run = function ( sel ) {
			var selInt = _intVal( sel );
			var i, ien;
			var aoData = settings.aoData;
	
			// Short cut - selector is a number and no options provided (default is
			// all records, so no need to check if the index is in there, since it
			// must be - dev error if the index doesn't exist).
			if ( selInt !== null && ! opts ) {
				return [ selInt ];
			}
	
			if ( ! rows ) {
				rows = _selector_row_indexes( settings, opts );
			}
	
			if ( selInt !== null && $.inArray( selInt, rows ) !== -1 ) {
				// Selector - integer
				return [ selInt ];
			}
			else if ( sel === null || sel === undefined || sel === '' ) {
				// Selector - none
				return rows;
			}
	
			// Selector - function
			if ( typeof sel === 'function' ) {
				return $.map( rows, function (idx) {
					var row = aoData[ idx ];
					return sel( idx, row._aData, row.nTr ) ? idx : null;
				} );
			}
	
			// Selector - node
			if ( sel.nodeName ) {
				var rowIdx = sel._DT_RowIndex;  // Property added by DT for fast lookup
				var cellIdx = sel._DT_CellIndex;
	
				if ( rowIdx !== undefined ) {
					// Make sure that the row is actually still present in the table
					return aoData[ rowIdx ] && aoData[ rowIdx ].nTr === sel ?
						[ rowIdx ] :
						[];
				}
				else if ( cellIdx ) {
					return aoData[ cellIdx.row ] && aoData[ cellIdx.row ].nTr === sel.parentNode ?
						[ cellIdx.row ] :
						[];
				}
				else {
					var host = $(sel).closest('*[data-dt-row]');
					return host.length ?
						[ host.data('dt-row') ] :
						[];
				}
			}
	
			// ID selector. Want to always be able to select rows by id, regardless
			// of if the tr element has been created or not, so can't rely upon
			// jQuery here - hence a custom implementation. This does not match
			// Sizzle's fast selector or HTML4 - in HTML5 the ID can be anything,
			// but to select it using a CSS selector engine (like Sizzle or
			// querySelect) it would need to need to be escaped for some characters.
			// DataTables simplifies this for row selectors since you can select
			// only a row. A # indicates an id any anything that follows is the id -
			// unescaped.
			if ( typeof sel === 'string' && sel.charAt(0) === '#' ) {
				// get row index from id
				var rowObj = settings.aIds[ sel.replace( /^#/, '' ) ];
				if ( rowObj !== undefined ) {
					return [ rowObj.idx ];
				}
	
				// need to fall through to jQuery in case there is DOM id that
				// matches
			}
			
			// Get nodes in the order from the `rows` array with null values removed
			var nodes = _removeEmpty(
				_pluck_order( settings.aoData, rows, 'nTr' )
			);
	
			// Selector - jQuery selector string, array of nodes or jQuery object/
			// As jQuery's .filter() allows jQuery objects to be passed in filter,
			// it also allows arrays, so this will cope with all three options
			return $(nodes)
				.filter( sel )
				.map( function () {
					return this._DT_RowIndex;
				} )
				.toArray();
		};
	
		return _selector_run( 'row', selector, run, settings, opts );
	};
	
	
	_api_register( 'rows()', function ( selector, opts ) {
		// argument shifting
		if ( selector === undefined ) {
			selector = '';
		}
		else if ( $.isPlainObject( selector ) ) {
			opts = selector;
			selector = '';
		}
	
		opts = _selector_opts( opts );
	
		var inst = this.iterator( 'table', function ( settings ) {
			return __row_selector( settings, selector, opts );
		}, 1 );
	
		// Want argument shifting here and in __row_selector?
		inst.selector.rows = selector;
		inst.selector.opts = opts;
	
		return inst;
	} );
	
	_api_register( 'rows().nodes()', function () {
		return this.iterator( 'row', function ( settings, row ) {
			return settings.aoData[ row ].nTr || undefined;
		}, 1 );
	} );
	
	_api_register( 'rows().data()', function () {
		return this.iterator( true, 'rows', function ( settings, rows ) {
			return _pluck_order( settings.aoData, rows, '_aData' );
		}, 1 );
	} );
	
	_api_registerPlural( 'rows().cache()', 'row().cache()', function ( type ) {
		return this.iterator( 'row', function ( settings, row ) {
			var r = settings.aoData[ row ];
			return type === 'search' ? r._aFilterData : r._aSortData;
		}, 1 );
	} );
	
	_api_registerPlural( 'rows().invalidate()', 'row().invalidate()', function ( src ) {
		return this.iterator( 'row', function ( settings, row ) {
			_fnInvalidate( settings, row, src );
		} );
	} );
	
	_api_registerPlural( 'rows().indexes()', 'row().index()', function () {
		return this.iterator( 'row', function ( settings, row ) {
			return row;
		}, 1 );
	} );
	
	_api_registerPlural( 'rows().ids()', 'row().id()', function ( hash ) {
		var a = [];
		var context = this.context;
	
		// `iterator` will drop undefined values, but in this case we want them
		for ( var i=0, ien=context.length ; i<ien ; i++ ) {
			for ( var j=0, jen=this[i].length ; j<jen ; j++ ) {
				var id = context[i].rowIdFn( context[i].aoData[ this[i][j] ]._aData );
				a.push( (hash === true ? '#' : '' )+ id );
			}
		}
	
		return new _Api( context, a );
	} );
	
	_api_registerPlural( 'rows().remove()', 'row().remove()', function () {
		var that = this;
	
		this.iterator( 'row', function ( settings, row, thatIdx ) {
			var data = settings.aoData;
			var rowData = data[ row ];
			var i, ien, j, jen;
			var loopRow, loopCells;
	
			data.splice( row, 1 );
	
			// Update the cached indexes
			for ( i=0, ien=data.length ; i<ien ; i++ ) {
				loopRow = data[i];
				loopCells = loopRow.anCells;
	
				// Rows
				if ( loopRow.nTr !== null ) {
					loopRow.nTr._DT_RowIndex = i;
				}
	
				// Cells
				if ( loopCells !== null ) {
					for ( j=0, jen=loopCells.length ; j<jen ; j++ ) {
						loopCells[j]._DT_CellIndex.row = i;
					}
				}
			}
	
			// Delete from the display arrays
			_fnDeleteIndex( settings.aiDisplayMaster, row );
			_fnDeleteIndex( settings.aiDisplay, row );
			_fnDeleteIndex( that[ thatIdx ], row, false ); // maintain local indexes
	
			// For server-side processing tables - subtract the deleted row from the count
			if ( settings._iRecordsDisplay > 0 ) {
				settings._iRecordsDisplay--;
			}
	
			// Check for an 'overflow' they case for displaying the table
			_fnLengthOverflow( settings );
	
			// Remove the row's ID reference if there is one
			var id = settings.rowIdFn( rowData._aData );
			if ( id !== undefined ) {
				delete settings.aIds[ id ];
			}
		} );
	
		this.iterator( 'table', function ( settings ) {
			for ( var i=0, ien=settings.aoData.length ; i<ien ; i++ ) {
				settings.aoData[i].idx = i;
			}
		} );
	
		return this;
	} );
	
	
	_api_register( 'rows.add()', function ( rows ) {
		var newRows = this.iterator( 'table', function ( settings ) {
				var row, i, ien;
				var out = [];
	
				for ( i=0, ien=rows.length ; i<ien ; i++ ) {
					row = rows[i];
	
					if ( row.nodeName && row.nodeName.toUpperCase() === 'TR' ) {
						out.push( _fnAddTr( settings, row )[0] );
					}
					else {
						out.push( _fnAddData( settings, row ) );
					}
				}
	
				return out;
			}, 1 );
	
		// Return an Api.rows() extended instance, so rows().nodes() etc can be used
		var modRows = this.rows( -1 );
		modRows.pop();
		$.merge( modRows, newRows );
	
		return modRows;
	} );
	
	
	
	
	
	/**
	 *
	 */
	_api_register( 'row()', function ( selector, opts ) {
		return _selector_first( this.rows( selector, opts ) );
	} );
	
	
	_api_register( 'row().data()', function ( data ) {
		var ctx = this.context;
	
		if ( data === undefined ) {
			// Get
			return ctx.length && this.length ?
				ctx[0].aoData[ this[0] ]._aData :
				undefined;
		}
	
		// Set
		var row = ctx[0].aoData[ this[0] ];
		row._aData = data;
	
		// If the DOM has an id, and the data source is an array
		if ( Array.isArray( data ) && row.nTr && row.nTr.id ) {
			_fnSetObjectDataFn( ctx[0].rowId )( data, row.nTr.id );
		}
	
		// Automatically invalidate
		_fnInvalidate( ctx[0], this[0], 'data' );
	
		return this;
	} );
	
	
	_api_register( 'row().node()', function () {
		var ctx = this.context;
	
		return ctx.length && this.length ?
			ctx[0].aoData[ this[0] ].nTr || null :
			null;
	} );
	
	
	_api_register( 'row.add()', function ( row ) {
		// Allow a jQuery object to be passed in - only a single row is added from
		// it though - the first element in the set
		if ( row instanceof $ && row.length ) {
			row = row[0];
		}
	
		var rows = this.iterator( 'table', function ( settings ) {
			if ( row.nodeName && row.nodeName.toUpperCase() === 'TR' ) {
				return _fnAddTr( settings, row )[0];
			}
			return _fnAddData( settings, row );
		} );
	
		// Return an Api.rows() extended instance, with the newly added row selected
		return this.row( rows[0] );
	} );
	
	
	$(document).on('plugin-init.dt', function (e, context) {
		var api = new _Api( context );
	
		api.on( 'stateSaveParams', function ( e, settings, d ) {
			// This could be more compact with the API, but it is a lot faster as a simple
			// internal loop
			var idFn = settings.rowIdFn;
			var data = settings.aoData;
			var ids = [];
	
			for (var i=0 ; i<data.length ; i++) {
				if (data[i]._detailsShow) {
					ids.push( '#' + idFn(data[i]._aData) );
				}
			}
	
			d.childRows = ids;
		})
	
		var loaded = api.state.loaded();
	
		if ( loaded && loaded.childRows ) {
			api
				.rows( $.map(loaded.childRows, function (id){
					return id.replace(/:/g, '\\:')
				}) )
				.every( function () {
					_fnCallbackFire( context, null, 'requestChild', [ this ] )
				});
		}
	});
	
	var __details_add = function ( ctx, row, data, klass )
	{
		// Convert to array of TR elements
		var rows = [];
		var addRow = function ( r, k ) {
			// Recursion to allow for arrays of jQuery objects
			if ( Array.isArray( r ) || r instanceof $ ) {
				for ( var i=0, ien=r.length ; i<ien ; i++ ) {
					addRow( r[i], k );
				}
				return;
			}
	
			// If we get a TR element, then just add it directly - up to the dev
			// to add the correct number of columns etc
			if ( r.nodeName && r.nodeName.toLowerCase() === 'tr' ) {
				rows.push( r );
			}
			else {
				// Otherwise create a row with a wrapper
				var created = $('<tr><td></td></tr>').addClass( k );
				$('td', created)
					.addClass( k )
					.html( r )
					[0].colSpan = _fnVisbleColumns( ctx );
	
				rows.push( created[0] );
			}
		};
	
		addRow( data, klass );
	
		if ( row._details ) {
			row._details.detach();
		}
	
		row._details = $(rows);
	
		// If the children were already shown, that state should be retained
		if ( row._detailsShow ) {
			row._details.insertAfter( row.nTr );
		}
	};
	
	
	// Make state saving of child row details async to allow them to be batch processed
	var __details_state = DataTable.util.throttle(
		function (ctx) {
			_fnSaveState( ctx[0] )
		},
		500
	);
	
	
	var __details_remove = function ( api, idx )
	{
		var ctx = api.context;
	
		if ( ctx.length ) {
			var row = ctx[0].aoData[ idx !== undefined ? idx : api[0] ];
	
			if ( row && row._details ) {
				row._details.remove();
	
				row._detailsShow = undefined;
				row._details = undefined;
				$( row.nTr ).removeClass( 'dt-hasChild' );
				__details_state( ctx );
			}
		}
	};
	
	
	var __details_display = function ( api, show ) {
		var ctx = api.context;
	
		if ( ctx.length && api.length ) {
			var row = ctx[0].aoData[ api[0] ];
	
			if ( row._details ) {
				row._detailsShow = show;
	
				if ( show ) {
					row._details.insertAfter( row.nTr );
					$( row.nTr ).addClass( 'dt-hasChild' );
				}
				else {
					row._details.detach();
					$( row.nTr ).removeClass( 'dt-hasChild' );
				}
	
				_fnCallbackFire( ctx[0], null, 'childRow', [ show, api.row( api[0] ) ] )
	
				__details_events( ctx[0] );
				__details_state( ctx );
			}
		}
	};
	
	
	var __details_events = function ( settings )
	{
		var api = new _Api( settings );
		var namespace = '.dt.DT_details';
		var drawEvent = 'draw'+namespace;
		var colvisEvent = 'column-sizing'+namespace;
		var destroyEvent = 'destroy'+namespace;
		var data = settings.aoData;
	
		api.off( drawEvent +' '+ colvisEvent +' '+ destroyEvent );
	
		if ( _pluck( data, '_details' ).length > 0 ) {
			// On each draw, insert the required elements into the document
			api.on( drawEvent, function ( e, ctx ) {
				if ( settings !== ctx ) {
					return;
				}
	
				api.rows( {page:'current'} ).eq(0).each( function (idx) {
					// Internal data grab
					var row = data[ idx ];
	
					if ( row._detailsShow ) {
						row._details.insertAfter( row.nTr );
					}
				} );
			} );
	
			// Column visibility change - update the colspan
			api.on( colvisEvent, function ( e, ctx, idx, vis ) {
				if ( settings !== ctx ) {
					return;
				}
	
				// Update the colspan for the details rows (note, only if it already has
				// a colspan)
				var row, visible = _fnVisbleColumns( ctx );
	
				for ( var i=0, ien=data.length ; i<ien ; i++ ) {
					row = data[i];
	
					if ( row._details ) {
						row._details.children('td[colspan]').attr('colspan', visible );
					}
				}
			} );
	
			// Table destroyed - nuke any child rows
			api.on( destroyEvent, function ( e, ctx ) {
				if ( settings !== ctx ) {
					return;
				}
	
				for ( var i=0, ien=data.length ; i<ien ; i++ ) {
					if ( data[i]._details ) {
						__details_remove( api, i );
					}
				}
			} );
		}
	};
	
	// Strings for the method names to help minification
	var _emp = '';
	var _child_obj = _emp+'row().child';
	var _child_mth = _child_obj+'()';
	
	// data can be:
	//  tr
	//  string
	//  jQuery or array of any of the above
	_api_register( _child_mth, function ( data, klass ) {
		var ctx = this.context;
	
		if ( data === undefined ) {
			// get
			return ctx.length && this.length ?
				ctx[0].aoData[ this[0] ]._details :
				undefined;
		}
		else if ( data === true ) {
			// show
			this.child.show();
		}
		else if ( data === false ) {
			// remove
			__details_remove( this );
		}
		else if ( ctx.length && this.length ) {
			// set
			__details_add( ctx[0], ctx[0].aoData[ this[0] ], data, klass );
		}
	
		return this;
	} );
	
	
	_api_register( [
		_child_obj+'.show()',
		_child_mth+'.show()' // only when `child()` was called with parameters (without
	], function ( show ) {   // it returns an object and this method is not executed)
		__details_display( this, true );
		return this;
	} );
	
	
	_api_register( [
		_child_obj+'.hide()',
		_child_mth+'.hide()' // only when `child()` was called with parameters (without
	], function () {         // it returns an object and this method is not executed)
		__details_display( this, false );
		return this;
	} );
	
	
	_api_register( [
		_child_obj+'.remove()',
		_child_mth+'.remove()' // only when `child()` was called with parameters (without
	], function () {           // it returns an object and this method is not executed)
		__details_remove( this );
		return this;
	} );
	
	
	_api_register( _child_obj+'.isShown()', function () {
		var ctx = this.context;
	
		if ( ctx.length && this.length ) {
			// _detailsShown as false or undefined will fall through to return false
			return ctx[0].aoData[ this[0] ]._detailsShow || false;
		}
		return false;
	} );
	
	
	
	/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
	 * Columns
	 *
	 * {integer}           - column index (>=0 count from left, <0 count from right)
	 * "{integer}:visIdx"  - visible column index (i.e. translate to column index)  (>=0 count from left, <0 count from right)
	 * "{integer}:visible" - alias for {integer}:visIdx  (>=0 count from left, <0 count from right)
	 * "{string}:name"     - column name
	 * "{string}"          - jQuery selector on column header nodes
	 *
	 */
	
	// can be an array of these items, comma separated list, or an array of comma
	// separated lists
	
	var __re_column_selector = /^([^:]+):(name|visIdx|visible)$/;
	
	
	// r1 and r2 are redundant - but it means that the parameters match for the
	// iterator callback in columns().data()
	var __columnData = function ( settings, column, r1, r2, rows ) {
		var a = [];
		for ( var row=0, ien=rows.length ; row<ien ; row++ ) {
			a.push( _fnGetCellData( settings, rows[row], column ) );
		}
		return a;
	};
	
	
	var __column_selector = function ( settings, selector, opts )
	{
		var
			columns = settings.aoColumns,
			names = _pluck( columns, 'sName' ),
			nodes = _pluck( columns, 'nTh' );
	
		var run = function ( s ) {
			var selInt = _intVal( s );
	
			// Selector - all
			if ( s === '' ) {
				return _range( columns.length );
			}
	
			// Selector - index
			if ( selInt !== null ) {
				return [ selInt >= 0 ?
					selInt : // Count from left
					columns.length + selInt // Count from right (+ because its a negative value)
				];
			}
	
			// Selector = function
			if ( typeof s === 'function' ) {
				var rows = _selector_row_indexes( settings, opts );
	
				return $.map( columns, function (col, idx) {
					return s(
							idx,
							__columnData( settings, idx, 0, 0, rows ),
							nodes[ idx ]
						) ? idx : null;
				} );
			}
	
			// jQuery or string selector
			var match = typeof s === 'string' ?
				s.match( __re_column_selector ) :
				'';
	
			if ( match ) {
				switch( match[2] ) {
					case 'visIdx':
					case 'visible':
						var idx = parseInt( match[1], 10 );
						// Visible index given, convert to column index
						if ( idx < 0 ) {
							// Counting from the right
							var visColumns = $.map( columns, function (col,i) {
								return col.bVisible ? i : null;
							} );
							return [ visColumns[ visColumns.length + idx ] ];
						}
						// Counting from the left
						return [ _fnVisibleToColumnIndex( settings, idx ) ];
	
					case 'name':
						// match by name. `names` is column index complete and in order
						return $.map( names, function (name, i) {
							return name === match[1] ? i : null;
						} );
	
					default:
						return [];
				}
			}
	
			// Cell in the table body
			if ( s.nodeName && s._DT_CellIndex ) {
				return [ s._DT_CellIndex.column ];
			}
	
			// jQuery selector on the TH elements for the columns
			var jqResult = $( nodes )
				.filter( s )
				.map( function () {
					return $.inArray( this, nodes ); // `nodes` is column index complete and in order
				} )
				.toArray();
	
			if ( jqResult.length || ! s.nodeName ) {
				return jqResult;
			}
	
			// Otherwise a node which might have a `dt-column` data attribute, or be
			// a child or such an element
			var host = $(s).closest('*[data-dt-column]');
			return host.length ?
				[ host.data('dt-column') ] :
				[];
		};
	
		return _selector_run( 'column', selector, run, settings, opts );
	};
	
	
	var __setColumnVis = function ( settings, column, vis ) {
		var
			cols = settings.aoColumns,
			col  = cols[ column ],
			data = settings.aoData,
			row, cells, i, ien, tr;
	
		// Get
		if ( vis === undefined ) {
			return col.bVisible;
		}
	
		// Set
		// No change
		if ( col.bVisible === vis ) {
			return;
		}
	
		if ( vis ) {
			// Insert column
			// Need to decide if we should use appendChild or insertBefore
			var insertBefore = $.inArray( true, _pluck(cols, 'bVisible'), column+1 );
	
			for ( i=0, ien=data.length ; i<ien ; i++ ) {
				tr = data[i].nTr;
				cells = data[i].anCells;
	
				if ( tr ) {
					// insertBefore can act like appendChild if 2nd arg is null
					tr.insertBefore( cells[ column ], cells[ insertBefore ] || null );
				}
			}
		}
		else {
			// Remove column
			$( _pluck( settings.aoData, 'anCells', column ) ).detach();
		}
	
		// Common actions
		col.bVisible = vis;
	};
	
	
	_api_register( 'columns()', function ( selector, opts ) {
		// argument shifting
		if ( selector === undefined ) {
			selector = '';
		}
		else if ( $.isPlainObject( selector ) ) {
			opts = selector;
			selector = '';
		}
	
		opts = _selector_opts( opts );
	
		var inst = this.iterator( 'table', function ( settings ) {
			return __column_selector( settings, selector, opts );
		}, 1 );
	
		// Want argument shifting here and in _row_selector?
		inst.selector.cols = selector;
		inst.selector.opts = opts;
	
		return inst;
	} );
	
	_api_registerPlural( 'columns().header()', 'column().header()', function ( selector, opts ) {
		return this.iterator( 'column', function ( settings, column ) {
			return settings.aoColumns[column].nTh;
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().footer()', 'column().footer()', function ( selector, opts ) {
		return this.iterator( 'column', function ( settings, column ) {
			return settings.aoColumns[column].nTf;
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().data()', 'column().data()', function () {
		return this.iterator( 'column-rows', __columnData, 1 );
	} );
	
	_api_registerPlural( 'columns().dataSrc()', 'column().dataSrc()', function () {
		return this.iterator( 'column', function ( settings, column ) {
			return settings.aoColumns[column].mData;
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().cache()', 'column().cache()', function ( type ) {
		return this.iterator( 'column-rows', function ( settings, column, i, j, rows ) {
			return _pluck_order( settings.aoData, rows,
				type === 'search' ? '_aFilterData' : '_aSortData', column
			);
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().nodes()', 'column().nodes()', function () {
		return this.iterator( 'column-rows', function ( settings, column, i, j, rows ) {
			return _pluck_order( settings.aoData, rows, 'anCells', column ) ;
		}, 1 );
	} );
	
	_api_registerPlural( 'columns().visible()', 'column().visible()', function ( vis, calc ) {
		var that = this;
		var ret = this.iterator( 'column', function ( settings, column ) {
			if ( vis === undefined ) {
				return settings.aoColumns[ column ].bVisible;
			} // else
			__setColumnVis( settings, column, vis );
		} );
	
		// Group the column visibility changes
		if ( vis !== undefined ) {
			this.iterator( 'table', function ( settings ) {
				// Redraw the header after changes
				_fnDrawHead( settings, settings.aoHeader );
				_fnDrawHead( settings, settings.aoFooter );
		
				// Update colspan for no records display. Child rows and extensions will use their own
				// listeners to do this - only need to update the empty table item here
				if ( ! settings.aiDisplay.length ) {
					$(settings.nTBody).find('td[colspan]').attr('colspan', _fnVisbleColumns(settings));
				}
		
				_fnSaveState( settings );
	
				// Second loop once the first is done for events
				that.iterator( 'column', function ( settings, column ) {
					_fnCallbackFire( settings, null, 'column-visibility', [settings, column, vis, calc] );
				} );
	
				if ( calc === undefined || calc ) {
					that.columns.adjust();
				}
			});
		}
	
		return ret;
	} );
	
	_api_registerPlural( 'columns().indexes()', 'column().index()', function ( type ) {
		return this.iterator( 'column', function ( settings, column ) {
			return type === 'visible' ?
				_fnColumnIndexToVisible( settings, column ) :
				column;
		}, 1 );
	} );
	
	_api_register( 'columns.adjust()', function () {
		return this.iterator( 'table', function ( settings ) {
			_fnAdjustColumnSizing( settings );
		}, 1 );
	} );
	
	_api_register( 'column.index()', function ( type, idx ) {
		if ( this.context.length !== 0 ) {
			var ctx = this.context[0];
	
			if ( type === 'fromVisible' || type === 'toData' ) {
				return _fnVisibleToColumnIndex( ctx, idx );
			}
			else if ( type === 'fromData' || type === 'toVisible' ) {
				return _fnColumnIndexToVisible( ctx, idx );
			}
		}
	} );
	
	_api_register( 'column()', function ( selector, opts ) {
		return _selector_first( this.columns( selector, opts ) );
	} );
	
	var __cell_selector = function ( settings, selector, opts )
	{
		var data = settings.aoData;
		var rows = _selector_row_indexes( settings, opts );
		var cells = _removeEmpty( _pluck_order( data, rows, 'anCells' ) );
		var allCells = $(_flatten( [], cells ));
		var row;
		var columns = settings.aoColumns.length;
		var a, i, ien, j, o, host;
	
		var run = function ( s ) {
			var fnSelector = typeof s === 'function';
	
			if ( s === null || s === undefined || fnSelector ) {
				// All cells and function selectors
				a = [];
	
				for ( i=0, ien=rows.length ; i<ien ; i++ ) {
					row = rows[i];
	
					for ( j=0 ; j<columns ; j++ ) {
						o = {
							row: row,
							column: j
						};
	
						if ( fnSelector ) {
							// Selector - function
							host = data[ row ];
	
							if ( s( o, _fnGetCellData(settings, row, j), host.anCells ? host.anCells[j] : null ) ) {
								a.push( o );
							}
						}
						else {
							// Selector - all
							a.push( o );
						}
					}
				}
	
				return a;
			}
			
			// Selector - index
			if ( $.isPlainObject( s ) ) {
				// Valid cell index and its in the array of selectable rows
				return s.column !== undefined && s.row !== undefined && $.inArray( s.row, rows ) !== -1 ?
					[s] :
					[];
			}
	
			// Selector - jQuery filtered cells
			var jqResult = allCells
				.filter( s )
				.map( function (i, el) {
					return { // use a new object, in case someone changes the values
						row:    el._DT_CellIndex.row,
						column: el._DT_CellIndex.column
	 				};
				} )
				.toArray();
	
			if ( jqResult.length || ! s.nodeName ) {
				return jqResult;
			}
	
			// Otherwise the selector is a node, and there is one last option - the
			// element might be a child of an element which has dt-row and dt-column
			// data attributes
			host = $(s).closest('*[data-dt-row]');
			return host.length ?
				[ {
					row: host.data('dt-row'),
					column: host.data('dt-column')
				} ] :
				[];
		};
	
		return _selector_run( 'cell', selector, run, settings, opts );
	};
	
	
	
	
	_api_register( 'cells()', function ( rowSelector, columnSelector, opts ) {
		// Argument shifting
		if ( $.isPlainObject( rowSelector ) ) {
			// Indexes
			if ( rowSelector.row === undefined ) {
				// Selector options in first parameter
				opts = rowSelector;
				rowSelector = null;
			}
			else {
				// Cell index objects in first parameter
				opts = columnSelector;
				columnSelector = null;
			}
		}
		if ( $.isPlainObject( columnSelector ) ) {
			opts = columnSelector;
			columnSelector = null;
		}
	
		// Cell selector
		if ( columnSelector === null || columnSelector === undefined ) {
			return this.iterator( 'table', function ( settings ) {
				return __cell_selector( settings, rowSelector, _selector_opts( opts ) );
			} );
		}
	
		// The default built in options need to apply to row and columns
		var internalOpts = opts ? {
			page: opts.page,
			order: opts.order,
			search: opts.search
		} : {};
	
		// Row + column selector
		var columns = this.columns( columnSelector, internalOpts );
		var rows = this.rows( rowSelector, internalOpts );
		var i, ien, j, jen;
	
		var cellsNoOpts = this.iterator( 'table', function ( settings, idx ) {
			var a = [];
	
			for ( i=0, ien=rows[idx].length ; i<ien ; i++ ) {
				for ( j=0, jen=columns[idx].length ; j<jen ; j++ ) {
					a.push( {
						row:    rows[idx][i],
						column: columns[idx][j]
					} );
				}
			}
	
			return a;
		}, 1 );
	
		// There is currently only one extension which uses a cell selector extension
		// It is a _major_ performance drag to run this if it isn't needed, so this is
		// an extension specific check at the moment
		var cells = opts && opts.selected ?
			this.cells( cellsNoOpts, opts ) :
			cellsNoOpts;
	
		$.extend( cells.selector, {
			cols: columnSelector,
			rows: rowSelector,
			opts: opts
		} );
	
		return cells;
	} );
	
	
	_api_registerPlural( 'cells().nodes()', 'cell().node()', function () {
		return this.iterator( 'cell', function ( settings, row, column ) {
			var data = settings.aoData[ row ];
	
			return data && data.anCells ?
				data.anCells[ column ] :
				undefined;
		}, 1 );
	} );
	
	
	_api_register( 'cells().data()', function () {
		return this.iterator( 'cell', function ( settings, row, column ) {
			return _fnGetCellData( settings, row, column );
		}, 1 );
	} );
	
	
	_api_registerPlural( 'cells().cache()', 'cell().cache()', function ( type ) {
		type = type === 'search' ? '_aFilterData' : '_aSortData';
	
		return this.iterator( 'cell', function ( settings, row, column ) {
			return settings.aoData[ row ][ type ][ column ];
		}, 1 );
	} );
	
	
	_api_registerPlural( 'cells().render()', 'cell().render()', function ( type ) {
		return this.iterator( 'cell', function ( settings, row, column ) {
			return _fnGetCellData( settings, row, column, type );
		}, 1 );
	} );
	
	
	_api_registerPlural( 'cells().indexes()', 'cell().index()', function () {
		return this.iterator( 'cell', function ( settings, row, column ) {
			return {
				row: row,
				column: column,
				columnVisible: _fnColumnIndexToVisible( settings, column )
			};
		}, 1 );
	} );
	
	
	_api_registerPlural( 'cells().invalidate()', 'cell().invalidate()', function ( src ) {
		return this.iterator( 'cell', function ( settings, row, column ) {
			_fnInvalidate( settings, row, src, column );
		} );
	} );
	
	
	
	_api_register( 'cell()', function ( rowSelector, columnSelector, opts ) {
		return _selector_first( this.cells( rowSelector, columnSelector, opts ) );
	} );
	
	
	_api_register( 'cell().data()', function ( data ) {
		var ctx = this.context;
		var cell = this[0];
	
		if ( data === undefined ) {
			// Get
			return ctx.length && cell.length ?
				_fnGetCellData( ctx[0], cell[0].row, cell[0].column ) :
				undefined;
		}
	
		// Set
		_fnSetCellData( ctx[0], cell[0].row, cell[0].column, data );
		_fnInvalidate( ctx[0], cell[0].row, 'data', cell[0].column );
	
		return this;
	} );
	
	
	
	/**
	 * Get current ordering (sorting) that has been applied to the table.
	 *
	 * @returns {array} 2D array containing the sorting information for the first
	 *   table in the current context. Each element in the parent array represents
	 *   a column being sorted upon (i.e. multi-sorting with two columns would have
	 *   2 inner arrays). The inner arrays may have 2 or 3 elements. The first is
	 *   the column index that the sorting condition applies to, the second is the
	 *   direction of the sort (`desc` or `asc`) and, optionally, the third is the
	 *   index of the sorting order from the `column.sorting` initialisation array.
	 *//**
	 * Set the ordering for the table.
	 *
	 * @param {integer} order Column index to sort upon.
	 * @param {string} direction Direction of the sort to be applied (`asc` or `desc`)
	 * @returns {DataTables.Api} this
	 *//**
	 * Set the ordering for the table.
	 *
	 * @param {array} order 1D array of sorting information to be applied.
	 * @param {array} [...] Optional additional sorting conditions
	 * @returns {DataTables.Api} this
	 *//**
	 * Set the ordering for the table.
	 *
	 * @param {array} order 2D array of sorting information to be applied.
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'order()', function ( order, dir ) {
		var ctx = this.context;
	
		if ( order === undefined ) {
			// get
			return ctx.length !== 0 ?
				ctx[0].aaSorting :
				undefined;
		}
	
		// set
		if ( typeof order === 'number' ) {
			// Simple column / direction passed in
			order = [ [ order, dir ] ];
		}
		else if ( order.length && ! Array.isArray( order[0] ) ) {
			// Arguments passed in (list of 1D arrays)
			order = Array.prototype.slice.call( arguments );
		}
		// otherwise a 2D array was passed in
	
		return this.iterator( 'table', function ( settings ) {
			settings.aaSorting = order.slice();
		} );
	} );
	
	
	/**
	 * Attach a sort listener to an element for a given column
	 *
	 * @param {node|jQuery|string} node Identifier for the element(s) to attach the
	 *   listener to. This can take the form of a single DOM node, a jQuery
	 *   collection of nodes or a jQuery selector which will identify the node(s).
	 * @param {integer} column the column that a click on this node will sort on
	 * @param {function} [callback] callback function when sort is run
	 * @returns {DataTables.Api} this
	 */
	_api_register( 'order.listener()', function ( node, column, callback ) {
		return this.iterator( 'table', function ( settings ) {
			_fnSortAttachListener( settings, node, column, callback );
		} );
	} );
	
	
	_api_register( 'order.fixed()', function ( set ) {
		if ( ! set ) {
			var ctx = this.context;
			var fixed = ctx.length ?
				ctx[0].aaSortingFixed :
				undefined;
	
			return Array.isArray( fixed ) ?
				{ pre: fixed } :
				fixed;
		}
	
		return this.iterator( 'table', function ( settings ) {
			settings.aaSortingFixed = $.extend( true, {}, set );
		} );
	} );
	
	
	// Order by the selected column(s)
	_api_register( [
		'columns().order()',
		'column().order()'
	], function ( dir ) {
		var that = this;
	
		return this.iterator( 'table', function ( settings, i ) {
			var sort = [];
	
			$.each( that[i], function (j, col) {
				sort.push( [ col, dir ] );
			} );
	
			settings.aaSorting = sort;
		} );
	} );
	
	
	
	_api_register( 'search()', function ( input, regex, smart, caseInsen ) {
		var ctx = this.context;
	
		if ( input === undefined ) {
			// get
			return ctx.length !== 0 ?
				ctx[0].oPreviousSearch.sSearch :
				undefined;
		}
	
		// set
		return this.iterator( 'table', function ( settings ) {
			if ( ! settings.oFeatures.bFilter ) {
				return;
			}
	
			_fnFilterComplete( settings, $.extend( {}, settings.oPreviousSearch, {
				"sSearch": input+"",
				"bRegex":  regex === null ? false : regex,
				"bSmart":  smart === null ? true  : smart,
				"bCaseInsensitive": caseInsen === null ? true : caseInsen
			} ), 1 );
		} );
	} );
	
	
	_api_registerPlural(
		'columns().search()',
		'column().search()',
		function ( input, regex, smart, caseInsen ) {
			return this.iterator( 'column', function ( settings, column ) {
				var preSearch = settings.aoPreSearchCols;
	
				if ( input === undefined ) {
					// get
					return preSearch[ column ].sSearch;
				}
	
				// set
				if ( ! settings.oFeatures.bFilter ) {
					return;
				}
	
				$.extend( preSearch[ column ], {
					"sSearch": input+"",
					"bRegex":  regex === null ? false : regex,
					"bSmart":  smart === null ? true  : smart,
					"bCaseInsensitive": caseInsen === null ? true : caseInsen
				} );
	
				_fnFilterComplete( settings, settings.oPreviousSearch, 1 );
			} );
		}
	);
	
	/*
	 * State API methods
	 */
	
	_api_register( 'state()', function () {
		return this.context.length ?
			this.context[0].oSavedState :
			null;
	} );
	
	
	_api_register( 'state.clear()', function () {
		return this.iterator( 'table', function ( settings ) {
			// Save an empty object
			settings.fnStateSaveCallback.call( settings.oInstance, settings, {} );
		} );
	} );
	
	
	_api_register( 'state.loaded()', function () {
		return this.context.length ?
			this.context[0].oLoadedState :
			null;
	} );
	
	
	_api_register( 'state.save()', function () {
		return this.iterator( 'table', function ( settings ) {
			_fnSaveState( settings );
		} );
	} );
	
	
	
	/**
	 * Provide a common method for plug-ins to check the version of DataTables being
	 * used, in order to ensure compatibility.
	 *
	 *  @param {string} version Version string to check for, in the format "X.Y.Z".
	 *    Note that the formats "X" and "X.Y" are also acceptable.
	 *  @returns {boolean} true if this version of DataTables is greater or equal to
	 *    the required version, or false if this version of DataTales is not
	 *    suitable
	 *  @static
	 *  @dtopt API-Static
	 *
	 *  @example
	 *    alert( $.fn.dataTable.versionCheck( '1.9.0' ) );
	 */
	DataTable.versionCheck = DataTable.fnVersionCheck = function( version )
	{
		var aThis = DataTable.version.split('.');
		var aThat = version.split('.');
		var iThis, iThat;
	
		for ( var i=0, iLen=aThat.length ; i<iLen ; i++ ) {
			iThis = parseInt( aThis[i], 10 ) || 0;
			iThat = parseInt( aThat[i], 10 ) || 0;
	
			// Parts are the same, keep comparing
			if (iThis === iThat) {
				continue;
			}
	
			// Parts are different, return immediately
			return iThis > iThat;
		}
	
		return true;
	};
	
	
	/**
	 * Check if a `<table>` node is a DataTable table already or not.
	 *
	 *  @param {node|jquery|string} table Table node, jQuery object or jQuery
	 *      selector for the table to test. Note that if more than more than one
	 *      table is passed on, only the first will be checked
	 *  @returns {boolean} true the table given is a DataTable, or false otherwise
	 *  @static
	 *  @dtopt API-Static
	 *
	 *  @example
	 *    if ( ! $.fn.DataTable.isDataTable( '#example' ) ) {
	 *      $('#example').dataTable();
	 *    }
	 */
	DataTable.isDataTable = DataTable.fnIsDataTable = function ( table )
	{
		var t = $(table).get(0);
		var is = false;
	
		if ( table instanceof DataTable.Api ) {
			return true;
		}
	
		$.each( DataTable.settings, function (i, o) {
			var head = o.nScrollHead ? $('table', o.nScrollHead)[0] : null;
			var foot = o.nScrollFoot ? $('table', o.nScrollFoot)[0] : null;
	
			if ( o.nTable === t || head === t || foot === t ) {
				is = true;
			}
		} );
	
		return is;
	};
	
	
	/**
	 * Get all DataTable tables that have been initialised - optionally you can
	 * select to get only currently visible tables.
	 *
	 *  @param {boolean} [visible=false] Flag to indicate if you want all (default)
	 *    or visible tables only.
	 *  @returns {array} Array of `table` nodes (not DataTable instances) which are
	 *    DataTables
	 *  @static
	 *  @dtopt API-Static
	 *
	 *  @example
	 *    $.each( $.fn.dataTable.tables(true), function () {
	 *      $(table).DataTable().columns.adjust();
	 *    } );
	 */
	DataTable.tables = DataTable.fnTables = function ( visible )
	{
		var api = false;
	
		if ( $.isPlainObject( visible ) ) {
			api = visible.api;
			visible = visible.visible;
		}
	
		var a = $.map( DataTable.settings, function (o) {
			if ( !visible || (visible && $(o.nTable).is(':visible')) ) {
				return o.nTable;
			}
		} );
	
		return api ?
			new _Api( a ) :
			a;
	};
	
	
	/**
	 * Convert from camel case parameters to Hungarian notation. This is made public
	 * for the extensions to provide the same ability as DataTables core to accept
	 * either the 1.9 style Hungarian notation, or the 1.10+ style camelCase
	 * parameters.
	 *
	 *  @param {object} src The model object which holds all parameters that can be
	 *    mapped.
	 *  @param {object} user The object to convert from camel case to Hungarian.
	 *  @param {boolean} force When set to `true`, properties which already have a
	 *    Hungarian value in the `user` object will be overwritten. Otherwise they
	 *    won't be.
	 */
	DataTable.camelToHungarian = _fnCamelToHungarian;
	
	
	
	/**
	 *
	 */
	_api_register( '$()', function ( selector, opts ) {
		var
			rows   = this.rows( opts ).nodes(), // Get all rows
			jqRows = $(rows);
	
		return $( [].concat(
			jqRows.filter( selector ).toArray(),
			jqRows.find( selector ).toArray()
		) );
	} );
	
	
	// jQuery functions to operate on the tables
	$.each( [ 'on', 'one', 'off' ], function (i, key) {
		_api_register( key+'()', function ( /* event, handler */ ) {
			var args = Array.prototype.slice.call(arguments);
	
			// Add the `dt` namespace automatically if it isn't already present
			args[0] = $.map( args[0].split( /\s/ ), function ( e ) {
				return ! e.match(/\.dt\b/) ?
					e+'.dt' :
					e;
				} ).join( ' ' );
	
			var inst = $( this.tables().nodes() );
			inst[key].apply( inst, args );
			return this;
		} );
	} );
	
	
	_api_register( 'clear()', function () {
		return this.iterator( 'table', function ( settings ) {
			_fnClearTable( settings );
		} );
	} );
	
	
	_api_register( 'settings()', function () {
		return new _Api( this.context, this.context );
	} );
	
	
	_api_register( 'init()', function () {
		var ctx = this.context;
		return ctx.length ? ctx[0].oInit : null;
	} );
	
	
	_api_register( 'data()', function () {
		return this.iterator( 'table', function ( settings ) {
			return _pluck( settings.aoData, '_aData' );
		} ).flatten();
	} );
	
	
	_api_register( 'destroy()', function ( remove ) {
		remove = remove || false;
	
		return this.iterator( 'table', function ( settings ) {
			var classes   = settings.oClasses;
			var table     = settings.nTable;
			var tbody     = settings.nTBody;
			var thead     = settings.nTHead;
			var tfoot     = settings.nTFoot;
			var jqTable   = $(table);
			var jqTbody   = $(tbody);
			var jqWrapper = $(settings.nTableWrapper);
			var rows      = $.map( settings.aoData, function (r) { return r.nTr; } );
			var i, ien;
	
			// Flag to note that the table is currently being destroyed - no action
			// should be taken
			settings.bDestroying = true;
	
			// Fire off the destroy callbacks for plug-ins etc
			_fnCallbackFire( settings, "aoDestroyCallback", "destroy", [settings] );
	
			// If not being removed from the document, make all columns visible
			if ( ! remove ) {
				new _Api( settings ).columns().visible( true );
			}
	
			// Blitz all `DT` namespaced events (these are internal events, the
			// lowercase, `dt` events are user subscribed and they are responsible
			// for removing them
			jqWrapper.off('.DT').find(':not(tbody *)').off('.DT');
			$(window).off('.DT-'+settings.sInstance);
	
			// When scrolling we had to break the table up - restore it
			if ( table != thead.parentNode ) {
				jqTable.children('thead').detach();
				jqTable.append( thead );
			}
	
			if ( tfoot && table != tfoot.parentNode ) {
				jqTable.children('tfoot').detach();
				jqTable.append( tfoot );
			}
	
			settings.aaSorting = [];
			settings.aaSortingFixed = [];
			_fnSortingClasses( settings );
	
			$( rows ).removeClass( settings.asStripeClasses.join(' ') );
	
			$('th, td', thead).removeClass( classes.sSortable+' '+
				classes.sSortableAsc+' '+classes.sSortableDesc+' '+classes.sSortableNone
			);
	
			// Add the TR elements back into the table in their original order
			jqTbody.children().detach();
			jqTbody.append( rows );
	
			var orig = settings.nTableWrapper.parentNode;
	
			// Remove the DataTables generated nodes, events and classes
			var removedMethod = remove ? 'remove' : 'detach';
			jqTable[ removedMethod ]();
			jqWrapper[ removedMethod ]();
	
			// If we need to reattach the table to the document
			if ( ! remove && orig ) {
				// insertBefore acts like appendChild if !arg[1]
				orig.insertBefore( table, settings.nTableReinsertBefore );
	
				// Restore the width of the original table - was read from the style property,
				// so we can restore directly to that
				jqTable
					.css( 'width', settings.sDestroyWidth )
					.removeClass( classes.sTable );
	
				// If the were originally stripe classes - then we add them back here.
				// Note this is not fool proof (for example if not all rows had stripe
				// classes - but it's a good effort without getting carried away
				ien = settings.asDestroyStripes.length;
	
				if ( ien ) {
					jqTbody.children().each( function (i) {
						$(this).addClass( settings.asDestroyStripes[i % ien] );
					} );
				}
			}
	
			/* Remove the settings object from the settings array */
			var idx = $.inArray( settings, DataTable.settings );
			if ( idx !== -1 ) {
				DataTable.settings.splice( idx, 1 );
			}
		} );
	} );
	
	
	// Add the `every()` method for rows, columns and cells in a compact form
	$.each( [ 'column', 'row', 'cell' ], function ( i, type ) {
		_api_register( type+'s().every()', function ( fn ) {
			var opts = this.selector.opts;
			var api = this;
	
			return this.iterator( type, function ( settings, arg1, arg2, arg3, arg4 ) {
				// Rows and columns:
				//  arg1 - index
				//  arg2 - table counter
				//  arg3 - loop counter
				//  arg4 - undefined
				// Cells:
				//  arg1 - row index
				//  arg2 - column index
				//  arg3 - table counter
				//  arg4 - loop counter
				fn.call(
					api[ type ](
						arg1,
						type==='cell' ? arg2 : opts,
						type==='cell' ? opts : undefined
					),
					arg1, arg2, arg3, arg4
				);
			} );
		} );
	} );
	
	
	// i18n method for extensions to be able to use the language object from the
	// DataTable
	_api_register( 'i18n()', function ( token, def, plural ) {
		var ctx = this.context[0];
		var resolved = _fnGetObjectDataFn( token )( ctx.oLanguage );
	
		if ( resolved === undefined ) {
			resolved = def;
		}
	
		if ( plural !== undefined && $.isPlainObject( resolved ) ) {
			resolved = resolved[ plural ] !== undefined ?
				resolved[ plural ] :
				resolved._;
		}
	
		return resolved.replace( '%d', plural ); // nb: plural might be undefined,
	} );	
	/**
	 * Version string for plug-ins to check compatibility. Allowed format is
	 * `a.b.c-d` where: a:int, b:int, c:int, d:string(dev|beta|alpha). `d` is used
	 * only for non-release builds. See http://semver.org/ for more information.
	 *  @member
	 *  @type string
	 *  @default Version number
	 */
	DataTable.version = "1.12.1";
	
	/**
	 * Private data store, containing all of the settings objects that are
	 * created for the tables on a given page.
	 *
	 * Note that the `DataTable.settings` object is aliased to
	 * `jQuery.fn.dataTableExt` through which it may be accessed and
	 * manipulated, or `jQuery.fn.dataTable.settings`.
	 *  @member
	 *  @type array
	 *  @default []
	 *  @private
	 */
	DataTable.settings = [];
	
	/**
	 * Object models container, for the various models that DataTables has
	 * available to it. These models define the objects that are used to hold
	 * the active state and configuration of the table.
	 *  @namespace
	 */
	DataTable.models = {};
	
	
	
	/**
	 * Template object for the way in which DataTables holds information about
	 * search information for the global filter and individual column filters.
	 *  @namespace
	 */
	DataTable.models.oSearch = {
		/**
		 * Flag to indicate if the filtering should be case insensitive or not
		 *  @type boolean
		 *  @default true
		 */
		"bCaseInsensitive": true,
	
		/**
		 * Applied search term
		 *  @type string
		 *  @default <i>Empty string</i>
		 */
		"sSearch": "",
	
		/**
		 * Flag to indicate if the search term should be interpreted as a
		 * regular expression (true) or not (false) and therefore and special
		 * regex characters escaped.
		 *  @type boolean
		 *  @default false
		 */
		"bRegex": false,
	
		/**
		 * Flag to indicate if DataTables is to use its smart filtering or not.
		 *  @type boolean
		 *  @default true
		 */
		"bSmart": true,
	
		/**
		 * Flag to indicate if DataTables should only trigger a search when
		 * the return key is pressed.
		 *  @type boolean
		 *  @default false
		 */
		"return": false
	};
	
	
	
	
	/**
	 * Template object for the way in which DataTables holds information about
	 * each individual row. This is the object format used for the settings
	 * aoData array.
	 *  @namespace
	 */
	DataTable.models.oRow = {
		/**
		 * TR element for the row
		 *  @type node
		 *  @default null
		 */
		"nTr": null,
	
		/**
		 * Array of TD elements for each row. This is null until the row has been
		 * created.
		 *  @type array nodes
		 *  @default []
		 */
		"anCells": null,
	
		/**
		 * Data object from the original data source for the row. This is either
		 * an array if using the traditional form of DataTables, or an object if
		 * using mData options. The exact type will depend on the passed in
		 * data from the data source, or will be an array if using DOM a data
		 * source.
		 *  @type array|object
		 *  @default []
		 */
		"_aData": [],
	
		/**
		 * Sorting data cache - this array is ostensibly the same length as the
		 * number of columns (although each index is generated only as it is
		 * needed), and holds the data that is used for sorting each column in the
		 * row. We do this cache generation at the start of the sort in order that
		 * the formatting of the sort data need be done only once for each cell
		 * per sort. This array should not be read from or written to by anything
		 * other than the master sorting methods.
		 *  @type array
		 *  @default null
		 *  @private
		 */
		"_aSortData": null,
	
		/**
		 * Per cell filtering data cache. As per the sort data cache, used to
		 * increase the performance of the filtering in DataTables
		 *  @type array
		 *  @default null
		 *  @private
		 */
		"_aFilterData": null,
	
		/**
		 * Filtering data cache. This is the same as the cell filtering cache, but
		 * in this case a string rather than an array. This is easily computed with
		 * a join on `_aFilterData`, but is provided as a cache so the join isn't
		 * needed on every search (memory traded for performance)
		 *  @type array
		 *  @default null
		 *  @private
		 */
		"_sFilterRow": null,
	
		/**
		 * Cache of the class name that DataTables has applied to the row, so we
		 * can quickly look at this variable rather than needing to do a DOM check
		 * on className for the nTr property.
		 *  @type string
		 *  @default <i>Empty string</i>
		 *  @private
		 */
		"_sRowStripe": "",
	
		/**
		 * Denote if the original data source was from the DOM, or the data source
		 * object. This is used for invalidating data, so DataTables can
		 * automatically read data from the original source, unless uninstructed
		 * otherwise.
		 *  @type string
		 *  @default null
		 *  @private
		 */
		"src": null,
	
		/**
		 * Index in the aoData array. This saves an indexOf lookup when we have the
		 * object, but want to know the index
		 *  @type integer
		 *  @default -1
		 *  @private
		 */
		"idx": -1
	};
	
	
	/**
	 * Template object for the column information object in DataTables. This object
	 * is held in the settings aoColumns array and contains all the information that
	 * DataTables needs about each individual column.
	 *
	 * Note that this object is related to {@link DataTable.defaults.column}
	 * but this one is the internal data store for DataTables's cache of columns.
	 * It should NOT be manipulated outside of DataTables. Any configuration should
	 * be done through the initialisation options.
	 *  @namespace
	 */
	DataTable.models.oColumn = {
		/**
		 * Column index. This could be worked out on-the-fly with $.inArray, but it
		 * is faster to just hold it as a variable
		 *  @type integer
		 *  @default null
		 */
		"idx": null,
	
		/**
		 * A list of the columns that sorting should occur on when this column
		 * is sorted. That this property is an array allows multi-column sorting
		 * to be defined for a column (for example first name / last name columns
		 * would benefit from this). The values are integers pointing to the
		 * columns to be sorted on (typically it will be a single integer pointing
		 * at itself, but that doesn't need to be the case).
		 *  @type array
		 */
		"aDataSort": null,
	
		/**
		 * Define the sorting directions that are applied to the column, in sequence
		 * as the column is repeatedly sorted upon - i.e. the first value is used
		 * as the sorting direction when the column if first sorted (clicked on).
		 * Sort it again (click again) and it will move on to the next index.
		 * Repeat until loop.
		 *  @type array
		 */
		"asSorting": null,
	
		/**
		 * Flag to indicate if the column is searchable, and thus should be included
		 * in the filtering or not.
		 *  @type boolean
		 */
		"bSearchable": null,
	
		/**
		 * Flag to indicate if the column is sortable or not.
		 *  @type boolean
		 */
		"bSortable": null,
	
		/**
		 * Flag to indicate if the column is currently visible in the table or not
		 *  @type boolean
		 */
		"bVisible": null,
	
		/**
		 * Store for manual type assignment using the `column.type` option. This
		 * is held in store so we can manipulate the column's `sType` property.
		 *  @type string
		 *  @default null
		 *  @private
		 */
		"_sManualType": null,
	
		/**
		 * Flag to indicate if HTML5 data attributes should be used as the data
		 * source for filtering or sorting. True is either are.
		 *  @type boolean
		 *  @default false
		 *  @private
		 */
		"_bAttrSrc": false,
	
		/**
		 * Developer definable function that is called whenever a cell is created (Ajax source,
		 * etc) or processed for input (DOM source). This can be used as a compliment to mRender
		 * allowing you to modify the DOM element (add background colour for example) when the
		 * element is available.
		 *  @type function
		 *  @param {element} nTd The TD node that has been created
		 *  @param {*} sData The Data for the cell
		 *  @param {array|object} oData The data for the whole row
		 *  @param {int} iRow The row index for the aoData data store
		 *  @default null
		 */
		"fnCreatedCell": null,
	
		/**
		 * Function to get data from a cell in a column. You should <b>never</b>
		 * access data directly through _aData internally in DataTables - always use
		 * the method attached to this property. It allows mData to function as
		 * required. This function is automatically assigned by the column
		 * initialisation method
		 *  @type function
		 *  @param {array|object} oData The data array/object for the array
		 *    (i.e. aoData[]._aData)
		 *  @param {string} sSpecific The specific data type you want to get -
		 *    'display', 'type' 'filter' 'sort'
		 *  @returns {*} The data for the cell from the given row's data
		 *  @default null
		 */
		"fnGetData": null,
	
		/**
		 * Function to set data for a cell in the column. You should <b>never</b>
		 * set the data directly to _aData internally in DataTables - always use
		 * this method. It allows mData to function as required. This function
		 * is automatically assigned by the column initialisation method
		 *  @type function
		 *  @param {array|object} oData The data array/object for the array
		 *    (i.e. aoData[]._aData)
		 *  @param {*} sValue Value to set
		 *  @default null
		 */
		"fnSetData": null,
	
		/**
		 * Property to read the value for the cells in the column from the data
		 * source array / object. If null, then the default content is used, if a
		 * function is given then the return from the function is used.
		 *  @type function|int|string|null
		 *  @default null
		 */
		"mData": null,
	
		/**
		 * Partner property to mData which is used (only when defined) to get
		 * the data - i.e. it is basically the same as mData, but without the
		 * 'set' option, and also the data fed to it is the result from mData.
		 * This is the rendering method to match the data method of mData.
		 *  @type function|int|string|null
		 *  @default null
		 */
		"mRender": null,
	
		/**
		 * Unique header TH/TD element for this column - this is what the sorting
		 * listener is attached to (if sorting is enabled.)
		 *  @type node
		 *  @default null
		 */
		"nTh": null,
	
		/**
		 * Unique footer TH/TD element for this column (if there is one). Not used
		 * in DataTables as such, but can be used for plug-ins to reference the
		 * footer for each column.
		 *  @type node
		 *  @default null
		 */
		"nTf": null,
	
		/**
		 * The class to apply to all TD elements in the table's TBODY for the column
		 *  @type string
		 *  @default null
		 */
		"sClass": null,
	
		/**
		 * When DataTables calculates the column widths to assign to each column,
		 * it finds the longest string in each column and then constructs a
		 * temporary table and reads the widths from that. The problem with this
		 * is that "mmm" is much wider then "iiii", but the latter is a longer
		 * string - thus the calculation can go wrong (doing it properly and putting
		 * it into an DOM object and measuring that is horribly(!) slow). Thus as
		 * a "work around" we provide this option. It will append its value to the
		 * text that is found to be the longest string for the column - i.e. padding.
		 *  @type string
		 */
		"sContentPadding": null,
	
		/**
		 * Allows a default value to be given for a column's data, and will be used
		 * whenever a null data source is encountered (this can be because mData
		 * is set to null, or because the data source itself is null).
		 *  @type string
		 *  @default null
		 */
		"sDefaultContent": null,
	
		/**
		 * Name for the column, allowing reference to the column by name as well as
		 * by index (needs a lookup to work by name).
		 *  @type string
		 */
		"sName": null,
	
		/**
		 * Custom sorting data type - defines which of the available plug-ins in
		 * afnSortData the custom sorting will use - if any is defined.
		 *  @type string
		 *  @default std
		 */
		"sSortDataType": 'std',
	
		/**
		 * Class to be applied to the header element when sorting on this column
		 *  @type string
		 *  @default null
		 */
		"sSortingClass": null,
	
		/**
		 * Class to be applied to the header element when sorting on this column -
		 * when jQuery UI theming is used.
		 *  @type string
		 *  @default null
		 */
		"sSortingClassJUI": null,
	
		/**
		 * Title of the column - what is seen in the TH element (nTh).
		 *  @type string
		 */
		"sTitle": null,
	
		/**
		 * Column sorting and filtering type
		 *  @type string
		 *  @default null
		 */
		"sType": null,
	
		/**
		 * Width of the column
		 *  @type string
		 *  @default null
		 */
		"sWidth": null,
	
		/**
		 * Width of the column when it was first "encountered"
		 *  @type string
		 *  @default null
		 */
		"sWidthOrig": null
	};
	
	
	/*
	 * Developer note: The properties of the object below are given in Hungarian
	 * notation, that was used as the interface for DataTables prior to v1.10, however
	 * from v1.10 onwards the primary interface is camel case. In order to avoid
	 * breaking backwards compatibility utterly with this change, the Hungarian
	 * version is still, internally the primary interface, but is is not documented
	 * - hence the @name tags in each doc comment. This allows a Javascript function
	 * to create a map from Hungarian notation to camel case (going the other direction
	 * would require each property to be listed, which would add around 3K to the size
	 * of DataTables, while this method is about a 0.5K hit).
	 *
	 * Ultimately this does pave the way for Hungarian notation to be dropped
	 * completely, but that is a massive amount of work and will break current
	 * installs (therefore is on-hold until v2).
	 */
	
	/**
	 * Initialisation options that can be given to DataTables at initialisation
	 * time.
	 *  @namespace
	 */
	DataTable.defaults = {
		/**
		 * An array of data to use for the table, passed in at initialisation which
		 * will be used in preference to any data which is already in the DOM. This is
		 * particularly useful for constructing tables purely in Javascript, for
		 * example with a custom Ajax call.
		 *  @type array
		 *  @default null
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.data
		 *
		 *  @example
		 *    // Using a 2D array data source
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "data": [
		 *          ['Trident', 'Internet Explorer 4.0', 'Win 95+', 4, 'X'],
		 *          ['Trident', 'Internet Explorer 5.0', 'Win 95+', 5, 'C'],
		 *        ],
		 *        "columns": [
		 *          { "title": "Engine" },
		 *          { "title": "Browser" },
		 *          { "title": "Platform" },
		 *          { "title": "Version" },
		 *          { "title": "Grade" }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using an array of objects as a data source (`data`)
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "data": [
		 *          {
		 *            "engine":   "Trident",
		 *            "browser":  "Internet Explorer 4.0",
		 *            "platform": "Win 95+",
		 *            "version":  4,
		 *            "grade":    "X"
		 *          },
		 *          {
		 *            "engine":   "Trident",
		 *            "browser":  "Internet Explorer 5.0",
		 *            "platform": "Win 95+",
		 *            "version":  5,
		 *            "grade":    "C"
		 *          }
		 *        ],
		 *        "columns": [
		 *          { "title": "Engine",   "data": "engine" },
		 *          { "title": "Browser",  "data": "browser" },
		 *          { "title": "Platform", "data": "platform" },
		 *          { "title": "Version",  "data": "version" },
		 *          { "title": "Grade",    "data": "grade" }
		 *        ]
		 *      } );
		 *    } );
		 */
		"aaData": null,
	
	
		/**
		 * If ordering is enabled, then DataTables will perform a first pass sort on
		 * initialisation. You can define which column(s) the sort is performed
		 * upon, and the sorting direction, with this variable. The `sorting` array
		 * should contain an array for each column to be sorted initially containing
		 * the column's index and a direction string ('asc' or 'desc').
		 *  @type array
		 *  @default [[0,'asc']]
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.order
		 *
		 *  @example
		 *    // Sort by 3rd column first, and then 4th column
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "order": [[2,'asc'], [3,'desc']]
		 *      } );
		 *    } );
		 *
		 *    // No initial sorting
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "order": []
		 *      } );
		 *    } );
		 */
		"aaSorting": [[0,'asc']],
	
	
		/**
		 * This parameter is basically identical to the `sorting` parameter, but
		 * cannot be overridden by user interaction with the table. What this means
		 * is that you could have a column (visible or hidden) which the sorting
		 * will always be forced on first - any sorting after that (from the user)
		 * will then be performed as required. This can be useful for grouping rows
		 * together.
		 *  @type array
		 *  @default null
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.orderFixed
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "orderFixed": [[0,'asc']]
		 *      } );
		 *    } )
		 */
		"aaSortingFixed": [],
	
	
		/**
		 * DataTables can be instructed to load data to display in the table from a
		 * Ajax source. This option defines how that Ajax call is made and where to.
		 *
		 * The `ajax` property has three different modes of operation, depending on
		 * how it is defined. These are:
		 *
		 * * `string` - Set the URL from where the data should be loaded from.
		 * * `object` - Define properties for `jQuery.ajax`.
		 * * `function` - Custom data get function
		 *
		 * `string`
		 * --------
		 *
		 * As a string, the `ajax` property simply defines the URL from which
		 * DataTables will load data.
		 *
		 * `object`
		 * --------
		 *
		 * As an object, the parameters in the object are passed to
		 * [jQuery.ajax](http://api.jquery.com/jQuery.ajax/) allowing fine control
		 * of the Ajax request. DataTables has a number of default parameters which
		 * you can override using this option. Please refer to the jQuery
		 * documentation for a full description of the options available, although
		 * the following parameters provide additional options in DataTables or
		 * require special consideration:
		 *
		 * * `data` - As with jQuery, `data` can be provided as an object, but it
		 *   can also be used as a function to manipulate the data DataTables sends
		 *   to the server. The function takes a single parameter, an object of
		 *   parameters with the values that DataTables has readied for sending. An
		 *   object may be returned which will be merged into the DataTables
		 *   defaults, or you can add the items to the object that was passed in and
		 *   not return anything from the function. This supersedes `fnServerParams`
		 *   from DataTables 1.9-.
		 *
		 * * `dataSrc` - By default DataTables will look for the property `data` (or
		 *   `aaData` for compatibility with DataTables 1.9-) when obtaining data
		 *   from an Ajax source or for server-side processing - this parameter
		 *   allows that property to be changed. You can use Javascript dotted
		 *   object notation to get a data source for multiple levels of nesting, or
		 *   it my be used as a function. As a function it takes a single parameter,
		 *   the JSON returned from the server, which can be manipulated as
		 *   required, with the returned value being that used by DataTables as the
		 *   data source for the table. This supersedes `sAjaxDataProp` from
		 *   DataTables 1.9-.
		 *
		 * * `success` - Should not be overridden it is used internally in
		 *   DataTables. To manipulate / transform the data returned by the server
		 *   use `ajax.dataSrc`, or use `ajax` as a function (see below).
		 *
		 * `function`
		 * ----------
		 *
		 * As a function, making the Ajax call is left up to yourself allowing
		 * complete control of the Ajax request. Indeed, if desired, a method other
		 * than Ajax could be used to obtain the required data, such as Web storage
		 * or an AIR database.
		 *
		 * The function is given four parameters and no return is required. The
		 * parameters are:
		 *
		 * 1. _object_ - Data to send to the server
		 * 2. _function_ - Callback function that must be executed when the required
		 *    data has been obtained. That data should be passed into the callback
		 *    as the only parameter
		 * 3. _object_ - DataTables settings object for the table
		 *
		 * Note that this supersedes `fnServerData` from DataTables 1.9-.
		 *
		 *  @type string|object|function
		 *  @default null
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.ajax
		 *  @since 1.10.0
		 *
		 * @example
		 *   // Get JSON data from a file via Ajax.
		 *   // Note DataTables expects data in the form `{ data: [ ...data... ] }` by default).
		 *   $('#example').dataTable( {
		 *     "ajax": "data.json"
		 *   } );
		 *
		 * @example
		 *   // Get JSON data from a file via Ajax, using `dataSrc` to change
		 *   // `data` to `tableData` (i.e. `{ tableData: [ ...data... ] }`)
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "dataSrc": "tableData"
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Get JSON data from a file via Ajax, using `dataSrc` to read data
		 *   // from a plain array rather than an array in an object
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "dataSrc": ""
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Manipulate the data returned from the server - add a link to data
		 *   // (note this can, should, be done using `render` for the column - this
		 *   // is just a simple example of how the data can be manipulated).
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "dataSrc": function ( json ) {
		 *         for ( var i=0, ien=json.length ; i<ien ; i++ ) {
		 *           json[i][0] = '<a href="/message/'+json[i][0]+'>View message</a>';
		 *         }
		 *         return json;
		 *       }
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Add data to the request
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "data": function ( d ) {
		 *         return {
		 *           "extra_search": $('#extra').val()
		 *         };
		 *       }
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Send request as POST
		 *   $('#example').dataTable( {
		 *     "ajax": {
		 *       "url": "data.json",
		 *       "type": "POST"
		 *     }
		 *   } );
		 *
		 * @example
		 *   // Get the data from localStorage (could interface with a form for
		 *   // adding, editing and removing rows).
		 *   $('#example').dataTable( {
		 *     "ajax": function (data, callback, settings) {
		 *       callback(
		 *         JSON.parse( localStorage.getItem('dataTablesData') )
		 *       );
		 *     }
		 *   } );
		 */
		"ajax": null,
	
	
		/**
		 * This parameter allows you to readily specify the entries in the length drop
		 * down menu that DataTables shows when pagination is enabled. It can be
		 * either a 1D array of options which will be used for both the displayed
		 * option and the value, or a 2D array which will use the array in the first
		 * position as the value, and the array in the second position as the
		 * displayed options (useful for language strings such as 'All').
		 *
		 * Note that the `pageLength` property will be automatically set to the
		 * first value given in this array, unless `pageLength` is also provided.
		 *  @type array
		 *  @default [ 10, 25, 50, 100 ]
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.lengthMenu
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "lengthMenu": [[10, 25, 50, -1], [10, 25, 50, "All"]]
		 *      } );
		 *    } );
		 */
		"aLengthMenu": [ 10, 25, 50, 100 ],
	
	
		/**
		 * The `columns` option in the initialisation parameter allows you to define
		 * details about the way individual columns behave. For a full list of
		 * column options that can be set, please see
		 * {@link DataTable.defaults.column}. Note that if you use `columns` to
		 * define your columns, you must have an entry in the array for every single
		 * column that you have in your table (these can be null if you don't which
		 * to specify any options).
		 *  @member
		 *
		 *  @name DataTable.defaults.column
		 */
		"aoColumns": null,
	
		/**
		 * Very similar to `columns`, `columnDefs` allows you to target a specific
		 * column, multiple columns, or all columns, using the `targets` property of
		 * each object in the array. This allows great flexibility when creating
		 * tables, as the `columnDefs` arrays can be of any length, targeting the
		 * columns you specifically want. `columnDefs` may use any of the column
		 * options available: {@link DataTable.defaults.column}, but it _must_
		 * have `targets` defined in each object in the array. Values in the `targets`
		 * array may be:
		 *   <ul>
		 *     <li>a string - class name will be matched on the TH for the column</li>
		 *     <li>0 or a positive integer - column index counting from the left</li>
		 *     <li>a negative integer - column index counting from the right</li>
		 *     <li>the string "_all" - all columns (i.e. assign a default)</li>
		 *   </ul>
		 *  @member
		 *
		 *  @name DataTable.defaults.columnDefs
		 */
		"aoColumnDefs": null,
	
	
		/**
		 * Basically the same as `search`, this parameter defines the individual column
		 * filtering state at initialisation time. The array must be of the same size
		 * as the number of columns, and each element be an object with the parameters
		 * `search` and `escapeRegex` (the latter is optional). 'null' is also
		 * accepted and the default will be used.
		 *  @type array
		 *  @default []
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.searchCols
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "searchCols": [
		 *          null,
		 *          { "search": "My filter" },
		 *          null,
		 *          { "search": "^[0-9]", "escapeRegex": false }
		 *        ]
		 *      } );
		 *    } )
		 */
		"aoSearchCols": [],
	
	
		/**
		 * An array of CSS classes that should be applied to displayed rows. This
		 * array may be of any length, and DataTables will apply each class
		 * sequentially, looping when required.
		 *  @type array
		 *  @default null <i>Will take the values determined by the `oClasses.stripe*`
		 *    options</i>
		 *
		 *  @dtopt Option
		 *  @name DataTable.defaults.stripeClasses
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stripeClasses": [ 'strip1', 'strip2', 'strip3' ]
		 *      } );
		 *    } )
		 */
		"asStripeClasses": null,
	
	
		/**
		 * Enable or disable automatic column width calculation. This can be disabled
		 * as an optimisation (it takes some time to calculate the widths) if the
		 * tables widths are passed in using `columns`.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.autoWidth
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "autoWidth": false
		 *      } );
		 *    } );
		 */
		"bAutoWidth": true,
	
	
		/**
		 * Deferred rendering can provide DataTables with a huge speed boost when you
		 * are using an Ajax or JS data source for the table. This option, when set to
		 * true, will cause DataTables to defer the creation of the table elements for
		 * each row until they are needed for a draw - saving a significant amount of
		 * time.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.deferRender
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "ajax": "sources/arrays.txt",
		 *        "deferRender": true
		 *      } );
		 *    } );
		 */
		"bDeferRender": false,
	
	
		/**
		 * Replace a DataTable which matches the given selector and replace it with
		 * one which has the properties of the new initialisation object passed. If no
		 * table matches the selector, then the new DataTable will be constructed as
		 * per normal.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.destroy
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "srollY": "200px",
		 *        "paginate": false
		 *      } );
		 *
		 *      // Some time later....
		 *      $('#example').dataTable( {
		 *        "filter": false,
		 *        "destroy": true
		 *      } );
		 *    } );
		 */
		"bDestroy": false,
	
	
		/**
		 * Enable or disable filtering of data. Filtering in DataTables is "smart" in
		 * that it allows the end user to input multiple words (space separated) and
		 * will match a row containing those words, even if not in the order that was
		 * specified (this allow matching across multiple columns). Note that if you
		 * wish to use filtering in DataTables this must remain 'true' - to remove the
		 * default filtering input box and retain filtering abilities, please use
		 * {@link DataTable.defaults.dom}.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.searching
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "searching": false
		 *      } );
		 *    } );
		 */
		"bFilter": true,
	
	
		/**
		 * Enable or disable the table information display. This shows information
		 * about the data that is currently visible on the page, including information
		 * about filtered data if that action is being performed.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.info
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "info": false
		 *      } );
		 *    } );
		 */
		"bInfo": true,
	
	
		/**
		 * Allows the end user to select the size of a formatted page from a select
		 * menu (sizes are 10, 25, 50 and 100). Requires pagination (`paginate`).
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.lengthChange
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "lengthChange": false
		 *      } );
		 *    } );
		 */
		"bLengthChange": true,
	
	
		/**
		 * Enable or disable pagination.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.paging
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "paging": false
		 *      } );
		 *    } );
		 */
		"bPaginate": true,
	
	
		/**
		 * Enable or disable the display of a 'processing' indicator when the table is
		 * being processed (e.g. a sort). This is particularly useful for tables with
		 * large amounts of data where it can take a noticeable amount of time to sort
		 * the entries.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.processing
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "processing": true
		 *      } );
		 *    } );
		 */
		"bProcessing": false,
	
	
		/**
		 * Retrieve the DataTables object for the given selector. Note that if the
		 * table has already been initialised, this parameter will cause DataTables
		 * to simply return the object that has already been set up - it will not take
		 * account of any changes you might have made to the initialisation object
		 * passed to DataTables (setting this parameter to true is an acknowledgement
		 * that you understand this). `destroy` can be used to reinitialise a table if
		 * you need.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.retrieve
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      initTable();
		 *      tableActions();
		 *    } );
		 *
		 *    function initTable ()
		 *    {
		 *      return $('#example').dataTable( {
		 *        "scrollY": "200px",
		 *        "paginate": false,
		 *        "retrieve": true
		 *      } );
		 *    }
		 *
		 *    function tableActions ()
		 *    {
		 *      var table = initTable();
		 *      // perform API operations with oTable
		 *    }
		 */
		"bRetrieve": false,
	
	
		/**
		 * When vertical (y) scrolling is enabled, DataTables will force the height of
		 * the table's viewport to the given height at all times (useful for layout).
		 * However, this can look odd when filtering data down to a small data set,
		 * and the footer is left "floating" further down. This parameter (when
		 * enabled) will cause DataTables to collapse the table's viewport down when
		 * the result set will fit within the given Y height.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.scrollCollapse
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "scrollY": "200",
		 *        "scrollCollapse": true
		 *      } );
		 *    } );
		 */
		"bScrollCollapse": false,
	
	
		/**
		 * Configure DataTables to use server-side processing. Note that the
		 * `ajax` parameter must also be given in order to give DataTables a
		 * source to obtain the required data for each draw.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Features
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.serverSide
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "serverSide": true,
		 *        "ajax": "xhr.php"
		 *      } );
		 *    } );
		 */
		"bServerSide": false,
	
	
		/**
		 * Enable or disable sorting of columns. Sorting of individual columns can be
		 * disabled by the `sortable` option for each column.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.ordering
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "ordering": false
		 *      } );
		 *    } );
		 */
		"bSort": true,
	
	
		/**
		 * Enable or display DataTables' ability to sort multiple columns at the
		 * same time (activated by shift-click by the user).
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.orderMulti
		 *
		 *  @example
		 *    // Disable multiple column sorting ability
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "orderMulti": false
		 *      } );
		 *    } );
		 */
		"bSortMulti": true,
	
	
		/**
		 * Allows control over whether DataTables should use the top (true) unique
		 * cell that is found for a single column, or the bottom (false - default).
		 * This is useful when using complex headers.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.orderCellsTop
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "orderCellsTop": true
		 *      } );
		 *    } );
		 */
		"bSortCellsTop": false,
	
	
		/**
		 * Enable or disable the addition of the classes `sorting\_1`, `sorting\_2` and
		 * `sorting\_3` to the columns which are currently being sorted on. This is
		 * presented as a feature switch as it can increase processing time (while
		 * classes are removed and added) so for large data sets you might want to
		 * turn this off.
		 *  @type boolean
		 *  @default true
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.orderClasses
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "orderClasses": false
		 *      } );
		 *    } );
		 */
		"bSortClasses": true,
	
	
		/**
		 * Enable or disable state saving. When enabled HTML5 `localStorage` will be
		 * used to save table display information such as pagination information,
		 * display length, filtering and sorting. As such when the end user reloads
		 * the page the display display will match what thy had previously set up.
		 *
		 * Due to the use of `localStorage` the default state saving is not supported
		 * in IE6 or 7. If state saving is required in those browsers, use
		 * `stateSaveCallback` to provide a storage solution such as cookies.
		 *  @type boolean
		 *  @default false
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.stateSave
		 *
		 *  @example
		 *    $(document).ready( function () {
		 *      $('#example').dataTable( {
		 *        "stateSave": true
		 *      } );
		 *    } );
		 */
		"bStateSave": false,
	
	
		/**
		 * This function is called when a TR element is created (and all TD child
		 * elements have been inserted), or registered if using a DOM source, allowing
		 * manipulation of the TR element (adding classes etc).
		 *  @type function
		 *  @param {node} row "TR" element for the current row
		 *  @param {array} data Raw data array for this row
		 *  @param {int} dataIndex The index of this row in the internal aoData array
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.createdRow
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "createdRow": function( row, data, dataIndex ) {
		 *          // Bold the grade for all 'A' grade browsers
		 *          if ( data[4] == "A" )
		 *          {
		 *            $('td:eq(4)', row).html( '<b>A</b>' );
		 *          }
		 *        }
		 *      } );
		 *    } );
		 */
		"fnCreatedRow": null,
	
	
		/**
		 * This function is called on every 'draw' event, and allows you to
		 * dynamically modify any aspect you want about the created DOM.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.drawCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "drawCallback": function( settings ) {
		 *          alert( 'DataTables has redrawn the table' );
		 *        }
		 *      } );
		 *    } );
		 */
		"fnDrawCallback": null,
	
	
		/**
		 * Identical to fnHeaderCallback() but for the table footer this function
		 * allows you to modify the table footer on every 'draw' event.
		 *  @type function
		 *  @param {node} foot "TR" element for the footer
		 *  @param {array} data Full table data (as derived from the original HTML)
		 *  @param {int} start Index for the current display starting point in the
		 *    display array
		 *  @param {int} end Index for the current display ending point in the
		 *    display array
		 *  @param {array int} display Index array to translate the visual position
		 *    to the full data array
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.footerCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "footerCallback": function( tfoot, data, start, end, display ) {
		 *          tfoot.getElementsByTagName('th')[0].innerHTML = "Starting index is "+start;
		 *        }
		 *      } );
		 *    } )
		 */
		"fnFooterCallback": null,
	
	
		/**
		 * When rendering large numbers in the information element for the table
		 * (i.e. "Showing 1 to 10 of 57 entries") DataTables will render large numbers
		 * to have a comma separator for the 'thousands' units (e.g. 1 million is
		 * rendered as "1,000,000") to help readability for the end user. This
		 * function will override the default method DataTables uses.
		 *  @type function
		 *  @member
		 *  @param {int} toFormat number to be formatted
		 *  @returns {string} formatted string for DataTables to show the number
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.formatNumber
		 *
		 *  @example
		 *    // Format a number using a single quote for the separator (note that
		 *    // this can also be done with the language.thousands option)
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "formatNumber": function ( toFormat ) {
		 *          return toFormat.toString().replace(
		 *            /\B(?=(\d{3})+(?!\d))/g, "'"
		 *          );
		 *        };
		 *      } );
		 *    } );
		 */
		"fnFormatNumber": function ( toFormat ) {
			return toFormat.toString().replace(
				/\B(?=(\d{3})+(?!\d))/g,
				this.oLanguage.sThousands
			);
		},
	
	
		/**
		 * This function is called on every 'draw' event, and allows you to
		 * dynamically modify the header row. This can be used to calculate and
		 * display useful information about the table.
		 *  @type function
		 *  @param {node} head "TR" element for the header
		 *  @param {array} data Full table data (as derived from the original HTML)
		 *  @param {int} start Index for the current display starting point in the
		 *    display array
		 *  @param {int} end Index for the current display ending point in the
		 *    display array
		 *  @param {array int} display Index array to translate the visual position
		 *    to the full data array
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.headerCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "fheaderCallback": function( head, data, start, end, display ) {
		 *          head.getElementsByTagName('th')[0].innerHTML = "Displaying "+(end-start)+" records";
		 *        }
		 *      } );
		 *    } )
		 */
		"fnHeaderCallback": null,
	
	
		/**
		 * The information element can be used to convey information about the current
		 * state of the table. Although the internationalisation options presented by
		 * DataTables are quite capable of dealing with most customisations, there may
		 * be times where you wish to customise the string further. This callback
		 * allows you to do exactly that.
		 *  @type function
		 *  @param {object} oSettings DataTables settings object
		 *  @param {int} start Starting position in data for the draw
		 *  @param {int} end End position in data for the draw
		 *  @param {int} max Total number of rows in the table (regardless of
		 *    filtering)
		 *  @param {int} total Total number of rows in the data set, after filtering
		 *  @param {string} pre The string that DataTables has formatted using it's
		 *    own rules
		 *  @returns {string} The string to be displayed in the information element.
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.infoCallback
		 *
		 *  @example
		 *    $('#example').dataTable( {
		 *      "infoCallback": function( settings, start, end, max, total, pre ) {
		 *        return start +" to "+ end;
		 *      }
		 *    } );
		 */
		"fnInfoCallback": null,
	
	
		/**
		 * Called when the table has been initialised. Normally DataTables will
		 * initialise sequentially and there will be no need for this function,
		 * however, this does not hold true when using external language information
		 * since that is obtained using an async XHR call.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @param {object} json The JSON object request from the server - only
		 *    present if client-side Ajax sourced data is used
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.initComplete
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "initComplete": function(settings, json) {
		 *          alert( 'DataTables has finished its initialisation.' );
		 *        }
		 *      } );
		 *    } )
		 */
		"fnInitComplete": null,
	
	
		/**
		 * Called at the very start of each table draw and can be used to cancel the
		 * draw by returning false, any other return (including undefined) results in
		 * the full draw occurring).
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @returns {boolean} False will cancel the draw, anything else (including no
		 *    return) will allow it to complete.
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.preDrawCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "preDrawCallback": function( settings ) {
		 *          if ( $('#test').val() == 1 ) {
		 *            return false;
		 *          }
		 *        }
		 *      } );
		 *    } );
		 */
		"fnPreDrawCallback": null,
	
	
		/**
		 * This function allows you to 'post process' each row after it have been
		 * generated for each table draw, but before it is rendered on screen. This
		 * function might be used for setting the row class name etc.
		 *  @type function
		 *  @param {node} row "TR" element for the current row
		 *  @param {array} data Raw data array for this row
		 *  @param {int} displayIndex The display index for the current table draw
		 *  @param {int} displayIndexFull The index of the data in the full list of
		 *    rows (after filtering)
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.rowCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "rowCallback": function( row, data, displayIndex, displayIndexFull ) {
		 *          // Bold the grade for all 'A' grade browsers
		 *          if ( data[4] == "A" ) {
		 *            $('td:eq(4)', row).html( '<b>A</b>' );
		 *          }
		 *        }
		 *      } );
		 *    } );
		 */
		"fnRowCallback": null,
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 * This parameter allows you to override the default function which obtains
		 * the data from the server so something more suitable for your application.
		 * For example you could use POST data, or pull information from a Gears or
		 * AIR database.
		 *  @type function
		 *  @member
		 *  @param {string} source HTTP source to obtain the data from (`ajax`)
		 *  @param {array} data A key/value pair object containing the data to send
		 *    to the server
		 *  @param {function} callback to be called on completion of the data get
		 *    process that will draw the data on the page.
		 *  @param {object} settings DataTables settings object
		 *
		 *  @dtopt Callbacks
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.serverData
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"fnServerData": null,
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 *  It is often useful to send extra data to the server when making an Ajax
		 * request - for example custom filtering information, and this callback
		 * function makes it trivial to send extra information to the server. The
		 * passed in parameter is the data set that has been constructed by
		 * DataTables, and you can add to this or modify it as you require.
		 *  @type function
		 *  @param {array} data Data array (array of objects which are name/value
		 *    pairs) that has been constructed by DataTables and will be sent to the
		 *    server. In the case of Ajax sourced data with server-side processing
		 *    this will be an empty array, for server-side processing there will be a
		 *    significant number of parameters!
		 *  @returns {undefined} Ensure that you modify the data array passed in,
		 *    as this is passed by reference.
		 *
		 *  @dtopt Callbacks
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.serverParams
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"fnServerParams": null,
	
	
		/**
		 * Load the table state. With this function you can define from where, and how, the
		 * state of a table is loaded. By default DataTables will load from `localStorage`
		 * but you might wish to use a server-side database or cookies.
		 *  @type function
		 *  @member
		 *  @param {object} settings DataTables settings object
		 *  @param {object} callback Callback that can be executed when done. It
		 *    should be passed the loaded state object.
		 *  @return {object} The DataTables state object to be loaded
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateLoadCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateLoadCallback": function (settings, callback) {
		 *          $.ajax( {
		 *            "url": "/state_load",
		 *            "dataType": "json",
		 *            "success": function (json) {
		 *              callback( json );
		 *            }
		 *          } );
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateLoadCallback": function ( settings ) {
			try {
				return JSON.parse(
					(settings.iStateDuration === -1 ? sessionStorage : localStorage).getItem(
						'DataTables_'+settings.sInstance+'_'+location.pathname
					)
				);
			} catch (e) {
				return {};
			}
		},
	
	
		/**
		 * Callback which allows modification of the saved state prior to loading that state.
		 * This callback is called when the table is loading state from the stored data, but
		 * prior to the settings object being modified by the saved state. Note that for
		 * plug-in authors, you should use the `stateLoadParams` event to load parameters for
		 * a plug-in.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @param {object} data The state object that is to be loaded
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateLoadParams
		 *
		 *  @example
		 *    // Remove a saved filter, so filtering is never loaded
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateLoadParams": function (settings, data) {
		 *          data.oSearch.sSearch = "";
		 *        }
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Disallow state loading by returning false
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateLoadParams": function (settings, data) {
		 *          return false;
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateLoadParams": null,
	
	
		/**
		 * Callback that is called when the state has been loaded from the state saving method
		 * and the DataTables settings object has been modified as a result of the loaded state.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @param {object} data The state object that was loaded
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateLoaded
		 *
		 *  @example
		 *    // Show an alert with the filtering value that was saved
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateLoaded": function (settings, data) {
		 *          alert( 'Saved filter was: '+data.oSearch.sSearch );
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateLoaded": null,
	
	
		/**
		 * Save the table state. This function allows you to define where and how the state
		 * information for the table is stored By default DataTables will use `localStorage`
		 * but you might wish to use a server-side database or cookies.
		 *  @type function
		 *  @member
		 *  @param {object} settings DataTables settings object
		 *  @param {object} data The state object to be saved
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateSaveCallback
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateSaveCallback": function (settings, data) {
		 *          // Send an Ajax request to the server with the state object
		 *          $.ajax( {
		 *            "url": "/state_save",
		 *            "data": data,
		 *            "dataType": "json",
		 *            "method": "POST"
		 *            "success": function () {}
		 *          } );
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateSaveCallback": function ( settings, data ) {
			try {
				(settings.iStateDuration === -1 ? sessionStorage : localStorage).setItem(
					'DataTables_'+settings.sInstance+'_'+location.pathname,
					JSON.stringify( data )
				);
			} catch (e) {}
		},
	
	
		/**
		 * Callback which allows modification of the state to be saved. Called when the table
		 * has changed state a new state save is required. This method allows modification of
		 * the state saving object prior to actually doing the save, including addition or
		 * other state properties or modification. Note that for plug-in authors, you should
		 * use the `stateSaveParams` event to save parameters for a plug-in.
		 *  @type function
		 *  @param {object} settings DataTables settings object
		 *  @param {object} data The state object to be saved
		 *
		 *  @dtopt Callbacks
		 *  @name DataTable.defaults.stateSaveParams
		 *
		 *  @example
		 *    // Remove a saved filter, so filtering is never saved
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateSave": true,
		 *        "stateSaveParams": function (settings, data) {
		 *          data.oSearch.sSearch = "";
		 *        }
		 *      } );
		 *    } );
		 */
		"fnStateSaveParams": null,
	
	
		/**
		 * Duration for which the saved state information is considered valid. After this period
		 * has elapsed the state will be returned to the default.
		 * Value is given in seconds.
		 *  @type int
		 *  @default 7200 <i>(2 hours)</i>
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.stateDuration
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "stateDuration": 60*60*24; // 1 day
		 *      } );
		 *    } )
		 */
		"iStateDuration": 7200,
	
	
		/**
		 * When enabled DataTables will not make a request to the server for the first
		 * page draw - rather it will use the data already on the page (no sorting etc
		 * will be applied to it), thus saving on an XHR at load time. `deferLoading`
		 * is used to indicate that deferred loading is required, but it is also used
		 * to tell DataTables how many records there are in the full table (allowing
		 * the information element and pagination to be displayed correctly). In the case
		 * where a filtering is applied to the table on initial load, this can be
		 * indicated by giving the parameter as an array, where the first element is
		 * the number of records available after filtering and the second element is the
		 * number of records without filtering (allowing the table information element
		 * to be shown correctly).
		 *  @type int | array
		 *  @default null
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.deferLoading
		 *
		 *  @example
		 *    // 57 records available in the table, no filtering applied
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "serverSide": true,
		 *        "ajax": "scripts/server_processing.php",
		 *        "deferLoading": 57
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // 57 records after filtering, 100 without filtering (an initial filter applied)
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "serverSide": true,
		 *        "ajax": "scripts/server_processing.php",
		 *        "deferLoading": [ 57, 100 ],
		 *        "search": {
		 *          "search": "my_filter"
		 *        }
		 *      } );
		 *    } );
		 */
		"iDeferLoading": null,
	
	
		/**
		 * Number of rows to display on a single page when using pagination. If
		 * feature enabled (`lengthChange`) then the end user will be able to override
		 * this to a custom setting using a pop-up menu.
		 *  @type int
		 *  @default 10
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.pageLength
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "pageLength": 50
		 *      } );
		 *    } )
		 */
		"iDisplayLength": 10,
	
	
		/**
		 * Define the starting point for data display when using DataTables with
		 * pagination. Note that this parameter is the number of records, rather than
		 * the page number, so if you have 10 records per page and want to start on
		 * the third page, it should be "20".
		 *  @type int
		 *  @default 0
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.displayStart
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "displayStart": 20
		 *      } );
		 *    } )
		 */
		"iDisplayStart": 0,
	
	
		/**
		 * By default DataTables allows keyboard navigation of the table (sorting, paging,
		 * and filtering) by adding a `tabindex` attribute to the required elements. This
		 * allows you to tab through the controls and press the enter key to activate them.
		 * The tabindex is default 0, meaning that the tab follows the flow of the document.
		 * You can overrule this using this parameter if you wish. Use a value of -1 to
		 * disable built-in keyboard navigation.
		 *  @type int
		 *  @default 0
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.tabIndex
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "tabIndex": 1
		 *      } );
		 *    } );
		 */
		"iTabIndex": 0,
	
	
		/**
		 * Classes that DataTables assigns to the various components and features
		 * that it adds to the HTML table. This allows classes to be configured
		 * during initialisation in addition to through the static
		 * {@link DataTable.ext.oStdClasses} object).
		 *  @namespace
		 *  @name DataTable.defaults.classes
		 */
		"oClasses": {},
	
	
		/**
		 * All strings that DataTables uses in the user interface that it creates
		 * are defined in this object, allowing you to modified them individually or
		 * completely replace them all as required.
		 *  @namespace
		 *  @name DataTable.defaults.language
		 */
		"oLanguage": {
			/**
			 * Strings that are used for WAI-ARIA labels and controls only (these are not
			 * actually visible on the page, but will be read by screenreaders, and thus
			 * must be internationalised as well).
			 *  @namespace
			 *  @name DataTable.defaults.language.aria
			 */
			"oAria": {
				/**
				 * ARIA label that is added to the table headers when the column may be
				 * sorted ascending by activing the column (click or return when focused).
				 * Note that the column header is prefixed to this string.
				 *  @type string
				 *  @default : activate to sort column ascending
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.aria.sortAscending
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "aria": {
				 *            "sortAscending": " - click/return to sort ascending"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sSortAscending": ": activate to sort column ascending",
	
				/**
				 * ARIA label that is added to the table headers when the column may be
				 * sorted descending by activing the column (click or return when focused).
				 * Note that the column header is prefixed to this string.
				 *  @type string
				 *  @default : activate to sort column ascending
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.aria.sortDescending
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "aria": {
				 *            "sortDescending": " - click/return to sort descending"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sSortDescending": ": activate to sort column descending"
			},
	
			/**
			 * Pagination string used by DataTables for the built-in pagination
			 * control types.
			 *  @namespace
			 *  @name DataTable.defaults.language.paginate
			 */
			"oPaginate": {
				/**
				 * Text to use when using the 'full_numbers' type of pagination for the
				 * button to take the user to the first page.
				 *  @type string
				 *  @default First
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.paginate.first
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "paginate": {
				 *            "first": "First page"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sFirst": "First",
	
	
				/**
				 * Text to use when using the 'full_numbers' type of pagination for the
				 * button to take the user to the last page.
				 *  @type string
				 *  @default Last
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.paginate.last
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "paginate": {
				 *            "last": "Last page"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sLast": "Last",
	
	
				/**
				 * Text to use for the 'next' pagination button (to take the user to the
				 * next page).
				 *  @type string
				 *  @default Next
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.paginate.next
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "paginate": {
				 *            "next": "Next page"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sNext": "Next",
	
	
				/**
				 * Text to use for the 'previous' pagination button (to take the user to
				 * the previous page).
				 *  @type string
				 *  @default Previous
				 *
				 *  @dtopt Language
				 *  @name DataTable.defaults.language.paginate.previous
				 *
				 *  @example
				 *    $(document).ready( function() {
				 *      $('#example').dataTable( {
				 *        "language": {
				 *          "paginate": {
				 *            "previous": "Previous page"
				 *          }
				 *        }
				 *      } );
				 *    } );
				 */
				"sPrevious": "Previous"
			},
	
			/**
			 * This string is shown in preference to `zeroRecords` when the table is
			 * empty of data (regardless of filtering). Note that this is an optional
			 * parameter - if it is not given, the value of `zeroRecords` will be used
			 * instead (either the default or given value).
			 *  @type string
			 *  @default No data available in table
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.emptyTable
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "emptyTable": "No data available in table"
			 *        }
			 *      } );
			 *    } );
			 */
			"sEmptyTable": "No data available in table",
	
	
			/**
			 * This string gives information to the end user about the information
			 * that is current on display on the page. The following tokens can be
			 * used in the string and will be dynamically replaced as the table
			 * display updates. This tokens can be placed anywhere in the string, or
			 * removed as needed by the language requires:
			 *
			 * * `\_START\_` - Display index of the first record on the current page
			 * * `\_END\_` - Display index of the last record on the current page
			 * * `\_TOTAL\_` - Number of records in the table after filtering
			 * * `\_MAX\_` - Number of records in the table without filtering
			 * * `\_PAGE\_` - Current page number
			 * * `\_PAGES\_` - Total number of pages of data in the table
			 *
			 *  @type string
			 *  @default Showing _START_ to _END_ of _TOTAL_ entries
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.info
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "info": "Showing page _PAGE_ of _PAGES_"
			 *        }
			 *      } );
			 *    } );
			 */
			"sInfo": "Showing _START_ to _END_ of _TOTAL_ entries",
	
	
			/**
			 * Display information string for when the table is empty. Typically the
			 * format of this string should match `info`.
			 *  @type string
			 *  @default Showing 0 to 0 of 0 entries
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.infoEmpty
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "infoEmpty": "No entries to show"
			 *        }
			 *      } );
			 *    } );
			 */
			"sInfoEmpty": "Showing 0 to 0 of 0 entries",
	
	
			/**
			 * When a user filters the information in a table, this string is appended
			 * to the information (`info`) to give an idea of how strong the filtering
			 * is. The variable _MAX_ is dynamically updated.
			 *  @type string
			 *  @default (filtered from _MAX_ total entries)
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.infoFiltered
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "infoFiltered": " - filtering from _MAX_ records"
			 *        }
			 *      } );
			 *    } );
			 */
			"sInfoFiltered": "(filtered from _MAX_ total entries)",
	
	
			/**
			 * If can be useful to append extra information to the info string at times,
			 * and this variable does exactly that. This information will be appended to
			 * the `info` (`infoEmpty` and `infoFiltered` in whatever combination they are
			 * being used) at all times.
			 *  @type string
			 *  @default <i>Empty string</i>
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.infoPostFix
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "infoPostFix": "All records shown are derived from real information."
			 *        }
			 *      } );
			 *    } );
			 */
			"sInfoPostFix": "",
	
	
			/**
			 * This decimal place operator is a little different from the other
			 * language options since DataTables doesn't output floating point
			 * numbers, so it won't ever use this for display of a number. Rather,
			 * what this parameter does is modify the sort methods of the table so
			 * that numbers which are in a format which has a character other than
			 * a period (`.`) as a decimal place will be sorted numerically.
			 *
			 * Note that numbers with different decimal places cannot be shown in
			 * the same table and still be sortable, the table must be consistent.
			 * However, multiple different tables on the page can use different
			 * decimal place characters.
			 *  @type string
			 *  @default 
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.decimal
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "decimal": ","
			 *          "thousands": "."
			 *        }
			 *      } );
			 *    } );
			 */
			"sDecimal": "",
	
	
			/**
			 * DataTables has a build in number formatter (`formatNumber`) which is
			 * used to format large numbers that are used in the table information.
			 * By default a comma is used, but this can be trivially changed to any
			 * character you wish with this parameter.
			 *  @type string
			 *  @default ,
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.thousands
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "thousands": "'"
			 *        }
			 *      } );
			 *    } );
			 */
			"sThousands": ",",
	
	
			/**
			 * Detail the action that will be taken when the drop down menu for the
			 * pagination length option is changed. The '_MENU_' variable is replaced
			 * with a default select list of 10, 25, 50 and 100, and can be replaced
			 * with a custom select box if required.
			 *  @type string
			 *  @default Show _MENU_ entries
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.lengthMenu
			 *
			 *  @example
			 *    // Language change only
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "lengthMenu": "Display _MENU_ records"
			 *        }
			 *      } );
			 *    } );
			 *
			 *  @example
			 *    // Language and options change
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "lengthMenu": 'Display <select>'+
			 *            '<option value="10">10</option>'+
			 *            '<option value="20">20</option>'+
			 *            '<option value="30">30</option>'+
			 *            '<option value="40">40</option>'+
			 *            '<option value="50">50</option>'+
			 *            '<option value="-1">All</option>'+
			 *            '</select> records'
			 *        }
			 *      } );
			 *    } );
			 */
			"sLengthMenu": "Show _MENU_ entries",
	
	
			/**
			 * When using Ajax sourced data and during the first draw when DataTables is
			 * gathering the data, this message is shown in an empty row in the table to
			 * indicate to the end user the the data is being loaded. Note that this
			 * parameter is not used when loading data by server-side processing, just
			 * Ajax sourced data with client-side processing.
			 *  @type string
			 *  @default Loading...
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.loadingRecords
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "loadingRecords": "Please wait - loading..."
			 *        }
			 *      } );
			 *    } );
			 */
			"sLoadingRecords": "Loading...",
	
	
			/**
			 * Text which is displayed when the table is processing a user action
			 * (usually a sort command or similar).
			 *  @type string
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.processing
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "processing": "DataTables is currently busy"
			 *        }
			 *      } );
			 *    } );
			 */
			"sProcessing": "",
	
	
			/**
			 * Details the actions that will be taken when the user types into the
			 * filtering input text box. The variable "_INPUT_", if used in the string,
			 * is replaced with the HTML text box for the filtering input allowing
			 * control over where it appears in the string. If "_INPUT_" is not given
			 * then the input box is appended to the string automatically.
			 *  @type string
			 *  @default Search:
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.search
			 *
			 *  @example
			 *    // Input text box will be appended at the end automatically
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "search": "Filter records:"
			 *        }
			 *      } );
			 *    } );
			 *
			 *  @example
			 *    // Specify where the filter should appear
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "search": "Apply filter _INPUT_ to table"
			 *        }
			 *      } );
			 *    } );
			 */
			"sSearch": "Search:",
	
	
			/**
			 * Assign a `placeholder` attribute to the search `input` element
			 *  @type string
			 *  @default 
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.searchPlaceholder
			 */
			"sSearchPlaceholder": "",
	
	
			/**
			 * All of the language information can be stored in a file on the
			 * server-side, which DataTables will look up if this parameter is passed.
			 * It must store the URL of the language file, which is in a JSON format,
			 * and the object has the same properties as the oLanguage object in the
			 * initialiser object (i.e. the above parameters). Please refer to one of
			 * the example language files to see how this works in action.
			 *  @type string
			 *  @default <i>Empty string - i.e. disabled</i>
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.url
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "url": "http://www.sprymedia.co.uk/dataTables/lang.txt"
			 *        }
			 *      } );
			 *    } );
			 */
			"sUrl": "",
	
	
			/**
			 * Text shown inside the table records when the is no information to be
			 * displayed after filtering. `emptyTable` is shown when there is simply no
			 * information in the table at all (regardless of filtering).
			 *  @type string
			 *  @default No matching records found
			 *
			 *  @dtopt Language
			 *  @name DataTable.defaults.language.zeroRecords
			 *
			 *  @example
			 *    $(document).ready( function() {
			 *      $('#example').dataTable( {
			 *        "language": {
			 *          "zeroRecords": "No records to display"
			 *        }
			 *      } );
			 *    } );
			 */
			"sZeroRecords": "No matching records found"
		},
	
	
		/**
		 * This parameter allows you to have define the global filtering state at
		 * initialisation time. As an object the `search` parameter must be
		 * defined, but all other parameters are optional. When `regex` is true,
		 * the search string will be treated as a regular expression, when false
		 * (default) it will be treated as a straight string. When `smart`
		 * DataTables will use it's smart filtering methods (to word match at
		 * any point in the data), when false this will not be done.
		 *  @namespace
		 *  @extends DataTable.models.oSearch
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.search
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "search": {"search": "Initial search"}
		 *      } );
		 *    } )
		 */
		"oSearch": $.extend( {}, DataTable.models.oSearch ),
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 * By default DataTables will look for the property `data` (or `aaData` for
		 * compatibility with DataTables 1.9-) when obtaining data from an Ajax
		 * source or for server-side processing - this parameter allows that
		 * property to be changed. You can use Javascript dotted object notation to
		 * get a data source for multiple levels of nesting.
		 *  @type string
		 *  @default data
		 *
		 *  @dtopt Options
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.ajaxDataProp
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"sAjaxDataProp": "data",
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 * You can instruct DataTables to load data from an external
		 * source using this parameter (use aData if you want to pass data in you
		 * already have). Simply provide a url a JSON object can be obtained from.
		 *  @type string
		 *  @default null
		 *
		 *  @dtopt Options
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.ajaxSource
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"sAjaxSource": null,
	
	
		/**
		 * This initialisation variable allows you to specify exactly where in the
		 * DOM you want DataTables to inject the various controls it adds to the page
		 * (for example you might want the pagination controls at the top of the
		 * table). DIV elements (with or without a custom class) can also be added to
		 * aid styling. The follow syntax is used:
		 *   <ul>
		 *     <li>The following options are allowed:
		 *       <ul>
		 *         <li>'l' - Length changing</li>
		 *         <li>'f' - Filtering input</li>
		 *         <li>'t' - The table!</li>
		 *         <li>'i' - Information</li>
		 *         <li>'p' - Pagination</li>
		 *         <li>'r' - pRocessing</li>
		 *       </ul>
		 *     </li>
		 *     <li>The following constants are allowed:
		 *       <ul>
		 *         <li>'H' - jQueryUI theme "header" classes ('fg-toolbar ui-widget-header ui-corner-tl ui-corner-tr ui-helper-clearfix')</li>
		 *         <li>'F' - jQueryUI theme "footer" classes ('fg-toolbar ui-widget-header ui-corner-bl ui-corner-br ui-helper-clearfix')</li>
		 *       </ul>
		 *     </li>
		 *     <li>The following syntax is expected:
		 *       <ul>
		 *         <li>'&lt;' and '&gt;' - div elements</li>
		 *         <li>'&lt;"class" and '&gt;' - div with a class</li>
		 *         <li>'&lt;"#id" and '&gt;' - div with an ID</li>
		 *       </ul>
		 *     </li>
		 *     <li>Examples:
		 *       <ul>
		 *         <li>'&lt;"wrapper"flipt&gt;'</li>
		 *         <li>'&lt;lf&lt;t&gt;ip&gt;'</li>
		 *       </ul>
		 *     </li>
		 *   </ul>
		 *  @type string
		 *  @default lfrtip <i>(when `jQueryUI` is false)</i> <b>or</b>
		 *    <"H"lfr>t<"F"ip> <i>(when `jQueryUI` is true)</i>
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.dom
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "dom": '&lt;"top"i&gt;rt&lt;"bottom"flp&gt;&lt;"clear"&gt;'
		 *      } );
		 *    } );
		 */
		"sDom": "lfrtip",
	
	
		/**
		 * Search delay option. This will throttle full table searches that use the
		 * DataTables provided search input element (it does not effect calls to
		 * `dt-api search()`, providing a delay before the search is made.
		 *  @type integer
		 *  @default 0
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.searchDelay
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "searchDelay": 200
		 *      } );
		 *    } )
		 */
		"searchDelay": null,
	
	
		/**
		 * DataTables features six different built-in options for the buttons to
		 * display for pagination control:
		 *
		 * * `numbers` - Page number buttons only
		 * * `simple` - 'Previous' and 'Next' buttons only
		 * * 'simple_numbers` - 'Previous' and 'Next' buttons, plus page numbers
		 * * `full` - 'First', 'Previous', 'Next' and 'Last' buttons
		 * * `full_numbers` - 'First', 'Previous', 'Next' and 'Last' buttons, plus page numbers
		 * * `first_last_numbers` - 'First' and 'Last' buttons, plus page numbers
		 *  
		 * Further methods can be added using {@link DataTable.ext.oPagination}.
		 *  @type string
		 *  @default simple_numbers
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.pagingType
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "pagingType": "full_numbers"
		 *      } );
		 *    } )
		 */
		"sPaginationType": "simple_numbers",
	
	
		/**
		 * Enable horizontal scrolling. When a table is too wide to fit into a
		 * certain layout, or you have a large number of columns in the table, you
		 * can enable x-scrolling to show the table in a viewport, which can be
		 * scrolled. This property can be `true` which will allow the table to
		 * scroll horizontally when needed, or any CSS unit, or a number (in which
		 * case it will be treated as a pixel measurement). Setting as simply `true`
		 * is recommended.
		 *  @type boolean|string
		 *  @default <i>blank string - i.e. disabled</i>
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.scrollX
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "scrollX": true,
		 *        "scrollCollapse": true
		 *      } );
		 *    } );
		 */
		"sScrollX": "",
	
	
		/**
		 * This property can be used to force a DataTable to use more width than it
		 * might otherwise do when x-scrolling is enabled. For example if you have a
		 * table which requires to be well spaced, this parameter is useful for
		 * "over-sizing" the table, and thus forcing scrolling. This property can by
		 * any CSS unit, or a number (in which case it will be treated as a pixel
		 * measurement).
		 *  @type string
		 *  @default <i>blank string - i.e. disabled</i>
		 *
		 *  @dtopt Options
		 *  @name DataTable.defaults.scrollXInner
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "scrollX": "100%",
		 *        "scrollXInner": "110%"
		 *      } );
		 *    } );
		 */
		"sScrollXInner": "",
	
	
		/**
		 * Enable vertical scrolling. Vertical scrolling will constrain the DataTable
		 * to the given height, and enable scrolling for any data which overflows the
		 * current viewport. This can be used as an alternative to paging to display
		 * a lot of data in a small area (although paging and scrolling can both be
		 * enabled at the same time). This property can be any CSS unit, or a number
		 * (in which case it will be treated as a pixel measurement).
		 *  @type string
		 *  @default <i>blank string - i.e. disabled</i>
		 *
		 *  @dtopt Features
		 *  @name DataTable.defaults.scrollY
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "scrollY": "200px",
		 *        "paginate": false
		 *      } );
		 *    } );
		 */
		"sScrollY": "",
	
	
		/**
		 * __Deprecated__ The functionality provided by this parameter has now been
		 * superseded by that provided through `ajax`, which should be used instead.
		 *
		 * Set the HTTP method that is used to make the Ajax call for server-side
		 * processing or Ajax sourced data.
		 *  @type string
		 *  @default GET
		 *
		 *  @dtopt Options
		 *  @dtopt Server-side
		 *  @name DataTable.defaults.serverMethod
		 *
		 *  @deprecated 1.10. Please use `ajax` for this functionality now.
		 */
		"sServerMethod": "GET",
	
	
		/**
		 * DataTables makes use of renderers when displaying HTML elements for
		 * a table. These renderers can be added or modified by plug-ins to
		 * generate suitable mark-up for a site. For example the Bootstrap
		 * integration plug-in for DataTables uses a paging button renderer to
		 * display pagination buttons in the mark-up required by Bootstrap.
		 *
		 * For further information about the renderers available see
		 * DataTable.ext.renderer
		 *  @type string|object
		 *  @default null
		 *
		 *  @name DataTable.defaults.renderer
		 *
		 */
		"renderer": null,
	
	
		/**
		 * Set the data property name that DataTables should use to get a row's id
		 * to set as the `id` property in the node.
		 *  @type string
		 *  @default DT_RowId
		 *
		 *  @name DataTable.defaults.rowId
		 */
		"rowId": "DT_RowId"
	};
	
	_fnHungarianMap( DataTable.defaults );
	
	
	
	/*
	 * Developer note - See note in model.defaults.js about the use of Hungarian
	 * notation and camel case.
	 */
	
	/**
	 * Column options that can be given to DataTables at initialisation time.
	 *  @namespace
	 */
	DataTable.defaults.column = {
		/**
		 * Define which column(s) an order will occur on for this column. This
		 * allows a column's ordering to take multiple columns into account when
		 * doing a sort or use the data from a different column. For example first
		 * name / last name columns make sense to do a multi-column sort over the
		 * two columns.
		 *  @type array|int
		 *  @default null <i>Takes the value of the column index automatically</i>
		 *
		 *  @name DataTable.defaults.column.orderData
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "orderData": [ 0, 1 ], "targets": [ 0 ] },
		 *          { "orderData": [ 1, 0 ], "targets": [ 1 ] },
		 *          { "orderData": 2, "targets": [ 2 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "orderData": [ 0, 1 ] },
		 *          { "orderData": [ 1, 0 ] },
		 *          { "orderData": 2 },
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"aDataSort": null,
		"iDataSort": -1,
	
	
		/**
		 * You can control the default ordering direction, and even alter the
		 * behaviour of the sort handler (i.e. only allow ascending ordering etc)
		 * using this parameter.
		 *  @type array
		 *  @default [ 'asc', 'desc' ]
		 *
		 *  @name DataTable.defaults.column.orderSequence
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "orderSequence": [ "asc" ], "targets": [ 1 ] },
		 *          { "orderSequence": [ "desc", "asc", "asc" ], "targets": [ 2 ] },
		 *          { "orderSequence": [ "desc" ], "targets": [ 3 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          null,
		 *          { "orderSequence": [ "asc" ] },
		 *          { "orderSequence": [ "desc", "asc", "asc" ] },
		 *          { "orderSequence": [ "desc" ] },
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"asSorting": [ 'asc', 'desc' ],
	
	
		/**
		 * Enable or disable filtering on the data in this column.
		 *  @type boolean
		 *  @default true
		 *
		 *  @name DataTable.defaults.column.searchable
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "searchable": false, "targets": [ 0 ] }
		 *        ] } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "searchable": false },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ] } );
		 *    } );
		 */
		"bSearchable": true,
	
	
		/**
		 * Enable or disable ordering on this column.
		 *  @type boolean
		 *  @default true
		 *
		 *  @name DataTable.defaults.column.orderable
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "orderable": false, "targets": [ 0 ] }
		 *        ] } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "orderable": false },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ] } );
		 *    } );
		 */
		"bSortable": true,
	
	
		/**
		 * Enable or disable the display of this column.
		 *  @type boolean
		 *  @default true
		 *
		 *  @name DataTable.defaults.column.visible
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "visible": false, "targets": [ 0 ] }
		 *        ] } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "visible": false },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ] } );
		 *    } );
		 */
		"bVisible": true,
	
	
		/**
		 * Developer definable function that is called whenever a cell is created (Ajax source,
		 * etc) or processed for input (DOM source). This can be used as a compliment to mRender
		 * allowing you to modify the DOM element (add background colour for example) when the
		 * element is available.
		 *  @type function
		 *  @param {element} td The TD node that has been created
		 *  @param {*} cellData The Data for the cell
		 *  @param {array|object} rowData The data for the whole row
		 *  @param {int} row The row index for the aoData data store
		 *  @param {int} col The column index for aoColumns
		 *
		 *  @name DataTable.defaults.column.createdCell
		 *  @dtopt Columns
		 *
		 *  @example
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [3],
		 *          "createdCell": function (td, cellData, rowData, row, col) {
		 *            if ( cellData == "1.7" ) {
		 *              $(td).css('color', 'blue')
		 *            }
		 *          }
		 *        } ]
		 *      });
		 *    } );
		 */
		"fnCreatedCell": null,
	
	
		/**
		 * This parameter has been replaced by `data` in DataTables to ensure naming
		 * consistency. `dataProp` can still be used, as there is backwards
		 * compatibility in DataTables for this option, but it is strongly
		 * recommended that you use `data` in preference to `dataProp`.
		 *  @name DataTable.defaults.column.dataProp
		 */
	
	
		/**
		 * This property can be used to read data from any data source property,
		 * including deeply nested objects / properties. `data` can be given in a
		 * number of different ways which effect its behaviour:
		 *
		 * * `integer` - treated as an array index for the data source. This is the
		 *   default that DataTables uses (incrementally increased for each column).
		 * * `string` - read an object property from the data source. There are
		 *   three 'special' options that can be used in the string to alter how
		 *   DataTables reads the data from the source object:
		 *    * `.` - Dotted Javascript notation. Just as you use a `.` in
		 *      Javascript to read from nested objects, so to can the options
		 *      specified in `data`. For example: `browser.version` or
		 *      `browser.name`. If your object parameter name contains a period, use
		 *      `\\` to escape it - i.e. `first\\.name`.
		 *    * `[]` - Array notation. DataTables can automatically combine data
		 *      from and array source, joining the data with the characters provided
		 *      between the two brackets. For example: `name[, ]` would provide a
		 *      comma-space separated list from the source array. If no characters
		 *      are provided between the brackets, the original array source is
		 *      returned.
		 *    * `()` - Function notation. Adding `()` to the end of a parameter will
		 *      execute a function of the name given. For example: `browser()` for a
		 *      simple function on the data source, `browser.version()` for a
		 *      function in a nested property or even `browser().version` to get an
		 *      object property if the function called returns an object. Note that
		 *      function notation is recommended for use in `render` rather than
		 *      `data` as it is much simpler to use as a renderer.
		 * * `null` - use the original data source for the row rather than plucking
		 *   data directly from it. This action has effects on two other
		 *   initialisation options:
		 *    * `defaultContent` - When null is given as the `data` option and
		 *      `defaultContent` is specified for the column, the value defined by
		 *      `defaultContent` will be used for the cell.
		 *    * `render` - When null is used for the `data` option and the `render`
		 *      option is specified for the column, the whole data source for the
		 *      row is used for the renderer.
		 * * `function` - the function given will be executed whenever DataTables
		 *   needs to set or get the data for a cell in the column. The function
		 *   takes three parameters:
		 *    * Parameters:
		 *      * `{array|object}` The data source for the row
		 *      * `{string}` The type call data requested - this will be 'set' when
		 *        setting data or 'filter', 'display', 'type', 'sort' or undefined
		 *        when gathering data. Note that when `undefined` is given for the
		 *        type DataTables expects to get the raw data for the object back<
		 *      * `{*}` Data to set when the second parameter is 'set'.
		 *    * Return:
		 *      * The return value from the function is not required when 'set' is
		 *        the type of call, but otherwise the return is what will be used
		 *        for the data requested.
		 *
		 * Note that `data` is a getter and setter option. If you just require
		 * formatting of data for output, you will likely want to use `render` which
		 * is simply a getter and thus simpler to use.
		 *
		 * Note that prior to DataTables 1.9.2 `data` was called `mDataProp`. The
		 * name change reflects the flexibility of this property and is consistent
		 * with the naming of mRender. If 'mDataProp' is given, then it will still
		 * be used by DataTables, as it automatically maps the old name to the new
		 * if required.
		 *
		 *  @type string|int|function|null
		 *  @default null <i>Use automatically calculated column index</i>
		 *
		 *  @name DataTable.defaults.column.data
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Read table data from objects
		 *    // JSON structure for each row:
		 *    //   {
		 *    //      "engine": {value},
		 *    //      "browser": {value},
		 *    //      "platform": {value},
		 *    //      "version": {value},
		 *    //      "grade": {value}
		 *    //   }
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "ajaxSource": "sources/objects.txt",
		 *        "columns": [
		 *          { "data": "engine" },
		 *          { "data": "browser" },
		 *          { "data": "platform" },
		 *          { "data": "version" },
		 *          { "data": "grade" }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Read information from deeply nested objects
		 *    // JSON structure for each row:
		 *    //   {
		 *    //      "engine": {value},
		 *    //      "browser": {value},
		 *    //      "platform": {
		 *    //         "inner": {value}
		 *    //      },
		 *    //      "details": [
		 *    //         {value}, {value}
		 *    //      ]
		 *    //   }
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "ajaxSource": "sources/deep.txt",
		 *        "columns": [
		 *          { "data": "engine" },
		 *          { "data": "browser" },
		 *          { "data": "platform.inner" },
		 *          { "data": "details.0" },
		 *          { "data": "details.1" }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `data` as a function to provide different information for
		 *    // sorting, filtering and display. In this case, currency (price)
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": function ( source, type, val ) {
		 *            if (type === 'set') {
		 *              source.price = val;
		 *              // Store the computed display and filter values for efficiency
		 *              source.price_display = val=="" ? "" : "$"+numberFormat(val);
		 *              source.price_filter  = val=="" ? "" : "$"+numberFormat(val)+" "+val;
		 *              return;
		 *            }
		 *            else if (type === 'display') {
		 *              return source.price_display;
		 *            }
		 *            else if (type === 'filter') {
		 *              return source.price_filter;
		 *            }
		 *            // 'sort', 'type' and undefined all just use the integer
		 *            return source.price;
		 *          }
		 *        } ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using default content
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": null,
		 *          "defaultContent": "Click to edit"
		 *        } ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using array notation - outputting a list from an array
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": "name[, ]"
		 *        } ]
		 *      } );
		 *    } );
		 *
		 */
		"mData": null,
	
	
		/**
		 * This property is the rendering partner to `data` and it is suggested that
		 * when you want to manipulate data for display (including filtering,
		 * sorting etc) without altering the underlying data for the table, use this
		 * property. `render` can be considered to be the the read only companion to
		 * `data` which is read / write (then as such more complex). Like `data`
		 * this option can be given in a number of different ways to effect its
		 * behaviour:
		 *
		 * * `integer` - treated as an array index for the data source. This is the
		 *   default that DataTables uses (incrementally increased for each column).
		 * * `string` - read an object property from the data source. There are
		 *   three 'special' options that can be used in the string to alter how
		 *   DataTables reads the data from the source object:
		 *    * `.` - Dotted Javascript notation. Just as you use a `.` in
		 *      Javascript to read from nested objects, so to can the options
		 *      specified in `data`. For example: `browser.version` or
		 *      `browser.name`. If your object parameter name contains a period, use
		 *      `\\` to escape it - i.e. `first\\.name`.
		 *    * `[]` - Array notation. DataTables can automatically combine data
		 *      from and array source, joining the data with the characters provided
		 *      between the two brackets. For example: `name[, ]` would provide a
		 *      comma-space separated list from the source array. If no characters
		 *      are provided between the brackets, the original array source is
		 *      returned.
		 *    * `()` - Function notation. Adding `()` to the end of a parameter will
		 *      execute a function of the name given. For example: `browser()` for a
		 *      simple function on the data source, `browser.version()` for a
		 *      function in a nested property or even `browser().version` to get an
		 *      object property if the function called returns an object.
		 * * `object` - use different data for the different data types requested by
		 *   DataTables ('filter', 'display', 'type' or 'sort'). The property names
		 *   of the object is the data type the property refers to and the value can
		 *   defined using an integer, string or function using the same rules as
		 *   `render` normally does. Note that an `_` option _must_ be specified.
		 *   This is the default value to use if you haven't specified a value for
		 *   the data type requested by DataTables.
		 * * `function` - the function given will be executed whenever DataTables
		 *   needs to set or get the data for a cell in the column. The function
		 *   takes three parameters:
		 *    * Parameters:
		 *      * {array|object} The data source for the row (based on `data`)
		 *      * {string} The type call data requested - this will be 'filter',
		 *        'display', 'type' or 'sort'.
		 *      * {array|object} The full data source for the row (not based on
		 *        `data`)
		 *    * Return:
		 *      * The return value from the function is what will be used for the
		 *        data requested.
		 *
		 *  @type string|int|function|object|null
		 *  @default null Use the data source value.
		 *
		 *  @name DataTable.defaults.column.render
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Create a comma separated list from an array of objects
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "ajaxSource": "sources/deep.txt",
		 *        "columns": [
		 *          { "data": "engine" },
		 *          { "data": "browser" },
		 *          {
		 *            "data": "platform",
		 *            "render": "[, ].name"
		 *          }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Execute a function to obtain data
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": null, // Use the full data source object for the renderer's source
		 *          "render": "browserName()"
		 *        } ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // As an object, extracting different data for the different types
		 *    // This would be used with a data source such as:
		 *    //   { "phone": 5552368, "phone_filter": "5552368 555-2368", "phone_display": "555-2368" }
		 *    // Here the `phone` integer is used for sorting and type detection, while `phone_filter`
		 *    // (which has both forms) is used for filtering for if a user inputs either format, while
		 *    // the formatted phone number is the one that is shown in the table.
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": null, // Use the full data source object for the renderer's source
		 *          "render": {
		 *            "_": "phone",
		 *            "filter": "phone_filter",
		 *            "display": "phone_display"
		 *          }
		 *        } ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Use as a function to create a link from the data source
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "data": "download_link",
		 *          "render": function ( data, type, full ) {
		 *            return '<a href="'+data+'">Download</a>';
		 *          }
		 *        } ]
		 *      } );
		 *    } );
		 */
		"mRender": null,
	
	
		/**
		 * Change the cell type created for the column - either TD cells or TH cells. This
		 * can be useful as TH cells have semantic meaning in the table body, allowing them
		 * to act as a header for a row (you may wish to add scope='row' to the TH elements).
		 *  @type string
		 *  @default td
		 *
		 *  @name DataTable.defaults.column.cellType
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Make the first column use TH cells
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [ {
		 *          "targets": [ 0 ],
		 *          "cellType": "th"
		 *        } ]
		 *      } );
		 *    } );
		 */
		"sCellType": "td",
	
	
		/**
		 * Class to give to each cell in this column.
		 *  @type string
		 *  @default <i>Empty string</i>
		 *
		 *  @name DataTable.defaults.column.class
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "class": "my_class", "targets": [ 0 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "class": "my_class" },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"sClass": "",
	
		/**
		 * When DataTables calculates the column widths to assign to each column,
		 * it finds the longest string in each column and then constructs a
		 * temporary table and reads the widths from that. The problem with this
		 * is that "mmm" is much wider then "iiii", but the latter is a longer
		 * string - thus the calculation can go wrong (doing it properly and putting
		 * it into an DOM object and measuring that is horribly(!) slow). Thus as
		 * a "work around" we provide this option. It will append its value to the
		 * text that is found to be the longest string for the column - i.e. padding.
		 * Generally you shouldn't need this!
		 *  @type string
		 *  @default <i>Empty string<i>
		 *
		 *  @name DataTable.defaults.column.contentPadding
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          null,
		 *          null,
		 *          null,
		 *          {
		 *            "contentPadding": "mmm"
		 *          }
		 *        ]
		 *      } );
		 *    } );
		 */
		"sContentPadding": "",
	
	
		/**
		 * Allows a default value to be given for a column's data, and will be used
		 * whenever a null data source is encountered (this can be because `data`
		 * is set to null, or because the data source itself is null).
		 *  @type string
		 *  @default null
		 *
		 *  @name DataTable.defaults.column.defaultContent
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          {
		 *            "data": null,
		 *            "defaultContent": "Edit",
		 *            "targets": [ -1 ]
		 *          }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          null,
		 *          null,
		 *          null,
		 *          {
		 *            "data": null,
		 *            "defaultContent": "Edit"
		 *          }
		 *        ]
		 *      } );
		 *    } );
		 */
		"sDefaultContent": null,
	
	
		/**
		 * This parameter is only used in DataTables' server-side processing. It can
		 * be exceptionally useful to know what columns are being displayed on the
		 * client side, and to map these to database fields. When defined, the names
		 * also allow DataTables to reorder information from the server if it comes
		 * back in an unexpected order (i.e. if you switch your columns around on the
		 * client-side, your server-side code does not also need updating).
		 *  @type string
		 *  @default <i>Empty string</i>
		 *
		 *  @name DataTable.defaults.column.name
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "name": "engine", "targets": [ 0 ] },
		 *          { "name": "browser", "targets": [ 1 ] },
		 *          { "name": "platform", "targets": [ 2 ] },
		 *          { "name": "version", "targets": [ 3 ] },
		 *          { "name": "grade", "targets": [ 4 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "name": "engine" },
		 *          { "name": "browser" },
		 *          { "name": "platform" },
		 *          { "name": "version" },
		 *          { "name": "grade" }
		 *        ]
		 *      } );
		 *    } );
		 */
		"sName": "",
	
	
		/**
		 * Defines a data source type for the ordering which can be used to read
		 * real-time information from the table (updating the internally cached
		 * version) prior to ordering. This allows ordering to occur on user
		 * editable elements such as form inputs.
		 *  @type string
		 *  @default std
		 *
		 *  @name DataTable.defaults.column.orderDataType
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "orderDataType": "dom-text", "targets": [ 2, 3 ] },
		 *          { "type": "numeric", "targets": [ 3 ] },
		 *          { "orderDataType": "dom-select", "targets": [ 4 ] },
		 *          { "orderDataType": "dom-checkbox", "targets": [ 5 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          null,
		 *          null,
		 *          { "orderDataType": "dom-text" },
		 *          { "orderDataType": "dom-text", "type": "numeric" },
		 *          { "orderDataType": "dom-select" },
		 *          { "orderDataType": "dom-checkbox" }
		 *        ]
		 *      } );
		 *    } );
		 */
		"sSortDataType": "std",
	
	
		/**
		 * The title of this column.
		 *  @type string
		 *  @default null <i>Derived from the 'TH' value for this column in the
		 *    original HTML table.</i>
		 *
		 *  @name DataTable.defaults.column.title
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "title": "My column title", "targets": [ 0 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "title": "My column title" },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"sTitle": null,
	
	
		/**
		 * The type allows you to specify how the data for this column will be
		 * ordered. Four types (string, numeric, date and html (which will strip
		 * HTML tags before ordering)) are currently available. Note that only date
		 * formats understood by Javascript's Date() object will be accepted as type
		 * date. For example: "Mar 26, 2008 5:03 PM". May take the values: 'string',
		 * 'numeric', 'date' or 'html' (by default). Further types can be adding
		 * through plug-ins.
		 *  @type string
		 *  @default null <i>Auto-detected from raw data</i>
		 *
		 *  @name DataTable.defaults.column.type
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "type": "html", "targets": [ 0 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "type": "html" },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"sType": null,
	
	
		/**
		 * Defining the width of the column, this parameter may take any CSS value
		 * (3em, 20px etc). DataTables applies 'smart' widths to columns which have not
		 * been given a specific width through this interface ensuring that the table
		 * remains readable.
		 *  @type string
		 *  @default null <i>Automatic</i>
		 *
		 *  @name DataTable.defaults.column.width
		 *  @dtopt Columns
		 *
		 *  @example
		 *    // Using `columnDefs`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columnDefs": [
		 *          { "width": "20%", "targets": [ 0 ] }
		 *        ]
		 *      } );
		 *    } );
		 *
		 *  @example
		 *    // Using `columns`
		 *    $(document).ready( function() {
		 *      $('#example').dataTable( {
		 *        "columns": [
		 *          { "width": "20%" },
		 *          null,
		 *          null,
		 *          null,
		 *          null
		 *        ]
		 *      } );
		 *    } );
		 */
		"sWidth": null
	};
	
	_fnHungarianMap( DataTable.defaults.column );
	
	
	
	/**
	 * DataTables settings object - this holds all the information needed for a
	 * given table, including configuration, data and current application of the
	 * table options. DataTables does not have a single instance for each DataTable
	 * with the settings attached to that instance, but rather instances of the
	 * DataTable "class" are created on-the-fly as needed (typically by a
	 * $().dataTable() call) and the settings object is then applied to that
	 * instance.
	 *
	 * Note that this object is related to {@link DataTable.defaults} but this
	 * one is the internal data store for DataTables's cache of columns. It should
	 * NOT be manipulated outside of DataTables. Any configuration should be done
	 * through the initialisation options.
	 *  @namespace
	 *  @todo Really should attach the settings object to individual instances so we
	 *    don't need to create new instances on each $().dataTable() call (if the
	 *    table already exists). It would also save passing oSettings around and
	 *    into every single function. However, this is a very significant
	 *    architecture change for DataTables and will almost certainly break
	 *    backwards compatibility with older installations. This is something that
	 *    will be done in 2.0.
	 */
	DataTable.models.oSettings = {
		/**
		 * Primary features of DataTables and their enablement state.
		 *  @namespace
		 */
		"oFeatures": {
	
			/**
			 * Flag to say if DataTables should automatically try to calculate the
			 * optimum table and columns widths (true) or not (false).
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bAutoWidth": null,
	
			/**
			 * Delay the creation of TR and TD elements until they are actually
			 * needed by a driven page draw. This can give a significant speed
			 * increase for Ajax source and Javascript source data, but makes no
			 * difference at all for DOM and server-side processing tables.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bDeferRender": null,
	
			/**
			 * Enable filtering on the table or not. Note that if this is disabled
			 * then there is no filtering at all on the table, including fnFilter.
			 * To just remove the filtering input use sDom and remove the 'f' option.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bFilter": null,
	
			/**
			 * Table information element (the 'Showing x of y records' div) enable
			 * flag.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bInfo": null,
	
			/**
			 * Present a user control allowing the end user to change the page size
			 * when pagination is enabled.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bLengthChange": null,
	
			/**
			 * Pagination enabled or not. Note that if this is disabled then length
			 * changing must also be disabled.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bPaginate": null,
	
			/**
			 * Processing indicator enable flag whenever DataTables is enacting a
			 * user request - typically an Ajax request for server-side processing.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bProcessing": null,
	
			/**
			 * Server-side processing enabled flag - when enabled DataTables will
			 * get all data from the server for every draw - there is no filtering,
			 * sorting or paging done on the client-side.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bServerSide": null,
	
			/**
			 * Sorting enablement flag.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bSort": null,
	
			/**
			 * Multi-column sorting
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bSortMulti": null,
	
			/**
			 * Apply a class to the columns which are being sorted to provide a
			 * visual highlight or not. This can slow things down when enabled since
			 * there is a lot of DOM interaction.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bSortClasses": null,
	
			/**
			 * State saving enablement flag.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bStateSave": null
		},
	
	
		/**
		 * Scrolling settings for a table.
		 *  @namespace
		 */
		"oScroll": {
			/**
			 * When the table is shorter in height than sScrollY, collapse the
			 * table container down to the height of the table (when true).
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type boolean
			 */
			"bCollapse": null,
	
			/**
			 * Width of the scrollbar for the web-browser's platform. Calculated
			 * during table initialisation.
			 *  @type int
			 *  @default 0
			 */
			"iBarWidth": 0,
	
			/**
			 * Viewport width for horizontal scrolling. Horizontal scrolling is
			 * disabled if an empty string.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type string
			 */
			"sX": null,
	
			/**
			 * Width to expand the table to when using x-scrolling. Typically you
			 * should not need to use this.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type string
			 *  @deprecated
			 */
			"sXInner": null,
	
			/**
			 * Viewport height for vertical scrolling. Vertical scrolling is disabled
			 * if an empty string.
			 * Note that this parameter will be set by the initialisation routine. To
			 * set a default use {@link DataTable.defaults}.
			 *  @type string
			 */
			"sY": null
		},
	
		/**
		 * Language information for the table.
		 *  @namespace
		 *  @extends DataTable.defaults.oLanguage
		 */
		"oLanguage": {
			/**
			 * Information callback function. See
			 * {@link DataTable.defaults.fnInfoCallback}
			 *  @type function
			 *  @default null
			 */
			"fnInfoCallback": null
		},
	
		/**
		 * Browser support parameters
		 *  @namespace
		 */
		"oBrowser": {
			/**
			 * Indicate if the browser incorrectly calculates width:100% inside a
			 * scrolling element (IE6/7)
			 *  @type boolean
			 *  @default false
			 */
			"bScrollOversize": false,
	
			/**
			 * Determine if the vertical scrollbar is on the right or left of the
			 * scrolling container - needed for rtl language layout, although not
			 * all browsers move the scrollbar (Safari).
			 *  @type boolean
			 *  @default false
			 */
			"bScrollbarLeft": false,
	
			/**
			 * Flag for if `getBoundingClientRect` is fully supported or not
			 *  @type boolean
			 *  @default false
			 */
			"bBounding": false,
	
			/**
			 * Browser scrollbar width
			 *  @type integer
			 *  @default 0
			 */
			"barWidth": 0
		},
	
	
		"ajax": null,
	
	
		/**
		 * Array referencing the nodes which are used for the features. The
		 * parameters of this object match what is allowed by sDom - i.e.
		 *   <ul>
		 *     <li>'l' - Length changing</li>
		 *     <li>'f' - Filtering input</li>
		 *     <li>'t' - The table!</li>
		 *     <li>'i' - Information</li>
		 *     <li>'p' - Pagination</li>
		 *     <li>'r' - pRocessing</li>
		 *   </ul>
		 *  @type array
		 *  @default []
		 */
		"aanFeatures": [],
	
		/**
		 * Store data information - see {@link DataTable.models.oRow} for detailed
		 * information.
		 *  @type array
		 *  @default []
		 */
		"aoData": [],
	
		/**
		 * Array of indexes which are in the current display (after filtering etc)
		 *  @type array
		 *  @default []
		 */
		"aiDisplay": [],
	
		/**
		 * Array of indexes for display - no filtering
		 *  @type array
		 *  @default []
		 */
		"aiDisplayMaster": [],
	
		/**
		 * Map of row ids to data indexes
		 *  @type object
		 *  @default {}
		 */
		"aIds": {},
	
		/**
		 * Store information about each column that is in use
		 *  @type array
		 *  @default []
		 */
		"aoColumns": [],
	
		/**
		 * Store information about the table's header
		 *  @type array
		 *  @default []
		 */
		"aoHeader": [],
	
		/**
		 * Store information about the table's footer
		 *  @type array
		 *  @default []
		 */
		"aoFooter": [],
	
		/**
		 * Store the applied global search information in case we want to force a
		 * research or compare the old search to a new one.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @namespace
		 *  @extends DataTable.models.oSearch
		 */
		"oPreviousSearch": {},
	
		/**
		 * Store the applied search for each column - see
		 * {@link DataTable.models.oSearch} for the format that is used for the
		 * filtering information for each column.
		 *  @type array
		 *  @default []
		 */
		"aoPreSearchCols": [],
	
		/**
		 * Sorting that is applied to the table. Note that the inner arrays are
		 * used in the following manner:
		 * <ul>
		 *   <li>Index 0 - column number</li>
		 *   <li>Index 1 - current sorting direction</li>
		 * </ul>
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type array
		 *  @todo These inner arrays should really be objects
		 */
		"aaSorting": null,
	
		/**
		 * Sorting that is always applied to the table (i.e. prefixed in front of
		 * aaSorting).
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type array
		 *  @default []
		 */
		"aaSortingFixed": [],
	
		/**
		 * Classes to use for the striping of a table.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type array
		 *  @default []
		 */
		"asStripeClasses": null,
	
		/**
		 * If restoring a table - we should restore its striping classes as well
		 *  @type array
		 *  @default []
		 */
		"asDestroyStripes": [],
	
		/**
		 * If restoring a table - we should restore its width
		 *  @type int
		 *  @default 0
		 */
		"sDestroyWidth": 0,
	
		/**
		 * Callback functions array for every time a row is inserted (i.e. on a draw).
		 *  @type array
		 *  @default []
		 */
		"aoRowCallback": [],
	
		/**
		 * Callback functions for the header on each draw.
		 *  @type array
		 *  @default []
		 */
		"aoHeaderCallback": [],
	
		/**
		 * Callback function for the footer on each draw.
		 *  @type array
		 *  @default []
		 */
		"aoFooterCallback": [],
	
		/**
		 * Array of callback functions for draw callback functions
		 *  @type array
		 *  @default []
		 */
		"aoDrawCallback": [],
	
		/**
		 * Array of callback functions for row created function
		 *  @type array
		 *  @default []
		 */
		"aoRowCreatedCallback": [],
	
		/**
		 * Callback functions for just before the table is redrawn. A return of
		 * false will be used to cancel the draw.
		 *  @type array
		 *  @default []
		 */
		"aoPreDrawCallback": [],
	
		/**
		 * Callback functions for when the table has been initialised.
		 *  @type array
		 *  @default []
		 */
		"aoInitComplete": [],
	
	
		/**
		 * Callbacks for modifying the settings to be stored for state saving, prior to
		 * saving state.
		 *  @type array
		 *  @default []
		 */
		"aoStateSaveParams": [],
	
		/**
		 * Callbacks for modifying the settings that have been stored for state saving
		 * prior to using the stored values to restore the state.
		 *  @type array
		 *  @default []
		 */
		"aoStateLoadParams": [],
	
		/**
		 * Callbacks for operating on the settings object once the saved state has been
		 * loaded
		 *  @type array
		 *  @default []
		 */
		"aoStateLoaded": [],
	
		/**
		 * Cache the table ID for quick access
		 *  @type string
		 *  @default <i>Empty string</i>
		 */
		"sTableId": "",
	
		/**
		 * The TABLE node for the main table
		 *  @type node
		 *  @default null
		 */
		"nTable": null,
	
		/**
		 * Permanent ref to the thead element
		 *  @type node
		 *  @default null
		 */
		"nTHead": null,
	
		/**
		 * Permanent ref to the tfoot element - if it exists
		 *  @type node
		 *  @default null
		 */
		"nTFoot": null,
	
		/**
		 * Permanent ref to the tbody element
		 *  @type node
		 *  @default null
		 */
		"nTBody": null,
	
		/**
		 * Cache the wrapper node (contains all DataTables controlled elements)
		 *  @type node
		 *  @default null
		 */
		"nTableWrapper": null,
	
		/**
		 * Indicate if when using server-side processing the loading of data
		 * should be deferred until the second draw.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type boolean
		 *  @default false
		 */
		"bDeferLoading": false,
	
		/**
		 * Indicate if all required information has been read in
		 *  @type boolean
		 *  @default false
		 */
		"bInitialised": false,
	
		/**
		 * Information about open rows. Each object in the array has the parameters
		 * 'nTr' and 'nParent'
		 *  @type array
		 *  @default []
		 */
		"aoOpenRows": [],
	
		/**
		 * Dictate the positioning of DataTables' control elements - see
		 * {@link DataTable.model.oInit.sDom}.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 *  @default null
		 */
		"sDom": null,
	
		/**
		 * Search delay (in mS)
		 *  @type integer
		 *  @default null
		 */
		"searchDelay": null,
	
		/**
		 * Which type of pagination should be used.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 *  @default two_button
		 */
		"sPaginationType": "two_button",
	
		/**
		 * The state duration (for `stateSave`) in seconds.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type int
		 *  @default 0
		 */
		"iStateDuration": 0,
	
		/**
		 * Array of callback functions for state saving. Each array element is an
		 * object with the following parameters:
		 *   <ul>
		 *     <li>function:fn - function to call. Takes two parameters, oSettings
		 *       and the JSON string to save that has been thus far created. Returns
		 *       a JSON string to be inserted into a json object
		 *       (i.e. '"param": [ 0, 1, 2]')</li>
		 *     <li>string:sName - name of callback</li>
		 *   </ul>
		 *  @type array
		 *  @default []
		 */
		"aoStateSave": [],
	
		/**
		 * Array of callback functions for state loading. Each array element is an
		 * object with the following parameters:
		 *   <ul>
		 *     <li>function:fn - function to call. Takes two parameters, oSettings
		 *       and the object stored. May return false to cancel state loading</li>
		 *     <li>string:sName - name of callback</li>
		 *   </ul>
		 *  @type array
		 *  @default []
		 */
		"aoStateLoad": [],
	
		/**
		 * State that was saved. Useful for back reference
		 *  @type object
		 *  @default null
		 */
		"oSavedState": null,
	
		/**
		 * State that was loaded. Useful for back reference
		 *  @type object
		 *  @default null
		 */
		"oLoadedState": null,
	
		/**
		 * Source url for AJAX data for the table.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 *  @default null
		 */
		"sAjaxSource": null,
	
		/**
		 * Property from a given object from which to read the table data from. This
		 * can be an empty string (when not server-side processing), in which case
		 * it is  assumed an an array is given directly.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 */
		"sAjaxDataProp": null,
	
		/**
		 * The last jQuery XHR object that was used for server-side data gathering.
		 * This can be used for working with the XHR information in one of the
		 * callbacks
		 *  @type object
		 *  @default null
		 */
		"jqXHR": null,
	
		/**
		 * JSON returned from the server in the last Ajax request
		 *  @type object
		 *  @default undefined
		 */
		"json": undefined,
	
		/**
		 * Data submitted as part of the last Ajax request
		 *  @type object
		 *  @default undefined
		 */
		"oAjaxData": undefined,
	
		/**
		 * Function to get the server-side data.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type function
		 */
		"fnServerData": null,
	
		/**
		 * Functions which are called prior to sending an Ajax request so extra
		 * parameters can easily be sent to the server
		 *  @type array
		 *  @default []
		 */
		"aoServerParams": [],
	
		/**
		 * Send the XHR HTTP method - GET or POST (could be PUT or DELETE if
		 * required).
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type string
		 */
		"sServerMethod": null,
	
		/**
		 * Format numbers for display.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type function
		 */
		"fnFormatNumber": null,
	
		/**
		 * List of options that can be used for the user selectable length menu.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type array
		 *  @default []
		 */
		"aLengthMenu": null,
	
		/**
		 * Counter for the draws that the table does. Also used as a tracker for
		 * server-side processing
		 *  @type int
		 *  @default 0
		 */
		"iDraw": 0,
	
		/**
		 * Indicate if a redraw is being done - useful for Ajax
		 *  @type boolean
		 *  @default false
		 */
		"bDrawing": false,
	
		/**
		 * Draw index (iDraw) of the last error when parsing the returned data
		 *  @type int
		 *  @default -1
		 */
		"iDrawError": -1,
	
		/**
		 * Paging display length
		 *  @type int
		 *  @default 10
		 */
		"_iDisplayLength": 10,
	
		/**
		 * Paging start point - aiDisplay index
		 *  @type int
		 *  @default 0
		 */
		"_iDisplayStart": 0,
	
		/**
		 * Server-side processing - number of records in the result set
		 * (i.e. before filtering), Use fnRecordsTotal rather than
		 * this property to get the value of the number of records, regardless of
		 * the server-side processing setting.
		 *  @type int
		 *  @default 0
		 *  @private
		 */
		"_iRecordsTotal": 0,
	
		/**
		 * Server-side processing - number of records in the current display set
		 * (i.e. after filtering). Use fnRecordsDisplay rather than
		 * this property to get the value of the number of records, regardless of
		 * the server-side processing setting.
		 *  @type boolean
		 *  @default 0
		 *  @private
		 */
		"_iRecordsDisplay": 0,
	
		/**
		 * The classes to use for the table
		 *  @type object
		 *  @default {}
		 */
		"oClasses": {},
	
		/**
		 * Flag attached to the settings object so you can check in the draw
		 * callback if filtering has been done in the draw. Deprecated in favour of
		 * events.
		 *  @type boolean
		 *  @default false
		 *  @deprecated
		 */
		"bFiltered": false,
	
		/**
		 * Flag attached to the settings object so you can check in the draw
		 * callback if sorting has been done in the draw. Deprecated in favour of
		 * events.
		 *  @type boolean
		 *  @default false
		 *  @deprecated
		 */
		"bSorted": false,
	
		/**
		 * Indicate that if multiple rows are in the header and there is more than
		 * one unique cell per column, if the top one (true) or bottom one (false)
		 * should be used for sorting / title by DataTables.
		 * Note that this parameter will be set by the initialisation routine. To
		 * set a default use {@link DataTable.defaults}.
		 *  @type boolean
		 */
		"bSortCellsTop": null,
	
		/**
		 * Initialisation object that is used for the table
		 *  @type object
		 *  @default null
		 */
		"oInit": null,
	
		/**
		 * Destroy callback functions - for plug-ins to attach themselves to the
		 * destroy so they can clean up markup and events.
		 *  @type array
		 *  @default []
		 */
		"aoDestroyCallback": [],
	
	
		/**
		 * Get the number of records in the current record set, before filtering
		 *  @type function
		 */
		"fnRecordsTotal": function ()
		{
			return _fnDataSource( this ) == 'ssp' ?
				this._iRecordsTotal * 1 :
				this.aiDisplayMaster.length;
		},
	
		/**
		 * Get the number of records in the current record set, after filtering
		 *  @type function
		 */
		"fnRecordsDisplay": function ()
		{
			return _fnDataSource( this ) == 'ssp' ?
				this._iRecordsDisplay * 1 :
				this.aiDisplay.length;
		},
	
		/**
		 * Get the display end point - aiDisplay index
		 *  @type function
		 */
		"fnDisplayEnd": function ()
		{
			var
				len      = this._iDisplayLength,
				start    = this._iDisplayStart,
				calc     = start + len,
				records  = this.aiDisplay.length,
				features = this.oFeatures,
				paginate = features.bPaginate;
	
			if ( features.bServerSide ) {
				return paginate === false || len === -1 ?
					start + records :
					Math.min( start+len, this._iRecordsDisplay );
			}
			else {
				return ! paginate || calc>records || len===-1 ?
					records :
					calc;
			}
		},
	
		/**
		 * The DataTables object for this table
		 *  @type object
		 *  @default null
		 */
		"oInstance": null,
	
		/**
		 * Unique identifier for each instance of the DataTables object. If there
		 * is an ID on the table node, then it takes that value, otherwise an
		 * incrementing internal counter is used.
		 *  @type string
		 *  @default null
		 */
		"sInstance": null,
	
		/**
		 * tabindex attribute value that is added to DataTables control elements, allowing
		 * keyboard navigation of the table and its controls.
		 */
		"iTabIndex": 0,
	
		/**
		 * DIV container for the footer scrolling table if scrolling
		 */
		"nScrollHead": null,
	
		/**
		 * DIV container for the footer scrolling table if scrolling
		 */
		"nScrollFoot": null,
	
		/**
		 * Last applied sort
		 *  @type array
		 *  @default []
		 */
		"aLastSort": [],
	
		/**
		 * Stored plug-in instances
		 *  @type object
		 *  @default {}
		 */
		"oPlugins": {},
	
		/**
		 * Function used to get a row's id from the row's data
		 *  @type function
		 *  @default null
		 */
		"rowIdFn": null,
	
		/**
		 * Data location where to store a row's id
		 *  @type string
		 *  @default null
		 */
		"rowId": null
	};
	
	/**
	 * Extension object for DataTables that is used to provide all extension
	 * options.
	 *
	 * Note that the `DataTable.ext` object is available through
	 * `jQuery.fn.dataTable.ext` where it may be accessed and manipulated. It is
	 * also aliased to `jQuery.fn.dataTableExt` for historic reasons.
	 *  @namespace
	 *  @extends DataTable.models.ext
	 */
	
	
	/**
	 * DataTables extensions
	 * 
	 * This namespace acts as a collection area for plug-ins that can be used to
	 * extend DataTables capabilities. Indeed many of the build in methods
	 * use this method to provide their own capabilities (sorting methods for
	 * example).
	 *
	 * Note that this namespace is aliased to `jQuery.fn.dataTableExt` for legacy
	 * reasons
	 *
	 *  @namespace
	 */
	DataTable.ext = _ext = {
		/**
		 * Buttons. For use with the Buttons extension for DataTables. This is
		 * defined here so other extensions can define buttons regardless of load
		 * order. It is _not_ used by DataTables core.
		 *
		 *  @type object
		 *  @default {}
		 */
		buttons: {},
	
	
		/**
		 * Element class names
		 *
		 *  @type object
		 *  @default {}
		 */
		classes: {},
	
	
		/**
		 * DataTables build type (expanded by the download builder)
		 *
		 *  @type string
		 */
		builder: "-source-",
	
	
		/**
		 * Error reporting.
		 * 
		 * How should DataTables report an error. Can take the value 'alert',
		 * 'throw', 'none' or a function.
		 *
		 *  @type string|function
		 *  @default alert
		 */
		errMode: "alert",
	
	
		/**
		 * Feature plug-ins.
		 * 
		 * This is an array of objects which describe the feature plug-ins that are
		 * available to DataTables. These feature plug-ins are then available for
		 * use through the `dom` initialisation option.
		 * 
		 * Each feature plug-in is described by an object which must have the
		 * following properties:
		 * 
		 * * `fnInit` - function that is used to initialise the plug-in,
		 * * `cFeature` - a character so the feature can be enabled by the `dom`
		 *   instillation option. This is case sensitive.
		 *
		 * The `fnInit` function has the following input parameters:
		 *
		 * 1. `{object}` DataTables settings object: see
		 *    {@link DataTable.models.oSettings}
		 *
		 * And the following return is expected:
		 * 
		 * * {node|null} The element which contains your feature. Note that the
		 *   return may also be void if your plug-in does not require to inject any
		 *   DOM elements into DataTables control (`dom`) - for example this might
		 *   be useful when developing a plug-in which allows table control via
		 *   keyboard entry
		 *
		 *  @type array
		 *
		 *  @example
		 *    $.fn.dataTable.ext.features.push( {
		 *      "fnInit": function( oSettings ) {
		 *        return new TableTools( { "oDTSettings": oSettings } );
		 *      },
		 *      "cFeature": "T"
		 *    } );
		 */
		feature: [],
	
	
		/**
		 * Row searching.
		 * 
		 * This method of searching is complimentary to the default type based
		 * searching, and a lot more comprehensive as it allows you complete control
		 * over the searching logic. Each element in this array is a function
		 * (parameters described below) that is called for every row in the table,
		 * and your logic decides if it should be included in the searching data set
		 * or not.
		 *
		 * Searching functions have the following input parameters:
		 *
		 * 1. `{object}` DataTables settings object: see
		 *    {@link DataTable.models.oSettings}
		 * 2. `{array|object}` Data for the row to be processed (same as the
		 *    original format that was passed in as the data source, or an array
		 *    from a DOM data source
		 * 3. `{int}` Row index ({@link DataTable.models.oSettings.aoData}), which
		 *    can be useful to retrieve the `TR` element if you need DOM interaction.
		 *
		 * And the following return is expected:
		 *
		 * * {boolean} Include the row in the searched result set (true) or not
		 *   (false)
		 *
		 * Note that as with the main search ability in DataTables, technically this
		 * is "filtering", since it is subtractive. However, for consistency in
		 * naming we call it searching here.
		 *
		 *  @type array
		 *  @default []
		 *
		 *  @example
		 *    // The following example shows custom search being applied to the
		 *    // fourth column (i.e. the data[3] index) based on two input values
		 *    // from the end-user, matching the data in a certain range.
		 *    $.fn.dataTable.ext.search.push(
		 *      function( settings, data, dataIndex ) {
		 *        var min = document.getElementById('min').value * 1;
		 *        var max = document.getElementById('max').value * 1;
		 *        var version = data[3] == "-" ? 0 : data[3]*1;
		 *
		 *        if ( min == "" && max == "" ) {
		 *          return true;
		 *        }
		 *        else if ( min == "" && version < max ) {
		 *          return true;
		 *        }
		 *        else if ( min < version && "" == max ) {
		 *          return true;
		 *        }
		 *        else if ( min < version && version < max ) {
		 *          return true;
		 *        }
		 *        return false;
		 *      }
		 *    );
		 */
		search: [],
	
	
		/**
		 * Selector extensions
		 *
		 * The `selector` option can be used to extend the options available for the
		 * selector modifier options (`selector-modifier` object data type) that
		 * each of the three built in selector types offer (row, column and cell +
		 * their plural counterparts). For example the Select extension uses this
		 * mechanism to provide an option to select only rows, columns and cells
		 * that have been marked as selected by the end user (`{selected: true}`),
		 * which can be used in conjunction with the existing built in selector
		 * options.
		 *
		 * Each property is an array to which functions can be pushed. The functions
		 * take three attributes:
		 *
		 * * Settings object for the host table
		 * * Options object (`selector-modifier` object type)
		 * * Array of selected item indexes
		 *
		 * The return is an array of the resulting item indexes after the custom
		 * selector has been applied.
		 *
		 *  @type object
		 */
		selector: {
			cell: [],
			column: [],
			row: []
		},
	
	
		/**
		 * Internal functions, exposed for used in plug-ins.
		 * 
		 * Please note that you should not need to use the internal methods for
		 * anything other than a plug-in (and even then, try to avoid if possible).
		 * The internal function may change between releases.
		 *
		 *  @type object
		 *  @default {}
		 */
		internal: {},
	
	
		/**
		 * Legacy configuration options. Enable and disable legacy options that
		 * are available in DataTables.
		 *
		 *  @type object
		 */
		legacy: {
			/**
			 * Enable / disable DataTables 1.9 compatible server-side processing
			 * requests
			 *
			 *  @type boolean
			 *  @default null
			 */
			ajax: null
		},
	
	
		/**
		 * Pagination plug-in methods.
		 * 
		 * Each entry in this object is a function and defines which buttons should
		 * be shown by the pagination rendering method that is used for the table:
		 * {@link DataTable.ext.renderer.pageButton}. The renderer addresses how the
		 * buttons are displayed in the document, while the functions here tell it
		 * what buttons to display. This is done by returning an array of button
		 * descriptions (what each button will do).
		 *
		 * Pagination types (the four built in options and any additional plug-in
		 * options defined here) can be used through the `paginationType`
		 * initialisation parameter.
		 *
		 * The functions defined take two parameters:
		 *
		 * 1. `{int} page` The current page index
		 * 2. `{int} pages` The number of pages in the table
		 *
		 * Each function is expected to return an array where each element of the
		 * array can be one of:
		 *
		 * * `first` - Jump to first page when activated
		 * * `last` - Jump to last page when activated
		 * * `previous` - Show previous page when activated
		 * * `next` - Show next page when activated
		 * * `{int}` - Show page of the index given
		 * * `{array}` - A nested array containing the above elements to add a
		 *   containing 'DIV' element (might be useful for styling).
		 *
		 * Note that DataTables v1.9- used this object slightly differently whereby
		 * an object with two functions would be defined for each plug-in. That
		 * ability is still supported by DataTables 1.10+ to provide backwards
		 * compatibility, but this option of use is now decremented and no longer
		 * documented in DataTables 1.10+.
		 *
		 *  @type object
		 *  @default {}
		 *
		 *  @example
		 *    // Show previous, next and current page buttons only
		 *    $.fn.dataTableExt.oPagination.current = function ( page, pages ) {
		 *      return [ 'previous', page, 'next' ];
		 *    };
		 */
		pager: {},
	
	
		renderer: {
			pageButton: {},
			header: {}
		},
	
	
		/**
		 * Ordering plug-ins - custom data source
		 * 
		 * The extension options for ordering of data available here is complimentary
		 * to the default type based ordering that DataTables typically uses. It
		 * allows much greater control over the the data that is being used to
		 * order a column, but is necessarily therefore more complex.
		 * 
		 * This type of ordering is useful if you want to do ordering based on data
		 * live from the DOM (for example the contents of an 'input' element) rather
		 * than just the static string that DataTables knows of.
		 * 
		 * The way these plug-ins work is that you create an array of the values you
		 * wish to be ordering for the column in question and then return that
		 * array. The data in the array much be in the index order of the rows in
		 * the table (not the currently ordering order!). Which order data gathering
		 * function is run here depends on the `dt-init columns.orderDataType`
		 * parameter that is used for the column (if any).
		 *
		 * The functions defined take two parameters:
		 *
		 * 1. `{object}` DataTables settings object: see
		 *    {@link DataTable.models.oSettings}
		 * 2. `{int}` Target column index
		 *
		 * Each function is expected to return an array:
		 *
		 * * `{array}` Data for the column to be ordering upon
		 *
		 *  @type array
		 *
		 *  @example
		 *    // Ordering using `input` node values
		 *    $.fn.dataTable.ext.order['dom-text'] = function  ( settings, col )
		 *    {
		 *      return this.api().column( col, {order:'index'} ).nodes().map( function ( td, i ) {
		 *        return $('input', td).val();
		 *      } );
		 *    }
		 */
		order: {},
	
	
		/**
		 * Type based plug-ins.
		 *
		 * Each column in DataTables has a type assigned to it, either by automatic
		 * detection or by direct assignment using the `type` option for the column.
		 * The type of a column will effect how it is ordering and search (plug-ins
		 * can also make use of the column type if required).
		 *
		 * @namespace
		 */
		type: {
			/**
			 * Type detection functions.
			 *
			 * The functions defined in this object are used to automatically detect
			 * a column's type, making initialisation of DataTables super easy, even
			 * when complex data is in the table.
			 *
			 * The functions defined take two parameters:
			 *
		     *  1. `{*}` Data from the column cell to be analysed
		     *  2. `{settings}` DataTables settings object. This can be used to
		     *     perform context specific type detection - for example detection
		     *     based on language settings such as using a comma for a decimal
		     *     place. Generally speaking the options from the settings will not
		     *     be required
			 *
			 * Each function is expected to return:
			 *
			 * * `{string|null}` Data type detected, or null if unknown (and thus
			 *   pass it on to the other type detection functions.
			 *
			 *  @type array
			 *
			 *  @example
			 *    // Currency type detection plug-in:
			 *    $.fn.dataTable.ext.type.detect.push(
			 *      function ( data, settings ) {
			 *        // Check the numeric part
			 *        if ( ! data.substring(1).match(/[0-9]/) ) {
			 *          return null;
			 *        }
			 *
			 *        // Check prefixed by currency
			 *        if ( data.charAt(0) == '$' || data.charAt(0) == '&pound;' ) {
			 *          return 'currency';
			 *        }
			 *        return null;
			 *      }
			 *    );
			 */
			detect: [],
	
	
			/**
			 * Type based search formatting.
			 *
			 * The type based searching functions can be used to pre-format the
			 * data to be search on. For example, it can be used to strip HTML
			 * tags or to de-format telephone numbers for numeric only searching.
			 *
			 * Note that is a search is not defined for a column of a given type,
			 * no search formatting will be performed.
			 * 
			 * Pre-processing of searching data plug-ins - When you assign the sType
			 * for a column (or have it automatically detected for you by DataTables
			 * or a type detection plug-in), you will typically be using this for
			 * custom sorting, but it can also be used to provide custom searching
			 * by allowing you to pre-processing the data and returning the data in
			 * the format that should be searched upon. This is done by adding
			 * functions this object with a parameter name which matches the sType
			 * for that target column. This is the corollary of <i>afnSortData</i>
			 * for searching data.
			 *
			 * The functions defined take a single parameter:
			 *
		     *  1. `{*}` Data from the column cell to be prepared for searching
			 *
			 * Each function is expected to return:
			 *
			 * * `{string|null}` Formatted string that will be used for the searching.
			 *
			 *  @type object
			 *  @default {}
			 *
			 *  @example
			 *    $.fn.dataTable.ext.type.search['title-numeric'] = function ( d ) {
			 *      return d.replace(/\n/g," ").replace( /<.*?>/g, "" );
			 *    }
			 */
			search: {},
	
	
			/**
			 * Type based ordering.
			 *
			 * The column type tells DataTables what ordering to apply to the table
			 * when a column is sorted upon. The order for each type that is defined,
			 * is defined by the functions available in this object.
			 *
			 * Each ordering option can be described by three properties added to
			 * this object:
			 *
			 * * `{type}-pre` - Pre-formatting function
			 * * `{type}-asc` - Ascending order function
			 * * `{type}-desc` - Descending order function
			 *
			 * All three can be used together, only `{type}-pre` or only
			 * `{type}-asc` and `{type}-desc` together. It is generally recommended
			 * that only `{type}-pre` is used, as this provides the optimal
			 * implementation in terms of speed, although the others are provided
			 * for compatibility with existing Javascript sort functions.
			 *
			 * `{type}-pre`: Functions defined take a single parameter:
			 *
		     *  1. `{*}` Data from the column cell to be prepared for ordering
			 *
			 * And return:
			 *
			 * * `{*}` Data to be sorted upon
			 *
			 * `{type}-asc` and `{type}-desc`: Functions are typical Javascript sort
			 * functions, taking two parameters:
			 *
		     *  1. `{*}` Data to compare to the second parameter
		     *  2. `{*}` Data to compare to the first parameter
			 *
			 * And returning:
			 *
			 * * `{*}` Ordering match: <0 if first parameter should be sorted lower
			 *   than the second parameter, ===0 if the two parameters are equal and
			 *   >0 if the first parameter should be sorted height than the second
			 *   parameter.
			 * 
			 *  @type object
			 *  @default {}
			 *
			 *  @example
			 *    // Numeric ordering of formatted numbers with a pre-formatter
			 *    $.extend( $.fn.dataTable.ext.type.order, {
			 *      "string-pre": function(x) {
			 *        a = (a === "-" || a === "") ? 0 : a.replace( /[^\d\-\.]/g, "" );
			 *        return parseFloat( a );
			 *      }
			 *    } );
			 *
			 *  @example
			 *    // Case-sensitive string ordering, with no pre-formatting method
			 *    $.extend( $.fn.dataTable.ext.order, {
			 *      "string-case-asc": function(x,y) {
			 *        return ((x < y) ? -1 : ((x > y) ? 1 : 0));
			 *      },
			 *      "string-case-desc": function(x,y) {
			 *        return ((x < y) ? 1 : ((x > y) ? -1 : 0));
			 *      }
			 *    } );
			 */
			order: {}
		},
	
		/**
		 * Unique DataTables instance counter
		 *
		 * @type int
		 * @private
		 */
		_unique: 0,
	
	
		//
		// Depreciated
		// The following properties are retained for backwards compatibility only.
		// The should not be used in new projects and will be removed in a future
		// version
		//
	
		/**
		 * Version check function.
		 *  @type function
		 *  @depreciated Since 1.10
		 */
		fnVersionCheck: DataTable.fnVersionCheck,
	
	
		/**
		 * Index for what 'this' index API functions should use
		 *  @type int
		 *  @deprecated Since v1.10
		 */
		iApiIndex: 0,
	
	
		/**
		 * jQuery UI class container
		 *  @type object
		 *  @deprecated Since v1.10
		 */
		oJUIClasses: {},
	
	
		/**
		 * Software version
		 *  @type string
		 *  @deprecated Since v1.10
		 */
		sVersion: DataTable.version
	};
	
	
	//
	// Backwards compatibility. Alias to pre 1.10 Hungarian notation counter parts
	//
	$.extend( _ext, {
		afnFiltering: _ext.search,
		aTypes:       _ext.type.detect,
		ofnSearch:    _ext.type.search,
		oSort:        _ext.type.order,
		afnSortData:  _ext.order,
		aoFeatures:   _ext.feature,
		oApi:         _ext.internal,
		oStdClasses:  _ext.classes,
		oPagination:  _ext.pager
	} );
	
	
	$.extend( DataTable.ext.classes, {
		"sTable": "dataTable",
		"sNoFooter": "no-footer",
	
		/* Paging buttons */
		"sPageButton": "paginate_button",
		"sPageButtonActive": "current",
		"sPageButtonDisabled": "disabled",
	
		/* Striping classes */
		"sStripeOdd": "odd",
		"sStripeEven": "even",
	
		/* Empty row */
		"sRowEmpty": "dataTables_empty",
	
		/* Features */
		"sWrapper": "dataTables_wrapper",
		"sFilter": "dataTables_filter",
		"sInfo": "dataTables_info",
		"sPaging": "dataTables_paginate paging_", /* Note that the type is postfixed */
		"sLength": "dataTables_length",
		"sProcessing": "dataTables_processing",
	
		/* Sorting */
		"sSortAsc": "sorting_asc",
		"sSortDesc": "sorting_desc",
		"sSortable": "sorting", /* Sortable in both directions */
		"sSortableAsc": "sorting_desc_disabled",
		"sSortableDesc": "sorting_asc_disabled",
		"sSortableNone": "sorting_disabled",
		"sSortColumn": "sorting_", /* Note that an int is postfixed for the sorting order */
	
		/* Filtering */
		"sFilterInput": "",
	
		/* Page length */
		"sLengthSelect": "",
	
		/* Scrolling */
		"sScrollWrapper": "dataTables_scroll",
		"sScrollHead": "dataTables_scrollHead",
		"sScrollHeadInner": "dataTables_scrollHeadInner",
		"sScrollBody": "dataTables_scrollBody",
		"sScrollFoot": "dataTables_scrollFoot",
		"sScrollFootInner": "dataTables_scrollFootInner",
	
		/* Misc */
		"sHeaderTH": "",
		"sFooterTH": "",
	
		// Deprecated
		"sSortJUIAsc": "",
		"sSortJUIDesc": "",
		"sSortJUI": "",
		"sSortJUIAscAllowed": "",
		"sSortJUIDescAllowed": "",
		"sSortJUIWrapper": "",
		"sSortIcon": "",
		"sJUIHeader": "",
		"sJUIFooter": ""
	} );
	
	
	var extPagination = DataTable.ext.pager;
	
	function _numbers ( page, pages ) {
		var
			numbers = [],
			buttons = extPagination.numbers_length,
			half = Math.floor( buttons / 2 ),
			i = 1;
	
		if ( pages <= buttons ) {
			numbers = _range( 0, pages );
		}
		else if ( page <= half ) {
			numbers = _range( 0, buttons-2 );
			numbers.push( 'ellipsis' );
			numbers.push( pages-1 );
		}
		else if ( page >= pages - 1 - half ) {
			numbers = _range( pages-(buttons-2), pages );
			numbers.splice( 0, 0, 'ellipsis' ); // no unshift in ie6
			numbers.splice( 0, 0, 0 );
		}
		else {
			numbers = _range( page-half+2, page+half-1 );
			numbers.push( 'ellipsis' );
			numbers.push( pages-1 );
			numbers.splice( 0, 0, 'ellipsis' );
			numbers.splice( 0, 0, 0 );
		}
	
		numbers.DT_el = 'span';
		return numbers;
	}
	
	
	$.extend( extPagination, {
		simple: function ( page, pages ) {
			return [ 'previous', 'next' ];
		},
	
		full: function ( page, pages ) {
			return [  'first', 'previous', 'next', 'last' ];
		},
	
		numbers: function ( page, pages ) {
			return [ _numbers(page, pages) ];
		},
	
		simple_numbers: function ( page, pages ) {
			return [ 'previous', _numbers(page, pages), 'next' ];
		},
	
		full_numbers: function ( page, pages ) {
			return [ 'first', 'previous', _numbers(page, pages), 'next', 'last' ];
		},
		
		first_last_numbers: function (page, pages) {
	 		return ['first', _numbers(page, pages), 'last'];
	 	},
	
		// For testing and plug-ins to use
		_numbers: _numbers,
	
		// Number of number buttons (including ellipsis) to show. _Must be odd!_
		numbers_length: 7
	} );
	
	
	$.extend( true, DataTable.ext.renderer, {
		pageButton: {
			_: function ( settings, host, idx, buttons, page, pages ) {
				var classes = settings.oClasses;
				var lang = settings.oLanguage.oPaginate;
				var aria = settings.oLanguage.oAria.paginate || {};
				var btnDisplay, btnClass, counter=0;
	
				var attach = function( container, buttons ) {
					var i, ien, node, button, tabIndex;
					var disabledClass = classes.sPageButtonDisabled;
					var clickHandler = function ( e ) {
						_fnPageChange( settings, e.data.action, true );
					};
	
					for ( i=0, ien=buttons.length ; i<ien ; i++ ) {
						button = buttons[i];
	
						if ( Array.isArray( button ) ) {
							var inner = $( '<'+(button.DT_el || 'div')+'/>' )
								.appendTo( container );
							attach( inner, button );
						}
						else {
							btnDisplay = null;
							btnClass = button;
							tabIndex = settings.iTabIndex;
	
							switch ( button ) {
								case 'ellipsis':
									container.append('<span class="ellipsis">&#x2026;</span>');
									break;
	
								case 'first':
									btnDisplay = lang.sFirst;
	
									if ( page === 0 ) {
										tabIndex = -1;
										btnClass += ' ' + disabledClass;
									}
									break;
	
								case 'previous':
									btnDisplay = lang.sPrevious;
	
									if ( page === 0 ) {
										tabIndex = -1;
										btnClass += ' ' + disabledClass;
									}
									break;
	
								case 'next':
									btnDisplay = lang.sNext;
	
									if ( pages === 0 || page === pages-1 ) {
										tabIndex = -1;
										btnClass += ' ' + disabledClass;
									}
									break;
	
								case 'last':
									btnDisplay = lang.sLast;
	
									if ( pages === 0 || page === pages-1 ) {
										tabIndex = -1;
										btnClass += ' ' + disabledClass;
									}
									break;
	
								default:
									btnDisplay = settings.fnFormatNumber( button + 1 );
									btnClass = page === button ?
										classes.sPageButtonActive : '';
									break;
							}
	
							if ( btnDisplay !== null ) {
								node = $('<a>', {
										'class': classes.sPageButton+' '+btnClass,
										'aria-controls': settings.sTableId,
										'aria-label': aria[ button ],
										'data-dt-idx': counter,
										'tabindex': tabIndex,
										'id': idx === 0 && typeof button === 'string' ?
											settings.sTableId +'_'+ button :
											null
									} )
									.html( btnDisplay )
									.appendTo( container );
	
								_fnBindAction(
									node, {action: button}, clickHandler
								);
	
								counter++;
							}
						}
					}
				};
	
				// IE9 throws an 'unknown error' if document.activeElement is used
				// inside an iframe or frame. Try / catch the error. Not good for
				// accessibility, but neither are frames.
				var activeEl;
	
				try {
					// Because this approach is destroying and recreating the paging
					// elements, focus is lost on the select button which is bad for
					// accessibility. So we want to restore focus once the draw has
					// completed
					activeEl = $(host).find(document.activeElement).data('dt-idx');
				}
				catch (e) {}
	
				attach( $(host).empty(), buttons );
	
				if ( activeEl !== undefined ) {
					$(host).find( '[data-dt-idx='+activeEl+']' ).trigger('focus');
				}
			}
		}
	} );
	
	
	
	// Built in type detection. See model.ext.aTypes for information about
	// what is required from this methods.
	$.extend( DataTable.ext.type.detect, [
		// Plain numbers - first since V8 detects some plain numbers as dates
		// e.g. Date.parse('55') (but not all, e.g. Date.parse('22')...).
		function ( d, settings )
		{
			var decimal = settings.oLanguage.sDecimal;
			return _isNumber( d, decimal ) ? 'num'+decimal : null;
		},
	
		// Dates (only those recognised by the browser's Date.parse)
		function ( d, settings )
		{
			// V8 tries _very_ hard to make a string passed into `Date.parse()`
			// valid, so we need to use a regex to restrict date formats. Use a
			// plug-in for anything other than ISO8601 style strings
			if ( d && !(d instanceof Date) && ! _re_date.test(d) ) {
				return null;
			}
			var parsed = Date.parse(d);
			return (parsed !== null && !isNaN(parsed)) || _empty(d) ? 'date' : null;
		},
	
		// Formatted numbers
		function ( d, settings )
		{
			var decimal = settings.oLanguage.sDecimal;
			return _isNumber( d, decimal, true ) ? 'num-fmt'+decimal : null;
		},
	
		// HTML numeric
		function ( d, settings )
		{
			var decimal = settings.oLanguage.sDecimal;
			return _htmlNumeric( d, decimal ) ? 'html-num'+decimal : null;
		},
	
		// HTML numeric, formatted
		function ( d, settings )
		{
			var decimal = settings.oLanguage.sDecimal;
			return _htmlNumeric( d, decimal, true ) ? 'html-num-fmt'+decimal : null;
		},
	
		// HTML (this is strict checking - there must be html)
		function ( d, settings )
		{
			return _empty( d ) || (typeof d === 'string' && d.indexOf('<') !== -1) ?
				'html' : null;
		}
	] );
	
	
	
	// Filter formatting functions. See model.ext.ofnSearch for information about
	// what is required from these methods.
	// 
	// Note that additional search methods are added for the html numbers and
	// html formatted numbers by `_addNumericSort()` when we know what the decimal
	// place is
	
	
	$.extend( DataTable.ext.type.search, {
		html: function ( data ) {
			return _empty(data) ?
				data :
				typeof data === 'string' ?
					data
						.replace( _re_new_lines, " " )
						.replace( _re_html, "" ) :
					'';
		},
	
		string: function ( data ) {
			return _empty(data) ?
				data :
				typeof data === 'string' ?
					data.replace( _re_new_lines, " " ) :
					data;
		}
	} );
	
	
	
	var __numericReplace = function ( d, decimalPlace, re1, re2 ) {
		if ( d !== 0 && (!d || d === '-') ) {
			return -Infinity;
		}
	
		// If a decimal place other than `.` is used, it needs to be given to the
		// function so we can detect it and replace with a `.` which is the only
		// decimal place Javascript recognises - it is not locale aware.
		if ( decimalPlace ) {
			d = _numToDecimal( d, decimalPlace );
		}
	
		if ( d.replace ) {
			if ( re1 ) {
				d = d.replace( re1, '' );
			}
	
			if ( re2 ) {
				d = d.replace( re2, '' );
			}
		}
	
		return d * 1;
	};
	
	
	// Add the numeric 'deformatting' functions for sorting and search. This is done
	// in a function to provide an easy ability for the language options to add
	// additional methods if a non-period decimal place is used.
	function _addNumericSort ( decimalPlace ) {
		$.each(
			{
				// Plain numbers
				"num": function ( d ) {
					return __numericReplace( d, decimalPlace );
				},
	
				// Formatted numbers
				"num-fmt": function ( d ) {
					return __numericReplace( d, decimalPlace, _re_formatted_numeric );
				},
	
				// HTML numeric
				"html-num": function ( d ) {
					return __numericReplace( d, decimalPlace, _re_html );
				},
	
				// HTML numeric, formatted
				"html-num-fmt": function ( d ) {
					return __numericReplace( d, decimalPlace, _re_html, _re_formatted_numeric );
				}
			},
			function ( key, fn ) {
				// Add the ordering method
				_ext.type.order[ key+decimalPlace+'-pre' ] = fn;
	
				// For HTML types add a search formatter that will strip the HTML
				if ( key.match(/^html\-/) ) {
					_ext.type.search[ key+decimalPlace ] = _ext.type.search.html;
				}
			}
		);
	}
	
	
	// Default sort methods
	$.extend( _ext.type.order, {
		// Dates
		"date-pre": function ( d ) {
			var ts = Date.parse( d );
			return isNaN(ts) ? -Infinity : ts;
		},
	
		// html
		"html-pre": function ( a ) {
			return _empty(a) ?
				'' :
				a.replace ?
					a.replace( /<.*?>/g, "" ).toLowerCase() :
					a+'';
		},
	
		// string
		"string-pre": function ( a ) {
			// This is a little complex, but faster than always calling toString,
			// http://jsperf.com/tostring-v-check
			return _empty(a) ?
				'' :
				typeof a === 'string' ?
					a.toLowerCase() :
					! a.toString ?
						'' :
						a.toString();
		},
	
		// string-asc and -desc are retained only for compatibility with the old
		// sort methods
		"string-asc": function ( x, y ) {
			return ((x < y) ? -1 : ((x > y) ? 1 : 0));
		},
	
		"string-desc": function ( x, y ) {
			return ((x < y) ? 1 : ((x > y) ? -1 : 0));
		}
	} );
	
	
	// Numeric sorting types - order doesn't matter here
	_addNumericSort( '' );
	
	
	$.extend( true, DataTable.ext.renderer, {
		header: {
			_: function ( settings, cell, column, classes ) {
				// No additional mark-up required
				// Attach a sort listener to update on sort - note that using the
				// `DT` namespace will allow the event to be removed automatically
				// on destroy, while the `dt` namespaced event is the one we are
				// listening for
				$(settings.nTable).on( 'order.dt.DT', function ( e, ctx, sorting, columns ) {
					if ( settings !== ctx ) { // need to check this this is the host
						return;               // table, not a nested one
					}
	
					var colIdx = column.idx;
	
					cell
						.removeClass(
							classes.sSortAsc +' '+
							classes.sSortDesc
						)
						.addClass( columns[ colIdx ] == 'asc' ?
							classes.sSortAsc : columns[ colIdx ] == 'desc' ?
								classes.sSortDesc :
								column.sSortingClass
						);
				} );
			},
	
			jqueryui: function ( settings, cell, column, classes ) {
				$('<div/>')
					.addClass( classes.sSortJUIWrapper )
					.append( cell.contents() )
					.append( $('<span/>')
						.addClass( classes.sSortIcon+' '+column.sSortingClassJUI )
					)
					.appendTo( cell );
	
				// Attach a sort listener to update on sort
				$(settings.nTable).on( 'order.dt.DT', function ( e, ctx, sorting, columns ) {
					if ( settings !== ctx ) {
						return;
					}
	
					var colIdx = column.idx;
	
					cell
						.removeClass( classes.sSortAsc +" "+classes.sSortDesc )
						.addClass( columns[ colIdx ] == 'asc' ?
							classes.sSortAsc : columns[ colIdx ] == 'desc' ?
								classes.sSortDesc :
								column.sSortingClass
						);
	
					cell
						.find( 'span.'+classes.sSortIcon )
						.removeClass(
							classes.sSortJUIAsc +" "+
							classes.sSortJUIDesc +" "+
							classes.sSortJUI +" "+
							classes.sSortJUIAscAllowed +" "+
							classes.sSortJUIDescAllowed
						)
						.addClass( columns[ colIdx ] == 'asc' ?
							classes.sSortJUIAsc : columns[ colIdx ] == 'desc' ?
								classes.sSortJUIDesc :
								column.sSortingClassJUI
						);
				} );
			}
		}
	} );
	
	/*
	 * Public helper functions. These aren't used internally by DataTables, or
	 * called by any of the options passed into DataTables, but they can be used
	 * externally by developers working with DataTables. They are helper functions
	 * to make working with DataTables a little bit easier.
	 */
	
	var __htmlEscapeEntities = function ( d ) {
		if (Array.isArray(d)) {
			d = d.join(',');
		}
	
		return typeof d === 'string' ?
			d
				.replace(/&/g, '&amp;')
				.replace(/</g, '&lt;')
				.replace(/>/g, '&gt;')
				.replace(/"/g, '&quot;') :
			d;
	};
	
	// Common logic for moment, luxon or a date action
	function __mld( dt, momentFn, luxonFn, dateFn, arg1 ) {
		if (window.moment) {
			return dt[momentFn]( arg1 );
		}
		else if (window.luxon) {
			return dt[luxonFn]( arg1 );
		}
		
		return dateFn ? dt[dateFn]( arg1 ) : dt;
	}
	
	
	var __mlWarning = false;
	function __mldObj (d, format, locale) {
		var dt;
	
		if (window.moment) {
			dt = window.moment.utc( d, format, locale, true );
	
			if (! dt.isValid()) {
				return null;
			}
		}
		else if (window.luxon) {
			dt = format
				? window.luxon.DateTime.fromFormat( d, format )
				: window.luxon.DateTime.fromISO( d );
	
			if (! dt.isValid) {
				return null;
			}
	
			dt.setLocale(locale);
		}
		else if (! format) {
			// No format given, must be ISO
			dt = new Date(d);
		}
		else {
			if (! __mlWarning) {
				alert('DataTables warning: Formatted date without Moment.js or Luxon - https://datatables.net/tn/17');
			}
	
			__mlWarning = true;
		}
	
		return dt;
	}
	
	// Wrapper for date, datetime and time which all operate the same way with the exception of
	// the output string for auto locale support
	function __mlHelper (localeString) {
		return function ( from, to, locale, def ) {
			// Luxon and Moment support
			// Argument shifting
			if ( arguments.length === 0 ) {
				locale = 'en';
				to = null; // means toLocaleString
				from = null; // means iso8601
			}
			else if ( arguments.length === 1 ) {
				locale = 'en';
				to = from;
				from = null;
			}
			else if ( arguments.length === 2 ) {
				locale = to;
				to = from;
				from = null;
			}
	
			var typeName = 'datetime-' + to;
	
			// Add type detection and sorting specific to this date format - we need to be able to identify
			// date type columns as such, rather than as numbers in extensions. Hence the need for this.
			if (! DataTable.ext.type.order[typeName]) {
				// The renderer will give the value to type detect as the type!
				DataTable.ext.type.detect.unshift(function (d) {
					return d === typeName ? typeName : false;
				});
	
				// The renderer gives us Moment, Luxon or Date obects for the sorting, all of which have a
				// `valueOf` which gives milliseconds epoch
				DataTable.ext.type.order[typeName + '-asc'] = function (a, b) {
					var x = a.valueOf();
					var y = b.valueOf();
	
					return x === y
						? 0
						: x < y
							? -1
							: 1;
				}
	
				DataTable.ext.type.order[typeName + '-desc'] = function (a, b) {
					var x = a.valueOf();
					var y = b.valueOf();
	
					return x === y
						? 0
						: x > y
							? -1
							: 1;
				}
			}
		
			return function ( d, type ) {
				// Allow for a default value
				if (d === null || d === undefined) {
					if (def === '--now') {
						// We treat everything as UTC further down, so no changes are
						// made, as such need to get the local date / time as if it were
						// UTC
						var local = new Date();
						d = new Date( Date.UTC(
							local.getFullYear(), local.getMonth(), local.getDate(),
							local.getHours(), local.getMinutes(), local.getSeconds()
						) );
					}
					else {
						d = '';
					}
				}
	
				if (type === 'type') {
					// Typing uses the type name for fast matching
					return typeName;
				}
	
				if (d === '') {
					return type !== 'sort'
						? ''
						: __mldObj('0000-01-01 00:00:00', null, locale);
				}
	
				// Shortcut. If `from` and `to` are the same, we are using the renderer to
				// format for ordering, not display - its already in the display format.
				if ( to !== null && from === to && type !== 'sort' && type !== 'type' && ! (d instanceof Date) ) {
					return d;
				}
	
				var dt = __mldObj(d, from, locale);
	
				if (dt === null) {
					return d;
				}
	
				if (type === 'sort') {
					return dt;
				}
				
				var formatted = to === null
					? __mld(dt, 'toDate', 'toJSDate', '')[localeString]()
					: __mld(dt, 'format', 'toFormat', 'toISOString', to);
	
				// XSS protection
				return type === 'display' ?
					__htmlEscapeEntities( formatted ) :
					formatted;
			};
		}
	}
	
	// Based on locale, determine standard number formatting
	// Fallback for legacy browsers is US English
	var __thousands = ',';
	var __decimal = '.';
	
	if (Intl) {
		try {
			var num = new Intl.NumberFormat().formatToParts(100000.1);
		
			for (var i=0 ; i<num.length ; i++) {
				if (num[i].type === 'group') {
					__thousands = num[i].value;
				}
				else if (num[i].type === 'decimal') {
					__decimal = num[i].value;
				}
			}
		}
		catch (e) {
			// noop
		}
	}
	
	// Formatted date time detection - use by declaring the formats you are going to use
	DataTable.datetime = function ( format, locale ) {
		var typeName = 'datetime-detect-' + format;
	
		if (! locale) {
			locale = 'en';
		}
	
		if (! DataTable.ext.type.order[typeName]) {
			DataTable.ext.type.detect.unshift(function (d) {
				var dt = __mldObj(d, format, locale);
				return d === '' || dt ? typeName : false;
			});
	
			DataTable.ext.type.order[typeName + '-pre'] = function (d) {
				return __mldObj(d, format, locale) || 0;
			}
		}
	}
	
	/**
	 * Helpers for `columns.render`.
	 *
	 * The options defined here can be used with the `columns.render` initialisation
	 * option to provide a display renderer. The following functions are defined:
	 *
	 * * `number` - Will format numeric data (defined by `columns.data`) for
	 *   display, retaining the original unformatted data for sorting and filtering.
	 *   It takes 5 parameters:
	 *   * `string` - Thousands grouping separator
	 *   * `string` - Decimal point indicator
	 *   * `integer` - Number of decimal points to show
	 *   * `string` (optional) - Prefix.
	 *   * `string` (optional) - Postfix (/suffix).
	 * * `text` - Escape HTML to help prevent XSS attacks. It has no optional
	 *   parameters.
	 *
	 * @example
	 *   // Column definition using the number renderer
	 *   {
	 *     data: "salary",
	 *     render: $.fn.dataTable.render.number( '\'', '.', 0, '$' )
	 *   }
	 *
	 * @namespace
	 */
	DataTable.render = {
		date: __mlHelper('toLocaleDateString'),
		datetime: __mlHelper('toLocaleString'),
		time: __mlHelper('toLocaleTimeString'),
		number: function ( thousands, decimal, precision, prefix, postfix ) {
			// Auto locale detection
			if (thousands === null || thousands === undefined) {
				thousands = __thousands;
			}
	
			if (decimal === null || decimal === undefined) {
				decimal = __decimal;
			}
	
			return {
				display: function ( d ) {
					if ( typeof d !== 'number' && typeof d !== 'string' ) {
						return d;
					}
	
					if (d === '' || d === null) {
						return d;
					}
	
					var negative = d < 0 ? '-' : '';
					var flo = parseFloat( d );
	
					// If NaN then there isn't much formatting that we can do - just
					// return immediately, escaping any HTML (this was supposed to
					// be a number after all)
					if ( isNaN( flo ) ) {
						return __htmlEscapeEntities( d );
					}
	
					flo = flo.toFixed( precision );
					d = Math.abs( flo );
	
					var intPart = parseInt( d, 10 );
					var floatPart = precision ?
						decimal+(d - intPart).toFixed( precision ).substring( 2 ):
						'';
	
					// If zero, then can't have a negative prefix
					if (intPart === 0 && parseFloat(floatPart) === 0) {
						negative = '';
					}
	
					return negative + (prefix||'') +
						intPart.toString().replace(
							/\B(?=(\d{3})+(?!\d))/g, thousands
						) +
						floatPart +
						(postfix||'');
				}
			};
		},
	
		text: function () {
			return {
				display: __htmlEscapeEntities,
				filter: __htmlEscapeEntities
			};
		}
	};
	
	
	/*
	 * This is really a good bit rubbish this method of exposing the internal methods
	 * publicly... - To be fixed in 2.0 using methods on the prototype
	 */
	
	
	/**
	 * Create a wrapper function for exporting an internal functions to an external API.
	 *  @param {string} fn API function name
	 *  @returns {function} wrapped function
	 *  @memberof DataTable#internal
	 */
	function _fnExternApiFunc (fn)
	{
		return function() {
			var args = [_fnSettingsFromNode( this[DataTable.ext.iApiIndex] )].concat(
				Array.prototype.slice.call(arguments)
			);
			return DataTable.ext.internal[fn].apply( this, args );
		};
	}
	
	
	/**
	 * Reference to internal functions for use by plug-in developers. Note that
	 * these methods are references to internal functions and are considered to be
	 * private. If you use these methods, be aware that they are liable to change
	 * between versions.
	 *  @namespace
	 */
	$.extend( DataTable.ext.internal, {
		_fnExternApiFunc: _fnExternApiFunc,
		_fnBuildAjax: _fnBuildAjax,
		_fnAjaxUpdate: _fnAjaxUpdate,
		_fnAjaxParameters: _fnAjaxParameters,
		_fnAjaxUpdateDraw: _fnAjaxUpdateDraw,
		_fnAjaxDataSrc: _fnAjaxDataSrc,
		_fnAddColumn: _fnAddColumn,
		_fnColumnOptions: _fnColumnOptions,
		_fnAdjustColumnSizing: _fnAdjustColumnSizing,
		_fnVisibleToColumnIndex: _fnVisibleToColumnIndex,
		_fnColumnIndexToVisible: _fnColumnIndexToVisible,
		_fnVisbleColumns: _fnVisbleColumns,
		_fnGetColumns: _fnGetColumns,
		_fnColumnTypes: _fnColumnTypes,
		_fnApplyColumnDefs: _fnApplyColumnDefs,
		_fnHungarianMap: _fnHungarianMap,
		_fnCamelToHungarian: _fnCamelToHungarian,
		_fnLanguageCompat: _fnLanguageCompat,
		_fnBrowserDetect: _fnBrowserDetect,
		_fnAddData: _fnAddData,
		_fnAddTr: _fnAddTr,
		_fnNodeToDataIndex: _fnNodeToDataIndex,
		_fnNodeToColumnIndex: _fnNodeToColumnIndex,
		_fnGetCellData: _fnGetCellData,
		_fnSetCellData: _fnSetCellData,
		_fnSplitObjNotation: _fnSplitObjNotation,
		_fnGetObjectDataFn: _fnGetObjectDataFn,
		_fnSetObjectDataFn: _fnSetObjectDataFn,
		_fnGetDataMaster: _fnGetDataMaster,
		_fnClearTable: _fnClearTable,
		_fnDeleteIndex: _fnDeleteIndex,
		_fnInvalidate: _fnInvalidate,
		_fnGetRowElements: _fnGetRowElements,
		_fnCreateTr: _fnCreateTr,
		_fnBuildHead: _fnBuildHead,
		_fnDrawHead: _fnDrawHead,
		_fnDraw: _fnDraw,
		_fnReDraw: _fnReDraw,
		_fnAddOptionsHtml: _fnAddOptionsHtml,
		_fnDetectHeader: _fnDetectHeader,
		_fnGetUniqueThs: _fnGetUniqueThs,
		_fnFeatureHtmlFilter: _fnFeatureHtmlFilter,
		_fnFilterComplete: _fnFilterComplete,
		_fnFilterCustom: _fnFilterCustom,
		_fnFilterColumn: _fnFilterColumn,
		_fnFilter: _fnFilter,
		_fnFilterCreateSearch: _fnFilterCreateSearch,
		_fnEscapeRegex: _fnEscapeRegex,
		_fnFilterData: _fnFilterData,
		_fnFeatureHtmlInfo: _fnFeatureHtmlInfo,
		_fnUpdateInfo: _fnUpdateInfo,
		_fnInfoMacros: _fnInfoMacros,
		_fnInitialise: _fnInitialise,
		_fnInitComplete: _fnInitComplete,
		_fnLengthChange: _fnLengthChange,
		_fnFeatureHtmlLength: _fnFeatureHtmlLength,
		_fnFeatureHtmlPaginate: _fnFeatureHtmlPaginate,
		_fnPageChange: _fnPageChange,
		_fnFeatureHtmlProcessing: _fnFeatureHtmlProcessing,
		_fnProcessingDisplay: _fnProcessingDisplay,
		_fnFeatureHtmlTable: _fnFeatureHtmlTable,
		_fnScrollDraw: _fnScrollDraw,
		_fnApplyToChildren: _fnApplyToChildren,
		_fnCalculateColumnWidths: _fnCalculateColumnWidths,
		_fnThrottle: _fnThrottle,
		_fnConvertToWidth: _fnConvertToWidth,
		_fnGetWidestNode: _fnGetWidestNode,
		_fnGetMaxLenString: _fnGetMaxLenString,
		_fnStringToCss: _fnStringToCss,
		_fnSortFlatten: _fnSortFlatten,
		_fnSort: _fnSort,
		_fnSortAria: _fnSortAria,
		_fnSortListener: _fnSortListener,
		_fnSortAttachListener: _fnSortAttachListener,
		_fnSortingClasses: _fnSortingClasses,
		_fnSortData: _fnSortData,
		_fnSaveState: _fnSaveState,
		_fnLoadState: _fnLoadState,
		_fnImplementState: _fnImplementState,
		_fnSettingsFromNode: _fnSettingsFromNode,
		_fnLog: _fnLog,
		_fnMap: _fnMap,
		_fnBindAction: _fnBindAction,
		_fnCallbackReg: _fnCallbackReg,
		_fnCallbackFire: _fnCallbackFire,
		_fnLengthOverflow: _fnLengthOverflow,
		_fnRenderer: _fnRenderer,
		_fnDataSource: _fnDataSource,
		_fnRowAttributes: _fnRowAttributes,
		_fnExtend: _fnExtend,
		_fnCalculateEnd: function () {} // Used by a lot of plug-ins, but redundant
		                                // in 1.10, so this dead-end function is
		                                // added to prevent errors
	} );
	
	
	// jQuery access
	$.fn.dataTable = DataTable;
	
	// Provide access to the host jQuery object (circular reference)
	DataTable.$ = $;
	
	// Legacy aliases
	$.fn.dataTableSettings = DataTable.settings;
	$.fn.dataTableExt = DataTable.ext;
	
	// With a capital `D` we return a DataTables API instance rather than a
	// jQuery object
	$.fn.DataTable = function ( opts ) {
		return $(this).dataTable( opts ).api();
	};
	
	// All properties that are available to $.fn.dataTable should also be
	// available on $.fn.DataTable
	$.each( DataTable, function ( prop, val ) {
		$.fn.DataTable[ prop ] = val;
	} );
	
	return DataTable;
}
\")).default;\n",
+       "    dt(window.$);\n",
+       "  }\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>table.dataTable td.dt-control{text-align:center;cursor:pointer}table.dataTable td.dt-control:before{height:1em;width:1em;margin-top:-9px;display:inline-block;color:white;border:.15em solid white;border-radius:1em;box-shadow:0 0 .2em #444;box-sizing:content-box;text-align:center;text-indent:0 !important;font-family:\"Courier New\",Courier,monospace;line-height:1em;content:\"+\";background-color:#31b131}table.dataTable tr.dt-hasChild td.dt-control:before{content:\"-\";background-color:#d33333}table.dataTable thead>tr>th.sorting,table.dataTable thead>tr>th.sorting_asc,table.dataTable thead>tr>th.sorting_desc,table.dataTable thead>tr>th.sorting_asc_disabled,table.dataTable thead>tr>th.sorting_desc_disabled,table.dataTable thead>tr>td.sorting,table.dataTable thead>tr>td.sorting_asc,table.dataTable thead>tr>td.sorting_desc,table.dataTable thead>tr>td.sorting_asc_disabled,table.dataTable 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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:itables.downsample:showing 744x11 of 6645x11 as nbytes=584760>65536=maxBytes. See https://mwouts.github.io/itables/downsampling.html\n"
+     ]
+    },
+    {
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+       "<table id=\"0243800c-2969-4ef8-b760-c6764031e055\" class=\"display\"style=\"max-width:100%\"><thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>R^2 Spearman</th>\n",
+       "      <th>Behaviour</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Moved</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Number of times repeated_x</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Level</th>\n",
+       "      <th>Number of times repeated_y</th>\n",
+       "    </tr>\n",
+       "  </thead><tbody><tr><td>Loading... (need <a href=https://mwouts.github.io/itables/troubleshooting.html>help</a>?)</td></tr></tbody></table>\n",
+       "<style>\n",
+       "table td {\n",
+       "    text-overflow: ellipsis;\n",
+       "    overflow: hidden;\n",
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+       "\n",
+       "table th {\n",
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+       "\n",
+       "thead input {\n",
+       "    width: 100%;\n",
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+       "\n",
+       "tfoot input {\n",
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+       "    box-sizing: border-box;\n",
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+       "\n",
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+       "<script>\n",
+       "    // We use a namespace per table (= the next curly bracket)\n",
+       "    // to avoid conflicts between the various tables' data & dt_args\n",
+       "    {\n",
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+       "        const data = [[0, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.817804, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [1, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.759333, \"Negative\", \"SWE\", \"3\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [2, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.936963, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [3, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.786708, \"Negative\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [4, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.924056, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [5, \"Adolescent fertility rate (births per 1,000 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\"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [11, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.992105, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [12, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.927189, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [13, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.897966, \"Negative\", \"OMN\", \"21\", \"Does not apply\", \"Persian Gulf\", 22, \"Demography\", \"secondary\", 40], [14, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941656, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [15, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.984804, \"Negative\", \"ISR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [16, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941309, \"Negative\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [17, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.902682, \"Negative\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [18, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.805222, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [19, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.863071, \"Negative\", \"LBR\", \"2\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [20, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.939188, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [21, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.821756, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [22, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.934848, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [23, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.844272, \"Negative\", \"GHA\", \"1\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [24, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.99911, \"Negative\", \"BGD\", \"2\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [25, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.992671, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 22, \"Demography\", \"secondary\", 40], [26, \"Adolescent fertility rate (births per 1,000 women 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22, \"Demography\", \"secondary\", 40], [32, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.940761, \"Negative\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [33, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.950571, \"Negative\", \"VEN\", \"1\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [34, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.89959, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [35, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.895844, \"Negative\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [36, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999778, \"Negative\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [37, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999123, \"Negative\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 22, \"Demography\", \"secondary\", 40], [38, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.999123, \"Negative\", \"USA\", \"13\", \"Does not apply\", \"Pair\", 22, \"Demography\", \"secondary\", 40], [39, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.995455, \"Positive\", \"CHN\", \"21\", \"Does not apply\", \"Pair\", 22, \"Demography\", \"secondary\", 40], [40, \"Commercial bank branches (per 100,000 adults)\", 0.801333, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [41, \"Commercial bank branches (per 100,000 adults)\", 0.75667, \"Negative\", \"FRA\", \"5\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [42, \"Commercial bank branches (per 100,000 adults)\", 0.782831, \"Negative\", \"GBR\", \"5\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [43, \"Commercial bank branches (per 100,000 adults)\", 0.838543, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [44, \"Commercial bank branches (per 100,000 adults)\", 0.836928, \"Negative\", \"NLD\", \"13\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [45, \"Commercial bank branches (per 100,000 adults)\", 0.933522, \"Negative\", \"QAT\", \"5\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [46, \"Commercial bank branches (per 100,000 adults)\", 0.755661, \"Negative\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [47, \"Commercial bank branches (per 100,000 adults)\", 0.875117, \"Negative\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [48, \"Commercial bank branches (per 100,000 adults)\", 0.895191, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [49, \"Commercial bank branches (per 100,000 adults)\", 0.76892, \"Positive\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [50, \"Commercial bank branches (per 100,000 adults)\", 0.81853, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [51, \"Commercial bank branches (per 100,000 adults)\", 0.835641, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [52, \"Commercial bank branches (per 100,000 adults)\", 0.843377, \"Positive\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [53, \"Commercial bank branches (per 100,000 adults)\", 0.877827, \"Positive\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 11, \"Economy\", \"secondary\", 26], [54, \"Commercial bank branches (per 100,000 adults)\", 0.881445, \"Positive\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [55, \"Commercial bank branches (per 100,000 adults)\", 0.787284, \"Positive\", \"ZAF\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [56, \"Commercial bank branches (per 100,000 adults)\", 0.907985, \"Positive\", \"MOZ\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [57, \"Commercial bank branches (per 100,000 adults)\", 0.892506, \"Positive\", \"CMR\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [58, \"Commercial bank branches (per 100,000 adults)\", 0.869419, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Economy\", \"secondary\", 26], [59, \"Commercial bank branches (per 100,000 adults)\", 0.960022, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [60, \"Commercial bank branches (per 100,000 adults)\", 0.980779, \"Positive\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [61, \"Commercial bank branches (per 100,000 adults)\", 0.806534, \"Positive\", \"IDN\", \"2\", \"Does not apply\", \"Asia\", 11, \"Economy\", \"secondary\", 26], [62, \"Commercial bank branches (per 100,000 adults)\", 0.814298, \"Positive\", \"MEX\", \"Does not apply\", \"Logarithmic\", \"Latam\", 11, \"Economy\", \"secondary\", 26], [63, \"Commercial bank branches (per 100,000 adults)\", 0.977356, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 11, \"Economy\", \"secondary\", 26], [64, \"Commercial bank branches (per 100,000 adults)\", 0.977356, \"Negative\", \"USA\", \"21\", \"Does not apply\", \"Pair\", 11, \"Economy\", \"secondary\", 26], [65, \"Commercial bank branches (per 100,000 adults)\", 0.768043, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 11, \"Economy\", \"secondary\", 26], [66, \"CO2 emissions (kt)\", 0.794276, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [67, \"CO2 emissions (kt)\", 0.761173, \"Negative\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [68, \"CO2 emissions (kt)\", 0.905449, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 30, \"Environment\", \"secondary\", 33], [69, \"CO2 emissions (kt)\", 0.971453, \"Positive\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [70, \"CO2 emissions (kt)\", 0.984775, \"Positive\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [71, \"CO2 emissions (kt)\", 0.960933, \"Positive\", \"SAU\", \"3\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [72, \"CO2 emissions (kt)\", 0.97328, \"Positive\", \"OMN\", \"5\", \"Does not apply\", \"Persian Gulf\", 30, \"Environment\", \"secondary\", 33], [73, \"CO2 emissions (kt)\", 0.829432, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [74, \"CO2 emissions (kt)\", 0.921508, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [75, \"CO2 emissions (kt)\", 0.941573, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [76, \"CO2 emissions (kt)\", 0.952939, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 30, \"Environment\", \"secondary\", 33], [77, \"CO2 emissions (kt)\", 0.789979, \"Positive\", \"SEN\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [78, \"CO2 emissions (kt)\", 0.783163, \"Positive\", \"ZAF\", \"1\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [79, \"CO2 emissions (kt)\", 0.833274, \"Positive\", \"LBR\", \"Does not apply\", \"Linear\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [80, \"CO2 emissions (kt)\", 0.930963, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [81, \"CO2 emissions (kt)\", 0.78301, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [82, \"CO2 emissions (kt)\", 0.85301, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 30, \"Environment\", \"secondary\", 33], [83, \"CO2 emissions (kt)\", 1.0, \"Positive\", \"BGD\", \"Does not apply\", \"Cuadratic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [84, \"CO2 emissions (kt)\", 0.992493, \"Positive\", \"IND\", \"Does not apply\", \"Linear\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [85, \"CO2 emissions (kt)\", 0.993956, \"Positive\", \"VNM\", \"1\", \"Does not apply\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [86, \"CO2 emissions (kt)\", 0.863207, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [87, \"CO2 emissions (kt)\", 0.902509, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [88, \"CO2 emissions (kt)\", 0.903608, \"Positive\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [89, \"CO2 emissions (kt)\", 0.959865, \"Positive\", \"KOR\", \"Does not apply\", \"Cubic\", \"Asia\", 30, \"Environment\", \"secondary\", 33], [90, \"CO2 emissions (kt)\", 0.821451, \"Positive\", \"MEX\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [91, \"CO2 emissions (kt)\", 0.855111, \"Positive\", \"BRA\", \"2\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [92, \"CO2 emissions (kt)\", 0.753552, \"Positive\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [93, \"CO2 emissions (kt)\", 0.944664, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [94, \"CO2 emissions (kt)\", 0.768742, \"Positive\", \"COL\", \"1\", \"Does not apply\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [95, \"CO2 emissions (kt)\", 0.925663, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [96, \"CO2 emissions (kt)\", 0.900419, \"Positive\", \"PAN\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [97, \"CO2 emissions (kt)\", 0.964184, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 30, \"Environment\", \"secondary\", 33], [98, \"CO2 emissions (kt)\", 0.98957, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 30, \"Environment\", \"secondary\", 33], [99, \"International migrant stock (% of population)\", 0.782956, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [100, \"International migrant stock (% of population)\", 0.807906, \"Positive\", \"FRA\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [101, \"International migrant stock (% of population)\", 0.770511, \"Positive\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [102, \"International migrant stock (% of population)\", 0.907327, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [103, \"International migrant stock (% of population)\", 0.900471, \"Positive\", \"ESP\", \"3\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [104, \"International migrant stock (% of population)\", 0.835596, \"Positive\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [105, \"International migrant stock (% of population)\", 0.968708, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [106, \"International migrant stock (% of population)\", 0.841846, \"Positive\", \"GRC\", \"5\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [107, \"International migrant stock (% of population)\", 0.831819, \"Positive\", \"AUT\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [108, \"International migrant stock (% of population)\", 0.898961, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 16, \"Migration\", \"secondary\", 31], [109, \"International migrant stock (% of population)\", 0.929941, \"Positive\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [110, \"International migrant stock (% of population)\", 0.927564, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [111, \"International migrant stock (% of population)\", 0.972132, \"Negative\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [112, \"International migrant stock (% of population)\", 0.903976, \"Positive\", \"YEM\", \"8\", \"Does not apply\", \"Persian Gulf\", 16, \"Migration\", \"secondary\", 31], [113, \"International migrant stock (% of population)\", 0.833059, \"Negative\", \"DZA\", \"3\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [114, \"International migrant stock (% of population)\", 0.972723, \"Negative\", \"ISR\", \"1\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [115, \"International migrant stock (% of population)\", 0.906777, \"Negative\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [116, \"International migrant stock (% of population)\", 0.783531, \"Positive\", \"MAR\", \"13\", \"Does not apply\", \"North Africa\", 16, \"Migration\", \"secondary\", 31], [117, \"International migrant stock (% of population)\", 0.775082, \"Negative\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [118, \"International migrant stock (% of population)\", 0.943912, \"Negative\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [119, \"International migrant stock (% of population)\", 0.971948, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [120, \"International migrant stock (% of population)\", 0.98406, \"Negative\", \"IND\", \"Does not apply\", \"Logarithmic\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [121, \"International migrant stock (% of population)\", 0.90581, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [122, \"International migrant stock (% of population)\", 0.874691, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [123, \"International migrant stock (% of population)\", 0.945693, \"Positive\", \"KOR\", \"Does not apply\", \"Linear\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [124, \"International migrant stock (% of population)\", 0.933482, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [125, \"International migrant stock (% of population)\", 0.912187, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [126, \"International migrant stock (% of population)\", 0.892956, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [127, \"International migrant stock (% of population)\", 0.989918, \"Positive\", \"PAN\", \"Does not apply\", \"Cubic\", \"Latam\", 16, \"Migration\", \"secondary\", 31], [128, \"International migrant stock (% of population)\", 0.987719, \"Positive\", \"USA\", \"Does not apply\", \"Cubic\", \"Pair\", 16, \"Migration\", \"secondary\", 31], [129, \"International migrant stock (% of population)\", 0.989918, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 16, \"Migration\", \"secondary\", 31], [130, \"CO2 emissions (metric tons per capita)\", 0.782806, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [131, \"CO2 emissions (metric tons per capita)\", 0.768845, \"Negative\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [132, \"CO2 emissions (metric tons per capita)\", 0.915538, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [133, \"CO2 emissions (metric tons per capita)\", 0.78562, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [134, \"CO2 emissions (metric tons per capita)\", 0.759154, \"Negative\", \"NLD\", \"5\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [135, \"CO2 emissions (metric tons per capita)\", 0.806797, \"Positive\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 18, \"Environment\", \"secondary\", 29], [136, \"CO2 emissions (metric tons per capita)\", 0.841863, \"Positive\", \"OMN\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Environment\", \"secondary\", 29], [137, \"CO2 emissions (metric tons per capita)\", 0.846222, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [138, \"CO2 emissions (metric tons per capita)\", 0.884204, \"Positive\", \"EGY\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [139, \"CO2 emissions (metric tons per capita)\", 0.788288, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [140, \"CO2 emissions (metric tons per capita)\", 0.895687, \"Positive\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [141, \"CO2 emissions (metric tons per capita)\", 0.936433, \"Positive\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [142, \"CO2 emissions (metric tons per capita)\", 0.782676, \"Positive\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [143, \"CO2 emissions (metric tons per capita)\", 0.905693, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [144, \"CO2 emissions (metric tons per capita)\", 0.786576, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [145, \"CO2 emissions (metric tons per capita)\", 0.997798, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [146, \"CO2 emissions (metric tons per capita)\", 0.98957, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [147, \"CO2 emissions (metric tons per capita)\", 0.991031, \"Positive\", \"VNM\", \"Does not apply\", \"Linear\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [148, \"CO2 emissions (metric tons per capita)\", 0.858441, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [149, \"CO2 emissions (metric tons per capita)\", 0.903207, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [150, \"CO2 emissions (metric tons per capita)\", 0.840909, \"Positive\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [151, \"CO2 emissions (metric tons per capita)\", 0.948395, \"Positive\", \"KOR\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [152, \"CO2 emissions (metric tons per capita)\", 0.866209, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [153, \"CO2 emissions (metric tons per capita)\", 0.880682, \"Positive\", \"PER\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [154, \"CO2 emissions (metric tons per capita)\", 0.895551, \"Positive\", \"CHL\", \"Does not apply\", \"Cubic\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [155, \"CO2 emissions (metric tons per capita)\", 0.757305, \"Positive\", \"PAN\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [156, \"CO2 emissions (metric tons per capita)\", 0.753095, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [157, \"CO2 emissions (metric tons per capita)\", 0.913924, \"Negative\", \"USA\", \"8\", \"Does not apply\", \"Pair\", 18, \"Environment\", \"secondary\", 29], [158, \"CO2 emissions (metric tons per capita)\", 0.976473, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 18, \"Environment\", \"secondary\", 29], [159, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.779372, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [160, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.798438, \"Positive\", \"FRA\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [161, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.782956, \"Positive\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [162, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.907327, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [163, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.800012, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [164, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.791365, \"Positive\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [165, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.968708, \"Positive\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [166, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.82583, \"Positive\", \"AUT\", \"3\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [167, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.843098, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [168, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.935625, \"Positive\", \"AZE\", \"5\", \"Does not apply\", \"Persian Gulf\", 29, \"Principal\", \"primary\", 35], [169, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.932399, \"Positive\", \"EGY\", \"2\", \"Does not apply\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [170, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.936453, \"Positive\", \"ISR\", \"Does not apply\", \"Cubic\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [171, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.920491, \"Positive\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [172, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.780022, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [173, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.820787, \"Positive\", \"ZAF\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [174, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.937635, \"Positive\", \"MOZ\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [175, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.848688, \"Positive\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [176, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.941264, \"Positive\", \"NGA\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [177, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.838169, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [178, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.98201, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [179, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.950622, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [180, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.967217, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [181, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.848688, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [182, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.927815, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 29, \"Principal\", \"primary\", 35], [183, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.93913, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [184, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.941701, \"Positive\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 29, \"Principal\", \"primary\", 35], [185, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.925869, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [186, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.831081, \"Positive\", \"BRA\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [187, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.752212, \"Positive\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [188, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.912985, \"Positive\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [189, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.853454, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [190, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.946165, \"Positive\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [191, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.899891, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [192, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.987719, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 29, \"Principal\", \"primary\", 35], [193, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.981854, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 29, \"Principal\", \"primary\", 35], [194, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.759092, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [195, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.898766, \"Positive\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [196, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.775622, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [197, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.801511, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [198, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.788969, \"Positive\", \"AUT\", \"2\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [199, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.857483, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [200, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.908522, \"Negative\", \"QAT\", \"1\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [201, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.892232, \"Negative\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [202, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.994777, \"Negative\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [203, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.947125, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [204, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.984385, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [205, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.891518, \"Negative\", \"DZA\", \"1\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [206, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.92803, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [207, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.819845, \"Positive\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [208, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.876163, \"Negative\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [209, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.902778, \"Negative\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [210, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.809567, \"Negative\", \"MOZ\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [211, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.907764, \"Negative\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [212, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.96926, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [213, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.86686, \"Negative\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [214, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.993913, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [215, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.987845, \"Negative\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [216, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.815973, \"Positive\", \"VNM\", \"21\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [217, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.904913, \"Negative\", \"PHL\", \"Does not apply\", \"Logarithmic\", \"Asia\", 18, \"Health\", \"secondary\", 33], [218, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.933133, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [219, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.754175, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [220, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.799358, \"Negative\", \"PER\", \"13\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [221, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.893013, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [222, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.896554, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [223, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.996522, \"Negative\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [224, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.821128, \"Negative\", \"CRI\", \"Does not apply\", \"Logarithmic\", \"Latam\", 18, \"Health\", \"secondary\", 33], [225, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.886364, \"Positive\", \"USA\", \"21\", \"Does not apply\", \"Pair\", 18, \"Health\", \"secondary\", 33], [226, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.833357, \"Negative\", \"CHN\", \"Does not apply\", \"Logarithmic\", \"Pair\", 18, \"Health\", \"secondary\", 33], [227, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.809179, \"Positive\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [228, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.773946, \"Positive\", \"FRA\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [229, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.815998, \"Positive\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [230, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.8799, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [231, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.751187, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [232, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.909632, \"Positive\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [233, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.873516, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [234, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.799967, \"Positive\", \"AUT\", \"Does not apply\", \"Logarithmic\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [235, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.829252, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [236, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.889718, \"Positive\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [237, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.977084, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [238, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.959094, \"Positive\", \"AZE\", \"21\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [239, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.847827, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [240, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.859402, \"Positive\", \"EGY\", \"Does not apply\", \"Linear\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [241, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.962303, \"Positive\", \"ISR\", \"Does not apply\", \"Cuadratic\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [242, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.932453, \"Positive\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [243, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.920393, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [244, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.904112, \"Positive\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [245, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.82775, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [246, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.784068, \"Positive\", \"NGA\", \"13\", \"Does not apply\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [247, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.843838, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [248, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.972127, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [249, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.955771, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [250, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.914644, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [251, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.864774, \"Positive\", \"THA\", \"1\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [252, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.897196, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [253, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.937594, \"Positive\", \"PHL\", \"2\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [254, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.918708, \"Positive\", \"KOR\", \"3\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [255, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.951463, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [256, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.751235, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [257, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.883617, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [258, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.893338, \"Positive\", \"VEN\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [259, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.904546, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [260, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.903191, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [261, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.979415, \"Positive\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [262, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.971504, \"Positive\", \"CRI\", \"2\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [263, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.949698, \"Positive\", \"USA\", \"2\", \"Does not apply\", \"Pair\", 28, \"Imports\", \"secondary\", 38], [264, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.925899, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 28, \"Imports\", \"secondary\", 38], [265, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.785897, \"Positive\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [266, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.832712, \"Positive\", \"FRA\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [267, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.792293, \"Positive\", \"SWE\", \"Does not apply\", \"Cuadratic\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [268, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.890853, \"Positive\", \"GBR\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [269, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.846758, \"Positive\", \"ESP\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [270, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.796993, \"Positive\", \"POL\", \"Does not apply\", \"Cuadratic\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [271, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.855869, \"Positive\", \"AUT\", \"2\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [272, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.940436, \"Positive\", \"SAU\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 15], [273, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.933687, \"Positive\", \"ISR\", \"Does not apply\", \"Cubic\", \"North Africa\", 12, \"Environment\", \"secondary\", 15], [274, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.836127, \"Positive\", \"ZAF\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 12, \"Environment\", \"secondary\", 15], [275, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.813553, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Environment\", \"secondary\", 15], [276, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.813553, \"Positive\", \"MEX\", \"Does not apply\", \"Linear\", \"Latam\", 12, \"Environment\", \"secondary\", 15], [277, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.7618, \"Positive\", \"VEN\", \"5\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 15], [278, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.945556, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 12, \"Environment\", \"secondary\", 15], [279, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.947754, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 12, \"Environment\", \"secondary\", 15], [280, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.768636, \"Negative\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [281, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.797226, \"Negative\", \"FRA\", \"3\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [282, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.785944, \"Negative\", \"SWE\", \"3\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [283, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.879221, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [284, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.868737, \"Negative\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [285, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.834752, \"Negative\", \"HRV\", \"2\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [286, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.944398, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [287, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.852272, \"Negative\", \"AUT\", \"1\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [288, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.863656, \"Negative\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [289, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.977434, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [290, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.977717, \"Negative\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [291, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.979206, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [292, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.909342, \"Negative\", \"AZE\", \"1\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [293, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.871188, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [294, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.922552, \"Negative\", \"OMN\", \"2\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [295, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.813387, \"Negative\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [296, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.95019, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [297, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.956329, \"Negative\", \"ISR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [298, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.945261, \"Negative\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [299, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.953349, \"Negative\", \"MAR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [300, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.939033, \"Negative\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [301, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.779079, \"Negative\", \"LBR\", \"1\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [302, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.939188, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [303, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.845139, \"Negative\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [304, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.934848, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [305, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.858043, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [306, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.842518, \"Negative\", \"BGD\", \"1\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [307, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.992671, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [308, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.993951, \"Negative\", \"VNM\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [309, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.900637, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [310, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.78477, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [311, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.947448, \"Negative\", \"KOR\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [312, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.898692, \"Negative\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [313, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.780573, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [314, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.801076, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [315, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.934719, \"Negative\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [316, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.887514, \"Negative\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [317, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.892492, \"Negative\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [318, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.927253, \"Negative\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [319, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.970669, \"Negative\", \"CRI\", \"Does not apply\", \"Logarithmic\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [320, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.823122, \"Negative\", \"USA\", \"Does not apply\", \"Logarithmic\", \"Pair\", 25, \"Mortality\", \"secondary\", 42], [321, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.989378, \"Negative\", \"CHN\", \"Does not apply\", \"Logarithmic\", \"Pair\", 25, \"Mortality\", \"secondary\", 42], [322, \"Time required to start a business (days)\", 0.792869, \"Negative\", \"DEU\", \"3\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [323, \"Time required to start a business (days)\", 0.762356, \"Negative\", \"FRA\", \"5\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [324, \"Time required to start a business (days)\", 0.847713, \"Negative\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [325, \"Time required to start a business (days)\", 0.810464, \"Negative\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [326, \"Time required to start a business (days)\", 0.905217, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [327, \"Time required to start a business (days)\", 0.816949, \"Negative\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [328, \"Time required to start a business (days)\", 0.885547, \"Negative\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [329, \"Time required to start a business (days)\", 0.841579, \"Negative\", \"SAU\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [330, \"Time required to start a business (days)\", 0.932239, \"Negative\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [331, \"Time required to start a business (days)\", 0.76496, \"Negative\", \"YEM\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [332, \"Time required to start a business (days)\", 0.873136, \"Negative\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [333, \"Time required to start a business (days)\", 0.830084, \"Negative\", \"DZA\", \"3\", \"Does not apply\", \"North Africa\", 11, \"Industry\", \"secondary\", 29], [334, \"Time required to start a business (days)\", 0.811387, \"Negative\", \"MAR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Industry\", \"secondary\", 29], [335, \"Time required to start a business (days)\", 0.782024, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [336, \"Time required to start a business (days)\", 0.793607, \"Negative\", \"ZAF\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [337, \"Time required to start a business (days)\", 0.751747, \"Negative\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [338, \"Time required to start a business (days)\", 0.820023, \"Negative\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [339, \"Time required to start a business (days)\", 0.874331, \"Negative\", \"CMR\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [340, \"Time required to start a business (days)\", 0.806947, \"Negative\", \"IND\", \"21\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [341, \"Time required to start a business (days)\", 0.965942, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [342, \"Time required to start a business (days)\", 0.83733, \"Negative\", \"THA\", \"5\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [343, \"Time required to start a business (days)\", 0.797064, \"Negative\", \"KOR\", \"5\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [344, \"Time required to start a business (days)\", 0.818574, \"Negative\", \"PER\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [345, \"Time required to start a business (days)\", 0.751607, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [346, \"Time required to start a business (days)\", 0.84776, \"Negative\", \"COL\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [347, \"Time required to start a business (days)\", 0.866647, \"Negative\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [348, \"Time required to start a business (days)\", 0.876316, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [349, \"Time required to start a business (days)\", 0.85138, \"Negative\", \"CRI\", \"8\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [350, \"Time required to start a business (days)\", 0.931818, \"Negative\", \"CHN\", \"21\", \"Does not apply\", \"Pair\", 11, \"Industry\", \"secondary\", 29], [351, \"Combustible renewables and waste (% of total energy)\", 0.759597, \"Positive\", \"DEU\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [352, \"Combustible renewables and waste (% of total energy)\", 0.793758, \"Positive\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [353, \"Combustible renewables and waste (% of total energy)\", 0.905255, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [354, \"Combustible renewables and waste (% of total energy)\", 0.915732, \"Positive\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [355, \"Combustible renewables and waste (% of total energy)\", 0.776496, \"Positive\", \"AUT\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [356, \"Combustible renewables and waste (% of total energy)\", 0.810504, \"Positive\", \"NLD\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [357, \"Combustible renewables and waste (% of total energy)\", 0.935853, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 25], [358, \"Combustible renewables and waste (% of total energy)\", 0.763706, \"Positive\", \"AZE\", \"Does not apply\", \"Linear\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 25], [359, \"Combustible renewables and waste (% of total energy)\", 0.780104, \"Negative\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [360, \"Combustible renewables and waste (% of total energy)\", 0.941407, \"Negative\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [361, \"Combustible renewables and waste (% of total energy)\", 0.78341, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [362, \"Combustible renewables and waste (% of total energy)\", 0.890534, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Environment\", \"secondary\", 25], [363, \"Combustible renewables and waste (% of total energy)\", 0.822012, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 12, \"Environment\", \"secondary\", 25], [364, \"Combustible renewables and waste (% of total energy)\", 0.982244, \"Negative\", \"BGD\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [365, \"Combustible renewables and waste (% of total energy)\", 0.978927, \"Negative\", \"IND\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [366, \"Combustible renewables and waste (% of total energy)\", 0.860249, \"Negative\", \"VNM\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [367, \"Combustible renewables and waste (% of total energy)\", 0.803047, \"Negative\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [368, \"Combustible renewables and waste (% of total energy)\", 0.918827, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [369, \"Combustible renewables and waste (% of total energy)\", 0.784859, \"Positive\", \"KOR\", \"3\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [370, \"Combustible renewables and waste (% of total energy)\", 0.915132, \"Negative\", \"MEX\", \"1\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 25], [371, \"Combustible renewables and waste (% of total energy)\", 0.930378, \"Negative\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 25], [6273, \"Food production index (2014-2016 = 100)\", 0.868933, \"Positive\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6274, \"Food production index (2014-2016 = 100)\", 0.834377, \"Positive\", \"MOZ\", \"Does not apply\", \"Linear\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6275, \"Food production index (2014-2016 = 100)\", 0.821876, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6276, \"Food production index (2014-2016 = 100)\", 0.858764, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6277, \"Food production index (2014-2016 = 100)\", 0.860215, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6278, \"Food production index (2014-2016 = 100)\", 0.995078, \"Positive\", \"BGD\", \"3\", \"Does not apply\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6279, \"Food production index (2014-2016 = 100)\", 0.98233, \"Positive\", \"IND\", \"1\", \"Does not apply\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6280, \"Food production index (2014-2016 = 100)\", 0.994324, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6281, \"Food production index (2014-2016 = 100)\", 0.807971, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6282, \"Food production index (2014-2016 = 100)\", 0.943786, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6283, \"Food production index (2014-2016 = 100)\", 0.942178, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6284, \"Food production index (2014-2016 = 100)\", 0.93495, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6285, \"Food production index (2014-2016 = 100)\", 0.76444, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6286, \"Food production index (2014-2016 = 100)\", 0.936928, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6287, \"Food production index (2014-2016 = 100)\", 0.896305, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6288, \"Food production index (2014-2016 = 100)\", 0.878859, \"Positive\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6289, \"Food production index (2014-2016 = 100)\", 0.966456, \"Positive\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6290, \"Food production index (2014-2016 = 100)\", 0.985531, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6291, \"Food production index (2014-2016 = 100)\", 0.946677, \"Positive\", \"USA\", \"1\", \"Does not apply\", \"Pair\", 24, \"Industry\", \"secondary\", 35], [6292, \"Food production index (2014-2016 = 100)\", 0.995055, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 24, \"Industry\", \"secondary\", 35], [6293, \"Time to obtain an electrical connection (days)\", 0.963721, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 11, \"R&D\", \"secondary\", 24], [6294, \"Time to obtain an electrical connection (days)\", 0.83291, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 11, \"R&D\", \"secondary\", 24], [6295, \"Time to obtain an electrical connection (days)\", 0.804939, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6296, \"Time to obtain an electrical connection (days)\", 0.75968, \"Negative\", \"TUR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6297, \"Time to obtain an electrical connection (days)\", 0.876713, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6298, \"Time to obtain an electrical connection (days)\", 0.852294, \"Positive\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6299, \"Time to obtain an electrical connection (days)\", 0.805169, \"Negative\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6300, \"Time to obtain an electrical connection (days)\", 0.92942, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6301, \"Time to obtain an electrical connection (days)\", 0.881377, \"Negative\", \"CMR\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6302, \"Time to obtain an electrical connection (days)\", 0.891739, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6303, \"Time to obtain an electrical connection (days)\", 0.843733, \"Positive\", \"GHA\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6304, \"Time to obtain an electrical connection (days)\", 0.877193, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6305, \"Time to obtain an electrical connection (days)\", 0.917544, \"Negative\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6306, \"Time to obtain an electrical connection (days)\", 0.901754, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6307, \"Time to obtain an electrical connection (days)\", 0.817729, \"Negative\", \"IDN\", \"8\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6308, \"Time to obtain an electrical connection (days)\", 0.810745, \"Negative\", \"PHL\", \"5\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6309, \"Time to obtain an electrical connection (days)\", 0.815051, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6310, \"Time to obtain an electrical connection (days)\", 0.909091, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6311, \"Time to obtain an electrical connection (days)\", 0.786273, \"Positive\", \"PER\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6312, \"Time to obtain an electrical connection (days)\", 0.818161, \"Negative\", \"VEN\", \"2\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6313, \"Time to obtain an electrical connection (days)\", 0.855364, \"Positive\", \"COL\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6314, \"Time to obtain an electrical connection (days)\", 0.817488, \"Negative\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6315, \"Time to obtain an electrical connection (days)\", 0.825397, \"Negative\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6316, \"Time to obtain an electrical connection (days)\", 0.845543, \"Negative\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6317, \"Suicide mortality rate (per 100,000 population)\", 0.855364, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 17, \"Mortality\", \"primary\", 25], [6318, \"Suicide mortality rate (per 100,000 population)\", 0.827624, \"Negative\", \"AUT\", \"13\", \"Does not apply\", \"Europe\", 17, \"Mortality\", \"primary\", 25], [6319, \"Suicide mortality rate (per 100,000 population)\", 0.773321, \"Negative\", \"QAT\", \"21\", \"Does not apply\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6320, \"Suicide mortality rate (per 100,000 population)\", 0.78697, \"Negative\", \"ARE\", \"3\", \"Does not apply\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6321, \"Suicide mortality rate (per 100,000 population)\", 0.872247, \"Positive\", \"SAU\", \"Does not apply\", \"Logarithmic\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6322, \"Suicide mortality rate (per 100,000 population)\", 0.827891, \"Negative\", \"YEM\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6323, \"Suicide mortality rate (per 100,000 population)\", 0.878636, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6324, \"Suicide mortality rate (per 100,000 population)\", 0.886364, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6325, \"Suicide mortality rate (per 100,000 population)\", 0.897716, \"Negative\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6326, \"Suicide mortality rate (per 100,000 population)\", 0.90973, \"Negative\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6327, \"Suicide mortality rate (per 100,000 population)\", 0.897565, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 17, \"Mortality\", \"primary\", 25], [6328, \"Suicide mortality rate (per 100,000 population)\", 0.966749, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 17, \"Mortality\", \"primary\", 25], [6329, \"Suicide mortality rate (per 100,000 population)\", 0.878685, \"Negative\", \"BGD\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6330, \"Suicide mortality rate (per 100,000 population)\", 0.915641, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6331, \"Suicide mortality rate (per 100,000 population)\", 0.869184, \"Positive\", \"VNM\", \"3\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6332, \"Suicide mortality rate (per 100,000 population)\", 0.882422, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6333, \"Suicide mortality rate (per 100,000 population)\", 0.831818, \"Negative\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6334, \"Suicide mortality rate (per 100,000 population)\", 0.952612, \"Positive\", \"MEX\", \"3\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6335, \"Suicide mortality rate (per 100,000 population)\", 0.756344, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6336, \"Suicide mortality rate (per 100,000 population)\", 0.902423, \"Negative\", \"VEN\", \"8\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6337, \"Suicide mortality rate (per 100,000 population)\", 0.776677, \"Negative\", \"COL\", \"Does not apply\", \"Logarithmic\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6338, \"Suicide mortality rate (per 100,000 population)\", 0.755201, \"Negative\", \"CHL\", \"21\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6339, \"Suicide mortality rate (per 100,000 population)\", 0.920313, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6340, \"Suicide mortality rate (per 100,000 population)\", 0.991231, \"Positive\", \"USA\", \"13\", \"Does not apply\", \"Pair\", 17, \"Mortality\", \"primary\", 25], [6341, \"Suicide mortality rate (per 100,000 population)\", 0.95768, \"Negative\", \"CHN\", \"5\", \"Does not apply\", \"Pair\", 17, \"Mortality\", \"primary\", 25], [6342, \"Firms using banks to finance investment (% of firms)\", 0.841751, \"Positive\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 12, \"Economy\", \"secondary\", 28], [6343, \"Firms using banks to finance investment (% of firms)\", 0.83291, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 12, \"Economy\", \"secondary\", 28], [6344, \"Firms using banks to finance investment (% of firms)\", 0.945216, \"Negative\", \"AZE\", \"21\", \"Does not apply\", \"Persian Gulf\", 12, \"Economy\", \"secondary\", 28], [6345, \"Firms using banks to finance investment (% of firms)\", 0.789777, \"Negative\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 12, \"Economy\", \"secondary\", 28], [6346, \"Firms using banks to finance investment (% of firms)\", 0.941656, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6347, \"Firms using banks to finance investment (% of firms)\", 0.75968, \"Negative\", \"TUR\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6348, \"Firms using banks to finance investment (% of firms)\", 0.876713, \"Positive\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6349, \"Firms using banks to finance investment (% of firms)\", 0.852294, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6350, \"Firms using banks to finance investment (% of firms)\", 0.805169, \"Negative\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6351, \"Firms using banks to finance investment (% of firms)\", 0.882199, \"Positive\", \"LBR\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6352, \"Firms using banks to finance investment (% of firms)\", 0.92942, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6353, \"Firms using banks to finance investment (% of firms)\", 0.759974, \"Negative\", \"CMR\", \"13\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6354, \"Firms using banks to finance investment (% of firms)\", 0.891739, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6355, \"Firms using banks to finance investment (% of firms)\", 0.843733, \"Positive\", \"GHA\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6356, \"Firms using banks to finance investment (% of firms)\", 0.877193, \"Negative\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6357, \"Firms using banks to finance investment (% of firms)\", 0.917544, \"Negative\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6358, \"Firms using banks to finance investment (% of firms)\", 0.901754, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6359, \"Firms using banks to finance investment (% of firms)\", 0.870956, \"Negative\", \"THA\", \"3\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6360, \"Firms using banks to finance investment (% of firms)\", 0.817729, \"Positive\", \"IDN\", \"8\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6361, \"Firms using banks to finance investment (% of firms)\", 0.810745, \"Negative\", \"PHL\", \"5\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6362, \"Firms using banks to finance investment (% of firms)\", 0.815051, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6363, \"Firms using banks to finance investment (% of firms)\", 0.909091, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6364, \"Firms using banks to finance investment (% of firms)\", 0.88294, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6365, \"Firms using banks to finance investment (% of firms)\", 0.818161, \"Negative\", \"VEN\", \"2\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6366, \"Firms using banks to finance investment (% of firms)\", 0.855364, \"Positive\", \"COL\", \"21\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6367, \"Firms using banks to finance investment (% of firms)\", 0.817488, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6368, \"Firms using banks to finance investment (% of firms)\", 0.825397, \"Negative\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6369, \"Firms using banks to finance investment (% of firms)\", 0.845543, \"Positive\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6370, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.768265, \"Negative\", \"GRC\", \"5\", \"Does not apply\", \"Europe\", 12, \"Imports\", \"secondary\", 21], [6371, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.933568, \"Negative\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6372, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.946379, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6373, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.829932, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6374, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.893727, \"Negative\", \"OMN\", \"Does not apply\", \"Logarithmic\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6375, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.827434, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Imports\", \"secondary\", 21], [6376, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.759141, \"Negative\", \"TUR\", \"2\", \"Does not apply\", \"North Africa\", 12, \"Imports\", \"secondary\", 21], [6377, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.904546, \"Negative\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Imports\", \"secondary\", 21], [6378, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.937705, \"Negative\", \"BGD\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6379, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.9145, \"Negative\", \"VNM\", \"8\", \"Does not apply\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6380, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.865531, \"Negative\", \"THA\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6381, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.898586, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6382, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.945048, \"Negative\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6383, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.929542, \"Negative\", \"KOR\", \"5\", \"Does not apply\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6384, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.949767, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6385, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.752078, \"Negative\", \"BRA\", \"1\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6386, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.919187, \"Negative\", \"VEN\", \"Does not apply\", \"Logarithmic\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6387, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.797346, \"Negative\", \"COL\", \"Does not apply\", \"Logarithmic\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6388, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.8055, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6389, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.831757, \"Negative\", \"USA\", \"Does not apply\", \"Logarithmic\", \"Pair\", 12, \"Imports\", \"secondary\", 21], [6390, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.857664, \"Negative\", \"CHN\", \"1\", \"Does not apply\", \"Pair\", 12, \"Imports\", \"secondary\", 21], [6391, \"People practicing open defecation (% of population)\", 0.85947, \"Negative\", \"GRC\", \"8\", \"Does not apply\", \"Europe\", 27, \"Health\", \"secondary\", 32], [6392, \"People practicing open defecation (% of population)\", 0.764246, \"Negative\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6393, \"People practicing open defecation (% of population)\", 0.961673, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6394, \"People practicing open defecation (% of population)\", 0.955118, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6395, \"People practicing open defecation (% of population)\", 0.905975, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6396, \"People practicing open defecation (% of population)\", 0.850746, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6397, \"People practicing open defecation (% of population)\", 0.913668, \"Negative\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6398, \"People practicing open defecation (% of population)\", 0.94435, \"Negative\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6399, \"People practicing open defecation (% of population)\", 0.924176, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6400, \"People practicing open defecation (% of population)\", 0.903327, \"Negative\", \"SEN\", \"Does not apply\", \"Linear\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6401, \"People practicing open defecation (% of population)\", 0.756276, \"Negative\", \"ZAF\", \"8\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6402, \"People practicing open defecation (% of population)\", 0.889166, \"Negative\", \"LBR\", \"1\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6403, \"People practicing open defecation (% of population)\", 0.948441, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6404, \"People practicing open defecation (% of population)\", 0.909027, \"Negative\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6405, \"People practicing open defecation (% of population)\", 0.964139, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6406, \"People practicing open defecation (% of population)\", 0.867409, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6407, \"People practicing open defecation (% of population)\", 0.996522, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6408, \"People practicing open defecation (% of population)\", 0.989149, \"Negative\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6409, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6410, \"People practicing open defecation (% of population)\", 0.865868, \"Negative\", \"THA\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6411, \"People practicing open defecation (% of population)\", 0.908314, \"Negative\", \"IDN\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6412, \"People practicing open defecation (% of population)\", 0.903327, \"Negative\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6413, \"People practicing open defecation (% of population)\", 0.939572, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6414, \"People practicing open defecation (% of population)\", 0.750815, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6415, \"People practicing open defecation (% of population)\", 0.751955, \"Negative\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6416, \"People practicing open defecation (% of population)\", 0.90475, \"Negative\", \"PER\", \"Does not apply\", \"Linear\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6417, \"People practicing open defecation (% of population)\", 0.890679, \"Negative\", \"VEN\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6418, \"People practicing open defecation (% of population)\", 0.897855, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6419, \"People practicing open defecation (% of population)\", 0.909091, \"Negative\", \"CHL\", \"21\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6420, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6421, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6422, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 27, \"Health\", \"secondary\", 32], [6423, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.829371, \"Negative\", \"AUT\", \"8\", \"Does not apply\", \"Europe\", 11, \"Environment\", \"secondary\", 18], [6424, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.848986, \"Negative\", \"QAT\", \"3\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6425, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.855782, \"Positive\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6426, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.964478, \"Negative\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6427, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.767146, \"Positive\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6428, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.805752, \"Positive\", \"MAR\", \"2\", \"Does not apply\", \"North Africa\", 11, \"Environment\", \"secondary\", 18], [6429, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.909825, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6430, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.855509, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6431, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.900159, \"Negative\", \"NGA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6432, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.853751, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6433, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.967802, \"Negative\", \"IND\", \"1\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6434, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.879127, \"Negative\", \"VNM\", \"5\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6435, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.922596, \"Negative\", \"IDN\", \"2\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6436, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.941576, \"Negative\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6437, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.895958, \"Negative\", \"MEX\", \"Does not apply\", \"Logarithmic\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6438, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.804632, \"Negative\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6439, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.87797, \"Negative\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6440, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.874924, \"Negative\", \"CHN\", \"1\", \"Does not apply\", \"Pair\", 11, \"Environment\", \"secondary\", 18], [6441, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.843001, \"Negative\", \"NLD\", \"Does not apply\", \"Logarithmic\", \"Europe\", 13, \"Agriculture\", \"secondary\", 21], [6442, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.853721, \"Positive\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6443, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.956311, \"Positive\", \"ARE\", \"3\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6444, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.983928, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6445, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.805605, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6446, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.972728, \"Positive\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6447, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.831716, \"Positive\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6448, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.795868, \"Positive\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6449, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.841358, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6450, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.790698, \"Positive\", \"LBR\", \"2\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6451, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.920335, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6452, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.774495, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6453, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.983907, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6454, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.961303, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6455, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.896246, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6456, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.936072, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6457, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.831515, \"Positive\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6458, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.758321, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6459, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.828788, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6460, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.792582, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6461, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.80934, \"Positive\", \"USA\", \"Does not apply\", \"Cubic\", \"Pair\", 13, \"Agriculture\", \"secondary\", 21], [6462, \"Access to clean fuels and technologies for cooking (% of population)\", 0.766483, \"Positive\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6463, \"Access to clean fuels and technologies for cooking (% of population)\", 0.996083, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6464, \"Access to clean fuels and technologies for cooking (% of population)\", 0.9509, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6465, \"Access to clean fuels and technologies for cooking (% of population)\", 0.853684, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6466, \"Access to clean fuels and technologies for cooking (% of population)\", 0.897511, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6467, \"Access to clean fuels and technologies for cooking (% of population)\", 0.957737, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6468, \"Access to clean fuels and technologies for cooking (% of population)\", 0.923174, \"Positive\", \"MAR\", \"8\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6469, \"Access to clean fuels and technologies for cooking (% of population)\", 0.878984, \"Negative\", \"SEN\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6470, \"Access to clean fuels and technologies for cooking (% of population)\", 0.756276, \"Positive\", \"ZAF\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6471, \"Access to clean fuels and technologies for cooking (% of population)\", 0.960204, \"Positive\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6472, \"Access to clean fuels and technologies for cooking (% of population)\", 0.872263, \"Positive\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6473, \"Access to clean fuels and technologies for cooking (% of population)\", 0.962628, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6474, \"Access to clean fuels and technologies for cooking (% of population)\", 0.864074, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6475, \"Access to clean fuels and technologies for cooking (% of population)\", 0.995617, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6476, \"Access to clean fuels and technologies for cooking (% of population)\", 0.989149, \"Positive\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6477, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6478, \"Access to clean fuels and technologies for cooking (% of population)\", 0.880003, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6479, \"Access to clean fuels and technologies for cooking (% of population)\", 0.908314, \"Positive\", \"IDN\", \"Does not apply\", \"Linear\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6480, \"Access to clean fuels and technologies for cooking (% of population)\", 0.826918, \"Positive\", \"PHL\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6481, \"Access to clean fuels and technologies for cooking (% of population)\", 0.753409, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6482, \"Access to clean fuels and technologies for cooking (% of population)\", 0.853102, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6483, \"Access to clean fuels and technologies for cooking (% of population)\", 0.90475, \"Positive\", \"PER\", \"Does not apply\", \"Linear\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6484, \"Access to clean fuels and technologies for cooking (% of population)\", 0.897855, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6485, \"Access to clean fuels and technologies for cooking (% of population)\", 0.945724, \"Positive\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6486, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6487, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 17, \"Internet\", \"secondary\", 26], [6488, \"Access to electricity (% of population)\", 0.755466, \"Positive\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6489, \"Access to electricity (% of population)\", 0.844248, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6490, \"Access to electricity (% of population)\", 0.825795, \"Positive\", \"YEM\", \"3\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6491, \"Access to electricity (% of population)\", 0.870107, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 16, \"Environment\", \"primary\", 26], [6492, \"Access to electricity (% of population)\", 0.914656, \"Positive\", \"MAR\", \"2\", \"Does not apply\", \"North Africa\", 16, \"Environment\", \"primary\", 26], [6493, \"Access to electricity (% of population)\", 0.780617, \"Positive\", \"SEN\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6494, \"Access to electricity (% of population)\", 0.798854, \"Positive\", \"ZAF\", \"2\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6495, \"Access to electricity (% of population)\", 0.848246, \"Positive\", \"LBR\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6496, \"Access to electricity (% of population)\", 0.924489, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6497, \"Access to electricity (% of population)\", 0.811965, \"Positive\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6498, \"Access to electricity (% of population)\", 0.829545, \"Positive\", \"NGA\", \"3\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6499, \"Access to electricity (% of population)\", 0.824265, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6500, \"Access to electricity (% of population)\", 0.993779, \"Positive\", \"BGD\", \"2\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6501, \"Access to electricity (% of population)\", 0.979171, \"Positive\", \"IND\", \"3\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6502, \"Access to electricity (% of population)\", 0.968614, \"Positive\", \"VNM\", \"3\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6503, \"Access to electricity (% of population)\", 0.872662, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6504, \"Access to electricity (% of population)\", 0.823824, \"Positive\", \"IDN\", \"5\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6505, \"Access to electricity (% of population)\", 0.909312, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6506, \"Access to electricity (% of population)\", 0.845838, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6507, \"Access to electricity (% of population)\", 0.776361, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6508, \"Access to electricity (% of population)\", 0.928139, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6509, \"Access to electricity (% of population)\", 0.780517, \"Positive\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6510, \"Access to electricity (% of population)\", 0.871062, \"Positive\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6511, \"Access to electricity (% of population)\", 0.977356, \"Positive\", \"PAN\", \"21\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6512, \"Access to electricity (% of population)\", 0.937042, \"Positive\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6513, \"Access to electricity (% of population)\", 0.963675, \"Positive\", \"CHN\", \"5\", \"Does not apply\", \"Pair\", 16, \"Environment\", \"primary\", 26], [6514, \"Prevalence of undernourishment (% of population)\", 0.766965, \"Positive\", \"IRQ\", \"3\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6515, \"Prevalence of undernourishment (% of population)\", 0.774883, \"Negative\", \"AZE\", \"2\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6516, \"Prevalence of undernourishment (% of population)\", 0.7825, \"Positive\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6517, \"Prevalence of undernourishment (% of population)\", 0.896934, \"Negative\", \"OMN\", \"Does not apply\", \"Cuadratic\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6518, \"Prevalence of undernourishment (% of population)\", 0.874165, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 14, \"Health\", \"secondary\", 21], [6519, \"Prevalence of undernourishment (% of population)\", 0.863113, \"Negative\", \"MAR\", \"8\", \"Does not apply\", \"North Africa\", 14, \"Health\", \"secondary\", 21], [6520, \"Prevalence of undernourishment (% of population)\", 0.877935, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6521, \"Prevalence of undernourishment (% of population)\", 0.809735, \"Negative\", \"MOZ\", \"Does not apply\", \"Cubic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6522, \"Prevalence of undernourishment (% of population)\", 0.912315, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6523, \"Prevalence of undernourishment (% of population)\", 0.959901, \"Positive\", \"NGA\", \"13\", \"Does not apply\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6524, \"Prevalence of undernourishment (% of population)\", 0.839859, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6525, \"Prevalence of undernourishment (% of population)\", 0.891, \"Negative\", \"BGD\", \"21\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6526, \"Prevalence of undernourishment (% of population)\", 0.996491, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6527, \"Prevalence of undernourishment (% of population)\", 0.836744, \"Negative\", \"THA\", \"Does not apply\", \"Logarithmic\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6528, \"Prevalence of undernourishment (% of population)\", 0.872487, \"Negative\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6529, \"Prevalence of undernourishment (% of population)\", 0.827297, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6530, \"Prevalence of undernourishment (% of population)\", 0.840925, \"Positive\", \"MEX\", \"3\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6531, \"Prevalence of undernourishment (% of population)\", 0.759618, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6532, \"Prevalence of undernourishment (% of population)\", 0.893013, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6533, \"Prevalence of undernourishment (% of population)\", 0.961103, \"Negative\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6534, \"Prevalence of undernourishment (% of population)\", 0.9, \"Negative\", \"CHN\", \"3\", \"Does not apply\", \"Pair\", 14, \"Health\", \"secondary\", 21], [6535, \"Cereal yield (kg per hectare)\", 0.779595, \"Positive\", \"QAT\", \"1\", \"Does not apply\", \"Persian Gulf\", 12, \"Agriculture\", \"secondary\", 18], [6536, \"Cereal yield (kg per hectare)\", 0.888932, \"Positive\", \"SAU\", \"5\", \"Does not apply\", \"Persian Gulf\", 12, \"Agriculture\", \"secondary\", 18], [6537, \"Cereal yield (kg per hectare)\", 0.788288, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Agriculture\", \"secondary\", 18], [6538, \"Cereal yield (kg per hectare)\", 0.87744, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Agriculture\", \"secondary\", 18], [6539, \"Cereal yield (kg per hectare)\", 0.774258, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 12, \"Agriculture\", \"secondary\", 18], [6540, \"Cereal yield (kg per hectare)\", 0.9936, \"Positive\", \"BGD\", \"3\", \"Does not apply\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6541, \"Cereal yield (kg per hectare)\", 0.961303, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6542, \"Cereal yield (kg per hectare)\", 0.983739, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6543, \"Cereal yield (kg per hectare)\", 0.894857, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6544, \"Cereal yield (kg per hectare)\", 0.939127, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6545, \"Cereal yield (kg per hectare)\", 0.903125, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6546, \"Cereal yield (kg per hectare)\", 0.804904, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6547, \"Cereal yield (kg per hectare)\", 0.907475, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6548, \"Cereal yield (kg per hectare)\", 0.794782, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6549, \"Cereal yield (kg per hectare)\", 0.836149, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6550, \"Cereal yield (kg per hectare)\", 0.750296, \"Positive\", \"PAN\", \"1\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6551, \"Cereal yield (kg per hectare)\", 0.911779, \"Positive\", \"USA\", \"3\", \"Does not apply\", \"Pair\", 12, \"Agriculture\", \"secondary\", 18], [6552, \"Cereal yield (kg per hectare)\", 0.977243, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 12, \"Agriculture\", \"secondary\", 18], [6553, \"Pregnant women receiving prenatal care (%)\", 0.803913, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6554, \"Pregnant women receiving prenatal care (%)\", 0.889643, \"Positive\", \"ARE\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6555, \"Pregnant women receiving prenatal care (%)\", 0.89992, \"Positive\", \"SAU\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6556, \"Pregnant women receiving prenatal care (%)\", 0.79933, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6557, \"Pregnant women receiving prenatal care (%)\", 0.908023, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6558, \"Pregnant women receiving prenatal care (%)\", 0.855771, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6559, \"Pregnant women receiving prenatal care (%)\", 0.898054, \"Positive\", \"EGY\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6560, \"Pregnant women receiving prenatal care (%)\", 0.95029, \"Positive\", \"TUR\", \"2\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6561, \"Pregnant women receiving prenatal care (%)\", 0.954106, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6562, \"Pregnant women receiving prenatal care (%)\", 0.904693, \"Positive\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6563, \"Pregnant women receiving prenatal care (%)\", 0.919914, \"Positive\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6564, \"Pregnant women receiving prenatal care (%)\", 0.878474, \"Positive\", \"MOZ\", \"21\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6565, \"Pregnant women receiving prenatal care (%)\", 0.858423, \"Positive\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6566, \"Pregnant women receiving prenatal care (%)\", 0.814632, \"Positive\", \"NGA\", \"21\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6567, \"Pregnant women receiving prenatal care (%)\", 0.983888, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6568, \"Pregnant women receiving prenatal care (%)\", 0.966606, \"Positive\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6569, \"Pregnant women receiving prenatal care (%)\", 0.956534, \"Positive\", \"VNM\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6570, \"Pregnant women receiving prenatal care (%)\", 0.861958, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6571, \"Pregnant women receiving prenatal care (%)\", 0.810999, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6572, \"Pregnant women receiving prenatal care (%)\", 0.898514, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6573, \"Pregnant women receiving prenatal care (%)\", 0.91284, \"Positive\", \"VEN\", \"3\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6574, \"Pregnant women receiving prenatal care (%)\", 0.932079, \"Positive\", \"COL\", \"1\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6575, \"Pregnant women receiving prenatal care (%)\", 0.954545, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6576, \"Pregnant women receiving prenatal care (%)\", 0.981598, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 18, \"Health\", \"secondary\", 24], [6577, \"Crop production index (2014-2016 = 100)\", 0.766012, \"Positive\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6578, \"Crop production index (2014-2016 = 100)\", 0.963749, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6579, \"Crop production index (2014-2016 = 100)\", 0.835085, \"Positive\", \"YEM\", \"Does not apply\", \"Cuadratic\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6580, \"Crop production index (2014-2016 = 100)\", 0.951834, \"Positive\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6581, \"Crop production index (2014-2016 = 100)\", 0.814074, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6582, \"Crop production index (2014-2016 = 100)\", 0.908494, \"Positive\", \"EGY\", \"Does not apply\", \"Cubic\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6583, \"Crop production index (2014-2016 = 100)\", 0.947974, \"Positive\", \"TUR\", \"13\", \"Does not apply\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6584, \"Crop production index (2014-2016 = 100)\", 0.87045, \"Positive\", \"MAR\", \"Does not apply\", \"Cuadratic\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6585, \"Crop production index (2014-2016 = 100)\", 0.804939, \"Negative\", \"SEN\", \"21\", \"Does not apply\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6586, \"Crop production index (2014-2016 = 100)\", 0.833037, \"Positive\", \"MOZ\", \"Does not apply\", \"Linear\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6587, \"Crop production index (2014-2016 = 100)\", 0.821211, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6588, \"Crop production index (2014-2016 = 100)\", 0.854241, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6589, \"Crop production index (2014-2016 = 100)\", 0.858855, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6590, \"Crop production index (2014-2016 = 100)\", 0.989486, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6591, \"Crop production index (2014-2016 = 100)\", 0.965283, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6592, \"Crop production index (2014-2016 = 100)\", 0.995787, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6593, \"Crop production index (2014-2016 = 100)\", 0.819882, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6594, \"Crop production index (2014-2016 = 100)\", 0.913211, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6595, \"Crop production index (2014-2016 = 100)\", 0.91721, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6596, \"Crop production index (2014-2016 = 100)\", 0.908215, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6597, \"Crop production index (2014-2016 = 100)\", 0.766164, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6598, \"Crop production index (2014-2016 = 100)\", 0.936928, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6599, \"Crop production index (2014-2016 = 100)\", 0.851083, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6600, \"Crop production index (2014-2016 = 100)\", 0.882284, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6601, \"Crop production index (2014-2016 = 100)\", 0.967446, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6602, \"Crop production index (2014-2016 = 100)\", 0.845314, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 21, \"Agriculture\", \"secondary\", 27], [6603, \"Crop production index (2014-2016 = 100)\", 0.998717, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 21, \"Agriculture\", \"secondary\", 27], [6604, \"Births attended by skilled health staff (% of total)\", 0.86053, \"Negative\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6605, \"Births attended by skilled health staff (% of total)\", 0.938926, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6606, \"Births attended by skilled health staff (% of total)\", 0.842853, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6607, \"Births attended by skilled health staff (% of total)\", 0.842834, \"Positive\", \"DZA\", \"2\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6608, \"Births attended by skilled health staff (% of total)\", 0.923878, \"Positive\", \"EGY\", \"2\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6609, \"Births attended by skilled health staff (% of total)\", 0.95508, \"Positive\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6610, \"Births attended by skilled health staff (% of total)\", 0.953389, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6611, \"Births attended by skilled health staff (% of total)\", 0.797257, \"Positive\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6612, \"Births attended by skilled health staff (% of total)\", 0.783475, \"Positive\", \"MOZ\", \"Does not apply\", \"Cubic\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6613, \"Births attended by skilled health staff (% of total)\", 0.858575, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6614, \"Births attended by skilled health staff (% of total)\", 0.977507, \"Positive\", \"BGD\", \"5\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6615, \"Births attended by skilled health staff (% of total)\", 0.982218, \"Positive\", \"IND\", \"Does not apply\", \"Linear\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6616, \"Births attended by skilled health staff (% of total)\", 0.95581, \"Positive\", \"VNM\", \"2\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6617, \"Births attended by skilled health staff (% of total)\", 0.876592, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6618, \"Births attended by skilled health staff (% of total)\", 0.939552, \"Positive\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6619, \"Births attended by skilled health staff (% of total)\", 0.844034, \"Positive\", \"MEX\", \"1\", \"Does not apply\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6620, \"Births attended by skilled health staff (% of total)\", 0.924618, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6621, \"Births attended by skilled health staff (% of total)\", 0.758834, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6622, \"Births attended by skilled health staff (% of total)\", 0.794656, \"Negative\", \"USA\", \"8\", \"Does not apply\", \"Pair\", 13, \"Mortality\", \"secondary\", 20], [6623, \"Births attended by skilled health staff (% of total)\", 0.950593, \"Positive\", \"CHN\", \"3\", \"Does not apply\", \"Pair\", 13, \"Mortality\", \"secondary\", 20], [6624, \"Completeness of birth registration (%)\", 0.86815, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6625, \"Completeness of birth registration (%)\", 0.840806, \"Positive\", \"EGY\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6626, \"Completeness of birth registration (%)\", 0.898573, \"Positive\", \"TUR\", \"1\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6627, \"Completeness of birth registration (%)\", 0.932084, \"Positive\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6628, \"Completeness of birth registration (%)\", 0.909636, \"Positive\", \"LBR\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6629, \"Completeness of birth registration (%)\", 0.8862, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6630, \"Completeness of birth registration (%)\", 0.790617, \"Negative\", \"CMR\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6631, \"Completeness of birth registration (%)\", 0.81349, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6632, \"Completeness of birth registration (%)\", 0.773855, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6633, \"Completeness of birth registration (%)\", 0.792328, \"Positive\", \"BGD\", \"5\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6634, \"Completeness of birth registration (%)\", 0.810707, \"Positive\", \"IND\", \"5\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6635, \"Completeness of birth registration (%)\", 0.781322, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6636, \"Completeness of birth registration (%)\", 0.901982, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6637, \"Completeness of birth registration (%)\", 0.894689, \"Positive\", \"MEX\", \"13\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6638, \"Completeness of birth registration (%)\", 0.781913, \"Positive\", \"BRA\", \"2\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6639, \"Completeness of birth registration (%)\", 0.855313, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6640, \"Completeness of birth registration (%)\", 0.839228, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6641, \"Completeness of birth registration (%)\", 0.792184, \"Negative\", \"VEN\", \"1\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6642, \"Completeness of birth registration (%)\", 0.840648, \"Positive\", \"COL\", \"2\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6643, \"Completeness of birth registration (%)\", 0.963636, \"Positive\", \"PAN\", \"21\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6644, \"Completeness of birth registration (%)\", 0.789474, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21]];\n",
+       "\n",
+       "        // Define the dt_args\n",
+       "        let dt_args = {\"dom\": \"ftpr\"};\n",
+       "        dt_args[\"data\"] = data;\n",
+       "\n",
+       "        // Display the table\n",
+       "        $(document).ready(function () {\n",
+       "            \n",
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+       "<table id=\"da270e15-dbe2-4c0c-907c-fcab717716fb\" class=\"display nowrap\"style=\"max-width:100%\"><thead><th>None</th><th>Indicator</th><th>R^2 Spearman</th><th>Behaviour</th><th>Country</th><th>Moved</th><th>Type</th><th>Continent</th><th>Number of times repeated_x</th><th>Group</th><th>Level</th><th>Number of times repeated_y</th></thead><tbody><tr><td>Loading... (need <a href=https://mwouts.github.io/itables/troubleshooting.html>help</a>?)</td></tr></tbody></table>\n",
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+       "        const data = [[0, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.817804, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [1, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.759333, \"Negative\", \"SWE\", \"3\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [2, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.936963, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [3, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.786708, \"Negative\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [4, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.924056, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 22, \"Demography\", \"secondary\", 40], [5, \"Adolescent fertility rate (births per 1,000 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22, \"Demography\", \"secondary\", 40], [16, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.941309, \"Negative\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [17, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.902682, \"Negative\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 22, \"Demography\", \"secondary\", 40], [18, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.805222, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [19, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.863071, \"Negative\", \"LBR\", \"2\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], [20, \"Adolescent fertility rate (births per 1,000 women ages 15-19)\", 0.939188, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 22, \"Demography\", \"secondary\", 40], 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[43, \"Commercial bank branches (per 100,000 adults)\", 0.838543, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [44, \"Commercial bank branches (per 100,000 adults)\", 0.836928, \"Negative\", \"NLD\", \"13\", \"Does not apply\", \"Europe\", 11, \"Economy\", \"secondary\", 26], [45, \"Commercial bank branches (per 100,000 adults)\", 0.933522, \"Negative\", \"QAT\", \"5\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [46, \"Commercial bank branches (per 100,000 adults)\", 0.755661, \"Negative\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [47, \"Commercial bank branches (per 100,000 adults)\", 0.875117, \"Negative\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", \"secondary\", 26], [48, \"Commercial bank branches (per 100,000 adults)\", 0.895191, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 11, \"Economy\", 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\"Migration\", \"secondary\", 31], [118, \"International migrant stock (% of population)\", 0.943912, \"Negative\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [119, \"International migrant stock (% of population)\", 0.971948, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Migration\", \"secondary\", 31], [120, \"International migrant stock (% of population)\", 0.98406, \"Negative\", \"IND\", \"Does not apply\", \"Logarithmic\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [121, \"International migrant stock (% of population)\", 0.90581, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [122, \"International migrant stock (% of population)\", 0.874691, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Migration\", \"secondary\", 31], [123, \"International migrant stock (% of population)\", 0.945693, \"Positive\", \"KOR\", \"Does not 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\"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 16, \"Migration\", \"secondary\", 31], [130, \"CO2 emissions (metric tons per capita)\", 0.782806, \"Negative\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [131, \"CO2 emissions (metric tons per capita)\", 0.768845, \"Negative\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [132, \"CO2 emissions (metric tons per capita)\", 0.915538, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [133, \"CO2 emissions (metric tons per capita)\", 0.78562, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [134, \"CO2 emissions (metric tons per capita)\", 0.759154, \"Negative\", \"NLD\", \"5\", \"Does not apply\", \"Europe\", 18, \"Environment\", \"secondary\", 29], [135, \"CO2 emissions (metric tons per capita)\", 0.806797, \"Positive\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 18, \"Environment\", \"secondary\", 29], [136, \"CO2 emissions (metric tons per capita)\", 0.841863, \"Positive\", \"OMN\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Environment\", \"secondary\", 29], [137, \"CO2 emissions (metric tons per capita)\", 0.846222, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [138, \"CO2 emissions (metric tons per capita)\", 0.884204, \"Positive\", \"EGY\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [139, \"CO2 emissions (metric tons per capita)\", 0.788288, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [140, \"CO2 emissions (metric tons per capita)\", 0.895687, \"Positive\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [141, \"CO2 emissions (metric tons per capita)\", 0.936433, \"Positive\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Environment\", \"secondary\", 29], [142, \"CO2 emissions (metric tons per capita)\", 0.782676, \"Positive\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [143, \"CO2 emissions (metric tons per capita)\", 0.905693, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [144, \"CO2 emissions (metric tons per capita)\", 0.786576, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Environment\", \"secondary\", 29], [145, \"CO2 emissions (metric tons per capita)\", 0.997798, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [146, \"CO2 emissions (metric tons per capita)\", 0.98957, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [147, \"CO2 emissions (metric tons per capita)\", 0.991031, \"Positive\", \"VNM\", \"Does not apply\", \"Linear\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [148, \"CO2 emissions (metric tons per capita)\", 0.858441, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [149, \"CO2 emissions (metric tons per capita)\", 0.903207, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [150, \"CO2 emissions (metric tons per capita)\", 0.840909, \"Positive\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [151, \"CO2 emissions (metric tons per capita)\", 0.948395, \"Positive\", \"KOR\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Environment\", \"secondary\", 29], [152, \"CO2 emissions (metric tons per capita)\", 0.866209, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [153, \"CO2 emissions (metric tons per capita)\", 0.880682, \"Positive\", \"PER\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [154, \"CO2 emissions (metric tons per capita)\", 0.895551, \"Positive\", \"CHL\", \"Does not apply\", \"Cubic\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [155, \"CO2 emissions (metric tons per capita)\", 0.757305, \"Positive\", \"PAN\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [156, \"CO2 emissions (metric tons per capita)\", 0.753095, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 18, \"Environment\", \"secondary\", 29], [157, \"CO2 emissions (metric tons per capita)\", 0.913924, \"Negative\", \"USA\", \"8\", \"Does not apply\", \"Pair\", 18, \"Environment\", \"secondary\", 29], [158, \"CO2 emissions (metric tons per capita)\", 0.976473, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 18, \"Environment\", \"secondary\", 29], [159, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.779372, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [160, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.798438, \"Positive\", \"FRA\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [161, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.782956, \"Positive\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [162, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.907327, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [163, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.800012, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [164, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.791365, \"Positive\", \"HRV\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [165, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.968708, \"Positive\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [166, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.82583, \"Positive\", \"AUT\", \"3\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [167, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.843098, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 29, \"Principal\", \"primary\", 35], [168, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.935625, \"Positive\", \"AZE\", \"5\", \"Does not apply\", \"Persian Gulf\", 29, \"Principal\", \"primary\", 35], [169, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.932399, \"Positive\", \"EGY\", \"2\", \"Does not apply\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [170, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.936453, \"Positive\", \"ISR\", \"Does not apply\", \"Cubic\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [171, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.920491, \"Positive\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [172, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.780022, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 29, \"Principal\", \"primary\", 35], [173, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.820787, \"Positive\", \"ZAF\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [174, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.937635, \"Positive\", \"MOZ\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [175, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.848688, \"Positive\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [176, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.941264, \"Positive\", \"NGA\", \"3\", \"Does not apply\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [177, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.838169, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 29, \"Principal\", \"primary\", 35], [178, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.98201, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [179, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.950622, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [180, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.967217, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [181, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.848688, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [182, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.927815, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 29, \"Principal\", \"primary\", 35], [183, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.93913, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 29, \"Principal\", \"primary\", 35], [184, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.941701, \"Positive\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 29, \"Principal\", \"primary\", 35], [185, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.925869, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [186, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.831081, \"Positive\", \"BRA\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [187, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.752212, \"Positive\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [188, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.912985, \"Positive\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [189, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.853454, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [190, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.946165, \"Positive\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 29, \"Principal\", \"primary\", 35], [191, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.899891, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 29, \"Principal\", \"primary\", 35], [192, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.987719, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 29, \"Principal\", \"primary\", 35], [193, \"GDP per unit of energy use (PPP $ per kg of oil equivalent)\", 0.981854, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 29, \"Principal\", \"primary\", 35], [194, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.759092, \"Positive\", \"DEU\", \"1\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [195, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.898766, \"Positive\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [196, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.775622, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [197, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.801511, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [198, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.788969, \"Positive\", \"AUT\", \"2\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [199, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.857483, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 18, \"Health\", \"secondary\", 33], [200, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.908522, \"Negative\", \"QAT\", \"1\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [201, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.892232, \"Negative\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [202, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.994777, \"Negative\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [203, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.947125, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [204, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.984385, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 33], [205, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.891518, \"Negative\", \"DZA\", \"1\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [206, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.92803, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [207, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.819845, \"Positive\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [208, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.876163, \"Negative\", \"MAR\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 33], [209, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.902778, \"Negative\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [210, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.809567, \"Negative\", \"MOZ\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [211, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.907764, \"Negative\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [212, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.96926, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [213, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.86686, \"Negative\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 33], [214, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.993913, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [215, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.987845, \"Negative\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [216, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.815973, \"Positive\", \"VNM\", \"21\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 33], [217, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.904913, \"Negative\", \"PHL\", \"Does not apply\", \"Logarithmic\", \"Asia\", 18, \"Health\", \"secondary\", 33], [218, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.933133, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [219, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.754175, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [220, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.799358, \"Negative\", \"PER\", \"13\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [221, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.893013, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [222, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.896554, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [223, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.996522, \"Negative\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 33], [224, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.821128, \"Negative\", \"CRI\", \"Does not apply\", \"Logarithmic\", \"Latam\", 18, \"Health\", \"secondary\", 33], [225, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.886364, \"Positive\", \"USA\", \"21\", \"Does not apply\", \"Pair\", 18, \"Health\", \"secondary\", 33], [226, \"Prevalence of anemia among children (% of children ages 6-59 months)\", 0.833357, \"Negative\", \"CHN\", \"Does not apply\", \"Logarithmic\", \"Pair\", 18, \"Health\", \"secondary\", 33], [227, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.809179, \"Positive\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [228, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.773946, \"Positive\", \"FRA\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [229, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.815998, \"Positive\", \"SWE\", \"2\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [230, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.8799, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [231, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.751187, \"Positive\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [232, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.909632, \"Positive\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [233, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.873516, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [234, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.799967, \"Positive\", \"AUT\", \"Does not apply\", \"Logarithmic\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [235, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.829252, \"Positive\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 28, \"Imports\", \"secondary\", 38], [236, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.889718, \"Positive\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [237, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.977084, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [238, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.959094, \"Positive\", \"AZE\", \"21\", \"Does not apply\", \"Persian Gulf\", 28, \"Imports\", \"secondary\", 38], [239, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.847827, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [240, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.859402, \"Positive\", \"EGY\", \"Does not apply\", \"Linear\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [241, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.962303, \"Positive\", \"ISR\", \"Does not apply\", \"Cuadratic\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [242, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.932453, \"Positive\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [243, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.920393, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 28, \"Imports\", \"secondary\", 38], [244, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.904112, \"Positive\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [245, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.82775, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [246, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.784068, \"Positive\", \"NGA\", \"13\", \"Does not apply\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [247, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.843838, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 28, \"Imports\", \"secondary\", 38], [248, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.972127, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [249, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.955771, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [250, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.914644, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [251, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.864774, \"Positive\", \"THA\", \"1\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [252, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.897196, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [253, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.937594, \"Positive\", \"PHL\", \"2\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [254, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.918708, \"Positive\", \"KOR\", \"3\", \"Does not apply\", \"Asia\", 28, \"Imports\", \"secondary\", 38], [255, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.951463, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [256, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.751235, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [257, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.883617, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [258, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.893338, \"Positive\", \"VEN\", \"5\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [259, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.904546, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [260, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.903191, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [261, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.979415, \"Positive\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [262, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.971504, \"Positive\", \"CRI\", \"2\", \"Does not apply\", \"Latam\", 28, \"Imports\", \"secondary\", 38], [263, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.949698, \"Positive\", \"USA\", \"2\", \"Does not apply\", \"Pair\", 28, \"Imports\", \"secondary\", 38], [264, \"Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)\", 0.925899, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 28, \"Imports\", \"secondary\", 38], [265, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.785897, \"Positive\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [266, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.832712, \"Positive\", \"FRA\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [267, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.792293, \"Positive\", \"SWE\", \"Does not apply\", \"Cuadratic\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [268, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.890853, \"Positive\", \"GBR\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [269, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.846758, \"Positive\", \"ESP\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [270, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.796993, \"Positive\", \"POL\", \"Does not apply\", \"Cuadratic\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [271, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.855869, \"Positive\", \"AUT\", \"2\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 15], [272, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.940436, \"Positive\", \"SAU\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 15], [273, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.933687, \"Positive\", \"ISR\", \"Does not apply\", \"Cubic\", \"North Africa\", 12, \"Environment\", \"secondary\", 15], [274, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.836127, \"Positive\", \"ZAF\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 12, \"Environment\", \"secondary\", 15], [275, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.813553, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Environment\", \"secondary\", 15], [276, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.813553, \"Positive\", \"MEX\", \"Does not apply\", \"Linear\", \"Latam\", 12, \"Environment\", \"secondary\", 15], [277, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.7618, \"Positive\", \"VEN\", \"5\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 15], [278, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.945556, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 12, \"Environment\", \"secondary\", 15], [279, \"HFC gas emissions (thousand metric tons of CO2 equivalent)\", 0.947754, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 12, \"Environment\", \"secondary\", 15], [280, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.768636, \"Negative\", \"DEU\", \"2\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [281, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.797226, \"Negative\", \"FRA\", \"3\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [282, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.785944, \"Negative\", \"SWE\", \"3\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [283, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.879221, \"Negative\", \"GBR\", \"13\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [284, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.868737, \"Negative\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [285, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.834752, \"Negative\", \"HRV\", \"2\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [286, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.944398, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [287, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.852272, \"Negative\", \"AUT\", \"1\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [288, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.863656, \"Negative\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 25, \"Mortality\", \"secondary\", 42], [289, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.977434, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [290, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.977717, \"Negative\", \"ARE\", \"5\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [291, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.979206, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [292, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.909342, \"Negative\", \"AZE\", \"1\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [293, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.871188, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [294, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.922552, \"Negative\", \"OMN\", \"2\", \"Does not apply\", \"Persian Gulf\", 25, \"Mortality\", \"secondary\", 42], [295, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.813387, \"Negative\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [296, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.95019, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [297, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.956329, \"Negative\", \"ISR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [298, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.945261, \"Negative\", \"TUR\", \"8\", \"Does not apply\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [299, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.953349, \"Negative\", \"MAR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 25, \"Mortality\", \"secondary\", 42], [300, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.939033, \"Negative\", \"SEN\", \"1\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [301, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.779079, \"Negative\", \"LBR\", \"1\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [302, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.939188, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [303, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.845139, \"Negative\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [304, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.934848, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [305, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.858043, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 25, \"Mortality\", \"secondary\", 42], [306, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.842518, \"Negative\", \"BGD\", \"1\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [307, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.992671, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [308, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.993951, \"Negative\", \"VNM\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [309, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.900637, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [310, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.78477, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [311, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.947448, \"Negative\", \"KOR\", \"Does not apply\", \"Logarithmic\", \"Asia\", 25, \"Mortality\", \"secondary\", 42], [312, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.898692, \"Negative\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [313, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.780573, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [314, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.801076, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [315, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.934719, \"Negative\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [316, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.887514, \"Negative\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [317, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.892492, \"Negative\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [318, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.927253, \"Negative\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [319, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.970669, \"Negative\", \"CRI\", \"Does not apply\", \"Logarithmic\", \"Latam\", 25, \"Mortality\", \"secondary\", 42], [320, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.823122, \"Negative\", \"USA\", \"Does not apply\", \"Logarithmic\", \"Pair\", 25, \"Mortality\", \"secondary\", 42], [321, \"Probability of dying among adolescents ages 10-14 years (per 1,000)\", 0.989378, \"Negative\", \"CHN\", \"Does not apply\", \"Logarithmic\", \"Pair\", 25, \"Mortality\", \"secondary\", 42], [322, \"Time required to start a business (days)\", 0.792869, \"Negative\", \"DEU\", \"3\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [323, \"Time required to start a business (days)\", 0.762356, \"Negative\", \"FRA\", \"5\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [324, \"Time required to start a business (days)\", 0.847713, \"Negative\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [325, \"Time required to start a business (days)\", 0.810464, \"Negative\", \"ESP\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [326, \"Time required to start a business (days)\", 0.905217, \"Negative\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [327, \"Time required to start a business (days)\", 0.816949, \"Negative\", \"NLD\", \"8\", \"Does not apply\", \"Europe\", 11, \"Industry\", \"secondary\", 29], [328, \"Time required to start a business (days)\", 0.885547, \"Negative\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [329, \"Time required to start a business (days)\", 0.841579, \"Negative\", \"SAU\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [330, \"Time required to start a business (days)\", 0.932239, \"Negative\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [331, \"Time required to start a business (days)\", 0.76496, \"Negative\", \"YEM\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [332, \"Time required to start a business (days)\", 0.873136, \"Negative\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Industry\", \"secondary\", 29], [333, \"Time required to start a business (days)\", 0.830084, \"Negative\", \"DZA\", \"3\", \"Does not apply\", \"North Africa\", 11, \"Industry\", \"secondary\", 29], [334, \"Time required to start a business (days)\", 0.811387, \"Negative\", \"MAR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"Industry\", \"secondary\", 29], [335, \"Time required to start a business (days)\", 0.782024, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [336, \"Time required to start a business (days)\", 0.793607, \"Negative\", \"ZAF\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [337, \"Time required to start a business (days)\", 0.751747, \"Negative\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [338, \"Time required to start a business (days)\", 0.820023, \"Negative\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [339, \"Time required to start a business (days)\", 0.874331, \"Negative\", \"CMR\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Industry\", \"secondary\", 29], [340, \"Time required to start a business (days)\", 0.806947, \"Negative\", \"IND\", \"21\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [341, \"Time required to start a business (days)\", 0.965942, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [342, \"Time required to start a business (days)\", 0.83733, \"Negative\", \"THA\", \"5\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [343, \"Time required to start a business (days)\", 0.797064, \"Negative\", \"KOR\", \"5\", \"Does not apply\", \"Asia\", 11, \"Industry\", \"secondary\", 29], [344, \"Time required to start a business (days)\", 0.818574, \"Negative\", \"PER\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [345, \"Time required to start a business (days)\", 0.751607, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [346, \"Time required to start a business (days)\", 0.84776, \"Negative\", \"COL\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [347, \"Time required to start a business (days)\", 0.866647, \"Negative\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [348, \"Time required to start a business (days)\", 0.876316, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [349, \"Time required to start a business (days)\", 0.85138, \"Negative\", \"CRI\", \"8\", \"Does not apply\", \"Latam\", 11, \"Industry\", \"secondary\", 29], [350, \"Time required to start a business (days)\", 0.931818, \"Negative\", \"CHN\", \"21\", \"Does not apply\", \"Pair\", 11, \"Industry\", \"secondary\", 29], [351, \"Combustible renewables and waste (% of total energy)\", 0.759597, \"Positive\", \"DEU\", \"3\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [352, \"Combustible renewables and waste (% of total energy)\", 0.793758, \"Positive\", \"SWE\", \"1\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [353, \"Combustible renewables and waste (% of total energy)\", 0.905255, \"Positive\", \"GBR\", \"8\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [354, \"Combustible renewables and waste (% of total energy)\", 0.915732, \"Positive\", \"POL\", \"8\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [355, \"Combustible renewables and waste (% of total energy)\", 0.776496, \"Positive\", \"AUT\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [356, \"Combustible renewables and waste (% of total energy)\", 0.810504, \"Positive\", \"NLD\", \"5\", \"Does not apply\", \"Europe\", 12, \"Environment\", \"secondary\", 25], [357, \"Combustible renewables and waste (% of total energy)\", 0.935853, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 25], [358, \"Combustible renewables and waste (% of total energy)\", 0.763706, \"Positive\", \"AZE\", \"Does not apply\", \"Linear\", \"Persian Gulf\", 12, \"Environment\", \"secondary\", 25], [359, \"Combustible renewables and waste (% of total energy)\", 0.780104, \"Negative\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [360, \"Combustible renewables and waste (% of total energy)\", 0.941407, \"Negative\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [361, \"Combustible renewables and waste (% of total energy)\", 0.78341, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Environment\", \"secondary\", 25], [362, \"Combustible renewables and waste (% of total energy)\", 0.890534, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Environment\", \"secondary\", 25], [363, \"Combustible renewables and waste (% of total energy)\", 0.822012, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 12, \"Environment\", \"secondary\", 25], [364, \"Combustible renewables and waste (% of total energy)\", 0.982244, \"Negative\", \"BGD\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [365, \"Combustible renewables and waste (% of total energy)\", 0.978927, \"Negative\", \"IND\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [366, \"Combustible renewables and waste (% of total energy)\", 0.860249, \"Negative\", \"VNM\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [367, \"Combustible renewables and waste (% of total energy)\", 0.803047, \"Negative\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [368, \"Combustible renewables and waste (% of total energy)\", 0.918827, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [369, \"Combustible renewables and waste (% of total energy)\", 0.784859, \"Positive\", \"KOR\", \"3\", \"Does not apply\", \"Asia\", 12, \"Environment\", \"secondary\", 25], [370, \"Combustible renewables and waste (% of total energy)\", 0.915132, \"Negative\", \"MEX\", \"1\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 25], [371, \"Combustible renewables and waste (% of total energy)\", 0.930378, \"Negative\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 12, \"Environment\", \"secondary\", 25], [6273, \"Food production index (2014-2016 = 100)\", 0.868933, \"Positive\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6274, \"Food production index (2014-2016 = 100)\", 0.834377, \"Positive\", \"MOZ\", \"Does not apply\", \"Linear\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6275, \"Food production index (2014-2016 = 100)\", 0.821876, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6276, \"Food production index (2014-2016 = 100)\", 0.858764, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6277, \"Food production index (2014-2016 = 100)\", 0.860215, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 24, \"Industry\", \"secondary\", 35], [6278, \"Food production index (2014-2016 = 100)\", 0.995078, \"Positive\", \"BGD\", \"3\", \"Does not apply\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6279, \"Food production index (2014-2016 = 100)\", 0.98233, \"Positive\", \"IND\", \"1\", \"Does not apply\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6280, \"Food production index (2014-2016 = 100)\", 0.994324, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6281, \"Food production index (2014-2016 = 100)\", 0.807971, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6282, \"Food production index (2014-2016 = 100)\", 0.943786, \"Positive\", \"IDN\", \"Does not apply\", \"Cuadratic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6283, \"Food production index (2014-2016 = 100)\", 0.942178, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 24, \"Industry\", \"secondary\", 35], [6284, \"Food production index (2014-2016 = 100)\", 0.93495, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6285, \"Food production index (2014-2016 = 100)\", 0.76444, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6286, \"Food production index (2014-2016 = 100)\", 0.936928, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6287, \"Food production index (2014-2016 = 100)\", 0.896305, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6288, \"Food production index (2014-2016 = 100)\", 0.878859, \"Positive\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6289, \"Food production index (2014-2016 = 100)\", 0.966456, \"Positive\", \"PAN\", \"2\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6290, \"Food production index (2014-2016 = 100)\", 0.985531, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 24, \"Industry\", \"secondary\", 35], [6291, \"Food production index (2014-2016 = 100)\", 0.946677, \"Positive\", \"USA\", \"1\", \"Does not apply\", \"Pair\", 24, \"Industry\", \"secondary\", 35], [6292, \"Food production index (2014-2016 = 100)\", 0.995055, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 24, \"Industry\", \"secondary\", 35], [6293, \"Time to obtain an electrical connection (days)\", 0.963721, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 11, \"R&D\", \"secondary\", 24], [6294, \"Time to obtain an electrical connection (days)\", 0.83291, \"Positive\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 11, \"R&D\", \"secondary\", 24], [6295, \"Time to obtain an electrical connection (days)\", 0.804939, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6296, \"Time to obtain an electrical connection (days)\", 0.75968, \"Negative\", \"TUR\", \"21\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6297, \"Time to obtain an electrical connection (days)\", 0.876713, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 11, \"R&D\", \"secondary\", 24], [6298, \"Time to obtain an electrical connection (days)\", 0.852294, \"Positive\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6299, \"Time to obtain an electrical connection (days)\", 0.805169, \"Negative\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6300, \"Time to obtain an electrical connection (days)\", 0.92942, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6301, \"Time to obtain an electrical connection (days)\", 0.881377, \"Negative\", \"CMR\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6302, \"Time to obtain an electrical connection (days)\", 0.891739, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6303, \"Time to obtain an electrical connection (days)\", 0.843733, \"Positive\", \"GHA\", \"5\", \"Does not apply\", \"South Africa\", 11, \"R&D\", \"secondary\", 24], [6304, \"Time to obtain an electrical connection (days)\", 0.877193, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6305, \"Time to obtain an electrical connection (days)\", 0.917544, \"Negative\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6306, \"Time to obtain an electrical connection (days)\", 0.901754, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6307, \"Time to obtain an electrical connection (days)\", 0.817729, \"Negative\", \"IDN\", \"8\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6308, \"Time to obtain an electrical connection (days)\", 0.810745, \"Negative\", \"PHL\", \"5\", \"Does not apply\", \"Asia\", 11, \"R&D\", \"secondary\", 24], [6309, \"Time to obtain an electrical connection (days)\", 0.815051, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6310, \"Time to obtain an electrical connection (days)\", 0.909091, \"Negative\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6311, \"Time to obtain an electrical connection (days)\", 0.786273, \"Positive\", \"PER\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6312, \"Time to obtain an electrical connection (days)\", 0.818161, \"Negative\", \"VEN\", \"2\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6313, \"Time to obtain an electrical connection (days)\", 0.855364, \"Positive\", \"COL\", \"21\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6314, \"Time to obtain an electrical connection (days)\", 0.817488, \"Negative\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6315, \"Time to obtain an electrical connection (days)\", 0.825397, \"Negative\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6316, \"Time to obtain an electrical connection (days)\", 0.845543, \"Negative\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 11, \"R&D\", \"secondary\", 24], [6317, \"Suicide mortality rate (per 100,000 population)\", 0.855364, \"Negative\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 17, \"Mortality\", \"primary\", 25], [6318, \"Suicide mortality rate (per 100,000 population)\", 0.827624, \"Negative\", \"AUT\", \"13\", \"Does not apply\", \"Europe\", 17, \"Mortality\", \"primary\", 25], [6319, \"Suicide mortality rate (per 100,000 population)\", 0.773321, \"Negative\", \"QAT\", \"21\", \"Does not apply\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6320, \"Suicide mortality rate (per 100,000 population)\", 0.78697, \"Negative\", \"ARE\", \"3\", \"Does not apply\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6321, \"Suicide mortality rate (per 100,000 population)\", 0.872247, \"Positive\", \"SAU\", \"Does not apply\", \"Logarithmic\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6322, \"Suicide mortality rate (per 100,000 population)\", 0.827891, \"Negative\", \"YEM\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 17, \"Mortality\", \"primary\", 25], [6323, \"Suicide mortality rate (per 100,000 population)\", 0.878636, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6324, \"Suicide mortality rate (per 100,000 population)\", 0.886364, \"Negative\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6325, \"Suicide mortality rate (per 100,000 population)\", 0.897716, \"Negative\", \"TUR\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6326, \"Suicide mortality rate (per 100,000 population)\", 0.90973, \"Negative\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 17, \"Mortality\", \"primary\", 25], [6327, \"Suicide mortality rate (per 100,000 population)\", 0.897565, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 17, \"Mortality\", \"primary\", 25], [6328, \"Suicide mortality rate (per 100,000 population)\", 0.966749, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 17, \"Mortality\", \"primary\", 25], [6329, \"Suicide mortality rate (per 100,000 population)\", 0.878685, \"Negative\", \"BGD\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6330, \"Suicide mortality rate (per 100,000 population)\", 0.915641, \"Negative\", \"IND\", \"2\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6331, \"Suicide mortality rate (per 100,000 population)\", 0.869184, \"Positive\", \"VNM\", \"3\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6332, \"Suicide mortality rate (per 100,000 population)\", 0.882422, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6333, \"Suicide mortality rate (per 100,000 population)\", 0.831818, \"Negative\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 17, \"Mortality\", \"primary\", 25], [6334, \"Suicide mortality rate (per 100,000 population)\", 0.952612, \"Positive\", \"MEX\", \"3\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6335, \"Suicide mortality rate (per 100,000 population)\", 0.756344, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6336, \"Suicide mortality rate (per 100,000 population)\", 0.902423, \"Negative\", \"VEN\", \"8\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6337, \"Suicide mortality rate (per 100,000 population)\", 0.776677, \"Negative\", \"COL\", \"Does not apply\", \"Logarithmic\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6338, \"Suicide mortality rate (per 100,000 population)\", 0.755201, \"Negative\", \"CHL\", \"21\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6339, \"Suicide mortality rate (per 100,000 population)\", 0.920313, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 17, \"Mortality\", \"primary\", 25], [6340, \"Suicide mortality rate (per 100,000 population)\", 0.991231, \"Positive\", \"USA\", \"13\", \"Does not apply\", \"Pair\", 17, \"Mortality\", \"primary\", 25], [6341, \"Suicide mortality rate (per 100,000 population)\", 0.95768, \"Negative\", \"CHN\", \"5\", \"Does not apply\", \"Pair\", 17, \"Mortality\", \"primary\", 25], [6342, \"Firms using banks to finance investment (% of firms)\", 0.841751, \"Positive\", \"POL\", \"21\", \"Does not apply\", \"Europe\", 12, \"Economy\", \"secondary\", 28], [6343, \"Firms using banks to finance investment (% of firms)\", 0.83291, \"Negative\", \"GRC\", \"13\", \"Does not apply\", \"Europe\", 12, \"Economy\", \"secondary\", 28], [6344, \"Firms using banks to finance investment (% of firms)\", 0.945216, \"Negative\", \"AZE\", \"21\", \"Does not apply\", \"Persian Gulf\", 12, \"Economy\", \"secondary\", 28], [6345, \"Firms using banks to finance investment (% of firms)\", 0.789777, \"Negative\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 12, \"Economy\", \"secondary\", 28], [6346, \"Firms using banks to finance investment (% of firms)\", 0.941656, \"Positive\", \"EGY\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6347, \"Firms using banks to finance investment (% of firms)\", 0.75968, \"Negative\", \"TUR\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6348, \"Firms using banks to finance investment (% of firms)\", 0.876713, \"Positive\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Economy\", \"secondary\", 28], [6349, \"Firms using banks to finance investment (% of firms)\", 0.852294, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6350, \"Firms using banks to finance investment (% of firms)\", 0.805169, \"Negative\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6351, \"Firms using banks to finance investment (% of firms)\", 0.882199, \"Positive\", \"LBR\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6352, \"Firms using banks to finance investment (% of firms)\", 0.92942, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6353, \"Firms using banks to finance investment (% of firms)\", 0.759974, \"Negative\", \"CMR\", \"13\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6354, \"Firms using banks to finance investment (% of firms)\", 0.891739, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6355, \"Firms using banks to finance investment (% of firms)\", 0.843733, \"Positive\", \"GHA\", \"5\", \"Does not apply\", \"South Africa\", 12, \"Economy\", \"secondary\", 28], [6356, \"Firms using banks to finance investment (% of firms)\", 0.877193, \"Negative\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6357, \"Firms using banks to finance investment (% of firms)\", 0.917544, \"Negative\", \"IND\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6358, \"Firms using banks to finance investment (% of firms)\", 0.901754, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6359, \"Firms using banks to finance investment (% of firms)\", 0.870956, \"Negative\", \"THA\", \"3\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6360, \"Firms using banks to finance investment (% of firms)\", 0.817729, \"Positive\", \"IDN\", \"8\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6361, \"Firms using banks to finance investment (% of firms)\", 0.810745, \"Negative\", \"PHL\", \"5\", \"Does not apply\", \"Asia\", 12, \"Economy\", \"secondary\", 28], [6362, \"Firms using banks to finance investment (% of firms)\", 0.815051, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6363, \"Firms using banks to finance investment (% of firms)\", 0.909091, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6364, \"Firms using banks to finance investment (% of firms)\", 0.88294, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6365, \"Firms using banks to finance investment (% of firms)\", 0.818161, \"Negative\", \"VEN\", \"2\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6366, \"Firms using banks to finance investment (% of firms)\", 0.855364, \"Positive\", \"COL\", \"21\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6367, \"Firms using banks to finance investment (% of firms)\", 0.817488, \"Positive\", \"CHL\", \"3\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6368, \"Firms using banks to finance investment (% of firms)\", 0.825397, \"Negative\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6369, \"Firms using banks to finance investment (% of firms)\", 0.845543, \"Positive\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 12, \"Economy\", \"secondary\", 28], [6370, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.768265, \"Negative\", \"GRC\", \"5\", \"Does not apply\", \"Europe\", 12, \"Imports\", \"secondary\", 21], [6371, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.933568, \"Negative\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6372, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.946379, \"Negative\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6373, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.829932, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6374, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.893727, \"Negative\", \"OMN\", \"Does not apply\", \"Logarithmic\", \"Persian Gulf\", 12, \"Imports\", \"secondary\", 21], [6375, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.827434, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Imports\", \"secondary\", 21], [6376, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.759141, \"Negative\", \"TUR\", \"2\", \"Does not apply\", \"North Africa\", 12, \"Imports\", \"secondary\", 21], [6377, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.904546, \"Negative\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 12, \"Imports\", \"secondary\", 21], [6378, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.937705, \"Negative\", \"BGD\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6379, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.9145, \"Negative\", \"VNM\", \"8\", \"Does not apply\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6380, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.865531, \"Negative\", \"THA\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6381, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.898586, \"Negative\", \"IDN\", \"Does not apply\", \"Logarithmic\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6382, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.945048, \"Negative\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6383, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.929542, \"Negative\", \"KOR\", \"5\", \"Does not apply\", \"Asia\", 12, \"Imports\", \"secondary\", 21], [6384, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.949767, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6385, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.752078, \"Negative\", \"BRA\", \"1\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6386, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.919187, \"Negative\", \"VEN\", \"Does not apply\", \"Logarithmic\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6387, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.797346, \"Negative\", \"COL\", \"Does not apply\", \"Logarithmic\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6388, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.8055, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 12, \"Imports\", \"secondary\", 21], [6389, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.831757, \"Negative\", \"USA\", \"Does not apply\", \"Logarithmic\", \"Pair\", 12, \"Imports\", \"secondary\", 21], [6390, \"Merchandise imports from high-income economies (% of total merchandise imports)\", 0.857664, \"Negative\", \"CHN\", \"1\", \"Does not apply\", \"Pair\", 12, \"Imports\", \"secondary\", 21], [6391, \"People practicing open defecation (% of population)\", 0.85947, \"Negative\", \"GRC\", \"8\", \"Does not apply\", \"Europe\", 27, \"Health\", \"secondary\", 32], [6392, \"People practicing open defecation (% of population)\", 0.764246, \"Negative\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6393, \"People practicing open defecation (% of population)\", 0.961673, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6394, \"People practicing open defecation (% of population)\", 0.955118, \"Negative\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6395, \"People practicing open defecation (% of population)\", 0.905975, \"Negative\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 27, \"Health\", \"secondary\", 32], [6396, \"People practicing open defecation (% of population)\", 0.850746, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6397, \"People practicing open defecation (% of population)\", 0.913668, \"Negative\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6398, \"People practicing open defecation (% of population)\", 0.94435, \"Negative\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6399, \"People practicing open defecation (% of population)\", 0.924176, \"Negative\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 27, \"Health\", \"secondary\", 32], [6400, \"People practicing open defecation (% of population)\", 0.903327, \"Negative\", \"SEN\", \"Does not apply\", \"Linear\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6401, \"People practicing open defecation (% of population)\", 0.756276, \"Negative\", \"ZAF\", \"8\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6402, \"People practicing open defecation (% of population)\", 0.889166, \"Negative\", \"LBR\", \"1\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6403, \"People practicing open defecation (% of population)\", 0.948441, \"Negative\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6404, \"People practicing open defecation (% of population)\", 0.909027, \"Negative\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6405, \"People practicing open defecation (% of population)\", 0.964139, \"Negative\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6406, \"People practicing open defecation (% of population)\", 0.867409, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 27, \"Health\", \"secondary\", 32], [6407, \"People practicing open defecation (% of population)\", 0.996522, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6408, \"People practicing open defecation (% of population)\", 0.989149, \"Negative\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6409, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6410, \"People practicing open defecation (% of population)\", 0.865868, \"Negative\", \"THA\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6411, \"People practicing open defecation (% of population)\", 0.908314, \"Negative\", \"IDN\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6412, \"People practicing open defecation (% of population)\", 0.903327, \"Negative\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 27, \"Health\", \"secondary\", 32], [6413, \"People practicing open defecation (% of population)\", 0.939572, \"Negative\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6414, \"People practicing open defecation (% of population)\", 0.750815, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6415, \"People practicing open defecation (% of population)\", 0.751955, \"Negative\", \"ARG\", \"Does not apply\", \"Cubic\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6416, \"People practicing open defecation (% of population)\", 0.90475, \"Negative\", \"PER\", \"Does not apply\", \"Linear\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6417, \"People practicing open defecation (% of population)\", 0.890679, \"Negative\", \"VEN\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6418, \"People practicing open defecation (% of population)\", 0.897855, \"Negative\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6419, \"People practicing open defecation (% of population)\", 0.909091, \"Negative\", \"CHL\", \"21\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6420, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"PAN\", \"13\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6421, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 27, \"Health\", \"secondary\", 32], [6422, \"People practicing open defecation (% of population)\", 0.999123, \"Negative\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 27, \"Health\", \"secondary\", 32], [6423, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.829371, \"Negative\", \"AUT\", \"8\", \"Does not apply\", \"Europe\", 11, \"Environment\", \"secondary\", 18], [6424, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.848986, \"Negative\", \"QAT\", \"3\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6425, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.855782, \"Positive\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6426, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.964478, \"Negative\", \"SAU\", \"2\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6427, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.767146, \"Positive\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 11, \"Environment\", \"secondary\", 18], [6428, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.805752, \"Positive\", \"MAR\", \"2\", \"Does not apply\", \"North Africa\", 11, \"Environment\", \"secondary\", 18], [6429, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.909825, \"Negative\", \"SEN\", \"5\", \"Does not apply\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6430, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.855509, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6431, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.900159, \"Negative\", \"NGA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 11, \"Environment\", \"secondary\", 18], [6432, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.853751, \"Negative\", \"BGD\", \"8\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6433, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.967802, \"Negative\", \"IND\", \"1\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6434, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.879127, \"Negative\", \"VNM\", \"5\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6435, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.922596, \"Negative\", \"IDN\", \"2\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6436, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.941576, \"Negative\", \"KOR\", \"1\", \"Does not apply\", \"Asia\", 11, \"Environment\", \"secondary\", 18], [6437, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.895958, \"Negative\", \"MEX\", \"Does not apply\", \"Logarithmic\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6438, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.804632, \"Negative\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6439, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.87797, \"Negative\", \"CHL\", \"1\", \"Does not apply\", \"Latam\", 11, \"Environment\", \"secondary\", 18], [6440, \"CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\", 0.874924, \"Negative\", \"CHN\", \"1\", \"Does not apply\", \"Pair\", 11, \"Environment\", \"secondary\", 18], [6441, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.843001, \"Negative\", \"NLD\", \"Does not apply\", \"Logarithmic\", \"Europe\", 13, \"Agriculture\", \"secondary\", 21], [6442, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.853721, \"Positive\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6443, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.956311, \"Positive\", \"ARE\", \"3\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6444, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.983928, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6445, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.805605, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6446, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.972728, \"Positive\", \"OMN\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Agriculture\", \"secondary\", 21], [6447, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.831716, \"Positive\", \"DZA\", \"8\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6448, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.795868, \"Positive\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6449, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.841358, \"Positive\", \"MAR\", \"1\", \"Does not apply\", \"North Africa\", 13, \"Agriculture\", \"secondary\", 21], [6450, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.790698, \"Positive\", \"LBR\", \"2\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6451, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.920335, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6452, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.774495, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 13, \"Agriculture\", \"secondary\", 21], [6453, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.983907, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6454, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.961303, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6455, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.896246, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6456, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.936072, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6457, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.831515, \"Positive\", \"PHL\", \"21\", \"Does not apply\", \"Asia\", 13, \"Agriculture\", \"secondary\", 21], [6458, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.758321, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6459, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.828788, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6460, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.792582, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 13, \"Agriculture\", \"secondary\", 21], [6461, \"Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)\", 0.80934, \"Positive\", \"USA\", \"Does not apply\", \"Cubic\", \"Pair\", 13, \"Agriculture\", \"secondary\", 21], [6462, \"Access to clean fuels and technologies for cooking (% of population)\", 0.766483, \"Positive\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6463, \"Access to clean fuels and technologies for cooking (% of population)\", 0.996083, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6464, \"Access to clean fuels and technologies for cooking (% of population)\", 0.9509, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 17, \"Internet\", \"secondary\", 26], [6465, \"Access to clean fuels and technologies for cooking (% of population)\", 0.853684, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6466, \"Access to clean fuels and technologies for cooking (% of population)\", 0.897511, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6467, \"Access to clean fuels and technologies for cooking (% of population)\", 0.957737, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6468, \"Access to clean fuels and technologies for cooking (% of population)\", 0.923174, \"Positive\", \"MAR\", \"8\", \"Does not apply\", \"North Africa\", 17, \"Internet\", \"secondary\", 26], [6469, \"Access to clean fuels and technologies for cooking (% of population)\", 0.878984, \"Negative\", \"SEN\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6470, \"Access to clean fuels and technologies for cooking (% of population)\", 0.756276, \"Positive\", \"ZAF\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6471, \"Access to clean fuels and technologies for cooking (% of population)\", 0.960204, \"Positive\", \"MOZ\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6472, \"Access to clean fuels and technologies for cooking (% of population)\", 0.872263, \"Positive\", \"CMR\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6473, \"Access to clean fuels and technologies for cooking (% of population)\", 0.962628, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6474, \"Access to clean fuels and technologies for cooking (% of population)\", 0.864074, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 17, \"Internet\", \"secondary\", 26], [6475, \"Access to clean fuels and technologies for cooking (% of population)\", 0.995617, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6476, \"Access to clean fuels and technologies for cooking (% of population)\", 0.989149, \"Positive\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6477, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6478, \"Access to clean fuels and technologies for cooking (% of population)\", 0.880003, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6479, \"Access to clean fuels and technologies for cooking (% of population)\", 0.908314, \"Positive\", \"IDN\", \"Does not apply\", \"Linear\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6480, \"Access to clean fuels and technologies for cooking (% of population)\", 0.826918, \"Positive\", \"PHL\", \"Does not apply\", \"Logarithmic\", \"Asia\", 17, \"Internet\", \"secondary\", 26], [6481, \"Access to clean fuels and technologies for cooking (% of population)\", 0.753409, \"Positive\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6482, \"Access to clean fuels and technologies for cooking (% of population)\", 0.853102, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6483, \"Access to clean fuels and technologies for cooking (% of population)\", 0.90475, \"Positive\", \"PER\", \"Does not apply\", \"Linear\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6484, \"Access to clean fuels and technologies for cooking (% of population)\", 0.897855, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6485, \"Access to clean fuels and technologies for cooking (% of population)\", 0.945724, \"Positive\", \"PAN\", \"5\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6486, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 17, \"Internet\", \"secondary\", 26], [6487, \"Access to clean fuels and technologies for cooking (% of population)\", 0.999123, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 17, \"Internet\", \"secondary\", 26], [6488, \"Access to electricity (% of population)\", 0.755466, \"Positive\", \"IRQ\", \"2\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6489, \"Access to electricity (% of population)\", 0.844248, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6490, \"Access to electricity (% of population)\", 0.825795, \"Positive\", \"YEM\", \"3\", \"Does not apply\", \"Persian Gulf\", 16, \"Environment\", \"primary\", 26], [6491, \"Access to electricity (% of population)\", 0.870107, \"Positive\", \"EGY\", \"5\", \"Does not apply\", \"North Africa\", 16, \"Environment\", \"primary\", 26], [6492, \"Access to electricity (% of population)\", 0.914656, \"Positive\", \"MAR\", \"2\", \"Does not apply\", \"North Africa\", 16, \"Environment\", \"primary\", 26], [6493, \"Access to electricity (% of population)\", 0.780617, \"Positive\", \"SEN\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6494, \"Access to electricity (% of population)\", 0.798854, \"Positive\", \"ZAF\", \"2\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6495, \"Access to electricity (% of population)\", 0.848246, \"Positive\", \"LBR\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6496, \"Access to electricity (% of population)\", 0.924489, \"Positive\", \"MOZ\", \"5\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6497, \"Access to electricity (% of population)\", 0.811965, \"Positive\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6498, \"Access to electricity (% of population)\", 0.829545, \"Positive\", \"NGA\", \"3\", \"Does not apply\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6499, \"Access to electricity (% of population)\", 0.824265, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 16, \"Environment\", \"primary\", 26], [6500, \"Access to electricity (% of population)\", 0.993779, \"Positive\", \"BGD\", \"2\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6501, \"Access to electricity (% of population)\", 0.979171, \"Positive\", \"IND\", \"3\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6502, \"Access to electricity (% of population)\", 0.968614, \"Positive\", \"VNM\", \"3\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6503, \"Access to electricity (% of population)\", 0.872662, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6504, \"Access to electricity (% of population)\", 0.823824, \"Positive\", \"IDN\", \"5\", \"Does not apply\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6505, \"Access to electricity (% of population)\", 0.909312, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 16, \"Environment\", \"primary\", 26], [6506, \"Access to electricity (% of population)\", 0.845838, \"Positive\", \"MEX\", \"5\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6507, \"Access to electricity (% of population)\", 0.776361, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6508, \"Access to electricity (% of population)\", 0.928139, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6509, \"Access to electricity (% of population)\", 0.780517, \"Positive\", \"COL\", \"3\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6510, \"Access to electricity (% of population)\", 0.871062, \"Positive\", \"CHL\", \"2\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6511, \"Access to electricity (% of population)\", 0.977356, \"Positive\", \"PAN\", \"21\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6512, \"Access to electricity (% of population)\", 0.937042, \"Positive\", \"CRI\", \"5\", \"Does not apply\", \"Latam\", 16, \"Environment\", \"primary\", 26], [6513, \"Access to electricity (% of population)\", 0.963675, \"Positive\", \"CHN\", \"5\", \"Does not apply\", \"Pair\", 16, \"Environment\", \"primary\", 26], [6514, \"Prevalence of undernourishment (% of population)\", 0.766965, \"Positive\", \"IRQ\", \"3\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6515, \"Prevalence of undernourishment (% of population)\", 0.774883, \"Negative\", \"AZE\", \"2\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6516, \"Prevalence of undernourishment (% of population)\", 0.7825, \"Positive\", \"YEM\", \"13\", \"Does not apply\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6517, \"Prevalence of undernourishment (% of population)\", 0.896934, \"Negative\", \"OMN\", \"Does not apply\", \"Cuadratic\", \"Persian Gulf\", 14, \"Health\", \"secondary\", 21], [6518, \"Prevalence of undernourishment (% of population)\", 0.874165, \"Negative\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 14, \"Health\", \"secondary\", 21], [6519, \"Prevalence of undernourishment (% of population)\", 0.863113, \"Negative\", \"MAR\", \"8\", \"Does not apply\", \"North Africa\", 14, \"Health\", \"secondary\", 21], [6520, \"Prevalence of undernourishment (% of population)\", 0.877935, \"Negative\", \"SEN\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6521, \"Prevalence of undernourishment (% of population)\", 0.809735, \"Negative\", \"MOZ\", \"Does not apply\", \"Cubic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6522, \"Prevalence of undernourishment (% of population)\", 0.912315, \"Negative\", \"CMR\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6523, \"Prevalence of undernourishment (% of population)\", 0.959901, \"Positive\", \"NGA\", \"13\", \"Does not apply\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6524, \"Prevalence of undernourishment (% of population)\", 0.839859, \"Negative\", \"GHA\", \"Does not apply\", \"Logarithmic\", \"South Africa\", 14, \"Health\", \"secondary\", 21], [6525, \"Prevalence of undernourishment (% of population)\", 0.891, \"Negative\", \"BGD\", \"21\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6526, \"Prevalence of undernourishment (% of population)\", 0.996491, \"Negative\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6527, \"Prevalence of undernourishment (% of population)\", 0.836744, \"Negative\", \"THA\", \"Does not apply\", \"Logarithmic\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6528, \"Prevalence of undernourishment (% of population)\", 0.872487, \"Negative\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6529, \"Prevalence of undernourishment (% of population)\", 0.827297, \"Negative\", \"PHL\", \"1\", \"Does not apply\", \"Asia\", 14, \"Health\", \"secondary\", 21], [6530, \"Prevalence of undernourishment (% of population)\", 0.840925, \"Positive\", \"MEX\", \"3\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6531, \"Prevalence of undernourishment (% of population)\", 0.759618, \"Negative\", \"BRA\", \"5\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6532, \"Prevalence of undernourishment (% of population)\", 0.893013, \"Positive\", \"VEN\", \"21\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6533, \"Prevalence of undernourishment (% of population)\", 0.961103, \"Negative\", \"PAN\", \"8\", \"Does not apply\", \"Latam\", 14, \"Health\", \"secondary\", 21], [6534, \"Prevalence of undernourishment (% of population)\", 0.9, \"Negative\", \"CHN\", \"3\", \"Does not apply\", \"Pair\", 14, \"Health\", \"secondary\", 21], [6535, \"Cereal yield (kg per hectare)\", 0.779595, \"Positive\", \"QAT\", \"1\", \"Does not apply\", \"Persian Gulf\", 12, \"Agriculture\", \"secondary\", 18], [6536, \"Cereal yield (kg per hectare)\", 0.888932, \"Positive\", \"SAU\", \"5\", \"Does not apply\", \"Persian Gulf\", 12, \"Agriculture\", \"secondary\", 18], [6537, \"Cereal yield (kg per hectare)\", 0.788288, \"Negative\", \"ISR\", \"21\", \"Does not apply\", \"North Africa\", 12, \"Agriculture\", \"secondary\", 18], [6538, \"Cereal yield (kg per hectare)\", 0.87744, \"Positive\", \"TUR\", \"5\", \"Does not apply\", \"North Africa\", 12, \"Agriculture\", \"secondary\", 18], [6539, \"Cereal yield (kg per hectare)\", 0.774258, \"Positive\", \"GHA\", \"Does not apply\", \"Cubic\", \"South Africa\", 12, \"Agriculture\", \"secondary\", 18], [6540, \"Cereal yield (kg per hectare)\", 0.9936, \"Positive\", \"BGD\", \"3\", \"Does not apply\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6541, \"Cereal yield (kg per hectare)\", 0.961303, \"Positive\", \"IND\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6542, \"Cereal yield (kg per hectare)\", 0.983739, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6543, \"Cereal yield (kg per hectare)\", 0.894857, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6544, \"Cereal yield (kg per hectare)\", 0.939127, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 12, \"Agriculture\", \"secondary\", 18], [6545, \"Cereal yield (kg per hectare)\", 0.903125, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6546, \"Cereal yield (kg per hectare)\", 0.804904, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6547, \"Cereal yield (kg per hectare)\", 0.907475, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6548, \"Cereal yield (kg per hectare)\", 0.794782, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6549, \"Cereal yield (kg per hectare)\", 0.836149, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6550, \"Cereal yield (kg per hectare)\", 0.750296, \"Positive\", \"PAN\", \"1\", \"Does not apply\", \"Latam\", 12, \"Agriculture\", \"secondary\", 18], [6551, \"Cereal yield (kg per hectare)\", 0.911779, \"Positive\", \"USA\", \"3\", \"Does not apply\", \"Pair\", 12, \"Agriculture\", \"secondary\", 18], [6552, \"Cereal yield (kg per hectare)\", 0.977243, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 12, \"Agriculture\", \"secondary\", 18], [6553, \"Pregnant women receiving prenatal care (%)\", 0.803913, \"Negative\", \"QAT\", \"8\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6554, \"Pregnant women receiving prenatal care (%)\", 0.889643, \"Positive\", \"ARE\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6555, \"Pregnant women receiving prenatal care (%)\", 0.89992, \"Positive\", \"SAU\", \"Does not apply\", \"Cubic\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6556, \"Pregnant women receiving prenatal care (%)\", 0.79933, \"Positive\", \"AZE\", \"13\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6557, \"Pregnant women receiving prenatal care (%)\", 0.908023, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 18, \"Health\", \"secondary\", 24], [6558, \"Pregnant women receiving prenatal care (%)\", 0.855771, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6559, \"Pregnant women receiving prenatal care (%)\", 0.898054, \"Positive\", \"EGY\", \"3\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6560, \"Pregnant women receiving prenatal care (%)\", 0.95029, \"Positive\", \"TUR\", \"2\", \"Does not apply\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6561, \"Pregnant women receiving prenatal care (%)\", 0.954106, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 18, \"Health\", \"secondary\", 24], [6562, \"Pregnant women receiving prenatal care (%)\", 0.904693, \"Positive\", \"SEN\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6563, \"Pregnant women receiving prenatal care (%)\", 0.919914, \"Positive\", \"LBR\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6564, \"Pregnant women receiving prenatal care (%)\", 0.878474, \"Positive\", \"MOZ\", \"21\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6565, \"Pregnant women receiving prenatal care (%)\", 0.858423, \"Positive\", \"CMR\", \"Does not apply\", \"Cubic\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6566, \"Pregnant women receiving prenatal care (%)\", 0.814632, \"Positive\", \"NGA\", \"21\", \"Does not apply\", \"South Africa\", 18, \"Health\", \"secondary\", 24], [6567, \"Pregnant women receiving prenatal care (%)\", 0.983888, \"Positive\", \"BGD\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6568, \"Pregnant women receiving prenatal care (%)\", 0.966606, \"Positive\", \"IND\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6569, \"Pregnant women receiving prenatal care (%)\", 0.956534, \"Positive\", \"VNM\", \"8\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6570, \"Pregnant women receiving prenatal care (%)\", 0.861958, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6571, \"Pregnant women receiving prenatal care (%)\", 0.810999, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 18, \"Health\", \"secondary\", 24], [6572, \"Pregnant women receiving prenatal care (%)\", 0.898514, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6573, \"Pregnant women receiving prenatal care (%)\", 0.91284, \"Positive\", \"VEN\", \"3\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6574, \"Pregnant women receiving prenatal care (%)\", 0.932079, \"Positive\", \"COL\", \"1\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6575, \"Pregnant women receiving prenatal care (%)\", 0.954545, \"Negative\", \"CRI\", \"21\", \"Does not apply\", \"Latam\", 18, \"Health\", \"secondary\", 24], [6576, \"Pregnant women receiving prenatal care (%)\", 0.981598, \"Positive\", \"CHN\", \"13\", \"Does not apply\", \"Pair\", 18, \"Health\", \"secondary\", 24], [6577, \"Crop production index (2014-2016 = 100)\", 0.766012, \"Positive\", \"QAT\", \"13\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6578, \"Crop production index (2014-2016 = 100)\", 0.963749, \"Positive\", \"AZE\", \"8\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6579, \"Crop production index (2014-2016 = 100)\", 0.835085, \"Positive\", \"YEM\", \"Does not apply\", \"Cuadratic\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6580, \"Crop production index (2014-2016 = 100)\", 0.951834, \"Positive\", \"OMN\", \"13\", \"Does not apply\", \"Persian Gulf\", 21, \"Agriculture\", \"secondary\", 27], [6581, \"Crop production index (2014-2016 = 100)\", 0.814074, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6582, \"Crop production index (2014-2016 = 100)\", 0.908494, \"Positive\", \"EGY\", \"Does not apply\", \"Cubic\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6583, \"Crop production index (2014-2016 = 100)\", 0.947974, \"Positive\", \"TUR\", \"13\", \"Does not apply\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6584, \"Crop production index (2014-2016 = 100)\", 0.87045, \"Positive\", \"MAR\", \"Does not apply\", \"Cuadratic\", \"North Africa\", 21, \"Agriculture\", \"secondary\", 27], [6585, \"Crop production index (2014-2016 = 100)\", 0.804939, \"Negative\", \"SEN\", \"21\", \"Does not apply\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6586, \"Crop production index (2014-2016 = 100)\", 0.833037, \"Positive\", \"MOZ\", \"Does not apply\", \"Linear\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6587, \"Crop production index (2014-2016 = 100)\", 0.821211, \"Positive\", \"CMR\", \"Does not apply\", \"Linear\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6588, \"Crop production index (2014-2016 = 100)\", 0.854241, \"Positive\", \"NGA\", \"5\", \"Does not apply\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6589, \"Crop production index (2014-2016 = 100)\", 0.858855, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 21, \"Agriculture\", \"secondary\", 27], [6590, \"Crop production index (2014-2016 = 100)\", 0.989486, \"Positive\", \"BGD\", \"13\", \"Does not apply\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6591, \"Crop production index (2014-2016 = 100)\", 0.965283, \"Positive\", \"IND\", \"Does not apply\", \"Cuadratic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6592, \"Crop production index (2014-2016 = 100)\", 0.995787, \"Positive\", \"VNM\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6593, \"Crop production index (2014-2016 = 100)\", 0.819882, \"Positive\", \"THA\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6594, \"Crop production index (2014-2016 = 100)\", 0.913211, \"Positive\", \"IDN\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6595, \"Crop production index (2014-2016 = 100)\", 0.91721, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 21, \"Agriculture\", \"secondary\", 27], [6596, \"Crop production index (2014-2016 = 100)\", 0.908215, \"Positive\", \"MEX\", \"8\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6597, \"Crop production index (2014-2016 = 100)\", 0.766164, \"Positive\", \"BRA\", \"3\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6598, \"Crop production index (2014-2016 = 100)\", 0.936928, \"Positive\", \"PER\", \"2\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6599, \"Crop production index (2014-2016 = 100)\", 0.851083, \"Positive\", \"COL\", \"8\", \"Does not apply\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6600, \"Crop production index (2014-2016 = 100)\", 0.882284, \"Positive\", \"CHL\", \"Does not apply\", \"Cuadratic\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6601, \"Crop production index (2014-2016 = 100)\", 0.967446, \"Positive\", \"CRI\", \"Does not apply\", \"Cubic\", \"Latam\", 21, \"Agriculture\", \"secondary\", 27], [6602, \"Crop production index (2014-2016 = 100)\", 0.845314, \"Positive\", \"USA\", \"Does not apply\", \"Linear\", \"Pair\", 21, \"Agriculture\", \"secondary\", 27], [6603, \"Crop production index (2014-2016 = 100)\", 0.998717, \"Positive\", \"CHN\", \"Does not apply\", \"Cubic\", \"Pair\", 21, \"Agriculture\", \"secondary\", 27], [6604, \"Births attended by skilled health staff (% of total)\", 0.86053, \"Negative\", \"ARE\", \"13\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6605, \"Births attended by skilled health staff (% of total)\", 0.938926, \"Positive\", \"SAU\", \"8\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6606, \"Births attended by skilled health staff (% of total)\", 0.842853, \"Positive\", \"YEM\", \"5\", \"Does not apply\", \"Persian Gulf\", 13, \"Mortality\", \"secondary\", 20], [6607, \"Births attended by skilled health staff (% of total)\", 0.842834, \"Positive\", \"DZA\", \"2\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6608, \"Births attended by skilled health staff (% of total)\", 0.923878, \"Positive\", \"EGY\", \"2\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6609, \"Births attended by skilled health staff (% of total)\", 0.95508, \"Positive\", \"TUR\", \"3\", \"Does not apply\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6610, \"Births attended by skilled health staff (% of total)\", 0.953389, \"Positive\", \"MAR\", \"Does not apply\", \"Cubic\", \"North Africa\", 13, \"Mortality\", \"secondary\", 20], [6611, \"Births attended by skilled health staff (% of total)\", 0.797257, \"Positive\", \"ZAF\", \"5\", \"Does not apply\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6612, \"Births attended by skilled health staff (% of total)\", 0.783475, \"Positive\", \"MOZ\", \"Does not apply\", \"Cubic\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6613, \"Births attended by skilled health staff (% of total)\", 0.858575, \"Positive\", \"GHA\", \"Does not apply\", \"Cuadratic\", \"South Africa\", 13, \"Mortality\", \"secondary\", 20], [6614, \"Births attended by skilled health staff (% of total)\", 0.977507, \"Positive\", \"BGD\", \"5\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6615, \"Births attended by skilled health staff (% of total)\", 0.982218, \"Positive\", \"IND\", \"Does not apply\", \"Linear\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6616, \"Births attended by skilled health staff (% of total)\", 0.95581, \"Positive\", \"VNM\", \"2\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6617, \"Births attended by skilled health staff (% of total)\", 0.876592, \"Positive\", \"IDN\", \"1\", \"Does not apply\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6618, \"Births attended by skilled health staff (% of total)\", 0.939552, \"Positive\", \"PHL\", \"Does not apply\", \"Linear\", \"Asia\", 13, \"Mortality\", \"secondary\", 20], [6619, \"Births attended by skilled health staff (% of total)\", 0.844034, \"Positive\", \"MEX\", \"1\", \"Does not apply\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6620, \"Births attended by skilled health staff (% of total)\", 0.924618, \"Positive\", \"PER\", \"Does not apply\", \"Cubic\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6621, \"Births attended by skilled health staff (% of total)\", 0.758834, \"Positive\", \"CRI\", \"1\", \"Does not apply\", \"Latam\", 13, \"Mortality\", \"secondary\", 20], [6622, \"Births attended by skilled health staff (% of total)\", 0.794656, \"Negative\", \"USA\", \"8\", \"Does not apply\", \"Pair\", 13, \"Mortality\", \"secondary\", 20], [6623, \"Births attended by skilled health staff (% of total)\", 0.950593, \"Positive\", \"CHN\", \"3\", \"Does not apply\", \"Pair\", 13, \"Mortality\", \"secondary\", 20], [6624, \"Completeness of birth registration (%)\", 0.86815, \"Positive\", \"DZA\", \"5\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6625, \"Completeness of birth registration (%)\", 0.840806, \"Positive\", \"EGY\", \"Does not apply\", \"Logarithmic\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6626, \"Completeness of birth registration (%)\", 0.898573, \"Positive\", \"TUR\", \"1\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6627, \"Completeness of birth registration (%)\", 0.932084, \"Positive\", \"MAR\", \"5\", \"Does not apply\", \"North Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6628, \"Completeness of birth registration (%)\", 0.909636, \"Positive\", \"LBR\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6629, \"Completeness of birth registration (%)\", 0.8862, \"Positive\", \"MOZ\", \"13\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6630, \"Completeness of birth registration (%)\", 0.790617, \"Negative\", \"CMR\", \"3\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6631, \"Completeness of birth registration (%)\", 0.81349, \"Positive\", \"NGA\", \"8\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6632, \"Completeness of birth registration (%)\", 0.773855, \"Positive\", \"GHA\", \"2\", \"Does not apply\", \"South Africa\", 11, \"Demoraphy\", \"secondary\", 21], [6633, \"Completeness of birth registration (%)\", 0.792328, \"Positive\", \"BGD\", \"5\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6634, \"Completeness of birth registration (%)\", 0.810707, \"Positive\", \"IND\", \"5\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6635, \"Completeness of birth registration (%)\", 0.781322, \"Positive\", \"VNM\", \"13\", \"Does not apply\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6636, \"Completeness of birth registration (%)\", 0.901982, \"Positive\", \"PHL\", \"Does not apply\", \"Cubic\", \"Asia\", 11, \"Demoraphy\", \"secondary\", 21], [6637, \"Completeness of birth registration (%)\", 0.894689, \"Positive\", \"MEX\", \"13\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6638, \"Completeness of birth registration (%)\", 0.781913, \"Positive\", \"BRA\", \"2\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6639, \"Completeness of birth registration (%)\", 0.855313, \"Positive\", \"ARG\", \"21\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6640, \"Completeness of birth registration (%)\", 0.839228, \"Positive\", \"PER\", \"5\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6641, \"Completeness of birth registration (%)\", 0.792184, \"Negative\", \"VEN\", \"1\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6642, \"Completeness of birth registration (%)\", 0.840648, \"Positive\", \"COL\", \"2\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6643, \"Completeness of birth registration (%)\", 0.963636, \"Positive\", \"PAN\", \"21\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21], [6644, \"Completeness of birth registration (%)\", 0.789474, \"Positive\", \"CRI\", \"13\", \"Does not apply\", \"Latam\", 11, \"Demoraphy\", \"secondary\", 21]];\n",
+       "\n",
+       "        // Define the dt_args\n",
+       "        let dt_args = {\"columnDefs\": [{\"className\": \"dt-left\", \"targets\": \"_all\"}], \"lengthMenu\": [5, 10, 20, 50, 100, 200], \"initComplete\": function () {\n",
+       "    // Apply the search\n",
+       "    this.api()\n",
+       "        .columns()\n",
+       "        .every(function () {\n",
+       "            const that = this;\n",
+       "\n",
+       "            $('input', this.header()).on('keyup change clear', function () {\n",
+       "                if (that.search() !== this.value) {\n",
+       "                    that.search(this.value).draw();\n",
+       "                }\n",
+       "            });\n",
+       "        });\n",
+       "}\n",
+       "};\n",
+       "        dt_args[\"data\"] = data;\n",
+       "\n",
+       "        // Display the table\n",
+       "        $(document).ready(function () {\n",
+       "            // Setup - add a text input to each header or footer cell\n",
+       "$('#da270e15-dbe2-4c0c-907c-fcab717716fb thead th').each(function () {\n",
+       "    let title = $(this).text();\n",
+       "    $(this).html('<input type=\"text\" placeholder=\"Search ' +\n",
+       "        // We use encodeURI to avoid this LGTM error:\n",
+       "        // https://lgtm.com/rules/1511866576920/\n",
+       "        encodeURI(title).replaceAll(\"%20\", \" \") +\n",
+       "        '\" />');\n",
+       "});\n",
+       "\n",
+       "            window.__itables_render('#da270e15-dbe2-4c0c-907c-fcab717716fb', dt_args);\n",
+       "        });\n",
+       "    }\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from itables import init_notebook_mode,show\n",
+    "\n",
+    "init_notebook_mode(all_interactive=False)\n",
+    "\n",
+    "import itables.options as opt\n",
+    "\n",
+    "show(pd.DataFrame(final),dom=\"ftpr\")\n",
+    "\n",
+    "opt.lengthMenu=[5,10,20,50,100,200]\n",
+    "\n",
+    "opt.classes=[\"display\",\"nowrap\"]\n",
+    "\n",
+    "show(final,columnDefs=[{\"className\": \"dt-left\", \"targets\": \"_all\"}],column_filters=\"header\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, there is a recount by the different columns, *Behaviour*, *Relationship* and *Time moved*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Type of behaviour</th>\n",
+       "      <th>Number of times repeated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>4723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Negative</td>\n",
+       "      <td>1922</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Type of behaviour  Number of times repeated\n",
+       "1          Positive                      4723\n",
+       "0          Negative                      1922"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columnssf=final.Behaviour.to_list()\n",
+    "columnsf=np.unique(columnssf)\n",
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "\n",
+    "final_Behaviour = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Type of behaviour','Number of times repeated'])\n",
+    "final_Behaviour=final_Behaviour.sort_values(by = 'Number of times repeated',ascending = False)\n",
+    "final_Behaviour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Type of relationship</th>\n",
+       "      <th>Number of times repeated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Linear</td>\n",
+       "      <td>1294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Logarithmic</td>\n",
+       "      <td>687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cubic</td>\n",
+       "      <td>419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cuadratic</td>\n",
+       "      <td>253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Type of relationship  Number of times repeated\n",
+       "3               Linear                      1294\n",
+       "4          Logarithmic                       687\n",
+       "1                Cubic                       419\n",
+       "0            Cuadratic                       253"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columnssf=final.Type.to_list()\n",
+    "columnsf=np.unique(columnssf)\n",
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "\n",
+    "final_type = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Type of relationship','Number of times repeated'])\n",
+    "final_type=final_type.sort_values(by = 'Number of times repeated',ascending = False)\n",
+    "final_type.drop([2], inplace=True)\n",
+    "final_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the table above, we can observe which is the most common type of relationship."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time moved</th>\n",
+       "      <th>Number of times repeated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Does not apply</td>\n",
+       "      <td>2653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8</td>\n",
+       "      <td>902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21</td>\n",
+       "      <td>474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>338</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Time moved  Number of times repeated\n",
+       "7  Does not apply                      2653\n",
+       "6               8                       902\n",
+       "5               5                       741\n",
+       "1              13                       607\n",
+       "0               1                       516\n",
+       "3              21                       474\n",
+       "2               2                       414\n",
+       "4               3                       338"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columnssf=final.Moved.to_list()\n",
+    "columnsf=np.unique(columnssf)\n",
+    "powerind=[]\n",
+    "for i in range(0, len(columnsf)):\n",
+    "    powerind.append(columnssf.count(columnsf[i]))\n",
+    "\n",
+    "final_moved = pd.DataFrame(list(zip(columnsf,powerind)), columns = ['Time moved','Number of times repeated'])\n",
+    "final_moved=final_moved.sort_values(by = 'Number of times repeated',ascending = False)\n",
+    "final_moved"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As said, the temporary displacement shows us correlation of an indicator with the GDP comparing different periods of time. In this table, we have made a recount to see which range of displacement is most repeated. As we can see, no displacement is the most common case, what means correlation year-by-year is majority in our sample."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization of results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Needed imports\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import plotly.express as px\n",
+    "\n",
+    "import plotly.graph_objects as go\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we’ve loaded the data, we can start right away to create widgets. These widgets are essentials to add interactivity to our visualizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
+               ],
+               [
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)"
+               ],
+               [
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
+               ],
+               [
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
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+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
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+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "Europe",
+            "method": "update"
+           },
+           {
+            "args": [
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+                "Agricultural land (% of land area)"
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+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
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+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)"
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+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
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+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
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+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Current health expenditure (% of GDP)"
+               ],
+               [
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
+               ],
+               [
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "Persian Gulf",
+            "method": "update"
+           },
+           {
+            "args": [
+             {
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+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)"
+               ],
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+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)"
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+                "Number of deaths ages 5-9 years",
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+                "Number of deaths ages 5-9 years",
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+                "Number of infant deaths",
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+                "Suicide mortality rate (per 100,000 population)",
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+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
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+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
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+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
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+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
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+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
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+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "North Africa",
+            "method": "update"
+           },
+           {
+            "args": [
+             {
+              "x": [
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+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)"
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+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)"
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+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
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+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)"
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+                "Gross value added at basic prices (GVA) (current US$)",
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+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
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+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
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+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
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+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
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+                "Current health expenditure per capita (current US$)",
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+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
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+                "Current health expenditure (% of GDP)"
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+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
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+                "CO2 emissions (kg per PPP $ of GDP)",
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+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
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+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
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+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "South Africa",
+            "method": "update"
+           },
+           {
+            "args": [
+             {
+              "x": [
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+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)"
+               ],
+               [
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)"
+               ],
+               [
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)"
+               ],
+               [
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
+               ],
+               [
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)"
+               ],
+               [
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
+               ],
+               [
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "Asia",
+            "method": "update"
+           },
+           {
+            "args": [
+             {
+              "x": [
+               [
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)"
+               ],
+               [
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)"
+               ],
+               [
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)"
+               ],
+               [
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "Gross value added at basic prices (GVA) (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
+               ],
+               [
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)"
+               ],
+               [
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
+               ],
+               [
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "Latam",
+            "method": "update"
+           },
+           {
+            "args": [
+             {
+              "x": [
+               [
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+                "GDP per unit of energy use (PPP $ per kg of oil equivalent)"
+               ],
+               [
+                "Agricultural land (% of land area)",
+                "Agricultural land (% of land area)"
+               ],
+               [
+                "Number of deaths ages 5-9 years",
+                "Number of deaths ages 5-9 years",
+                "Number of infant deaths",
+                "Number of infant deaths",
+                "Suicide mortality rate (per 100,000 population)",
+                "Suicide mortality rate (per 100,000 population)"
+               ],
+               [
+                "Gross value added at basic prices (GVA) (current US$)",
+                "GNI (current US$)",
+                "GNI (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Households and NPISHs Final consumption expenditure (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Industry (including construction), value added (current US$)",
+                "Consumer price index (2010 = 100)",
+                "Consumer price index (2010 = 100)"
+               ],
+               [
+                "Current health expenditure per capita (current US$)",
+                "Current health expenditure per capita (current US$)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Prevalence of current tobacco use (% of adults)",
+                "Current health expenditure (% of GDP)",
+                "Current health expenditure (% of GDP)"
+               ],
+               [
+                "Export value index (2000 = 100)",
+                "Export value index (2000 = 100)"
+               ],
+               [
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "CO2 emissions (kg per PPP $ of GDP)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Renewable internal freshwater resources per capita (cubic meters)",
+                "Access to electricity (% of population)"
+               ],
+               [
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (national estimate)",
+                "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)"
+               ],
+               [
+                "Fixed broadband subscriptions (per 100 people)",
+                "Fixed broadband subscriptions (per 100 people)"
+               ],
+               [
+                "Employment in industry (% of total employment) (modeled ILO estimate)",
+                "Employment in industry (% of total employment) (modeled ILO estimate)"
+               ],
+               [
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city",
+                "Population in largest city"
+               ],
+               [
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+                "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
+               ]
+              ]
+             }
+            ],
+            "label": "Pair",
+            "method": "update"
+           }
+          ],
+          "direction": "down",
+          "showactive": true,
+          "type": "dropdown"
+         }
+        ],
+        "xaxis": {
+         "anchor": "y",
+         "categoryorder": "total descending",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Indicator"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "count"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#COUNT HISTOGRAM: Graph for seeing the frequency of the primary indicators for each region.\n",
+    "selected_primary=final.loc[final['Level']=='primary']\n",
+    "\n",
+    "fig=px.histogram(selected_primary,x='Indicator',histfunc=\"count\",height=700,color='Group',text_auto=True,title=\"Indicators frequency by continents\").update_xaxes(categoryorder=\"total descending\")\n",
+    "continents=list(selected_primary['Continent'].unique())\n",
+    "buttons = []\n",
+    "\n",
+    "for continent in continents:\n",
+    "    selected_primary_c = selected_primary.loc[(selected_primary['Continent'] == continent)]\n",
+    "    fig_continent = px.histogram(selected_primary_c, x='Indicator', color='Group').update_xaxes(categoryorder=\"total descending\")\n",
+    "    buttons.append(\n",
+    "        dict(\n",
+    "            label=continent,\n",
+    "            method=\"update\",\n",
+    "            args=[\n",
+    "                {\n",
+    "                    \"x\": [trace['x'] for trace in fig_continent._data],\n",
+    "                }\n",
+    "            ]\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    updatemenus=[\n",
+    "        dict(\n",
+    "            type=\"dropdown\",\n",
+    "            direction=\"down\",\n",
+    "            showactive=True,\n",
+    "            buttons=buttons\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
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+         "branchvalues": "total",
+         "customdata": [
+          [
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+          [
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+          [
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+          [
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+          [
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+          [
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+          [
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+          "Prevalence of current tobacco use (% of adults)/Asia/BGD",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia/BGD",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia/BGD",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/BGD",
+          "Suicide mortality rate (per 100,000 population)/Asia/BGD",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/BGD",
+          "Access to electricity (% of population)/Latam/BRA",
+          "Agricultural land (% of land area)/Latam/BRA",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/BRA",
+          "Consumer price index (2010 = 100)/Latam/BRA",
+          "Current health expenditure per capita (current US$)/Latam/BRA",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam/BRA",
+          "Export value index (2000 = 100)/Latam/BRA",
+          "Fixed broadband subscriptions (per 100 people)/Latam/BRA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/BRA",
+          "GNI (current US$)/Latam/BRA",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/BRA",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/BRA",
+          "Industry (including construction), value added (current US$)/Latam/BRA",
+          "Number of deaths ages 5-9 years/Latam/BRA",
+          "Number of infant deaths/Latam/BRA",
+          "Population in largest city/Latam/BRA",
+          "Prevalence of current tobacco use (% of adults)/Latam/BRA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/BRA",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/BRA",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/BRA",
+          "Suicide mortality rate (per 100,000 population)/Latam/BRA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/BRA",
+          "Access to electricity (% of population)/Latam/CHL",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/CHL",
+          "Consumer price index (2010 = 100)/Latam/CHL",
+          "Current health expenditure (% of GDP)/Latam/CHL",
+          "Current health expenditure per capita (current US$)/Latam/CHL",
+          "Export value index (2000 = 100)/Latam/CHL",
+          "Fixed broadband subscriptions (per 100 people)/Latam/CHL",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/CHL",
+          "GNI (current US$)/Latam/CHL",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/CHL",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/CHL",
+          "Industry (including construction), value added (current US$)/Latam/CHL",
+          "Number of deaths ages 5-9 years/Latam/CHL",
+          "Number of infant deaths/Latam/CHL",
+          "Population in largest city/Latam/CHL",
+          "Prevalence of current tobacco use (% of adults)/Latam/CHL",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/CHL",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/CHL",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/CHL",
+          "Suicide mortality rate (per 100,000 population)/Latam/CHL",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/CHL",
+          "Access to electricity (% of population)/Pair/CHN",
+          "Agricultural land (% of land area)/Pair/CHN",
+          "CO2 emissions (kg per PPP $ of GDP)/Pair/CHN",
+          "Consumer price index (2010 = 100)/Pair/CHN",
+          "Current health expenditure (% of GDP)/Pair/CHN",
+          "Current health expenditure per capita (current US$)/Pair/CHN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Pair/CHN",
+          "Export value index (2000 = 100)/Pair/CHN",
+          "Fixed broadband subscriptions (per 100 people)/Pair/CHN",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Pair/CHN",
+          "GNI (current US$)/Pair/CHN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Pair/CHN",
+          "Industry (including construction), value added (current US$)/Pair/CHN",
+          "Number of deaths ages 5-9 years/Pair/CHN",
+          "Number of infant deaths/Pair/CHN",
+          "Population in largest city/Pair/CHN",
+          "Prevalence of current tobacco use (% of adults)/Pair/CHN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Pair/CHN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Pair/CHN",
+          "Suicide mortality rate (per 100,000 population)/Pair/CHN",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Pair/CHN",
+          "Access to electricity (% of population)/South Africa/CMR",
+          "Agricultural land (% of land area)/South Africa/CMR",
+          "Consumer price index (2010 = 100)/South Africa/CMR",
+          "Current health expenditure (% of GDP)/South Africa/CMR",
+          "Current health expenditure per capita (current US$)/South Africa/CMR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa/CMR",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/CMR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/CMR",
+          "GNI (current US$)/South Africa/CMR",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/CMR",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/CMR",
+          "Industry (including construction), value added (current US$)/South Africa/CMR",
+          "Number of deaths ages 5-9 years/South Africa/CMR",
+          "Number of infant deaths/South Africa/CMR",
+          "Population in largest city/South Africa/CMR",
+          "Prevalence of current tobacco use (% of adults)/South Africa/CMR",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/CMR",
+          "Access to electricity (% of population)/Latam/COL",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/COL",
+          "Consumer price index (2010 = 100)/Latam/COL",
+          "Current health expenditure (% of GDP)/Latam/COL",
+          "Current health expenditure per capita (current US$)/Latam/COL",
+          "Export value index (2000 = 100)/Latam/COL",
+          "Fixed broadband subscriptions (per 100 people)/Latam/COL",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/COL",
+          "GNI (current US$)/Latam/COL",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/COL",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/COL",
+          "Industry (including construction), value added (current US$)/Latam/COL",
+          "Number of deaths ages 5-9 years/Latam/COL",
+          "Number of infant deaths/Latam/COL",
+          "Population in largest city/Latam/COL",
+          "Prevalence of current tobacco use (% of adults)/Latam/COL",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/COL",
+          "Suicide mortality rate (per 100,000 population)/Latam/COL",
+          "Access to electricity (% of population)/Latam/CRI",
+          "Agricultural land (% of land area)/Latam/CRI",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/CRI",
+          "Consumer price index (2010 = 100)/Latam/CRI",
+          "Current health expenditure (% of GDP)/Latam/CRI",
+          "Current health expenditure per capita (current US$)/Latam/CRI",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam/CRI",
+          "Export value index (2000 = 100)/Latam/CRI",
+          "Fixed broadband subscriptions (per 100 people)/Latam/CRI",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/CRI",
+          "GNI (current US$)/Latam/CRI",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/CRI",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/CRI",
+          "Industry (including construction), value added (current US$)/Latam/CRI",
+          "Number of deaths ages 5-9 years/Latam/CRI",
+          "Number of infant deaths/Latam/CRI",
+          "Population in largest city/Latam/CRI",
+          "Prevalence of current tobacco use (% of adults)/Latam/CRI",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/CRI",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/CRI",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/CRI",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/CRI",
+          "Agricultural land (% of land area)/Europe/DEU",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/DEU",
+          "Consumer price index (2010 = 100)/Europe/DEU",
+          "Current health expenditure per capita (current US$)/Europe/DEU",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/DEU",
+          "Export value index (2000 = 100)/Europe/DEU",
+          "Fixed broadband subscriptions (per 100 people)/Europe/DEU",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/DEU",
+          "GNI (current US$)/Europe/DEU",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/DEU",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/DEU",
+          "Industry (including construction), value added (current US$)/Europe/DEU",
+          "Number of deaths ages 5-9 years/Europe/DEU",
+          "Prevalence of current tobacco use (% of adults)/Europe/DEU",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/DEU",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/DEU",
+          "Agricultural land (% of land area)/North Africa/DZA",
+          "Consumer price index (2010 = 100)/North Africa/DZA",
+          "Current health expenditure per capita (current US$)/North Africa/DZA",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa/DZA",
+          "Export value index (2000 = 100)/North Africa/DZA",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/DZA",
+          "GNI (current US$)/North Africa/DZA",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/DZA",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/DZA",
+          "Industry (including construction), value added (current US$)/North Africa/DZA",
+          "Number of deaths ages 5-9 years/North Africa/DZA",
+          "Population in largest city/North Africa/DZA",
+          "Prevalence of current tobacco use (% of adults)/North Africa/DZA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa/DZA",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa/DZA",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/DZA",
+          "Suicide mortality rate (per 100,000 population)/North Africa/DZA",
+          "Access to electricity (% of population)/North Africa/EGY",
+          "Agricultural land (% of land area)/North Africa/EGY",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa/EGY",
+          "Consumer price index (2010 = 100)/North Africa/EGY",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa/EGY",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/EGY",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa/EGY",
+          "GNI (current US$)/North Africa/EGY",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/EGY",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/EGY",
+          "Industry (including construction), value added (current US$)/North Africa/EGY",
+          "Number of deaths ages 5-9 years/North Africa/EGY",
+          "Number of infant deaths/North Africa/EGY",
+          "Population in largest city/North Africa/EGY",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/EGY",
+          "Suicide mortality rate (per 100,000 population)/North Africa/EGY",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa/EGY",
+          "Agricultural land (% of land area)/Europe/ESP",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/ESP",
+          "Consumer price index (2010 = 100)/Europe/ESP",
+          "Current health expenditure (% of GDP)/Europe/ESP",
+          "Current health expenditure per capita (current US$)/Europe/ESP",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/ESP",
+          "Export value index (2000 = 100)/Europe/ESP",
+          "Fixed broadband subscriptions (per 100 people)/Europe/ESP",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/ESP",
+          "GNI (current US$)/Europe/ESP",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/ESP",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/ESP",
+          "Industry (including construction), value added (current US$)/Europe/ESP",
+          "Number of deaths ages 5-9 years/Europe/ESP",
+          "Population in largest city/Europe/ESP",
+          "Prevalence of current tobacco use (% of adults)/Europe/ESP",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/ESP",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/ESP",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/ESP",
+          "Agricultural land (% of land area)/Europe/FRA",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/FRA",
+          "Consumer price index (2010 = 100)/Europe/FRA",
+          "Current health expenditure (% of GDP)/Europe/FRA",
+          "Current health expenditure per capita (current US$)/Europe/FRA",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/FRA",
+          "Export value index (2000 = 100)/Europe/FRA",
+          "Fixed broadband subscriptions (per 100 people)/Europe/FRA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/FRA",
+          "GNI (current US$)/Europe/FRA",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/FRA",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/FRA",
+          "Industry (including construction), value added (current US$)/Europe/FRA",
+          "Number of deaths ages 5-9 years/Europe/FRA",
+          "Number of infant deaths/Europe/FRA",
+          "Population in largest city/Europe/FRA",
+          "Prevalence of current tobacco use (% of adults)/Europe/FRA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/FRA",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/FRA",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/FRA",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/GBR",
+          "Consumer price index (2010 = 100)/Europe/GBR",
+          "Current health expenditure (% of GDP)/Europe/GBR",
+          "Current health expenditure per capita (current US$)/Europe/GBR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/GBR",
+          "Export value index (2000 = 100)/Europe/GBR",
+          "Fixed broadband subscriptions (per 100 people)/Europe/GBR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/GBR",
+          "GNI (current US$)/Europe/GBR",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/GBR",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/GBR",
+          "Industry (including construction), value added (current US$)/Europe/GBR",
+          "Number of deaths ages 5-9 years/Europe/GBR",
+          "Number of infant deaths/Europe/GBR",
+          "Population in largest city/Europe/GBR",
+          "Prevalence of current tobacco use (% of adults)/Europe/GBR",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/GBR",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/GBR",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/GBR",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/GBR",
+          "Access to electricity (% of population)/South Africa/GHA",
+          "Consumer price index (2010 = 100)/South Africa/GHA",
+          "Current health expenditure per capita (current US$)/South Africa/GHA",
+          "Export value index (2000 = 100)/South Africa/GHA",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/GHA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/GHA",
+          "GNI (current US$)/South Africa/GHA",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/GHA",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/GHA",
+          "Industry (including construction), value added (current US$)/South Africa/GHA",
+          "Number of deaths ages 5-9 years/South Africa/GHA",
+          "Number of infant deaths/South Africa/GHA",
+          "Population in largest city/South Africa/GHA",
+          "Prevalence of current tobacco use (% of adults)/South Africa/GHA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/GHA",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/GHA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa/GHA",
+          "Agricultural land (% of land area)/Europe/GRC",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/GRC",
+          "Consumer price index (2010 = 100)/Europe/GRC",
+          "Current health expenditure (% of GDP)/Europe/GRC",
+          "Current health expenditure per capita (current US$)/Europe/GRC",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/GRC",
+          "Fixed broadband subscriptions (per 100 people)/Europe/GRC",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/GRC",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/GRC",
+          "Industry (including construction), value added (current US$)/Europe/GRC",
+          "Number of deaths ages 5-9 years/Europe/GRC",
+          "Number of infant deaths/Europe/GRC",
+          "Population in largest city/Europe/GRC",
+          "Prevalence of current tobacco use (% of adults)/Europe/GRC",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/GRC",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/GRC",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/HRV",
+          "Current health expenditure per capita (current US$)/Europe/HRV",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/HRV",
+          "Fixed broadband subscriptions (per 100 people)/Europe/HRV",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/HRV",
+          "GNI (current US$)/Europe/HRV",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/HRV",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/HRV",
+          "Industry (including construction), value added (current US$)/Europe/HRV",
+          "Number of deaths ages 5-9 years/Europe/HRV",
+          "Number of infant deaths/Europe/HRV",
+          "Population in largest city/Europe/HRV",
+          "Prevalence of current tobacco use (% of adults)/Europe/HRV",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/HRV",
+          "Access to electricity (% of population)/Asia/IDN",
+          "Agricultural land (% of land area)/Asia/IDN",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/IDN",
+          "Consumer price index (2010 = 100)/Asia/IDN",
+          "Current health expenditure (% of GDP)/Asia/IDN",
+          "Current health expenditure per capita (current US$)/Asia/IDN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/IDN",
+          "Export value index (2000 = 100)/Asia/IDN",
+          "Fixed broadband subscriptions (per 100 people)/Asia/IDN",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/IDN",
+          "GNI (current US$)/Asia/IDN",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/IDN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/IDN",
+          "Industry (including construction), value added (current US$)/Asia/IDN",
+          "Number of deaths ages 5-9 years/Asia/IDN",
+          "Number of infant deaths/Asia/IDN",
+          "Population in largest city/Asia/IDN",
+          "Prevalence of current tobacco use (% of adults)/Asia/IDN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/IDN",
+          "Suicide mortality rate (per 100,000 population)/Asia/IDN",
+          "Access to electricity (% of population)/Asia/IND",
+          "Agricultural land (% of land area)/Asia/IND",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/IND",
+          "Consumer price index (2010 = 100)/Asia/IND",
+          "Current health expenditure (% of GDP)/Asia/IND",
+          "Current health expenditure per capita (current US$)/Asia/IND",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/IND",
+          "Export value index (2000 = 100)/Asia/IND",
+          "Fixed broadband subscriptions (per 100 people)/Asia/IND",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/IND",
+          "GNI (current US$)/Asia/IND",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/IND",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/IND",
+          "Industry (including construction), value added (current US$)/Asia/IND",
+          "Number of deaths ages 5-9 years/Asia/IND",
+          "Number of infant deaths/Asia/IND",
+          "Population in largest city/Asia/IND",
+          "Prevalence of current tobacco use (% of adults)/Asia/IND",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia/IND",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/IND",
+          "Suicide mortality rate (per 100,000 population)/Asia/IND",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/IND",
+          "Access to electricity (% of population)/Persian Gulf/IRQ",
+          "Export value index (2000 = 100)/Persian Gulf/IRQ",
+          "GNI (current US$)/Persian Gulf/IRQ",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/IRQ",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/IRQ",
+          "Industry (including construction), value added (current US$)/Persian Gulf/IRQ",
+          "Number of infant deaths/Persian Gulf/IRQ",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/IRQ",
+          "Agricultural land (% of land area)/North Africa/ISR",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa/ISR",
+          "Consumer price index (2010 = 100)/North Africa/ISR",
+          "Current health expenditure per capita (current US$)/North Africa/ISR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa/ISR",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/ISR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa/ISR",
+          "GNI (current US$)/North Africa/ISR",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/ISR",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/ISR",
+          "Industry (including construction), value added (current US$)/North Africa/ISR",
+          "Number of deaths ages 5-9 years/North Africa/ISR",
+          "Number of infant deaths/North Africa/ISR",
+          "Population in largest city/North Africa/ISR",
+          "Prevalence of current tobacco use (% of adults)/North Africa/ISR",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa/ISR",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa/ISR",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/ISR",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa/ISR",
+          "Agricultural land (% of land area)/Asia/KOR",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/KOR",
+          "Consumer price index (2010 = 100)/Asia/KOR",
+          "Current health expenditure (% of GDP)/Asia/KOR",
+          "Current health expenditure per capita (current US$)/Asia/KOR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/KOR",
+          "Export value index (2000 = 100)/Asia/KOR",
+          "Fixed broadband subscriptions (per 100 people)/Asia/KOR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/KOR",
+          "GNI (current US$)/Asia/KOR",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/KOR",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/KOR",
+          "Industry (including construction), value added (current US$)/Asia/KOR",
+          "Number of deaths ages 5-9 years/Asia/KOR",
+          "Number of infant deaths/Asia/KOR",
+          "Prevalence of current tobacco use (% of adults)/Asia/KOR",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia/KOR",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia/KOR",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/KOR",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/KOR",
+          "Access to electricity (% of population)/South Africa/LBR",
+          "Agricultural land (% of land area)/South Africa/LBR",
+          "Consumer price index (2010 = 100)/South Africa/LBR",
+          "Current health expenditure per capita (current US$)/South Africa/LBR",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/LBR",
+          "GNI (current US$)/South Africa/LBR",
+          "Industry (including construction), value added (current US$)/South Africa/LBR",
+          "Number of infant deaths/South Africa/LBR",
+          "Prevalence of current tobacco use (% of adults)/South Africa/LBR",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/LBR",
+          "Access to electricity (% of population)/North Africa/MAR",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa/MAR",
+          "Consumer price index (2010 = 100)/North Africa/MAR",
+          "Current health expenditure per capita (current US$)/North Africa/MAR",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa/MAR",
+          "Export value index (2000 = 100)/North Africa/MAR",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/MAR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa/MAR",
+          "GNI (current US$)/North Africa/MAR",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/MAR",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/MAR",
+          "Industry (including construction), value added (current US$)/North Africa/MAR",
+          "Number of deaths ages 5-9 years/North Africa/MAR",
+          "Number of infant deaths/North Africa/MAR",
+          "Population in largest city/North Africa/MAR",
+          "Prevalence of current tobacco use (% of adults)/North Africa/MAR",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/MAR",
+          "Suicide mortality rate (per 100,000 population)/North Africa/MAR",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa/MAR",
+          "Access to electricity (% of population)/Latam/MEX",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/MEX",
+          "Consumer price index (2010 = 100)/Latam/MEX",
+          "Current health expenditure per capita (current US$)/Latam/MEX",
+          "Export value index (2000 = 100)/Latam/MEX",
+          "Fixed broadband subscriptions (per 100 people)/Latam/MEX",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/MEX",
+          "GNI (current US$)/Latam/MEX",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/MEX",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/MEX",
+          "Industry (including construction), value added (current US$)/Latam/MEX",
+          "Number of deaths ages 5-9 years/Latam/MEX",
+          "Number of infant deaths/Latam/MEX",
+          "Population in largest city/Latam/MEX",
+          "Prevalence of current tobacco use (% of adults)/Latam/MEX",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/MEX",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/MEX",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/MEX",
+          "Suicide mortality rate (per 100,000 population)/Latam/MEX",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/MEX",
+          "Access to electricity (% of population)/South Africa/MOZ",
+          "Agricultural land (% of land area)/South Africa/MOZ",
+          "Consumer price index (2010 = 100)/South Africa/MOZ",
+          "Current health expenditure (% of GDP)/South Africa/MOZ",
+          "Current health expenditure per capita (current US$)/South Africa/MOZ",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa/MOZ",
+          "Export value index (2000 = 100)/South Africa/MOZ",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/MOZ",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/MOZ",
+          "GNI (current US$)/South Africa/MOZ",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/MOZ",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/MOZ",
+          "Industry (including construction), value added (current US$)/South Africa/MOZ",
+          "Number of deaths ages 5-9 years/South Africa/MOZ",
+          "Number of infant deaths/South Africa/MOZ",
+          "Population in largest city/South Africa/MOZ",
+          "Prevalence of current tobacco use (% of adults)/South Africa/MOZ",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/MOZ",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa/MOZ",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/MOZ",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa/MOZ",
+          "Access to electricity (% of population)/South Africa/NGA",
+          "Agricultural land (% of land area)/South Africa/NGA",
+          "CO2 emissions (kg per PPP $ of GDP)/South Africa/NGA",
+          "Consumer price index (2010 = 100)/South Africa/NGA",
+          "Current health expenditure per capita (current US$)/South Africa/NGA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/NGA",
+          "GNI (current US$)/South Africa/NGA",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/NGA",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/NGA",
+          "Industry (including construction), value added (current US$)/South Africa/NGA",
+          "Number of deaths ages 5-9 years/South Africa/NGA",
+          "Population in largest city/South Africa/NGA",
+          "Prevalence of current tobacco use (% of adults)/South Africa/NGA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/NGA",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/NGA",
+          "Suicide mortality rate (per 100,000 population)/South Africa/NGA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa/NGA",
+          "Agricultural land (% of land area)/Europe/NLD",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/NLD",
+          "Consumer price index (2010 = 100)/Europe/NLD",
+          "Current health expenditure (% of GDP)/Europe/NLD",
+          "Current health expenditure per capita (current US$)/Europe/NLD",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/NLD",
+          "Export value index (2000 = 100)/Europe/NLD",
+          "Fixed broadband subscriptions (per 100 people)/Europe/NLD",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/NLD",
+          "GNI (current US$)/Europe/NLD",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/NLD",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/NLD",
+          "Industry (including construction), value added (current US$)/Europe/NLD",
+          "Number of deaths ages 5-9 years/Europe/NLD",
+          "Number of infant deaths/Europe/NLD",
+          "Population in largest city/Europe/NLD",
+          "Prevalence of current tobacco use (% of adults)/Europe/NLD",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe/NLD",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/NLD",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/NLD",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/NLD",
+          "CO2 emissions (kg per PPP $ of GDP)/Persian Gulf/OMN",
+          "Consumer price index (2010 = 100)/Persian Gulf/OMN",
+          "Current health expenditure per capita (current US$)/Persian Gulf/OMN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf/OMN",
+          "Export value index (2000 = 100)/Persian Gulf/OMN",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/OMN",
+          "GNI (current US$)/Persian Gulf/OMN",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/OMN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/OMN",
+          "Industry (including construction), value added (current US$)/Persian Gulf/OMN",
+          "Number of deaths ages 5-9 years/Persian Gulf/OMN",
+          "Number of infant deaths/Persian Gulf/OMN",
+          "Population in largest city/Persian Gulf/OMN",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/OMN",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/OMN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf/OMN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/OMN",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf/OMN",
+          "Access to electricity (% of population)/Latam/PAN",
+          "Agricultural land (% of land area)/Latam/PAN",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/PAN",
+          "Consumer price index (2010 = 100)/Latam/PAN",
+          "Current health expenditure per capita (current US$)/Latam/PAN",
+          "Export value index (2000 = 100)/Latam/PAN",
+          "Fixed broadband subscriptions (per 100 people)/Latam/PAN",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam/PAN",
+          "GNI (current US$)/Latam/PAN",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/PAN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/PAN",
+          "Industry (including construction), value added (current US$)/Latam/PAN",
+          "Number of deaths ages 5-9 years/Latam/PAN",
+          "Number of infant deaths/Latam/PAN",
+          "Population in largest city/Latam/PAN",
+          "Prevalence of current tobacco use (% of adults)/Latam/PAN",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam/PAN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/PAN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/PAN",
+          "Suicide mortality rate (per 100,000 population)/Latam/PAN",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/PAN",
+          "Access to electricity (% of population)/Latam/PER",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam/PER",
+          "Consumer price index (2010 = 100)/Latam/PER",
+          "Current health expenditure (% of GDP)/Latam/PER",
+          "Current health expenditure per capita (current US$)/Latam/PER",
+          "Export value index (2000 = 100)/Latam/PER",
+          "Fixed broadband subscriptions (per 100 people)/Latam/PER",
+          "GNI (current US$)/Latam/PER",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/PER",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/PER",
+          "Industry (including construction), value added (current US$)/Latam/PER",
+          "Number of deaths ages 5-9 years/Latam/PER",
+          "Number of infant deaths/Latam/PER",
+          "Population in largest city/Latam/PER",
+          "Prevalence of current tobacco use (% of adults)/Latam/PER",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/PER",
+          "Access to electricity (% of population)/Asia/PHL",
+          "Agricultural land (% of land area)/Asia/PHL",
+          "Consumer price index (2010 = 100)/Asia/PHL",
+          "Current health expenditure per capita (current US$)/Asia/PHL",
+          "Export value index (2000 = 100)/Asia/PHL",
+          "Fixed broadband subscriptions (per 100 people)/Asia/PHL",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/PHL",
+          "GNI (current US$)/Asia/PHL",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/PHL",
+          "Industry (including construction), value added (current US$)/Asia/PHL",
+          "Number of infant deaths/Asia/PHL",
+          "Population in largest city/Asia/PHL",
+          "Prevalence of current tobacco use (% of adults)/Asia/PHL",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/PHL",
+          "Suicide mortality rate (per 100,000 population)/Asia/PHL",
+          "Agricultural land (% of land area)/Europe/POL",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/POL",
+          "Consumer price index (2010 = 100)/Europe/POL",
+          "Current health expenditure (% of GDP)/Europe/POL",
+          "Current health expenditure per capita (current US$)/Europe/POL",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/POL",
+          "Export value index (2000 = 100)/Europe/POL",
+          "Fixed broadband subscriptions (per 100 people)/Europe/POL",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/POL",
+          "GNI (current US$)/Europe/POL",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/POL",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/POL",
+          "Industry (including construction), value added (current US$)/Europe/POL",
+          "Number of deaths ages 5-9 years/Europe/POL",
+          "Number of infant deaths/Europe/POL",
+          "Population in largest city/Europe/POL",
+          "Prevalence of current tobacco use (% of adults)/Europe/POL",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe/POL",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe/POL",
+          "Suicide mortality rate (per 100,000 population)/Europe/POL",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe/POL",
+          "Consumer price index (2010 = 100)/Persian Gulf/QAT",
+          "Current health expenditure per capita (current US$)/Persian Gulf/QAT",
+          "Export value index (2000 = 100)/Persian Gulf/QAT",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/QAT",
+          "GNI (current US$)/Persian Gulf/QAT",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/QAT",
+          "Number of deaths ages 5-9 years/Persian Gulf/QAT",
+          "Population in largest city/Persian Gulf/QAT",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/QAT",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/QAT",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf/QAT",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/QAT",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf/QAT",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf/QAT",
+          "Consumer price index (2010 = 100)/Persian Gulf/SAU",
+          "Current health expenditure per capita (current US$)/Persian Gulf/SAU",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf/SAU",
+          "Export value index (2000 = 100)/Persian Gulf/SAU",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/SAU",
+          "GNI (current US$)/Persian Gulf/SAU",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/SAU",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf/SAU",
+          "Industry (including construction), value added (current US$)/Persian Gulf/SAU",
+          "Number of deaths ages 5-9 years/Persian Gulf/SAU",
+          "Number of infant deaths/Persian Gulf/SAU",
+          "Population in largest city/Persian Gulf/SAU",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/SAU",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf/SAU",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/SAU",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf/SAU",
+          "Access to electricity (% of population)/South Africa/SEN",
+          "Consumer price index (2010 = 100)/South Africa/SEN",
+          "Current health expenditure per capita (current US$)/South Africa/SEN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa/SEN",
+          "Export value index (2000 = 100)/South Africa/SEN",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/SEN",
+          "GNI (current US$)/South Africa/SEN",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/SEN",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/SEN",
+          "Industry (including construction), value added (current US$)/South Africa/SEN",
+          "Number of deaths ages 5-9 years/South Africa/SEN",
+          "Number of infant deaths/South Africa/SEN",
+          "Population in largest city/South Africa/SEN",
+          "Prevalence of current tobacco use (% of adults)/South Africa/SEN",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/SEN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa/SEN",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/SEN",
+          "Suicide mortality rate (per 100,000 population)/South Africa/SEN",
+          "Agricultural land (% of land area)/Europe/SWE",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe/SWE",
+          "Consumer price index (2010 = 100)/Europe/SWE",
+          "Current health expenditure (% of GDP)/Europe/SWE",
+          "Current health expenditure per capita (current US$)/Europe/SWE",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe/SWE",
+          "Export value index (2000 = 100)/Europe/SWE",
+          "Fixed broadband subscriptions (per 100 people)/Europe/SWE",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe/SWE",
+          "GNI (current US$)/Europe/SWE",
+          "Gross value added at basic prices (GVA) (current US$)/Europe/SWE",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe/SWE",
+          "Industry (including construction), value added (current US$)/Europe/SWE",
+          "Number of infant deaths/Europe/SWE",
+          "Prevalence of current tobacco use (% of adults)/Europe/SWE",
+          "Access to electricity (% of population)/Asia/THA",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia/THA",
+          "Consumer price index (2010 = 100)/Asia/THA",
+          "Current health expenditure (% of GDP)/Asia/THA",
+          "Current health expenditure per capita (current US$)/Asia/THA",
+          "Export value index (2000 = 100)/Asia/THA",
+          "Fixed broadband subscriptions (per 100 people)/Asia/THA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/THA",
+          "GNI (current US$)/Asia/THA",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/THA",
+          "Industry (including construction), value added (current US$)/Asia/THA",
+          "Number of deaths ages 5-9 years/Asia/THA",
+          "Number of infant deaths/Asia/THA",
+          "Population in largest city/Asia/THA",
+          "Prevalence of current tobacco use (% of adults)/Asia/THA",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/THA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/THA",
+          "Agricultural land (% of land area)/North Africa/TUR",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa/TUR",
+          "Consumer price index (2010 = 100)/North Africa/TUR",
+          "Export value index (2000 = 100)/North Africa/TUR",
+          "Fixed broadband subscriptions (per 100 people)/North Africa/TUR",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa/TUR",
+          "GNI (current US$)/North Africa/TUR",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa/TUR",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa/TUR",
+          "Industry (including construction), value added (current US$)/North Africa/TUR",
+          "Number of deaths ages 5-9 years/North Africa/TUR",
+          "Number of infant deaths/North Africa/TUR",
+          "Population in largest city/North Africa/TUR",
+          "Prevalence of current tobacco use (% of adults)/North Africa/TUR",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa/TUR",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa/TUR",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa/TUR",
+          "Suicide mortality rate (per 100,000 population)/North Africa/TUR",
+          "Agricultural land (% of land area)/Pair/USA",
+          "CO2 emissions (kg per PPP $ of GDP)/Pair/USA",
+          "Consumer price index (2010 = 100)/Pair/USA",
+          "Current health expenditure (% of GDP)/Pair/USA",
+          "Current health expenditure per capita (current US$)/Pair/USA",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Pair/USA",
+          "Export value index (2000 = 100)/Pair/USA",
+          "Fixed broadband subscriptions (per 100 people)/Pair/USA",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Pair/USA",
+          "GNI (current US$)/Pair/USA",
+          "Gross value added at basic prices (GVA) (current US$)/Pair/USA",
+          "Households and NPISHs Final consumption expenditure (current US$)/Pair/USA",
+          "Industry (including construction), value added (current US$)/Pair/USA",
+          "Number of deaths ages 5-9 years/Pair/USA",
+          "Number of infant deaths/Pair/USA",
+          "Population in largest city/Pair/USA",
+          "Prevalence of current tobacco use (% of adults)/Pair/USA",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Pair/USA",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Pair/USA",
+          "Renewable internal freshwater resources per capita (cubic meters)/Pair/USA",
+          "Suicide mortality rate (per 100,000 population)/Pair/USA",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Pair/USA",
+          "Agricultural land (% of land area)/Latam/VEN",
+          "Consumer price index (2010 = 100)/Latam/VEN",
+          "Current health expenditure per capita (current US$)/Latam/VEN",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam/VEN",
+          "Export value index (2000 = 100)/Latam/VEN",
+          "Fixed broadband subscriptions (per 100 people)/Latam/VEN",
+          "GNI (current US$)/Latam/VEN",
+          "Gross value added at basic prices (GVA) (current US$)/Latam/VEN",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam/VEN",
+          "Industry (including construction), value added (current US$)/Latam/VEN",
+          "Population in largest city/Latam/VEN",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam/VEN",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam/VEN",
+          "Suicide mortality rate (per 100,000 population)/Latam/VEN",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam/VEN",
+          "Access to electricity (% of population)/Asia/VNM",
+          "Agricultural land (% of land area)/Asia/VNM",
+          "Consumer price index (2010 = 100)/Asia/VNM",
+          "Current health expenditure (% of GDP)/Asia/VNM",
+          "Current health expenditure per capita (current US$)/Asia/VNM",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia/VNM",
+          "Export value index (2000 = 100)/Asia/VNM",
+          "Fixed broadband subscriptions (per 100 people)/Asia/VNM",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia/VNM",
+          "GNI (current US$)/Asia/VNM",
+          "Gross value added at basic prices (GVA) (current US$)/Asia/VNM",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia/VNM",
+          "Industry (including construction), value added (current US$)/Asia/VNM",
+          "Number of deaths ages 5-9 years/Asia/VNM",
+          "Number of infant deaths/Asia/VNM",
+          "Population in largest city/Asia/VNM",
+          "Prevalence of current tobacco use (% of adults)/Asia/VNM",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia/VNM",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia/VNM",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia/VNM",
+          "Suicide mortality rate (per 100,000 population)/Asia/VNM",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia/VNM",
+          "Access to electricity (% of population)/Persian Gulf/YEM",
+          "Agricultural land (% of land area)/Persian Gulf/YEM",
+          "Consumer price index (2010 = 100)/Persian Gulf/YEM",
+          "Current health expenditure (% of GDP)/Persian Gulf/YEM",
+          "Current health expenditure per capita (current US$)/Persian Gulf/YEM",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf/YEM",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf/YEM",
+          "GNI (current US$)/Persian Gulf/YEM",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf/YEM",
+          "Industry (including construction), value added (current US$)/Persian Gulf/YEM",
+          "Number of deaths ages 5-9 years/Persian Gulf/YEM",
+          "Population in largest city/Persian Gulf/YEM",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf/YEM",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf/YEM",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf/YEM",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf/YEM",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf/YEM",
+          "Access to electricity (% of population)/South Africa/ZAF",
+          "CO2 emissions (kg per PPP $ of GDP)/South Africa/ZAF",
+          "Consumer price index (2010 = 100)/South Africa/ZAF",
+          "Current health expenditure per capita (current US$)/South Africa/ZAF",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa/ZAF",
+          "Export value index (2000 = 100)/South Africa/ZAF",
+          "Fixed broadband subscriptions (per 100 people)/South Africa/ZAF",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa/ZAF",
+          "GNI (current US$)/South Africa/ZAF",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa/ZAF",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa/ZAF",
+          "Industry (including construction), value added (current US$)/South Africa/ZAF",
+          "Population in largest city/South Africa/ZAF",
+          "Prevalence of current tobacco use (% of adults)/South Africa/ZAF",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa/ZAF",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa/ZAF",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa/ZAF",
+          "Access to electricity (% of population)/Asia",
+          "Agricultural land (% of land area)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Gross value added at basic prices (GVA) (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Suicide mortality rate (per 100,000 population)/Asia",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "Suicide mortality rate (per 100,000 population)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "Access to electricity (% of population)/Latam",
+          "Agricultural land (% of land area)/Latam",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure (% of GDP)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam",
+          "Industry (including construction), value added (current US$)/Latam",
+          "Number of deaths ages 5-9 years/Latam",
+          "Number of infant deaths/Latam",
+          "Population in largest city/Latam",
+          "Prevalence of current tobacco use (% of adults)/Latam",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam",
+          "Suicide mortality rate (per 100,000 population)/Latam",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam",
+          "Access to electricity (% of population)/North Africa",
+          "Agricultural land (% of land area)/North Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa",
+          "Consumer price index (2010 = 100)/North Africa",
+          "Current health expenditure per capita (current US$)/North Africa",
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+          "Export value index (2000 = 100)/North Africa",
+          "Fixed broadband subscriptions (per 100 people)/North Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa",
+          "GNI (current US$)/North Africa",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa",
+          "Industry (including construction), value added (current US$)/North Africa",
+          "Number of deaths ages 5-9 years/North Africa",
+          "Number of infant deaths/North Africa",
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+          "Prevalence of current tobacco use (% of adults)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Suicide mortality rate (per 100,000 population)/North Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa",
+          "Access to electricity (% of population)/Pair",
+          "Agricultural land (% of land area)/Pair",
+          "CO2 emissions (kg per PPP $ of GDP)/Pair",
+          "Consumer price index (2010 = 100)/Pair",
+          "Current health expenditure (% of GDP)/Pair",
+          "Current health expenditure per capita (current US$)/Pair",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Pair",
+          "Export value index (2000 = 100)/Pair",
+          "Fixed broadband subscriptions (per 100 people)/Pair",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Pair",
+          "GNI (current US$)/Pair",
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+          "Households and NPISHs Final consumption expenditure (current US$)/Pair",
+          "Industry (including construction), value added (current US$)/Pair",
+          "Number of deaths ages 5-9 years/Pair",
+          "Number of infant deaths/Pair",
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+          "Ratio of female to male labor force participation rate (%) (national estimate)/Pair",
+          "Renewable internal freshwater resources per capita (cubic meters)/Pair",
+          "Suicide mortality rate (per 100,000 population)/Pair",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Pair",
+          "Access to electricity (% of population)/Persian Gulf",
+          "Agricultural land (% of land area)/Persian Gulf",
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+          "Consumer price index (2010 = 100)/Persian Gulf",
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+          "Current health expenditure per capita (current US$)/Persian Gulf",
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+          "Export value index (2000 = 100)/Persian Gulf",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Persian Gulf",
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+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Industry (including construction), value added (current US$)/Persian Gulf",
+          "Number of deaths ages 5-9 years/Persian Gulf",
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+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf",
+          "Access to electricity (% of population)/South Africa",
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+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
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+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)"
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+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "Access to electricity (% of population)/Asia",
+          "Agricultural land (% of land area)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Gross value added at basic prices (GVA) (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Suicide mortality rate (per 100,000 population)/Asia",
+          "Access to electricity (% of population)/Asia",
+          "Agricultural land (% of land area)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Gross value added at basic prices (GVA) (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Suicide mortality rate (per 100,000 population)/Asia",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia",
+          "Access to electricity (% of population)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Industry (including construction), value added (current US$)/Persian Gulf",
+          "Number of infant deaths/Persian Gulf",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf",
+          "Agricultural land (% of land area)/North Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa",
+          "Consumer price index (2010 = 100)/North Africa",
+          "Current health expenditure per capita (current US$)/North Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa",
+          "Fixed broadband subscriptions (per 100 people)/North Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa",
+          "GNI (current US$)/North Africa",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa",
+          "Industry (including construction), value added (current US$)/North Africa",
+          "Number of deaths ages 5-9 years/North Africa",
+          "Number of infant deaths/North Africa",
+          "Population in largest city/North Africa",
+          "Prevalence of current tobacco use (% of adults)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa",
+          "Agricultural land (% of land area)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Gross value added at basic prices (GVA) (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Asia",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia",
+          "Access to electricity (% of population)/South Africa",
+          "Agricultural land (% of land area)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "Fixed broadband subscriptions (per 100 people)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of infant deaths/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Access to electricity (% of population)/North Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa",
+          "Consumer price index (2010 = 100)/North Africa",
+          "Current health expenditure per capita (current US$)/North Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/North Africa",
+          "Export value index (2000 = 100)/North Africa",
+          "Fixed broadband subscriptions (per 100 people)/North Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa",
+          "GNI (current US$)/North Africa",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa",
+          "Industry (including construction), value added (current US$)/North Africa",
+          "Number of deaths ages 5-9 years/North Africa",
+          "Number of infant deaths/North Africa",
+          "Population in largest city/North Africa",
+          "Prevalence of current tobacco use (% of adults)/North Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Suicide mortality rate (per 100,000 population)/North Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/North Africa",
+          "Access to electricity (% of population)/Latam",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam",
+          "Industry (including construction), value added (current US$)/Latam",
+          "Number of deaths ages 5-9 years/Latam",
+          "Number of infant deaths/Latam",
+          "Population in largest city/Latam",
+          "Prevalence of current tobacco use (% of adults)/Latam",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam",
+          "Suicide mortality rate (per 100,000 population)/Latam",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam",
+          "Access to electricity (% of population)/South Africa",
+          "Agricultural land (% of land area)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure (% of GDP)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa",
+          "Export value index (2000 = 100)/South Africa",
+          "Fixed broadband subscriptions (per 100 people)/South Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of deaths ages 5-9 years/South Africa",
+          "Number of infant deaths/South Africa",
+          "Population in largest city/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa",
+          "Access to electricity (% of population)/South Africa",
+          "Agricultural land (% of land area)/South Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of deaths ages 5-9 years/South Africa",
+          "Population in largest city/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Suicide mortality rate (per 100,000 population)/South Africa",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/South Africa",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Persian Gulf",
+          "Consumer price index (2010 = 100)/Persian Gulf",
+          "Current health expenditure per capita (current US$)/Persian Gulf",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Industry (including construction), value added (current US$)/Persian Gulf",
+          "Number of deaths ages 5-9 years/Persian Gulf",
+          "Number of infant deaths/Persian Gulf",
+          "Population in largest city/Persian Gulf",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf",
+          "Access to electricity (% of population)/Latam",
+          "Agricultural land (% of land area)/Latam",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam",
+          "Industry (including construction), value added (current US$)/Latam",
+          "Number of deaths ages 5-9 years/Latam",
+          "Number of infant deaths/Latam",
+          "Population in largest city/Latam",
+          "Prevalence of current tobacco use (% of adults)/Latam",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Latam",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Latam",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam",
+          "Suicide mortality rate (per 100,000 population)/Latam",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Latam",
+          "Access to electricity (% of population)/Latam",
+          "CO2 emissions (kg per PPP $ of GDP)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure (% of GDP)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
+          "Households and NPISHs Final consumption expenditure (current US$)/Latam",
+          "Industry (including construction), value added (current US$)/Latam",
+          "Number of deaths ages 5-9 years/Latam",
+          "Number of infant deaths/Latam",
+          "Population in largest city/Latam",
+          "Prevalence of current tobacco use (% of adults)/Latam",
+          "Renewable internal freshwater resources per capita (cubic meters)/Latam",
+          "Access to electricity (% of population)/Asia",
+          "Agricultural land (% of land area)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Suicide mortality rate (per 100,000 population)/Asia",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of deaths ages 5-9 years/Europe",
+          "Number of infant deaths/Europe",
+          "Population in largest city/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Europe",
+          "Renewable internal freshwater resources per capita (cubic meters)/Europe",
+          "Suicide mortality rate (per 100,000 population)/Europe",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Europe",
+          "Consumer price index (2010 = 100)/Persian Gulf",
+          "Current health expenditure per capita (current US$)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Number of deaths ages 5-9 years/Persian Gulf",
+          "Population in largest city/Persian Gulf",
+          "Prevalence of current tobacco use (% of adults)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Persian Gulf",
+          "Consumer price index (2010 = 100)/Persian Gulf",
+          "Current health expenditure per capita (current US$)/Persian Gulf",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Persian Gulf",
+          "Export value index (2000 = 100)/Persian Gulf",
+          "Fixed broadband subscriptions (per 100 people)/Persian Gulf",
+          "GNI (current US$)/Persian Gulf",
+          "Gross value added at basic prices (GVA) (current US$)/Persian Gulf",
+          "Households and NPISHs Final consumption expenditure (current US$)/Persian Gulf",
+          "Industry (including construction), value added (current US$)/Persian Gulf",
+          "Number of deaths ages 5-9 years/Persian Gulf",
+          "Number of infant deaths/Persian Gulf",
+          "Population in largest city/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Persian Gulf",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Persian Gulf",
+          "Renewable internal freshwater resources per capita (cubic meters)/Persian Gulf",
+          "Suicide mortality rate (per 100,000 population)/Persian Gulf",
+          "Access to electricity (% of population)/South Africa",
+          "Consumer price index (2010 = 100)/South Africa",
+          "Current health expenditure per capita (current US$)/South Africa",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/South Africa",
+          "Export value index (2000 = 100)/South Africa",
+          "Fixed broadband subscriptions (per 100 people)/South Africa",
+          "GNI (current US$)/South Africa",
+          "Gross value added at basic prices (GVA) (current US$)/South Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/South Africa",
+          "Industry (including construction), value added (current US$)/South Africa",
+          "Number of deaths ages 5-9 years/South Africa",
+          "Number of infant deaths/South Africa",
+          "Population in largest city/South Africa",
+          "Prevalence of current tobacco use (% of adults)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/South Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/South Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/South Africa",
+          "Suicide mortality rate (per 100,000 population)/South Africa",
+          "Agricultural land (% of land area)/Europe",
+          "CO2 emissions (kg per PPP $ of GDP)/Europe",
+          "Consumer price index (2010 = 100)/Europe",
+          "Current health expenditure (% of GDP)/Europe",
+          "Current health expenditure per capita (current US$)/Europe",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Europe",
+          "Export value index (2000 = 100)/Europe",
+          "Fixed broadband subscriptions (per 100 people)/Europe",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Europe",
+          "GNI (current US$)/Europe",
+          "Gross value added at basic prices (GVA) (current US$)/Europe",
+          "Households and NPISHs Final consumption expenditure (current US$)/Europe",
+          "Industry (including construction), value added (current US$)/Europe",
+          "Number of infant deaths/Europe",
+          "Prevalence of current tobacco use (% of adults)/Europe",
+          "Access to electricity (% of population)/Asia",
+          "CO2 emissions (kg per PPP $ of GDP)/Asia",
+          "Consumer price index (2010 = 100)/Asia",
+          "Current health expenditure (% of GDP)/Asia",
+          "Current health expenditure per capita (current US$)/Asia",
+          "Export value index (2000 = 100)/Asia",
+          "Fixed broadband subscriptions (per 100 people)/Asia",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Asia",
+          "GNI (current US$)/Asia",
+          "Households and NPISHs Final consumption expenditure (current US$)/Asia",
+          "Industry (including construction), value added (current US$)/Asia",
+          "Number of deaths ages 5-9 years/Asia",
+          "Number of infant deaths/Asia",
+          "Population in largest city/Asia",
+          "Prevalence of current tobacco use (% of adults)/Asia",
+          "Renewable internal freshwater resources per capita (cubic meters)/Asia",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Asia",
+          "Agricultural land (% of land area)/North Africa",
+          "CO2 emissions (kg per PPP $ of GDP)/North Africa",
+          "Consumer price index (2010 = 100)/North Africa",
+          "Export value index (2000 = 100)/North Africa",
+          "Fixed broadband subscriptions (per 100 people)/North Africa",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/North Africa",
+          "GNI (current US$)/North Africa",
+          "Gross value added at basic prices (GVA) (current US$)/North Africa",
+          "Households and NPISHs Final consumption expenditure (current US$)/North Africa",
+          "Industry (including construction), value added (current US$)/North Africa",
+          "Number of deaths ages 5-9 years/North Africa",
+          "Number of infant deaths/North Africa",
+          "Population in largest city/North Africa",
+          "Prevalence of current tobacco use (% of adults)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/North Africa",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/North Africa",
+          "Renewable internal freshwater resources per capita (cubic meters)/North Africa",
+          "Suicide mortality rate (per 100,000 population)/North Africa",
+          "Agricultural land (% of land area)/Pair",
+          "CO2 emissions (kg per PPP $ of GDP)/Pair",
+          "Consumer price index (2010 = 100)/Pair",
+          "Current health expenditure (% of GDP)/Pair",
+          "Current health expenditure per capita (current US$)/Pair",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Pair",
+          "Export value index (2000 = 100)/Pair",
+          "Fixed broadband subscriptions (per 100 people)/Pair",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)/Pair",
+          "GNI (current US$)/Pair",
+          "Gross value added at basic prices (GVA) (current US$)/Pair",
+          "Households and NPISHs Final consumption expenditure (current US$)/Pair",
+          "Industry (including construction), value added (current US$)/Pair",
+          "Number of deaths ages 5-9 years/Pair",
+          "Number of infant deaths/Pair",
+          "Population in largest city/Pair",
+          "Prevalence of current tobacco use (% of adults)/Pair",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)/Pair",
+          "Ratio of female to male labor force participation rate (%) (national estimate)/Pair",
+          "Renewable internal freshwater resources per capita (cubic meters)/Pair",
+          "Suicide mortality rate (per 100,000 population)/Pair",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)/Pair",
+          "Agricultural land (% of land area)/Latam",
+          "Consumer price index (2010 = 100)/Latam",
+          "Current health expenditure per capita (current US$)/Latam",
+          "Employment in industry (% of total employment) (modeled ILO estimate)/Latam",
+          "Export value index (2000 = 100)/Latam",
+          "Fixed broadband subscriptions (per 100 people)/Latam",
+          "GNI (current US$)/Latam",
+          "Gross value added at basic prices (GVA) (current US$)/Latam",
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+            "ticks": ""
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+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
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+           "zerolinecolor": "white",
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+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#TREEMAP: Graph for seeing the correlation worldwide.\n",
+    "\n",
+    "#By indicator.\n",
+    "\n",
+    "fig2=px.treemap(selected_primary,path=['Indicator','Continent','Country'],values='R^2 Spearman',color='R^2 Spearman',color_continuous_scale='RdBu_r')\n",
+    "\n",
+    "fig2.show()\n",
+    "\n",
+    "#By continent\n",
+    "\n",
+    "fig2d=px.treemap(selected_primary,path=['Continent','Country','Group'],values='R^2 Spearman',color='R^2 Spearman',color_continuous_scale='RdBu_r')\n",
+    "fig2d.show()\n",
+    "\n",
+    "#By group of interest\n",
+    "\n",
+    "fig2dd=px.treemap(selected_primary,path=['Group','Continent','Country'],values='R^2 Spearman',color='R^2 Spearman',color_continuous_scale='RdBu_r')\n",
+    "\n",
+    "fig2dd.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "lastdf=final.dropna()\n",
+    "\n",
+    "lastdf.isna().sum()\n",
+    "\n",
+    "from ipywidgets import widgets, HBox\n",
+    "\n",
+    "out = widgets.Output()\n",
+    "\n",
+    "def output_treemap(path):\n",
+    "\n",
+    "    figA = px.treemap(lastdf, path=path, values='R^2 Spearman',\n",
+    "\n",
+    "                  color='R^2 Spearman',\n",
+    "\n",
+    "                  color_continuous_scale='RdBu_r')\n",
+    "\n",
+    "    figA.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
+    "\n",
+    "    out.clear_output(wait=True)\n",
+    "\n",
+    "    with out:\n",
+    "\n",
+    "        figA.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_1_dropdown = widgets.Dropdown(\n",
+    "\n",
+    "    options=['Continent', 'Country','Group','Indicator'],\n",
+    "\n",
+    "    value='Continent',\n",
+    "\n",
+    "    description='Path 1',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "path_2_dropdown = widgets.Dropdown(\n",
+    "\n",
+    "    options=['Continent', 'Country','Group','Indicator'],\n",
+    "\n",
+    "    value='Country',\n",
+    "\n",
+    "    description='Path 2',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "path_3_dropdown=widgets.Dropdown(\n",
+    "\n",
+    "    options=['Continent', 'Country','Group','Indicator'],\n",
+    "\n",
+    "    value='Group',\n",
+    "\n",
+    "    description='Path 3',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "path_4_dropdown=widgets.Dropdown(\n",
+    "\n",
+    "    options=['Continent', 'Country','Group','Indicator'],\n",
+    "\n",
+    "    value='Indicator',\n",
+    "\n",
+    "    description='Path 4',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "ok_button = widgets.Button(\n",
+    "\n",
+    "    description='Ready to go',\n",
+    "\n",
+    "    disabled=False,\n",
+    "\n",
+    "    button_style='info', # 'success', 'info', 'warning', 'danger' or ''\n",
+    "\n",
+    "    icon='check' # (FontAwesome names without the `fa-` prefix)\n",
+    "\n",
+    ")\n",
+    "\n",
+    "ok_button.on_click(lambda _: output_treemap([px.Constant(\"World\"), path_1_dropdown.value, path_2_dropdown.value,path_3_dropdown.value,path_4_dropdown.value]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efff9c080b6049fd9f89865b23843ce6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Dropdown(description='Path 1', options=('Continent', 'Country', 'Group', 'Indicator'), value='C…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e53ca788e13c4c0dbaba5cb95476da99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "application/vnd.plotly.v1+json": {
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+          "World/Persian Gulf/YEM/Environment/Access to electricity (% of population)",
+          "World/South Africa/ZAF/Environment/Access to electricity (% of population)",
+          "World/Persian Gulf/ARE/Economy/Adjusted net national income (current US$)",
+          "World/Latam/ARG/Economy/Adjusted net national income (current US$)",
+          "World/Europe/AUT/Economy/Adjusted net national income (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted net national income (current US$)",
+          "World/Asia/BGD/Economy/Adjusted net national income (current US$)",
+          "World/Latam/BRA/Economy/Adjusted net national income (current US$)",
+          "World/Latam/CHL/Economy/Adjusted net national income (current US$)",
+          "World/Pair/CHN/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted net national income (current US$)",
+          "World/Latam/COL/Economy/Adjusted net national income (current US$)",
+          "World/Latam/CRI/Economy/Adjusted net national income (current US$)",
+          "World/Europe/DEU/Economy/Adjusted net national income (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted net national income (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted net national income (current US$)",
+          "World/Europe/ESP/Economy/Adjusted net national income (current US$)",
+          "World/Europe/FRA/Economy/Adjusted net national income (current US$)",
+          "World/Europe/GBR/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted net national income (current US$)",
+          "World/Europe/HRV/Economy/Adjusted net national income (current US$)",
+          "World/Asia/IDN/Economy/Adjusted net national income (current US$)",
+          "World/Asia/IND/Economy/Adjusted net national income (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted net national income (current US$)",
+          "World/Asia/KOR/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted net national income (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted net national income (current US$)",
+          "World/Latam/MEX/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted net national income (current US$)",
+          "World/Europe/NLD/Economy/Adjusted net national income (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted net national income (current US$)",
+          "World/Latam/PAN/Economy/Adjusted net national income (current US$)",
+          "World/Latam/PER/Economy/Adjusted net national income (current US$)",
+          "World/Asia/PHL/Economy/Adjusted net national income (current US$)",
+          "World/Europe/POL/Economy/Adjusted net national income (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted net national income (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted net national income (current US$)",
+          "World/Europe/SWE/Economy/Adjusted net national income (current US$)",
+          "World/Asia/THA/Economy/Adjusted net national income (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted net national income (current US$)",
+          "World/Pair/USA/Economy/Adjusted net national income (current US$)",
+          "World/Latam/VEN/Economy/Adjusted net national income (current US$)",
+          "World/Asia/VNM/Economy/Adjusted net national income (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted net national income (current US$)",
+          "World/Latam/ARG/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/AUT/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/BGD/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/BRA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/CHL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Pair/CHN/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/COL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/CRI/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/DEU/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/ESP/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/FRA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/GBR/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/HRV/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/IDN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/IND/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/KOR/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/MEX/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/NLD/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/PAN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/PER/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/PHL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/POL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/SWE/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/THA/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Pair/USA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/VEN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/VNM/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/ESP/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/HRV/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/SWE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/ESP/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/HRV/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/SWE/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: net national savings (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/ARE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/ARG/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/AUT/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/AZE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/BGD/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/BRA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/CHL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Pair/CHN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/CMR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/COL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/CRI/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/DEU/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/EGY/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/GBR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/GHA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/GRC/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/HRV/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/IND/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/ISR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/KOR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/LBR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/MAR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/MEX/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/MOZ/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/NGA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/NLD/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/OMN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/PAN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/PER/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/PHL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/POL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/QAT/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/SAU/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/SEN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/SWE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/TUR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Pair/USA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/VEN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/VNM/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/YEM/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/AUT/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/BGD/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/BRA/Agriculture/Agricultural land (% of land area)",
+          "World/Pair/CHN/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/CMR/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/CRI/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/DEU/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/DZA/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/EGY/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/ESP/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/FRA/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/GRC/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/IDN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/IND/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/ISR/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/KOR/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/LBR/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/NGA/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/NLD/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/PAN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/PHL/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/POL/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/SWE/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/TUR/Agriculture/Agricultural land (% of land area)",
+          "World/Pair/USA/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/VEN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/VNM/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/ARE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CHL/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/HRV/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/TUR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/AUT/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/BRA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/CHL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/COL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/DZA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/EGY/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/GRC/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/HRV/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/IDN/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/KOR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/MAR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/MEX/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/PHL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/POL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/THA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/TUR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Pair/USA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/VEN/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/VNM/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/BGD/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/BRA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/CHL/Agriculture/Aquaculture production (metric tons)",
+          "World/Pair/CHN/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/CMR/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/COL/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/CRI/Agriculture/Aquaculture production (metric tons)",
+          "World/North Africa/EGY/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/FRA/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/GHA/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/IDN/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/IND/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/KOR/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/MEX/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/NGA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/PER/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/PHL/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/POL/Agriculture/Aquaculture production (metric tons)",
+          "World/Persian Gulf/SAU/Agriculture/Aquaculture production (metric tons)",
+          "World/North Africa/TUR/Agriculture/Aquaculture production (metric tons)",
+          "World/Pair/USA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/VEN/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/VNM/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/ZAF/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/AUT/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (% of land area)",
+          "World/Asia/BGD/Agriculture/Arable land (% of land area)",
+          "World/Latam/BRA/Agriculture/Arable land (% of land area)",
+          "World/Latam/CHL/Agriculture/Arable land (% of land area)",
+          "World/Pair/CHN/Agriculture/Arable land (% of land area)",
+          "World/South Africa/CMR/Agriculture/Arable land (% of land area)",
+          "World/Latam/COL/Agriculture/Arable land (% of land area)",
+          "World/Latam/CRI/Agriculture/Arable land (% of land area)",
+          "World/South Africa/GHA/Agriculture/Arable land (% of land area)",
+          "World/Europe/GRC/Agriculture/Arable land (% of land area)",
+          "World/Asia/IDN/Agriculture/Arable land (% of land area)",
+          "World/Asia/IND/Agriculture/Arable land (% of land area)",
+          "World/Asia/KOR/Agriculture/Arable land (% of land area)",
+          "World/South Africa/LBR/Agriculture/Arable land (% of land area)",
+          "World/North Africa/MAR/Agriculture/Arable land (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Arable land (% of land area)",
+          "World/Latam/PAN/Agriculture/Arable land (% of land area)",
+          "World/Europe/POL/Agriculture/Arable land (% of land area)",
+          "World/Europe/SWE/Agriculture/Arable land (% of land area)",
+          "World/North Africa/TUR/Agriculture/Arable land (% of land area)",
+          "World/Pair/USA/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/ARE/Agriculture/Arable land (hectares per person)",
+          "World/Latam/ARG/Agriculture/Arable land (hectares per person)",
+          "World/Europe/AUT/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (hectares per person)",
+          "World/Asia/BGD/Agriculture/Arable land (hectares per person)",
+          "World/Latam/CHL/Agriculture/Arable land (hectares per person)",
+          "World/Pair/CHN/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/CMR/Agriculture/Arable land (hectares per person)",
+          "World/Latam/COL/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/DZA/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/EGY/Agriculture/Arable land (hectares per person)",
+          "World/Europe/ESP/Agriculture/Arable land (hectares per person)",
+          "World/Europe/FRA/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/GHA/Agriculture/Arable land (hectares per person)",
+          "World/Europe/GRC/Agriculture/Arable land (hectares per person)",
+          "World/Asia/IND/Agriculture/Arable land (hectares per person)",
+          "World/Asia/KOR/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/LBR/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/MAR/Agriculture/Arable land (hectares per person)",
+          "World/Latam/MEX/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/MOZ/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/NGA/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/OMN/Agriculture/Arable land (hectares per person)",
+          "World/Latam/PAN/Agriculture/Arable land (hectares per person)",
+          "World/Latam/PER/Agriculture/Arable land (hectares per person)",
+          "World/Asia/PHL/Agriculture/Arable land (hectares per person)",
+          "World/Europe/POL/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/QAT/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/SAU/Agriculture/Arable land (hectares per person)",
+          "World/Europe/SWE/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/TUR/Agriculture/Arable land (hectares per person)",
+          "World/Pair/USA/Agriculture/Arable land (hectares per person)",
+          "World/Latam/VEN/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/ZAF/Agriculture/Arable land (hectares per person)",
+          "World/Europe/AUT/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (hectares)",
+          "World/Asia/BGD/Agriculture/Arable land (hectares)",
+          "World/Latam/BRA/Agriculture/Arable land (hectares)",
+          "World/Latam/CHL/Agriculture/Arable land (hectares)",
+          "World/Pair/CHN/Agriculture/Arable land (hectares)",
+          "World/South Africa/CMR/Agriculture/Arable land (hectares)",
+          "World/Latam/COL/Agriculture/Arable land (hectares)",
+          "World/Latam/CRI/Agriculture/Arable land (hectares)",
+          "World/South Africa/GHA/Agriculture/Arable land (hectares)",
+          "World/Europe/GRC/Agriculture/Arable land (hectares)",
+          "World/Asia/IDN/Agriculture/Arable land (hectares)",
+          "World/Asia/IND/Agriculture/Arable land (hectares)",
+          "World/Asia/KOR/Agriculture/Arable land (hectares)",
+          "World/South Africa/LBR/Agriculture/Arable land (hectares)",
+          "World/North Africa/MAR/Agriculture/Arable land (hectares)",
+          "World/South Africa/MOZ/Agriculture/Arable land (hectares)",
+          "World/Latam/PAN/Agriculture/Arable land (hectares)",
+          "World/Europe/POL/Agriculture/Arable land (hectares)",
+          "World/Europe/SWE/Agriculture/Arable land (hectares)",
+          "World/North Africa/TUR/Agriculture/Arable land (hectares)",
+          "World/Pair/USA/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/ARE/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/ARG/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/AZE/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/BGD/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Pair/CHN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/CMR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/COL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/CRI/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/DEU/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/DZA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/EGY/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/FRA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/GHA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/HRV/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/IDN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/IND/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/ISR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/LBR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/MAR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/MEX/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/MOZ/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/NGA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/PAN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/PER/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/PHL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/POL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/SAU/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/SEN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/THA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/TUR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Pair/USA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/VEN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/VNM/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/YEM/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/ZAF/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/ARE/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/BGD/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Pair/CHN/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/CRI/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/DZA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/EGY/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/GHA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/IDN/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/IND/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/MAR/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/MEX/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/MOZ/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/PER/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/PHL/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/SAU/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/TUR/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Pair/USA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/VNM/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/YEM/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/ZAF/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/ARE/Economy/Broad money (% of GDP)",
+          "World/Latam/ARG/Economy/Broad money (% of GDP)",
+          "World/Persian Gulf/AZE/Economy/Broad money (% of GDP)",
+          "World/Asia/BGD/Economy/Broad money (% of GDP)",
+          "World/Latam/BRA/Economy/Broad money (% of GDP)",
+          "World/Pair/CHN/Economy/Broad money (% of GDP)",
+          "World/South Africa/CMR/Economy/Broad money (% of GDP)",
+          "World/North Africa/DZA/Economy/Broad money (% of GDP)",
+          "World/Europe/GBR/Economy/Broad money (% of GDP)",
+          "World/Europe/HRV/Economy/Broad money (% of GDP)",
+          "World/Asia/IND/Economy/Broad money (% of GDP)",
+          "World/North Africa/ISR/Economy/Broad money (% of GDP)",
+          "World/Asia/KOR/Economy/Broad money (% of GDP)",
+          "World/North Africa/MAR/Economy/Broad money (% of GDP)",
+          "World/Latam/MEX/Economy/Broad money (% of GDP)",
+          "World/South Africa/MOZ/Economy/Broad money (% of GDP)",
+          "World/Latam/PER/Economy/Broad money (% of GDP)",
+          "World/Asia/PHL/Economy/Broad money (% of GDP)",
+          "World/Europe/POL/Economy/Broad money (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Broad money (% of GDP)",
+          "World/South Africa/SEN/Economy/Broad money (% of GDP)",
+          "World/North Africa/TUR/Economy/Broad money (% of GDP)",
+          "World/Pair/USA/Economy/Broad money (% of GDP)",
+          "World/Latam/VEN/Economy/Broad money (% of GDP)",
+          "World/Asia/VNM/Economy/Broad money (% of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/PHL/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/VNM/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/PHL/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/VNM/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/ARG/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/BRA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/CHL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/COL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/ESP/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/IDN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/MAR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/PER/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/TUR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/South Africa/ZAF/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions (kt)",
+          "World/Latam/ARG/Environment/CO2 emissions (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions (kt)",
+          "World/Latam/CHL/Environment/CO2 emissions (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions (kt)",
+          "World/Latam/COL/Environment/CO2 emissions (kt)",
+          "World/Latam/CRI/Environment/CO2 emissions (kt)",
+          "World/Europe/DEU/Environment/CO2 emissions (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kt)",
+          "World/Europe/GBR/Environment/CO2 emissions (kt)",
+          "World/South Africa/GHA/Environment/CO2 emissions (kt)",
+          "World/Asia/IDN/Environment/CO2 emissions (kt)",
+          "World/Asia/IND/Environment/CO2 emissions (kt)",
+          "World/Asia/KOR/Environment/CO2 emissions (kt)",
+          "World/South Africa/LBR/Environment/CO2 emissions (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions (kt)",
+          "World/Latam/MEX/Environment/CO2 emissions (kt)",
+          "World/South Africa/MOZ/Environment/CO2 emissions (kt)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kt)",
+          "World/Latam/PAN/Environment/CO2 emissions (kt)",
+          "World/Latam/PER/Environment/CO2 emissions (kt)",
+          "World/Asia/PHL/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kt)",
+          "World/South Africa/SEN/Environment/CO2 emissions (kt)",
+          "World/Europe/SWE/Environment/CO2 emissions (kt)",
+          "World/Asia/THA/Environment/CO2 emissions (kt)",
+          "World/North Africa/TUR/Environment/CO2 emissions (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions (kt)",
+          "World/South Africa/ZAF/Environment/CO2 emissions (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/BRA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/CHL/Environment/CO2 emissions (metric tons per capita)",
+          "World/Pair/CHN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/CRI/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/DEU/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/DZA/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/EGY/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/GBR/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/GHA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/GRC/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/IDN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/IND/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/ISR/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/KOR/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/MAR/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/MOZ/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/NLD/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/PAN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/PER/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/PHL/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/SEN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/SWE/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/THA/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/TUR/Environment/CO2 emissions (metric tons per capita)",
+          "World/Pair/USA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/VNM/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/ARG/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/COL/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/ESP/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/IND/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/ISR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/KOR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/MEX/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/South Africa/NGA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/NLD/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/PER/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/POL/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/THA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/TUR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Pair/USA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/CHL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/CMR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/CRI/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/GBR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/GHA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/IDN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/IND/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/MOZ/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/NLD/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/PAN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/PER/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/PHL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/POL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/THA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Europe/AUT/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/BGD/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/BRA/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/CHL/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Pair/CHN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/CMR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/IDN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/IND/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/KOR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/North Africa/MAR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/MEX/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/NGA/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/SEN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/VNM/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/ARE/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/AZE/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/BGD/Industry/Capture fisheries production (metric tons)",
+          "World/Latam/CHL/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/CMR/Industry/Capture fisheries production (metric tons)",
+          "World/Latam/CRI/Industry/Capture fisheries production (metric tons)",
+          "World/Europe/GRC/Industry/Capture fisheries production (metric tons)",
+          "World/Europe/HRV/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/IDN/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/IND/Industry/Capture fisheries production (metric tons)",
+          "World/North Africa/ISR/Industry/Capture fisheries production (metric tons)",
+          "World/North Africa/MAR/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/MOZ/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/NGA/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/OMN/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/PHL/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/SAU/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/THA/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/VNM/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/BGD/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/BRA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/CHL/Agriculture/Cereal yield (kg per hectare)",
+          "World/Pair/CHN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/COL/Agriculture/Cereal yield (kg per hectare)",
+          "World/South Africa/GHA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/IDN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/IND/Agriculture/Cereal yield (kg per hectare)",
+          "World/North Africa/ISR/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/MEX/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/PAN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/PER/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/PHL/Agriculture/Cereal yield (kg per hectare)",
+          "World/Persian Gulf/QAT/Agriculture/Cereal yield (kg per hectare)",
+          "World/Persian Gulf/SAU/Agriculture/Cereal yield (kg per hectare)",
+          "World/North Africa/TUR/Agriculture/Cereal yield (kg per hectare)",
+          "World/Pair/USA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/VNM/Agriculture/Cereal yield (kg per hectare)",
+          "World/Europe/AUT/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/AZE/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/BGD/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Pair/CHN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/COL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/DEU/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/DZA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/GBR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/South Africa/GHA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/IDN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/IND/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/KOR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/MAR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/MEX/Environment/Combustible renewables and waste (% of total energy)",
+          "World/South Africa/MOZ/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/NLD/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/PAN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/PER/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/PHL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/POL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/SAU/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/SWE/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/TUR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Pair/USA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/VNM/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/ARE/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/BGD/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Pair/CHN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/CMR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Latam/CRI/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/DEU/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/DZA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/EGY/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/FRA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/GBR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/GHA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/IDN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/IND/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/ISR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/MAR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Latam/MEX/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/MOZ/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/NLD/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/OMN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/POL/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/QAT/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/SEN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/TUR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Pair/USA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/YEM/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/ZAF/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/AUT/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/AZE/Exports/Commercial service exports (current US$)",
+          "World/Asia/BGD/Exports/Commercial service exports (current US$)",
+          "World/Latam/BRA/Exports/Commercial service exports (current US$)",
+          "World/Latam/CHL/Exports/Commercial service exports (current US$)",
+          "World/Pair/CHN/Exports/Commercial service exports (current US$)",
+          "World/South Africa/CMR/Exports/Commercial service exports (current US$)",
+          "World/Latam/COL/Exports/Commercial service exports (current US$)",
+          "World/Latam/CRI/Exports/Commercial service exports (current US$)",
+          "World/Europe/DEU/Exports/Commercial service exports (current US$)",
+          "World/North Africa/DZA/Exports/Commercial service exports (current US$)",
+          "World/Europe/ESP/Exports/Commercial service exports (current US$)",
+          "World/Europe/FRA/Exports/Commercial service exports (current US$)",
+          "World/Europe/GBR/Exports/Commercial service exports (current US$)",
+          "World/South Africa/GHA/Exports/Commercial service exports (current US$)",
+          "World/Europe/GRC/Exports/Commercial service exports (current US$)",
+          "World/Europe/HRV/Exports/Commercial service exports (current US$)",
+          "World/Asia/IDN/Exports/Commercial service exports (current US$)",
+          "World/Asia/IND/Exports/Commercial service exports (current US$)",
+          "World/North Africa/ISR/Exports/Commercial service exports (current US$)",
+          "World/Asia/KOR/Exports/Commercial service exports (current US$)",
+          "World/North Africa/MAR/Exports/Commercial service exports (current US$)",
+          "World/Europe/NLD/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/OMN/Exports/Commercial service exports (current US$)",
+          "World/Latam/PAN/Exports/Commercial service exports (current US$)",
+          "World/Latam/PER/Exports/Commercial service exports (current US$)",
+          "World/Asia/PHL/Exports/Commercial service exports (current US$)",
+          "World/Europe/POL/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/QAT/Exports/Commercial service exports (current US$)",
+          "World/South Africa/SEN/Exports/Commercial service exports (current US$)",
+          "World/Europe/SWE/Exports/Commercial service exports (current US$)",
+          "World/Asia/THA/Exports/Commercial service exports (current US$)",
+          "World/North Africa/TUR/Exports/Commercial service exports (current US$)",
+          "World/Pair/USA/Exports/Commercial service exports (current US$)",
+          "World/Asia/VNM/Exports/Commercial service exports (current US$)",
+          "World/South Africa/ZAF/Exports/Commercial service exports (current US$)",
+          "World/Latam/ARG/Imports/Commercial service imports (current US$)",
+          "World/Europe/AUT/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/AZE/Imports/Commercial service imports (current US$)",
+          "World/Asia/BGD/Imports/Commercial service imports (current US$)",
+          "World/Latam/BRA/Imports/Commercial service imports (current US$)",
+          "World/Latam/CHL/Imports/Commercial service imports (current US$)",
+          "World/Pair/CHN/Imports/Commercial service imports (current US$)",
+          "World/South Africa/CMR/Imports/Commercial service imports (current US$)",
+          "World/Latam/COL/Imports/Commercial service imports (current US$)",
+          "World/Latam/CRI/Imports/Commercial service imports (current US$)",
+          "World/Europe/DEU/Imports/Commercial service imports (current US$)",
+          "World/North Africa/DZA/Imports/Commercial service imports (current US$)",
+          "World/North Africa/EGY/Imports/Commercial service imports (current US$)",
+          "World/Europe/ESP/Imports/Commercial service imports (current US$)",
+          "World/Europe/FRA/Imports/Commercial service imports (current US$)",
+          "World/Europe/GBR/Imports/Commercial service imports (current US$)",
+          "World/South Africa/GHA/Imports/Commercial service imports (current US$)",
+          "World/Europe/HRV/Imports/Commercial service imports (current US$)",
+          "World/Asia/IDN/Imports/Commercial service imports (current US$)",
+          "World/Asia/IND/Imports/Commercial service imports (current US$)",
+          "World/North Africa/ISR/Imports/Commercial service imports (current US$)",
+          "World/Asia/KOR/Imports/Commercial service imports (current US$)",
+          "World/North Africa/MAR/Imports/Commercial service imports (current US$)",
+          "World/Latam/MEX/Imports/Commercial service imports (current US$)",
+          "World/South Africa/MOZ/Imports/Commercial service imports (current US$)",
+          "World/South Africa/NGA/Imports/Commercial service imports (current US$)",
+          "World/Europe/NLD/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/OMN/Imports/Commercial service imports (current US$)",
+          "World/Latam/PAN/Imports/Commercial service imports (current US$)",
+          "World/Latam/PER/Imports/Commercial service imports (current US$)",
+          "World/Asia/PHL/Imports/Commercial service imports (current US$)",
+          "World/Europe/POL/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/QAT/Imports/Commercial service imports (current US$)",
+          "World/South Africa/SEN/Imports/Commercial service imports (current US$)",
+          "World/Europe/SWE/Imports/Commercial service imports (current US$)",
+          "World/Asia/THA/Imports/Commercial service imports (current US$)",
+          "World/North Africa/TUR/Imports/Commercial service imports (current US$)",
+          "World/Pair/USA/Imports/Commercial service imports (current US$)",
+          "World/Asia/VNM/Imports/Commercial service imports (current US$)",
+          "World/South Africa/ZAF/Imports/Commercial service imports (current US$)",
+          "World/Latam/ARG/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/BGD/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/BRA/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/CMR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/COL/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/CRI/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/DZA/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/EGY/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/GHA/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/IND/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/LBR/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/MAR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/MEX/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/MOZ/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/NGA/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/PAN/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/PER/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/PHL/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/TUR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/VEN/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/VNM/Demoraphy/Completeness of birth registration (%)",
+          "World/Persian Gulf/ARE/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/AUT/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/AZE/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/BGD/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/BRA/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/CHL/Economy/Consumer price index (2010 = 100)",
+          "World/Pair/CHN/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/CMR/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/COL/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/CRI/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/DEU/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/DZA/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/EGY/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/ESP/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/FRA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/GBR/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/GHA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/GRC/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/IDN/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/IND/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/ISR/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/KOR/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/LBR/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/MAR/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/MEX/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/MOZ/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/NGA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/NLD/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/OMN/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/PAN/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/PER/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/PHL/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/POL/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/QAT/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/SAU/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/SEN/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/SWE/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/THA/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/TUR/Economy/Consumer price index (2010 = 100)",
+          "World/Pair/USA/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/VEN/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/VNM/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/YEM/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/ZAF/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/ARE/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/BGD/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/CHL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Pair/CHN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/CMR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/COL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/CRI/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/DEU/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/ESP/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/FRA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/GBR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/GHA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/IDN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/IND/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/ISR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/KOR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/LBR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/MAR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/MEX/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/MOZ/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/NLD/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/OMN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/PAN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/PER/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/PHL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/POL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/QAT/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/SAU/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/SWE/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/THA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/TUR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Pair/USA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/VNM/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/ZAF/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/AZE/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/BGD/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/BRA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/CHL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Pair/CHN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/CMR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/COL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/CRI/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/IDN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/IND/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/MEX/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/PER/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/PHL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/THA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Pair/USA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/VNM/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/ARG/Health/Current health expenditure (% of GDP)",
+          "World/Europe/AUT/Health/Current health expenditure (% of GDP)",
+          "World/Latam/CHL/Health/Current health expenditure (% of GDP)",
+          "World/Pair/CHN/Health/Current health expenditure (% of GDP)",
+          "World/South Africa/CMR/Health/Current health expenditure (% of GDP)",
+          "World/Latam/COL/Health/Current health expenditure (% of GDP)",
+          "World/Latam/CRI/Health/Current health expenditure (% of GDP)",
+          "World/Europe/ESP/Health/Current health expenditure (% of GDP)",
+          "World/Europe/FRA/Health/Current health expenditure (% of GDP)",
+          "World/Europe/GBR/Health/Current health expenditure (% of GDP)",
+          "World/Europe/GRC/Health/Current health expenditure (% of GDP)",
+          "World/Asia/IDN/Health/Current health expenditure (% of GDP)",
+          "World/Asia/IND/Health/Current health expenditure (% of GDP)",
+          "World/Asia/KOR/Health/Current health expenditure (% of GDP)",
+          "World/South Africa/MOZ/Health/Current health expenditure (% of GDP)",
+          "World/Europe/NLD/Health/Current health expenditure (% of GDP)",
+          "World/Latam/PER/Health/Current health expenditure (% of GDP)",
+          "World/Europe/POL/Health/Current health expenditure (% of GDP)",
+          "World/Europe/SWE/Health/Current health expenditure (% of GDP)",
+          "World/Asia/THA/Health/Current health expenditure (% of GDP)",
+          "World/Pair/USA/Health/Current health expenditure (% of GDP)",
+          "World/Asia/VNM/Health/Current health expenditure (% of GDP)",
+          "World/Persian Gulf/YEM/Health/Current health expenditure (% of GDP)",
+          "World/Persian Gulf/ARE/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Current health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/CMR/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/GHA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/GRC/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/MEX/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/MOZ/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/NGA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/OMN/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/SEN/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Current health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/VEN/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/BGD/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/CHL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Pair/CHN/Economy/Customs and other import duties (% of tax revenue)",
+          "World/South Africa/CMR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/COL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/CRI/Economy/Customs and other import duties (% of tax revenue)",
+          "World/North Africa/EGY/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/ESP/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/HRV/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/IND/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/KOR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/North Africa/MAR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/MEX/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/PAN/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/PER/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/POL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/THA/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/BGD/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/CHL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Pair/CHN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/CMR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/COL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/CRI/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/DEU/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/DZA/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/EGY/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/FRA/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/GRC/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/HRV/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/IND/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/KOR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/LBR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/MEX/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/OMN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/PER/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/POL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/SAU/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/SEN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/SWE/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/TUR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/VEN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/VNM/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/ARE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/AZE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/BGD/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/CHL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Pair/CHN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/CMR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/COL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/CRI/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/DEU/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/DZA/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/EGY/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/GRC/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/HRV/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/IND/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/KOR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/LBR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/MAR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/MEX/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/OMN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/PAN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/PER/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/POL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/SAU/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/SEN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/SWE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/TUR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/VEN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/VNM/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/ARG/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/GRC/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/MEX/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/MOZ/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/OMN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/TUR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/CMR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/EGY/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/GHA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/NGA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/SEN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/AUT/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/BGD/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/BRA/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/CHL/Environment/Electric power consumption (kWh per capita)",
+          "World/Pair/CHN/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/CMR/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/COL/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/CRI/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/DZA/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/EGY/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/GBR/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/GRC/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/HRV/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/IDN/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/IND/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/KOR/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/MAR/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/MEX/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/MOZ/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/NGA/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/OMN/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/PAN/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/PER/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/PHL/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/POL/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/SAU/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/SEN/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/SWE/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/THA/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/TUR/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/VNM/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/YEM/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/ARE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CHL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/COL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/GHA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/LBR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/MEX/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/NGA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PAN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PER/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/PHL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/QAT/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/TUR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GRC/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/HRV/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/SAU/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Pair/USA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/ARE/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CHL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/COL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/GHA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GRC/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/HRV/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/LBR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/MEX/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/NGA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PAN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PER/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/PHL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/THA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/TUR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Pair/USA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/BGD/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Pair/CHN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/CMR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/CRI/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/DEU/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/DZA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/GBR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/GHA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/IDN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/IND/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/ISR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/KOR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/MEX/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/MOZ/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/NGA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/NLD/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Persian Gulf/OMN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/PAN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/PER/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/PHL/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/POL/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/SWE/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/THA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/TUR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Pair/USA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/VEN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/BGD/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/BRA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/CHL/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Pair/CHN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/CRI/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/DZA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/GBR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/IDN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/IND/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/KOR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/MAR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/South Africa/MOZ/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/South Africa/NGA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/NLD/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Persian Gulf/OMN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/PAN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/PER/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Persian Gulf/SAU/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/THA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/TUR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Pair/USA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/VNM/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/AUT/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/AZE/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/BGD/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Pair/CHN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/CMR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/COL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/CRI/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/DEU/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/DZA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/ESP/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/FRA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/GBR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/GHA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/HRV/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/IDN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/IND/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/ISR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/KOR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/MEX/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/MOZ/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/NGA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/NLD/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/PAN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/PHL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/POL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/SAU/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/SWE/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/TUR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Pair/USA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/ZAF/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/ARE/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/DEU/Exports/Export unit value index (2000 = 100)",
+          "World/North Africa/DZA/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/FRA/Exports/Export unit value index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/GRC/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/HRV/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/POL/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/SWE/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/THA/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/YEM/Exports/Export unit value index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Exports/Export value index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export value index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export value index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export value index (2000 = 100)",
+          "World/Latam/CHL/Exports/Export value index (2000 = 100)",
+          "World/Pair/CHN/Exports/Export value index (2000 = 100)",
+          "World/Latam/COL/Exports/Export value index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export value index (2000 = 100)",
+          "World/Europe/DEU/Exports/Export value index (2000 = 100)",
+          "World/North Africa/DZA/Exports/Export value index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export value index (2000 = 100)",
+          "World/Europe/FRA/Exports/Export value index (2000 = 100)",
+          "World/Europe/GBR/Exports/Export value index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export value index (2000 = 100)",
+          "World/Asia/IDN/Exports/Export value index (2000 = 100)",
+          "World/Asia/IND/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/IRQ/Exports/Export value index (2000 = 100)",
+          "World/Asia/KOR/Exports/Export value index (2000 = 100)",
+          "World/North Africa/MAR/Exports/Export value index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export value index (2000 = 100)",
+          "World/South Africa/MOZ/Exports/Export value index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/OMN/Exports/Export value index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export value index (2000 = 100)",
+          "World/Latam/PER/Exports/Export value index (2000 = 100)",
+          "World/Asia/PHL/Exports/Export value index (2000 = 100)",
+          "World/Europe/POL/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/SAU/Exports/Export value index (2000 = 100)",
+          "World/South Africa/SEN/Exports/Export value index (2000 = 100)",
+          "World/Europe/SWE/Exports/Export value index (2000 = 100)",
+          "World/Asia/THA/Exports/Export value index (2000 = 100)",
+          "World/North Africa/TUR/Exports/Export value index (2000 = 100)",
+          "World/Pair/USA/Exports/Export value index (2000 = 100)",
+          "World/Latam/VEN/Exports/Export value index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export value index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Exports/Export volume index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export volume index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export volume index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export volume index (2000 = 100)",
+          "World/Latam/CHL/Exports/Export volume index (2000 = 100)",
+          "World/Pair/CHN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/COL/Exports/Export volume index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export volume index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export volume index (2000 = 100)",
+          "World/Europe/GRC/Exports/Export volume index (2000 = 100)",
+          "World/Asia/IDN/Exports/Export volume index (2000 = 100)",
+          "World/Asia/IND/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/ISR/Exports/Export volume index (2000 = 100)",
+          "World/Asia/KOR/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/MAR/Exports/Export volume index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/MOZ/Exports/Export volume index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/OMN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/PER/Exports/Export volume index (2000 = 100)",
+          "World/Asia/PHL/Exports/Export volume index (2000 = 100)",
+          "World/Europe/POL/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/QAT/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/SAU/Exports/Export volume index (2000 = 100)",
+          "World/Asia/THA/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/TUR/Exports/Export volume index (2000 = 100)",
+          "World/Pair/USA/Exports/Export volume index (2000 = 100)",
+          "World/Latam/VEN/Exports/Export volume index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/YEM/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export volume index (2000 = 100)",
+          "World/Europe/AUT/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/BGD/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/BRA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/CHL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Pair/CHN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/COL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/CRI/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/DEU/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/ESP/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/FRA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/GBR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/HRV/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/IDN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/IND/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/IRQ/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/KOR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/MEX/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/MOZ/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/NLD/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/PAN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/PER/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/PHL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/POL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/SWE/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/THA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Pair/USA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/VEN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/VNM/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/ARE/Exports/Exports of goods and services (current US$)",
+          "World/Europe/AUT/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/AZE/Exports/Exports of goods and services (current US$)",
+          "World/Asia/BGD/Exports/Exports of goods and services (current US$)",
+          "World/Latam/BRA/Exports/Exports of goods and services (current US$)",
+          "World/Latam/CHL/Exports/Exports of goods and services (current US$)",
+          "World/Pair/CHN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/COL/Exports/Exports of goods and services (current US$)",
+          "World/Latam/CRI/Exports/Exports of goods and services (current US$)",
+          "World/Europe/DEU/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/DZA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/ESP/Exports/Exports of goods and services (current US$)",
+          "World/Europe/FRA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/GBR/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/GHA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/HRV/Exports/Exports of goods and services (current US$)",
+          "World/Asia/IDN/Exports/Exports of goods and services (current US$)",
+          "World/Asia/IND/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/IRQ/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/ISR/Exports/Exports of goods and services (current US$)",
+          "World/Asia/KOR/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/MAR/Exports/Exports of goods and services (current US$)",
+          "World/Latam/MEX/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/MOZ/Exports/Exports of goods and services (current US$)",
+          "World/Europe/NLD/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/OMN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/PAN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/PER/Exports/Exports of goods and services (current US$)",
+          "World/Asia/PHL/Exports/Exports of goods and services (current US$)",
+          "World/Europe/POL/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/QAT/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/SAU/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/SEN/Exports/Exports of goods and services (current US$)",
+          "World/Europe/SWE/Exports/Exports of goods and services (current US$)",
+          "World/Asia/THA/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/TUR/Exports/Exports of goods and services (current US$)",
+          "World/Pair/USA/Exports/Exports of goods and services (current US$)",
+          "World/Latam/VEN/Exports/Exports of goods and services (current US$)",
+          "World/Asia/VNM/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/ZAF/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/ARE/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/Final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/ARG/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/CHL/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/COL/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/CRI/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/EGY/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/GHA/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/GRC/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/HRV/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/IDN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/IND/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/LBR/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/MAR/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/MEX/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/MOZ/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/NGA/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/PAN/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/PER/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/PHL/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/POL/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/SEN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/THA/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/TUR/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/VEN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/VNM/R&D/Firms offering formal training (% of firms)",
+          "World/Persian Gulf/YEM/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/ZAF/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/ARG/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Persian Gulf/AZE/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/BGD/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/CHL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/CMR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/COL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/CRI/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/EGY/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/GHA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Europe/GRC/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/IDN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/IND/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/LBR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/MAR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/MEX/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/MOZ/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/NGA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/PAN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/PER/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/PHL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Europe/POL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/SEN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/THA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/TUR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/VEN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/VNM/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Persian Gulf/YEM/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/ZAF/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/ARG/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/AZE/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/BGD/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/CHL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/CMR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/COL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/CRI/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/North Africa/EGY/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/GHA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Europe/GRC/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/IDN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/LBR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/MEX/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/MOZ/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/NGA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/PAN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/PER/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/PHL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Europe/POL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/SEN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/THA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/North Africa/TUR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/VEN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/VNM/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/YEM/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/ZAF/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/ARE/Internet/Fixed broadband subscriptions",
+          "World/Latam/ARG/Internet/Fixed broadband subscriptions",
+          "World/Europe/AUT/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/AZE/Internet/Fixed broadband subscriptions",
+          "World/Asia/BGD/Internet/Fixed broadband subscriptions",
+          "World/Latam/BRA/Internet/Fixed broadband subscriptions",
+          "World/Latam/CHL/Internet/Fixed broadband subscriptions",
+          "World/Pair/CHN/Internet/Fixed broadband subscriptions",
+          "World/South Africa/CMR/Internet/Fixed broadband subscriptions",
+          "World/Latam/COL/Internet/Fixed broadband subscriptions",
+          "World/Latam/CRI/Internet/Fixed broadband subscriptions",
+          "World/Europe/DEU/Internet/Fixed broadband subscriptions",
+          "World/North Africa/DZA/Internet/Fixed broadband subscriptions",
+          "World/North Africa/EGY/Internet/Fixed broadband subscriptions",
+          "World/Europe/ESP/Internet/Fixed broadband subscriptions",
+          "World/Europe/FRA/Internet/Fixed broadband subscriptions",
+          "World/Europe/GBR/Internet/Fixed broadband subscriptions",
+          "World/South Africa/GHA/Internet/Fixed broadband subscriptions",
+          "World/Europe/GRC/Internet/Fixed broadband subscriptions",
+          "World/Europe/HRV/Internet/Fixed broadband subscriptions",
+          "World/Asia/IDN/Internet/Fixed broadband subscriptions",
+          "World/Asia/IND/Internet/Fixed broadband subscriptions",
+          "World/North Africa/ISR/Internet/Fixed broadband subscriptions",
+          "World/Asia/KOR/Internet/Fixed broadband subscriptions",
+          "World/South Africa/LBR/Internet/Fixed broadband subscriptions",
+          "World/North Africa/MAR/Internet/Fixed broadband subscriptions",
+          "World/Latam/MEX/Internet/Fixed broadband subscriptions",
+          "World/South Africa/MOZ/Internet/Fixed broadband subscriptions",
+          "World/Europe/NLD/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/OMN/Internet/Fixed broadband subscriptions",
+          "World/Latam/PAN/Internet/Fixed broadband subscriptions",
+          "World/Latam/PER/Internet/Fixed broadband subscriptions",
+          "World/Asia/PHL/Internet/Fixed broadband subscriptions",
+          "World/Europe/POL/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/QAT/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/SAU/Internet/Fixed broadband subscriptions",
+          "World/South Africa/SEN/Internet/Fixed broadband subscriptions",
+          "World/Europe/SWE/Internet/Fixed broadband subscriptions",
+          "World/Asia/THA/Internet/Fixed broadband subscriptions",
+          "World/North Africa/TUR/Internet/Fixed broadband subscriptions",
+          "World/Pair/USA/Internet/Fixed broadband subscriptions",
+          "World/Latam/VEN/Internet/Fixed broadband subscriptions",
+          "World/Asia/VNM/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/YEM/Internet/Fixed broadband subscriptions",
+          "World/South Africa/ZAF/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/ARE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/ARG/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/AUT/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/AZE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/BGD/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/BRA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/CHL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Pair/CHN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/CMR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/COL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/CRI/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/DEU/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/DZA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/EGY/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/ESP/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/FRA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/GBR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/GHA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/GRC/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/HRV/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/IDN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/IND/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/ISR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/KOR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/LBR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/MAR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/MEX/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/MOZ/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/NLD/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/OMN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/PAN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/PER/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/PHL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/POL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/QAT/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/SAU/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/SEN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/SWE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/THA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/TUR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Pair/USA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/VEN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/VNM/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/YEM/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/ZAF/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/ARE/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/AZE/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/BGD/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/BRA/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/CHL/Industry/Food production index (2014-2016 = 100)",
+          "World/Pair/CHN/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/CMR/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/COL/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/CRI/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/IDN/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/IND/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/ISR/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/LBR/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/MEX/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Industry/Food production index (2014-2016 = 100)",
+          "World/Europe/NLD/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/PAN/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/PER/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/PHL/Industry/Food production index (2014-2016 = 100)",
+          "World/Europe/POL/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/SAU/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/THA/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Industry/Food production index (2014-2016 = 100)",
+          "World/Pair/USA/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/VNM/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/ZAF/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/ARE/Demography/Forest area (% of land area)",
+          "World/Latam/ARG/Demography/Forest area (% of land area)",
+          "World/Europe/AUT/Demography/Forest area (% of land area)",
+          "World/Persian Gulf/AZE/Demography/Forest area (% of land area)",
+          "World/Asia/BGD/Demography/Forest area (% of land area)",
+          "World/Latam/BRA/Demography/Forest area (% of land area)",
+          "World/Latam/CHL/Demography/Forest area (% of land area)",
+          "World/Pair/CHN/Demography/Forest area (% of land area)",
+          "World/South Africa/CMR/Demography/Forest area (% of land area)",
+          "World/Latam/COL/Demography/Forest area (% of land area)",
+          "World/Latam/CRI/Demography/Forest area (% of land area)",
+          "World/North Africa/DZA/Demography/Forest area (% of land area)",
+          "World/North Africa/EGY/Demography/Forest area (% of land area)",
+          "World/Europe/ESP/Demography/Forest area (% of land area)",
+          "World/Europe/FRA/Demography/Forest area (% of land area)",
+          "World/Europe/GBR/Demography/Forest area (% of land area)",
+          "World/Europe/GRC/Demography/Forest area (% of land area)",
+          "World/Europe/HRV/Demography/Forest area (% of land area)",
+          "World/Asia/IDN/Demography/Forest area (% of land area)",
+          "World/Asia/IND/Demography/Forest area (% of land area)",
+          "World/Asia/KOR/Demography/Forest area (% of land area)",
+          "World/South Africa/LBR/Demography/Forest area (% of land area)",
+          "World/North Africa/MAR/Demography/Forest area (% of land area)",
+          "World/Latam/MEX/Demography/Forest area (% of land area)",
+          "World/South Africa/MOZ/Demography/Forest area (% of land area)",
+          "World/South Africa/NGA/Demography/Forest area (% of land area)",
+          "World/Europe/NLD/Demography/Forest area (% of land area)",
+          "World/Latam/PAN/Demography/Forest area (% of land area)",
+          "World/Latam/PER/Demography/Forest area (% of land area)",
+          "World/Europe/POL/Demography/Forest area (% of land area)",
+          "World/South Africa/SEN/Demography/Forest area (% of land area)",
+          "World/North Africa/TUR/Demography/Forest area (% of land area)",
+          "World/Pair/USA/Demography/Forest area (% of land area)",
+          "World/Latam/VEN/Demography/Forest area (% of land area)",
+          "World/Asia/VNM/Demography/Forest area (% of land area)",
+          "World/South Africa/ZAF/Demography/Forest area (% of land area)",
+          "World/Persian Gulf/ARE/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/AUT/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/AZE/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/BGD/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/BRA/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/CHL/Principal/GDP deflator (base year varies by country)",
+          "World/Pair/CHN/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/CMR/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/COL/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/CRI/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/DEU/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/DZA/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/EGY/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/ESP/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/FRA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/GBR/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/GHA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/GRC/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/HRV/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/IDN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/IND/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/ISR/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/KOR/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/LBR/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/MAR/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/MEX/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/MOZ/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/NGA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/NLD/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/OMN/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/PAN/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/PER/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/PHL/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/POL/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/QAT/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/SAU/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/SEN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/THA/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/TUR/Principal/GDP deflator (base year varies by country)",
+          "World/Pair/USA/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/VEN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/VNM/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/YEM/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/ZAF/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/ARE/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/AUT/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/AZE/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/BGD/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/BRA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/CHL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Pair/CHN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/CMR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/COL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/CRI/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/DEU/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/DZA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/EGY/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/ESP/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/FRA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/GBR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/GHA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/GRC/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/HRV/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/IDN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/IND/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/ISR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/KOR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/LBR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/MAR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/MEX/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/MOZ/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/NGA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/NLD/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/OMN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/PAN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/PER/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/PHL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/POL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/QAT/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/SAU/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/SEN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/THA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/TUR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Pair/USA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/VEN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/VNM/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/YEM/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/ZAF/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/ARE/Principal/GDP per capita (current US$)",
+          "World/Latam/ARG/Principal/GDP per capita (current US$)",
+          "World/Europe/AUT/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/AZE/Principal/GDP per capita (current US$)",
+          "World/Asia/BGD/Principal/GDP per capita (current US$)",
+          "World/Latam/BRA/Principal/GDP per capita (current US$)",
+          "World/Latam/CHL/Principal/GDP per capita (current US$)",
+          "World/Pair/CHN/Principal/GDP per capita (current US$)",
+          "World/South Africa/CMR/Principal/GDP per capita (current US$)",
+          "World/Latam/COL/Principal/GDP per capita (current US$)",
+          "World/Latam/CRI/Principal/GDP per capita (current US$)",
+          "World/Europe/DEU/Principal/GDP per capita (current US$)",
+          "World/North Africa/DZA/Principal/GDP per capita (current US$)",
+          "World/North Africa/EGY/Principal/GDP per capita (current US$)",
+          "World/Europe/ESP/Principal/GDP per capita (current US$)",
+          "World/Europe/FRA/Principal/GDP per capita (current US$)",
+          "World/Europe/GBR/Principal/GDP per capita (current US$)",
+          "World/South Africa/GHA/Principal/GDP per capita (current US$)",
+          "World/Europe/GRC/Principal/GDP per capita (current US$)",
+          "World/Europe/HRV/Principal/GDP per capita (current US$)",
+          "World/Asia/IDN/Principal/GDP per capita (current US$)",
+          "World/Asia/IND/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/IRQ/Principal/GDP per capita (current US$)",
+          "World/North Africa/ISR/Principal/GDP per capita (current US$)",
+          "World/Asia/KOR/Principal/GDP per capita (current US$)",
+          "World/North Africa/MAR/Principal/GDP per capita (current US$)",
+          "World/Latam/MEX/Principal/GDP per capita (current US$)",
+          "World/South Africa/MOZ/Principal/GDP per capita (current US$)",
+          "World/South Africa/NGA/Principal/GDP per capita (current US$)",
+          "World/Europe/NLD/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/OMN/Principal/GDP per capita (current US$)",
+          "World/Latam/PAN/Principal/GDP per capita (current US$)",
+          "World/Latam/PER/Principal/GDP per capita (current US$)",
+          "World/Asia/PHL/Principal/GDP per capita (current US$)",
+          "World/Europe/POL/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/QAT/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/SAU/Principal/GDP per capita (current US$)",
+          "World/South Africa/SEN/Principal/GDP per capita (current US$)",
+          "World/Europe/SWE/Principal/GDP per capita (current US$)",
+          "World/Asia/THA/Principal/GDP per capita (current US$)",
+          "World/North Africa/TUR/Principal/GDP per capita (current US$)",
+          "World/Pair/USA/Principal/GDP per capita (current US$)",
+          "World/Latam/VEN/Principal/GDP per capita (current US$)",
+          "World/Asia/VNM/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/YEM/Principal/GDP per capita (current US$)",
+          "World/South Africa/ZAF/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/ARE/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/ARG/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/AZE/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/BGD/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/BRA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/CHL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Pair/CHN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/CMR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/COL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/CRI/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/EGY/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/FRA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/GBR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/GHA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/GRC/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/IDN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/IND/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/ISR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/KOR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/MAR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/MOZ/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/NGA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/OMN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/PAN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/PER/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/PHL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/POL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/QAT/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/SAU/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/SEN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/THA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/TUR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Pair/USA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/VNM/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/ZAF/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/ARG/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/AUT/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/AZE/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/BGD/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/BRA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/CHL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Pair/CHN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/CMR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/COL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/CRI/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/DEU/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/EGY/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/ESP/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/FRA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/GBR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/GHA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/HRV/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/IDN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/IND/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/ISR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/KOR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/MAR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/MEX/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/MOZ/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/NGA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/NLD/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/PAN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/PHL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/POL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/SWE/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/THA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/TUR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Pair/USA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/VNM/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/ZAF/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/AUT/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/AZE/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/BGD/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Pair/CHN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/CMR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/COL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/CRI/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/DEU/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/DZA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/ESP/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/FRA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/GBR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/GHA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/HRV/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/IDN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/IND/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/ISR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/KOR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/MEX/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/MOZ/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/NGA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/NLD/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/PAN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/PHL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/POL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/SAU/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/SWE/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/TUR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Pair/USA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/ZAF/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/ARE/Economy/GNI (current US$)",
+          "World/Latam/ARG/Economy/GNI (current US$)",
+          "World/Europe/AUT/Economy/GNI (current US$)",
+          "World/Persian Gulf/AZE/Economy/GNI (current US$)",
+          "World/Asia/BGD/Economy/GNI (current US$)",
+          "World/Latam/BRA/Economy/GNI (current US$)",
+          "World/Latam/CHL/Economy/GNI (current US$)",
+          "World/Pair/CHN/Economy/GNI (current US$)",
+          "World/South Africa/CMR/Economy/GNI (current US$)",
+          "World/Latam/COL/Economy/GNI (current US$)",
+          "World/Latam/CRI/Economy/GNI (current US$)",
+          "World/Europe/DEU/Economy/GNI (current US$)",
+          "World/North Africa/DZA/Economy/GNI (current US$)",
+          "World/North Africa/EGY/Economy/GNI (current US$)",
+          "World/Europe/ESP/Economy/GNI (current US$)",
+          "World/Europe/FRA/Economy/GNI (current US$)",
+          "World/Europe/GBR/Economy/GNI (current US$)",
+          "World/South Africa/GHA/Economy/GNI (current US$)",
+          "World/Europe/HRV/Economy/GNI (current US$)",
+          "World/Asia/IDN/Economy/GNI (current US$)",
+          "World/Asia/IND/Economy/GNI (current US$)",
+          "World/Persian Gulf/IRQ/Economy/GNI (current US$)",
+          "World/North Africa/ISR/Economy/GNI (current US$)",
+          "World/Asia/KOR/Economy/GNI (current US$)",
+          "World/South Africa/LBR/Economy/GNI (current US$)",
+          "World/North Africa/MAR/Economy/GNI (current US$)",
+          "World/Latam/MEX/Economy/GNI (current US$)",
+          "World/South Africa/MOZ/Economy/GNI (current US$)",
+          "World/South Africa/NGA/Economy/GNI (current US$)",
+          "World/Europe/NLD/Economy/GNI (current US$)",
+          "World/Persian Gulf/OMN/Economy/GNI (current US$)",
+          "World/Latam/PAN/Economy/GNI (current US$)",
+          "World/Latam/PER/Economy/GNI (current US$)",
+          "World/Asia/PHL/Economy/GNI (current US$)",
+          "World/Europe/POL/Economy/GNI (current US$)",
+          "World/Persian Gulf/QAT/Economy/GNI (current US$)",
+          "World/Persian Gulf/SAU/Economy/GNI (current US$)",
+          "World/South Africa/SEN/Economy/GNI (current US$)",
+          "World/Europe/SWE/Economy/GNI (current US$)",
+          "World/Asia/THA/Economy/GNI (current US$)",
+          "World/North Africa/TUR/Economy/GNI (current US$)",
+          "World/Pair/USA/Economy/GNI (current US$)",
+          "World/Latam/VEN/Economy/GNI (current US$)",
+          "World/Asia/VNM/Economy/GNI (current US$)",
+          "World/Persian Gulf/YEM/Economy/GNI (current US$)",
+          "World/South Africa/ZAF/Economy/GNI (current US$)",
+          "World/Persian Gulf/ARE/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/General government final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/General government final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/AUT/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/BRA/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/Goods exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/COL/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/FRA/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/IDN/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/IND/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/IRQ/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/LBR/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/MEX/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/MOZ/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/PER/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/PHL/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/POL/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/THA/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Goods exports (BoP, current US$)",
+          "World/Pair/USA/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/VEN/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/VNM/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/ARG/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/AUT/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/BGD/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/BRA/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/CHL/Imports/Goods imports (BoP, current US$)",
+          "World/Pair/CHN/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/COL/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/CRI/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/DEU/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/ESP/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/FRA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/GBR/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/GRC/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/HRV/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/IDN/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/IND/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/KOR/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/LBR/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/MEX/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/NLD/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/PER/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/PHL/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/POL/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/SWE/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/THA/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Goods imports (BoP, current US$)",
+          "World/Pair/USA/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/VNM/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/YEM/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross capital formation (current US$)",
+          "World/Latam/ARG/Economy/Gross capital formation (current US$)",
+          "World/Europe/AUT/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross capital formation (current US$)",
+          "World/Asia/BGD/Economy/Gross capital formation (current US$)",
+          "World/Latam/BRA/Economy/Gross capital formation (current US$)",
+          "World/Latam/CHL/Economy/Gross capital formation (current US$)",
+          "World/Pair/CHN/Economy/Gross capital formation (current US$)",
+          "World/South Africa/CMR/Economy/Gross capital formation (current US$)",
+          "World/Latam/COL/Economy/Gross capital formation (current US$)",
+          "World/Latam/CRI/Economy/Gross capital formation (current US$)",
+          "World/Europe/DEU/Economy/Gross capital formation (current US$)",
+          "World/North Africa/DZA/Economy/Gross capital formation (current US$)",
+          "World/North Africa/EGY/Economy/Gross capital formation (current US$)",
+          "World/Europe/ESP/Economy/Gross capital formation (current US$)",
+          "World/Europe/FRA/Economy/Gross capital formation (current US$)",
+          "World/Europe/GBR/Economy/Gross capital formation (current US$)",
+          "World/South Africa/GHA/Economy/Gross capital formation (current US$)",
+          "World/Europe/HRV/Economy/Gross capital formation (current US$)",
+          "World/Asia/IDN/Economy/Gross capital formation (current US$)",
+          "World/Asia/IND/Economy/Gross capital formation (current US$)",
+          "World/North Africa/ISR/Economy/Gross capital formation (current US$)",
+          "World/Asia/KOR/Economy/Gross capital formation (current US$)",
+          "World/North Africa/MAR/Economy/Gross capital formation (current US$)",
+          "World/Latam/MEX/Economy/Gross capital formation (current US$)",
+          "World/South Africa/MOZ/Economy/Gross capital formation (current US$)",
+          "World/South Africa/NGA/Economy/Gross capital formation (current US$)",
+          "World/Europe/NLD/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross capital formation (current US$)",
+          "World/Latam/PAN/Economy/Gross capital formation (current US$)",
+          "World/Latam/PER/Economy/Gross capital formation (current US$)",
+          "World/Asia/PHL/Economy/Gross capital formation (current US$)",
+          "World/Europe/POL/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross capital formation (current US$)",
+          "World/South Africa/SEN/Economy/Gross capital formation (current US$)",
+          "World/Europe/SWE/Economy/Gross capital formation (current US$)",
+          "World/Asia/THA/Economy/Gross capital formation (current US$)",
+          "World/North Africa/TUR/Economy/Gross capital formation (current US$)",
+          "World/Pair/USA/Economy/Gross capital formation (current US$)",
+          "World/Latam/VEN/Economy/Gross capital formation (current US$)",
+          "World/Asia/VNM/Economy/Gross capital formation (current US$)",
+          "World/South Africa/ZAF/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross domestic savings (current US$)",
+          "World/Latam/ARG/Economy/Gross domestic savings (current US$)",
+          "World/Europe/AUT/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross domestic savings (current US$)",
+          "World/Asia/BGD/Economy/Gross domestic savings (current US$)",
+          "World/Latam/BRA/Economy/Gross domestic savings (current US$)",
+          "World/Latam/CHL/Economy/Gross domestic savings (current US$)",
+          "World/Pair/CHN/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/CMR/Economy/Gross domestic savings (current US$)",
+          "World/Latam/COL/Economy/Gross domestic savings (current US$)",
+          "World/Latam/CRI/Economy/Gross domestic savings (current US$)",
+          "World/Europe/DEU/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/DZA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/ESP/Economy/Gross domestic savings (current US$)",
+          "World/Europe/FRA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/GBR/Economy/Gross domestic savings (current US$)",
+          "World/Europe/HRV/Economy/Gross domestic savings (current US$)",
+          "World/Asia/IDN/Economy/Gross domestic savings (current US$)",
+          "World/Asia/IND/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/ISR/Economy/Gross domestic savings (current US$)",
+          "World/Asia/KOR/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/MAR/Economy/Gross domestic savings (current US$)",
+          "World/Latam/MEX/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/NGA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/NLD/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross domestic savings (current US$)",
+          "World/Latam/PAN/Economy/Gross domestic savings (current US$)",
+          "World/Latam/PER/Economy/Gross domestic savings (current US$)",
+          "World/Asia/PHL/Economy/Gross domestic savings (current US$)",
+          "World/Europe/POL/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/SEN/Economy/Gross domestic savings (current US$)",
+          "World/Europe/SWE/Economy/Gross domestic savings (current US$)",
+          "World/Asia/THA/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/TUR/Economy/Gross domestic savings (current US$)",
+          "World/Pair/USA/Economy/Gross domestic savings (current US$)",
+          "World/Asia/VNM/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/ZAF/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/ARG/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/AUT/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/BGD/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/BRA/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/CHL/Economy/Gross fixed capital formation (current US$)",
+          "World/Pair/CHN/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/CMR/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/COL/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/CRI/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/DEU/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/DZA/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/EGY/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/FRA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/GBR/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/GHA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/HRV/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/IDN/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/IND/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/ISR/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/KOR/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/MAR/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/MEX/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/MOZ/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/NGA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/NLD/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/PAN/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/PER/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/PHL/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/POL/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/SEN/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/SWE/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/THA/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/TUR/Economy/Gross fixed capital formation (current US$)",
+          "World/Pair/USA/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/VEN/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/VNM/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/ZAF/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross national expenditure (current US$)",
+          "World/Latam/ARG/Economy/Gross national expenditure (current US$)",
+          "World/Europe/AUT/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross national expenditure (current US$)",
+          "World/Asia/BGD/Economy/Gross national expenditure (current US$)",
+          "World/Latam/BRA/Economy/Gross national expenditure (current US$)",
+          "World/Latam/CHL/Economy/Gross national expenditure (current US$)",
+          "World/Pair/CHN/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Gross national expenditure (current US$)",
+          "World/Latam/COL/Economy/Gross national expenditure (current US$)",
+          "World/Latam/CRI/Economy/Gross national expenditure (current US$)",
+          "World/Europe/DEU/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Gross national expenditure (current US$)",
+          "World/Europe/ESP/Economy/Gross national expenditure (current US$)",
+          "World/Europe/FRA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/GBR/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/GRC/Economy/Gross national expenditure (current US$)",
+          "World/Europe/HRV/Economy/Gross national expenditure (current US$)",
+          "World/Asia/IDN/Economy/Gross national expenditure (current US$)",
+          "World/Asia/IND/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Gross national expenditure (current US$)",
+          "World/Asia/KOR/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Gross national expenditure (current US$)",
+          "World/Latam/MEX/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/NLD/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross national expenditure (current US$)",
+          "World/Latam/PAN/Economy/Gross national expenditure (current US$)",
+          "World/Latam/PER/Economy/Gross national expenditure (current US$)",
+          "World/Asia/PHL/Economy/Gross national expenditure (current US$)",
+          "World/Europe/POL/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Gross national expenditure (current US$)",
+          "World/Europe/SWE/Economy/Gross national expenditure (current US$)",
+          "World/Asia/THA/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Gross national expenditure (current US$)",
+          "World/Pair/USA/Economy/Gross national expenditure (current US$)",
+          "World/Latam/VEN/Economy/Gross national expenditure (current US$)",
+          "World/Asia/VNM/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/AUT/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/AZE/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/BGD/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/BRA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/CHL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Pair/CHN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/CMR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/COL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/CRI/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/DEU/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/DZA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/EGY/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/ESP/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/FRA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/GBR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/GHA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/GRC/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/IND/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/ISR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/KOR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/MAR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/MEX/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/MOZ/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/NGA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/NLD/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/OMN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/PAN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/PER/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/PHL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/POL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/SAU/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Pair/USA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/VEN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/VNM/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/ZAF/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/ARG/Economy/Gross savings (current US$)",
+          "World/Europe/AUT/Economy/Gross savings (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross savings (current US$)",
+          "World/Asia/BGD/Economy/Gross savings (current US$)",
+          "World/Latam/BRA/Economy/Gross savings (current US$)",
+          "World/Latam/CHL/Economy/Gross savings (current US$)",
+          "World/Pair/CHN/Economy/Gross savings (current US$)",
+          "World/South Africa/CMR/Economy/Gross savings (current US$)",
+          "World/Latam/COL/Economy/Gross savings (current US$)",
+          "World/Latam/CRI/Economy/Gross savings (current US$)",
+          "World/Europe/DEU/Economy/Gross savings (current US$)",
+          "World/North Africa/EGY/Economy/Gross savings (current US$)",
+          "World/Europe/ESP/Economy/Gross savings (current US$)",
+          "World/Europe/FRA/Economy/Gross savings (current US$)",
+          "World/Europe/GBR/Economy/Gross savings (current US$)",
+          "World/South Africa/GHA/Economy/Gross savings (current US$)",
+          "World/Europe/HRV/Economy/Gross savings (current US$)",
+          "World/Asia/IDN/Economy/Gross savings (current US$)",
+          "World/Asia/IND/Economy/Gross savings (current US$)",
+          "World/North Africa/ISR/Economy/Gross savings (current US$)",
+          "World/Asia/KOR/Economy/Gross savings (current US$)",
+          "World/North Africa/MAR/Economy/Gross savings (current US$)",
+          "World/Latam/MEX/Economy/Gross savings (current US$)",
+          "World/South Africa/MOZ/Economy/Gross savings (current US$)",
+          "World/South Africa/NGA/Economy/Gross savings (current US$)",
+          "World/Europe/NLD/Economy/Gross savings (current US$)",
+          "World/Latam/PAN/Economy/Gross savings (current US$)",
+          "World/Latam/PER/Economy/Gross savings (current US$)",
+          "World/Asia/PHL/Economy/Gross savings (current US$)",
+          "World/Europe/POL/Economy/Gross savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross savings (current US$)",
+          "World/South Africa/SEN/Economy/Gross savings (current US$)",
+          "World/Europe/SWE/Economy/Gross savings (current US$)",
+          "World/Asia/THA/Economy/Gross savings (current US$)",
+          "World/North Africa/TUR/Economy/Gross savings (current US$)",
+          "World/Pair/USA/Economy/Gross savings (current US$)",
+          "World/Asia/VNM/Economy/Gross savings (current US$)",
+          "World/South Africa/ZAF/Economy/Gross savings (current US$)",
+          "World/Latam/ARG/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/AUT/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/BGD/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/BRA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/CHL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/CMR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/COL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/CRI/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/DEU/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/DZA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/EGY/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/ESP/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/FRA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/GBR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/GHA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/GRC/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/HRV/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/IDN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/IND/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/ISR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/KOR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/MAR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/MEX/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/MOZ/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/NGA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/NLD/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/PAN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/PER/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/POL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/SEN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/SWE/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/TUR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Pair/USA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/VEN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/VNM/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/YEM/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/ZAF/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/AUT/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/DEU/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/ESP/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/POL/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/VEN/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/ARG/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/AUT/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/AZE/Health/Hospital beds (per 1,000 people)",
+          "World/Asia/BGD/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/BRA/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/CHL/Health/Hospital beds (per 1,000 people)",
+          "World/Pair/CHN/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/CMR/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/COL/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/CRI/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/DEU/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/EGY/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/ESP/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/FRA/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/GBR/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/ISR/Health/Hospital beds (per 1,000 people)",
+          "World/Asia/KOR/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/LBR/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/MEX/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/MOZ/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/NGA/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/NLD/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/OMN/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/SAU/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/TUR/Health/Hospital beds (per 1,000 people)",
+          "World/Pair/USA/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/ZAF/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/ARE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/ICT service exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/CMR/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/DZA/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/GRC/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/HRV/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/IND/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/MEX/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/NGA/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/PAN/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/POL/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/THA/Exports/ICT service exports (BoP, current US$)",
+          "World/Pair/USA/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import unit value index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import unit value index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/ESP/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/FRA/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/GRC/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/HRV/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import unit value index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/POL/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/SWE/Imports/Import unit value index (2000 = 100)",
+          "World/Asia/THA/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Imports/Import value index (2000 = 100)",
+          "World/Latam/ARG/Imports/Import value index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import value index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import value index (2000 = 100)",
+          "World/Latam/BRA/Imports/Import value index (2000 = 100)",
+          "World/Latam/CHL/Imports/Import value index (2000 = 100)",
+          "World/Pair/CHN/Imports/Import value index (2000 = 100)",
+          "World/Latam/COL/Imports/Import value index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import value index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import value index (2000 = 100)",
+          "World/North Africa/DZA/Imports/Import value index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import value index (2000 = 100)",
+          "World/Europe/ESP/Imports/Import value index (2000 = 100)",
+          "World/Europe/FRA/Imports/Import value index (2000 = 100)",
+          "World/Europe/GBR/Imports/Import value index (2000 = 100)",
+          "World/Europe/GRC/Imports/Import value index (2000 = 100)",
+          "World/Europe/HRV/Imports/Import value index (2000 = 100)",
+          "World/Asia/IDN/Imports/Import value index (2000 = 100)",
+          "World/Asia/IND/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/IRQ/Imports/Import value index (2000 = 100)",
+          "World/North Africa/ISR/Imports/Import value index (2000 = 100)",
+          "World/Asia/KOR/Imports/Import value index (2000 = 100)",
+          "World/North Africa/MAR/Imports/Import value index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import value index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import value index (2000 = 100)",
+          "World/South Africa/NGA/Imports/Import value index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/OMN/Imports/Import value index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import value index (2000 = 100)",
+          "World/Latam/PER/Imports/Import value index (2000 = 100)",
+          "World/Asia/PHL/Imports/Import value index (2000 = 100)",
+          "World/Europe/POL/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/SAU/Imports/Import value index (2000 = 100)",
+          "World/South Africa/SEN/Imports/Import value index (2000 = 100)",
+          "World/Europe/SWE/Imports/Import value index (2000 = 100)",
+          "World/Asia/THA/Imports/Import value index (2000 = 100)",
+          "World/North Africa/TUR/Imports/Import value index (2000 = 100)",
+          "World/Pair/USA/Imports/Import value index (2000 = 100)",
+          "World/Asia/VNM/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/YEM/Imports/Import value index (2000 = 100)",
+          "World/South Africa/ZAF/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Imports/Import volume index (2000 = 100)",
+          "World/Latam/ARG/Imports/Import volume index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import volume index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import volume index (2000 = 100)",
+          "World/Latam/BRA/Imports/Import volume index (2000 = 100)",
+          "World/Latam/CHL/Imports/Import volume index (2000 = 100)",
+          "World/Pair/CHN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/COL/Imports/Import volume index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import volume index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/DZA/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import volume index (2000 = 100)",
+          "World/Europe/GBR/Imports/Import volume index (2000 = 100)",
+          "World/Asia/IDN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/IND/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/ISR/Imports/Import volume index (2000 = 100)",
+          "World/Asia/KOR/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/MAR/Imports/Import volume index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/NGA/Imports/Import volume index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/OMN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/PER/Imports/Import volume index (2000 = 100)",
+          "World/Asia/PHL/Imports/Import volume index (2000 = 100)",
+          "World/Europe/POL/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/SAU/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/SEN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/THA/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/TUR/Imports/Import volume index (2000 = 100)",
+          "World/Pair/USA/Imports/Import volume index (2000 = 100)",
+          "World/Latam/VEN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/VNM/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/YEM/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/ZAF/Imports/Import volume index (2000 = 100)",
+          "World/Latam/ARG/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/AUT/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/BGD/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/BRA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/CHL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Pair/CHN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/CMR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/COL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/CRI/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/DEU/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/ESP/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/FRA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/GBR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/GRC/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/HRV/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/IDN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/IND/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/KOR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/MEX/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/NLD/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/PAN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/PER/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/PHL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/POL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/SAU/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/SWE/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/THA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Pair/USA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/VNM/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/YEM/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/ARE/Imports/Imports of goods and services (current US$)",
+          "World/Latam/ARG/Imports/Imports of goods and services (current US$)",
+          "World/Europe/AUT/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/AZE/Imports/Imports of goods and services (current US$)",
+          "World/Asia/BGD/Imports/Imports of goods and services (current US$)",
+          "World/Latam/BRA/Imports/Imports of goods and services (current US$)",
+          "World/Latam/CHL/Imports/Imports of goods and services (current US$)",
+          "World/Pair/CHN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/COL/Imports/Imports of goods and services (current US$)",
+          "World/Latam/CRI/Imports/Imports of goods and services (current US$)",
+          "World/Europe/DEU/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/DZA/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/EGY/Imports/Imports of goods and services (current US$)",
+          "World/Europe/ESP/Imports/Imports of goods and services (current US$)",
+          "World/Europe/FRA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/GBR/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/GHA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/GRC/Imports/Imports of goods and services (current US$)",
+          "World/Europe/HRV/Imports/Imports of goods and services (current US$)",
+          "World/Asia/IDN/Imports/Imports of goods and services (current US$)",
+          "World/Asia/IND/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/IRQ/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/ISR/Imports/Imports of goods and services (current US$)",
+          "World/Asia/KOR/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/MAR/Imports/Imports of goods and services (current US$)",
+          "World/Latam/MEX/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/MOZ/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/NGA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/NLD/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/OMN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/PAN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/PER/Imports/Imports of goods and services (current US$)",
+          "World/Asia/PHL/Imports/Imports of goods and services (current US$)",
+          "World/Europe/POL/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/QAT/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/SAU/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/SEN/Imports/Imports of goods and services (current US$)",
+          "World/Europe/SWE/Imports/Imports of goods and services (current US$)",
+          "World/Asia/THA/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/TUR/Imports/Imports of goods and services (current US$)",
+          "World/Pair/USA/Imports/Imports of goods and services (current US$)",
+          "World/Latam/VEN/Imports/Imports of goods and services (current US$)",
+          "World/Asia/VNM/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/ZAF/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/ARE/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/AUT/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/AZE/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/BRA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Pair/CHN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/CMR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/CRI/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/EGY/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/ESP/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/GBR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/GHA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/HRV/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/IDN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/IND/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/IRQ/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/ISR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/LBR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/MOZ/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/OMN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/PER/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/POL/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/QAT/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/SAU/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/SEN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/THA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/TUR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Pair/USA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/VNM/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/YEM/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/ARE/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/ARG/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/AUT/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/AZE/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/BGD/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/BRA/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/CHL/Economy/Industry (including construction), value added (current US$)",
+          "World/Pair/CHN/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/CMR/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/COL/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/CRI/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/DEU/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/DZA/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/EGY/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/ESP/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/FRA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/GBR/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/GHA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/GRC/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/HRV/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/IDN/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/IND/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/ISR/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/KOR/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/LBR/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/MAR/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/MEX/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/MOZ/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/NGA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/NLD/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/OMN/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/PAN/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/PER/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/PHL/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/POL/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/SAU/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/SEN/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/SWE/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/THA/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/TUR/Economy/Industry (including construction), value added (current US$)",
+          "World/Pair/USA/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/VEN/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/VNM/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/YEM/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/ZAF/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/BRA/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Pair/CHN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/COL/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/CRI/Mortality/Intentional homicides (per 100,000 people)",
+          "World/North Africa/EGY/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/ESP/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/GBR/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/IDN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/IND/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/IRQ/Mortality/Intentional homicides (per 100,000 people)",
+          "World/North Africa/ISR/Mortality/Intentional homicides (per 100,000 people)",
+          "World/South Africa/MOZ/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/NLD/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/PAN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/PER/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/POL/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/THA/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/VNM/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/YEM/Mortality/Intentional homicides (per 100,000 people)",
+          "World/South Africa/ZAF/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/ARE/Migration/International migrant stock (% of population)",
+          "World/Europe/AUT/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/AZE/Migration/International migrant stock (% of population)",
+          "World/Latam/CHL/Migration/International migrant stock (% of population)",
+          "World/Pair/CHN/Migration/International migrant stock (% of population)",
+          "World/Europe/DEU/Migration/International migrant stock (% of population)",
+          "World/North Africa/DZA/Migration/International migrant stock (% of population)",
+          "World/Europe/ESP/Migration/International migrant stock (% of population)",
+          "World/Europe/FRA/Migration/International migrant stock (% of population)",
+          "World/Europe/GBR/Migration/International migrant stock (% of population)",
+          "World/Europe/GRC/Migration/International migrant stock (% of population)",
+          "World/Europe/HRV/Migration/International migrant stock (% of population)",
+          "World/Asia/IND/Migration/International migrant stock (% of population)",
+          "World/North Africa/ISR/Migration/International migrant stock (% of population)",
+          "World/Asia/KOR/Migration/International migrant stock (% of population)",
+          "World/North Africa/MAR/Migration/International migrant stock (% of population)",
+          "World/Latam/MEX/Migration/International migrant stock (% of population)",
+          "World/South Africa/MOZ/Migration/International migrant stock (% of population)",
+          "World/South Africa/NGA/Migration/International migrant stock (% of population)",
+          "World/Europe/NLD/Migration/International migrant stock (% of population)",
+          "World/Latam/PAN/Migration/International migrant stock (% of population)",
+          "World/Latam/PER/Migration/International migrant stock (% of population)",
+          "World/Europe/POL/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/SAU/Migration/International migrant stock (% of population)",
+          "World/South Africa/SEN/Migration/International migrant stock (% of population)",
+          "World/Europe/SWE/Migration/International migrant stock (% of population)",
+          "World/Asia/THA/Migration/International migrant stock (% of population)",
+          "World/North Africa/TUR/Migration/International migrant stock (% of population)",
+          "World/Pair/USA/Migration/International migrant stock (% of population)",
+          "World/Asia/VNM/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/YEM/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/ARE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/AUT/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/AZE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/BRA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/CHL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Pair/CHN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/COL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/CRI/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/DEU/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/DZA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/EGY/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/GHA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/GRC/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/IDN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/IND/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/IRQ/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/ISR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/KOR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/LBR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/MEX/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/MOZ/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/NGA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/NLD/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/OMN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/PAN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/PER/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/PHL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/POL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/QAT/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/SWE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/TUR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Pair/USA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/VEN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/VNM/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/YEM/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/ZAF/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/ARE/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/ARG/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/AZE/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/BGD/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/CHL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Pair/CHN/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/CMR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/COL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/CRI/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/EGY/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/ESP/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/GBR/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/GHA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/GRC/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/HRV/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/IDN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/IND/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/ISR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/KOR/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/LBR/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/MAR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/MEX/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/MOZ/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/NGA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/NLD/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/PAN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/PER/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/PHL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/POL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/QAT/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/SAU/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/SEN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/THA/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/TUR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Pair/USA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/VNM/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/YEM/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/ARE/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/ARG/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/AZE/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/BGD/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/CHL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Pair/CHN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/CMR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/COL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/CRI/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/EGY/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/ESP/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/GBR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/GHA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/GRC/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/HRV/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/IDN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/IND/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/ISR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/KOR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/LBR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/MAR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/MEX/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/MOZ/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/NGA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/NLD/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/PAN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/PER/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/PHL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/POL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/QAT/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/SAU/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/SEN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/THA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/TUR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Pair/USA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/VEN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/VNM/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/YEM/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/ARE/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/ARG/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/AUT/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/AZE/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/BGD/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/BRA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Pair/CHN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/COL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/CRI/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/GBR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/IDN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/IND/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/ISR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/KOR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/LBR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/MEX/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/NLD/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/PAN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/PER/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/PHL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/POL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/SAU/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/THA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Pair/USA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/VEN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/VNM/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/ZAF/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/ARE/Health/Low-birthweight babies (% of births)",
+          "World/Asia/BGD/Health/Low-birthweight babies (% of births)",
+          "World/Latam/BRA/Health/Low-birthweight babies (% of births)",
+          "World/Latam/CHL/Health/Low-birthweight babies (% of births)",
+          "World/Pair/CHN/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/CMR/Health/Low-birthweight babies (% of births)",
+          "World/Latam/COL/Health/Low-birthweight babies (% of births)",
+          "World/Latam/CRI/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/DZA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/ESP/Health/Low-birthweight babies (% of births)",
+          "World/Europe/FRA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/GBR/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/GHA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/HRV/Health/Low-birthweight babies (% of births)",
+          "World/Asia/IDN/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/ISR/Health/Low-birthweight babies (% of births)",
+          "World/Asia/KOR/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/MAR/Health/Low-birthweight babies (% of births)",
+          "World/Latam/MEX/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/MOZ/Health/Low-birthweight babies (% of births)",
+          "World/Europe/NLD/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/OMN/Health/Low-birthweight babies (% of births)",
+          "World/Latam/PAN/Health/Low-birthweight babies (% of births)",
+          "World/Latam/PER/Health/Low-birthweight babies (% of births)",
+          "World/Asia/PHL/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/QAT/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/SEN/Health/Low-birthweight babies (% of births)",
+          "World/Asia/THA/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/TUR/Health/Low-birthweight babies (% of births)",
+          "World/Asia/VNM/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/ZAF/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/ARE/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/BGD/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/CHL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Pair/CHN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/COL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/South Africa/GHA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Europe/HRV/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/IDN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/IND/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/North Africa/ISR/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/KOR/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/MEX/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/OMN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/PAN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Europe/POL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/QAT/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/SAU/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/THA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Pair/USA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/VNM/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/South Africa/ZAF/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/ARE/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/ARG/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/AZE/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/BGD/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/CHL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Pair/CHN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/CMR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/CRI/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/DZA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/EGY/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/ESP/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/GBR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/GHA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/HRV/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/IDN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/IND/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/ISR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/KOR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/LBR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/MAR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/MEX/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/MOZ/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/NGA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/NLD/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/PAN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/PER/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/PHL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/POL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/QAT/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/SAU/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/SEN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/THA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/TUR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Pair/USA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/VNM/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/YEM/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports (current US$)",
+          "World/Europe/AUT/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/AZE/Exports/Merchandise exports (current US$)",
+          "World/Asia/BGD/Exports/Merchandise exports (current US$)",
+          "World/Latam/BRA/Exports/Merchandise exports (current US$)",
+          "World/Latam/CHL/Exports/Merchandise exports (current US$)",
+          "World/Pair/CHN/Exports/Merchandise exports (current US$)",
+          "World/Latam/COL/Exports/Merchandise exports (current US$)",
+          "World/Latam/CRI/Exports/Merchandise exports (current US$)",
+          "World/Europe/DEU/Exports/Merchandise exports (current US$)",
+          "World/North Africa/EGY/Exports/Merchandise exports (current US$)",
+          "World/Europe/ESP/Exports/Merchandise exports (current US$)",
+          "World/Europe/FRA/Exports/Merchandise exports (current US$)",
+          "World/Europe/GBR/Exports/Merchandise exports (current US$)",
+          "World/South Africa/GHA/Exports/Merchandise exports (current US$)",
+          "World/Asia/IDN/Exports/Merchandise exports (current US$)",
+          "World/Asia/IND/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports (current US$)",
+          "World/North Africa/ISR/Exports/Merchandise exports (current US$)",
+          "World/Asia/KOR/Exports/Merchandise exports (current US$)",
+          "World/North Africa/MAR/Exports/Merchandise exports (current US$)",
+          "World/Latam/MEX/Exports/Merchandise exports (current US$)",
+          "World/South Africa/MOZ/Exports/Merchandise exports (current US$)",
+          "World/Europe/NLD/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/OMN/Exports/Merchandise exports (current US$)",
+          "World/Latam/PAN/Exports/Merchandise exports (current US$)",
+          "World/Latam/PER/Exports/Merchandise exports (current US$)",
+          "World/Asia/PHL/Exports/Merchandise exports (current US$)",
+          "World/Europe/POL/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/SAU/Exports/Merchandise exports (current US$)",
+          "World/South Africa/SEN/Exports/Merchandise exports (current US$)",
+          "World/Europe/SWE/Exports/Merchandise exports (current US$)",
+          "World/Asia/THA/Exports/Merchandise exports (current US$)",
+          "World/North Africa/TUR/Exports/Merchandise exports (current US$)",
+          "World/Pair/USA/Exports/Merchandise exports (current US$)",
+          "World/Latam/VEN/Exports/Merchandise exports (current US$)",
+          "World/Asia/VNM/Exports/Merchandise exports (current US$)",
+          "World/South Africa/ZAF/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/AUT/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/AZE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/BGD/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/BRA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/CHL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Pair/CHN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/COL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/CRI/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/DEU/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/DZA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/EGY/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/ESP/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/FRA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/GBR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/GHA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/IDN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/IND/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/KOR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/MAR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/MEX/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/MOZ/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/NGA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/NLD/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/OMN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/PER/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/PHL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/POL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/SEN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/SWE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/THA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/TUR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Pair/USA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/VEN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/VNM/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/ZAF/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Persian Gulf/AZE/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/BGD/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/CHL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Pair/CHN/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/COL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/DEU/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/ESP/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/GBR/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/GRC/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/IDN/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/North Africa/ISR/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/KOR/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/South Africa/LBR/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/MEX/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/South Africa/MOZ/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/South Africa/NGA/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/NLD/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/PAN/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/PER/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/PHL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/POL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/SWE/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/THA/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Pair/USA/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/VEN/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/South Africa/ZAF/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/AUT/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/BRA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/CHL/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Pair/CHN/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/COL/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/DEU/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/ESP/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/FRA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/GRC/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Asia/IDN/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Asia/IND/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/North Africa/ISR/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Asia/KOR/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/MEX/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/South Africa/MOZ/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/South Africa/NGA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/NLD/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/OMN/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/PAN/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/PER/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Asia/PHL/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/SAU/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Pair/USA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/South Africa/ZAF/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/ARE/Imports/Merchandise imports (current US$)",
+          "World/Latam/ARG/Imports/Merchandise imports (current US$)",
+          "World/Europe/AUT/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/AZE/Imports/Merchandise imports (current US$)",
+          "World/Asia/BGD/Imports/Merchandise imports (current US$)",
+          "World/Latam/BRA/Imports/Merchandise imports (current US$)",
+          "World/Latam/CHL/Imports/Merchandise imports (current US$)",
+          "World/Pair/CHN/Imports/Merchandise imports (current US$)",
+          "World/Latam/COL/Imports/Merchandise imports (current US$)",
+          "World/Latam/CRI/Imports/Merchandise imports (current US$)",
+          "World/Europe/DEU/Imports/Merchandise imports (current US$)",
+          "World/North Africa/DZA/Imports/Merchandise imports (current US$)",
+          "World/North Africa/EGY/Imports/Merchandise imports (current US$)",
+          "World/Europe/ESP/Imports/Merchandise imports (current US$)",
+          "World/Europe/FRA/Imports/Merchandise imports (current US$)",
+          "World/Europe/GBR/Imports/Merchandise imports (current US$)",
+          "World/Europe/GRC/Imports/Merchandise imports (current US$)",
+          "World/Europe/HRV/Imports/Merchandise imports (current US$)",
+          "World/Asia/IDN/Imports/Merchandise imports (current US$)",
+          "World/Asia/IND/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/IRQ/Imports/Merchandise imports (current US$)",
+          "World/North Africa/ISR/Imports/Merchandise imports (current US$)",
+          "World/Asia/KOR/Imports/Merchandise imports (current US$)",
+          "World/North Africa/MAR/Imports/Merchandise imports (current US$)",
+          "World/Latam/MEX/Imports/Merchandise imports (current US$)",
+          "World/South Africa/MOZ/Imports/Merchandise imports (current US$)",
+          "World/South Africa/NGA/Imports/Merchandise imports (current US$)",
+          "World/Europe/NLD/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports (current US$)",
+          "World/Latam/PAN/Imports/Merchandise imports (current US$)",
+          "World/Latam/PER/Imports/Merchandise imports (current US$)",
+          "World/Asia/PHL/Imports/Merchandise imports (current US$)",
+          "World/Europe/POL/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports (current US$)",
+          "World/South Africa/SEN/Imports/Merchandise imports (current US$)",
+          "World/Europe/SWE/Imports/Merchandise imports (current US$)",
+          "World/Asia/THA/Imports/Merchandise imports (current US$)",
+          "World/North Africa/TUR/Imports/Merchandise imports (current US$)",
+          "World/Pair/USA/Imports/Merchandise imports (current US$)",
+          "World/Asia/VNM/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/YEM/Imports/Merchandise imports (current US$)",
+          "World/South Africa/ZAF/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/ARE/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/ARG/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/AUT/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Persian Gulf/AZE/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/BGD/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/BRA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/CHL/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Pair/CHN/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/CMR/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/COL/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/CRI/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/DEU/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/North Africa/DZA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/North Africa/EGY/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/ESP/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/FRA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/GBR/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/GHA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/GRC/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/HRV/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/IDN/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/IND/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Persian Gulf/IRQ/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/North Africa/ISR/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/KOR/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/North Africa/MAR/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/MEX/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/MOZ/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/NGA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/NLD/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/PAN/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/PER/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/PHL/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/POL/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/SEN/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/SWE/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/THA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/North Africa/TUR/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Pair/USA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/VNM/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/ZAF/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/BGD/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/BRA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Pair/CHN/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Europe/GRC/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/IDN/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/KOR/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/South Africa/NGA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/PHL/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/THA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/VEN/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/VNM/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Persian Gulf/YEM/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Europe/AUT/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Persian Gulf/AZE/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/BGD/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/BRA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/CHL/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Pair/CHN/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/South Africa/CMR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/DEU/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/EGY/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/ESP/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/FRA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/GBR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/South Africa/GHA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/GRC/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/IDN/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/IND/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/ISR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/KOR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/MAR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/South Africa/MOZ/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/South Africa/NGA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/NLD/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/PAN/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/PER/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/PHL/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/POL/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/SWE/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/THA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/VEN/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/VNM/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/ARG/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/BRA/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/CHL/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/EGY/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/ESP/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/GBR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/ISR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/South Africa/LBR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/MAR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/PAN/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/PER/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/POL/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/SWE/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/VEN/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/South Africa/ZAF/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/AUT/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/AZE/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/BGD/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Pair/CHN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/CMR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/EGY/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/ESP/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/FRA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/GBR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/GHA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/GRC/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/IND/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/ISR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/KOR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/MAR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/MOZ/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/NLD/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/PER/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/POL/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/SEN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/YEM/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/AUT/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Methane emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/COL/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/DEU/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/ESP/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/FRA/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Methane emissions (% change from 1990)",
+          "World/South Africa/GHA/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/KOR/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/MAR/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/MEX/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/PER/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/PHL/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/POL/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/SAU/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/TUR/Environment/Methane emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/ARG/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/COL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/IND/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/PER/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/POL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Pair/USA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/COL/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/IRQ/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/POL/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/THA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Military/Military expenditure (current USD)",
+          "World/Europe/AUT/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/AZE/Military/Military expenditure (current USD)",
+          "World/Asia/BGD/Military/Military expenditure (current USD)",
+          "World/Latam/BRA/Military/Military expenditure (current USD)",
+          "World/Latam/CHL/Military/Military expenditure (current USD)",
+          "World/Pair/CHN/Military/Military expenditure (current USD)",
+          "World/South Africa/CMR/Military/Military expenditure (current USD)",
+          "World/Latam/COL/Military/Military expenditure (current USD)",
+          "World/North Africa/DZA/Military/Military expenditure (current USD)",
+          "World/North Africa/EGY/Military/Military expenditure (current USD)",
+          "World/Europe/ESP/Military/Military expenditure (current USD)",
+          "World/Europe/FRA/Military/Military expenditure (current USD)",
+          "World/South Africa/GHA/Military/Military expenditure (current USD)",
+          "World/Europe/GRC/Military/Military expenditure (current USD)",
+          "World/Asia/IDN/Military/Military expenditure (current USD)",
+          "World/Asia/IND/Military/Military expenditure (current USD)",
+          "World/North Africa/ISR/Military/Military expenditure (current USD)",
+          "World/Asia/KOR/Military/Military expenditure (current USD)",
+          "World/North Africa/MAR/Military/Military expenditure (current USD)",
+          "World/Latam/MEX/Military/Military expenditure (current USD)",
+          "World/South Africa/MOZ/Military/Military expenditure (current USD)",
+          "World/South Africa/NGA/Military/Military expenditure (current USD)",
+          "World/Europe/NLD/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/OMN/Military/Military expenditure (current USD)",
+          "World/Latam/PER/Military/Military expenditure (current USD)",
+          "World/Asia/PHL/Military/Military expenditure (current USD)",
+          "World/Europe/POL/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/SAU/Military/Military expenditure (current USD)",
+          "World/South Africa/SEN/Military/Military expenditure (current USD)",
+          "World/Asia/THA/Military/Military expenditure (current USD)",
+          "World/North Africa/TUR/Military/Military expenditure (current USD)",
+          "World/Pair/USA/Military/Military expenditure (current USD)",
+          "World/Asia/VNM/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/YEM/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/ARE/Internet/Mobile cellular subscriptions",
+          "World/Latam/BRA/Internet/Mobile cellular subscriptions",
+          "World/Latam/CHL/Internet/Mobile cellular subscriptions",
+          "World/Pair/CHN/Internet/Mobile cellular subscriptions",
+          "World/Europe/DEU/Internet/Mobile cellular subscriptions",
+          "World/North Africa/DZA/Internet/Mobile cellular subscriptions",
+          "World/North Africa/EGY/Internet/Mobile cellular subscriptions",
+          "World/Europe/FRA/Internet/Mobile cellular subscriptions",
+          "World/Europe/GBR/Internet/Mobile cellular subscriptions",
+          "World/Europe/HRV/Internet/Mobile cellular subscriptions",
+          "World/Asia/IDN/Internet/Mobile cellular subscriptions",
+          "World/North Africa/ISR/Internet/Mobile cellular subscriptions",
+          "World/Asia/KOR/Internet/Mobile cellular subscriptions",
+          "World/North Africa/MAR/Internet/Mobile cellular subscriptions",
+          "World/Latam/MEX/Internet/Mobile cellular subscriptions",
+          "World/Europe/NLD/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/OMN/Internet/Mobile cellular subscriptions",
+          "World/Latam/PER/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/QAT/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/SAU/Internet/Mobile cellular subscriptions",
+          "World/Europe/SWE/Internet/Mobile cellular subscriptions",
+          "World/Asia/THA/Internet/Mobile cellular subscriptions",
+          "World/North Africa/TUR/Internet/Mobile cellular subscriptions",
+          "World/Pair/USA/Internet/Mobile cellular subscriptions",
+          "World/South Africa/ZAF/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/ARE/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/BRA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/CHL/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Pair/CHN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/DEU/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/DZA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/EGY/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/FRA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/HRV/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/IDN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/ISR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/KOR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/MAR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/MEX/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/NLD/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/OMN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/PER/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/QAT/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/SAU/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/SWE/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/THA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/TUR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Pair/USA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/VEN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/South Africa/ZAF/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/ARE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/AZE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/BGD/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/CHL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Pair/CHN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/CMR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/COL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/CRI/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/DEU/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/DZA/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/EGY/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/FRA/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/GRC/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/HRV/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/IND/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/KOR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/LBR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/MAR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/MEX/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/OMN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/PAN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/PER/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/POL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/QAT/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/SAU/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/SEN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/SWE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/TUR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/VEN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/VNM/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/AUT/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/AZE/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/BGD/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Pair/CHN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/CMR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/DEU/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/DZA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/ESP/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/GBR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/GRC/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/HRV/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/IDN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/ISR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/KOR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/MAR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Latam/MEX/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/MOZ/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/NGA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/NLD/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/OMN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/PHL/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/POL/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/QAT/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/SAU/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/SWE/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/THA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/TUR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Latam/VEN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/VNM/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/YEM/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/ARE/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/AUT/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Latam/BRA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Latam/COL/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/North Africa/DZA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/North Africa/EGY/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/ESP/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/GHA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/KOR/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/LBR/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/NLD/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/SWE/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/VNM/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/ZAF/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/BGD/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/BRA/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/COL/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/CRI/Economy/Net primary income (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Net primary income (BoP, current US$)",
+          "World/Europe/FRA/Economy/Net primary income (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/IDN/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/IND/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/KOR/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/MEX/Economy/Net primary income (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/PAN/Economy/Net primary income (BoP, current US$)",
+          "World/Europe/POL/Economy/Net primary income (BoP, current US$)",
+          "World/Persian Gulf/QAT/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/THA/Economy/Net primary income (BoP, current US$)",
+          "World/North Africa/TUR/Economy/Net primary income (BoP, current US$)",
+          "World/Pair/USA/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/VEN/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/VNM/Economy/Net primary income (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/BGD/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/BRA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/COL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/CRI/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/North Africa/EGY/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Europe/FRA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/IND/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/KOR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/LBR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/MEX/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/NGA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/PAN/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/PHL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Europe/POL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/THA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/North Africa/TUR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Pair/USA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/VNM/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/ZAF/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/BGD/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/COL/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/CRI/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/DEU/Economy/Net secondary income (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/ESP/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/FRA/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/GBR/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/IND/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/KOR/Economy/Net secondary income (BoP, current US$)",
+          "World/North Africa/MAR/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/MEX/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/NGA/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/PER/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/PHL/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/POL/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/SAU/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/SWE/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/THA/Economy/Net secondary income (BoP, current US$)",
+          "World/Pair/USA/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/VEN/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/VNM/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/ARE/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/AUT/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/COL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/CRI/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/DZA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/EGY/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/ESP/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/FRA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/GBR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/GHA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/IND/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/ISR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/MAR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/MEX/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/NGA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/PAN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/PER/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/PHL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/POL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/QAT/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/SEN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/SWE/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/THA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Pair/USA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/VNM/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/YEM/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/ZAF/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/AUT/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/COL/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/CRI/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/ESP/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/GRC/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/SWE/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/ZAF/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CHL/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/THA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/VEN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/BRA/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 10-14 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/IDN/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/IRQ/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 10-14 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/ARE/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/ARG/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 15-19 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/PHL/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 15-19 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/VEN/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 20-24 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/PHL/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/VEN/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/ARE/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/ARG/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/BRA/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 5-9 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/EGY/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/GHA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/IDN/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 5-9 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/ARE/Mortality/Number of infant deaths",
+          "World/Latam/ARG/Mortality/Number of infant deaths",
+          "World/Europe/AUT/Mortality/Number of infant deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of infant deaths",
+          "World/Asia/BGD/Mortality/Number of infant deaths",
+          "World/Latam/BRA/Mortality/Number of infant deaths",
+          "World/Latam/CHL/Mortality/Number of infant deaths",
+          "World/Pair/CHN/Mortality/Number of infant deaths",
+          "World/South Africa/CMR/Mortality/Number of infant deaths",
+          "World/Latam/COL/Mortality/Number of infant deaths",
+          "World/Latam/CRI/Mortality/Number of infant deaths",
+          "World/North Africa/EGY/Mortality/Number of infant deaths",
+          "World/Europe/FRA/Mortality/Number of infant deaths",
+          "World/Europe/GBR/Mortality/Number of infant deaths",
+          "World/South Africa/GHA/Mortality/Number of infant deaths",
+          "World/Europe/GRC/Mortality/Number of infant deaths",
+          "World/Europe/HRV/Mortality/Number of infant deaths",
+          "World/Asia/IDN/Mortality/Number of infant deaths",
+          "World/Asia/IND/Mortality/Number of infant deaths",
+          "World/Persian Gulf/IRQ/Mortality/Number of infant deaths",
+          "World/North Africa/ISR/Mortality/Number of infant deaths",
+          "World/Asia/KOR/Mortality/Number of infant deaths",
+          "World/South Africa/LBR/Mortality/Number of infant deaths",
+          "World/North Africa/MAR/Mortality/Number of infant deaths",
+          "World/Latam/MEX/Mortality/Number of infant deaths",
+          "World/South Africa/MOZ/Mortality/Number of infant deaths",
+          "World/Europe/NLD/Mortality/Number of infant deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of infant deaths",
+          "World/Latam/PAN/Mortality/Number of infant deaths",
+          "World/Latam/PER/Mortality/Number of infant deaths",
+          "World/Asia/PHL/Mortality/Number of infant deaths",
+          "World/Europe/POL/Mortality/Number of infant deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of infant deaths",
+          "World/South Africa/SEN/Mortality/Number of infant deaths",
+          "World/Europe/SWE/Mortality/Number of infant deaths",
+          "World/Asia/THA/Mortality/Number of infant deaths",
+          "World/North Africa/TUR/Mortality/Number of infant deaths",
+          "World/Pair/USA/Mortality/Number of infant deaths",
+          "World/Asia/VNM/Mortality/Number of infant deaths",
+          "World/Latam/ARG/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of maternal deaths",
+          "World/Asia/BGD/Mortality/Number of maternal deaths",
+          "World/Latam/BRA/Mortality/Number of maternal deaths",
+          "World/Latam/CHL/Mortality/Number of maternal deaths",
+          "World/Pair/CHN/Mortality/Number of maternal deaths",
+          "World/South Africa/CMR/Mortality/Number of maternal deaths",
+          "World/Latam/COL/Mortality/Number of maternal deaths",
+          "World/Latam/CRI/Mortality/Number of maternal deaths",
+          "World/North Africa/DZA/Mortality/Number of maternal deaths",
+          "World/North Africa/EGY/Mortality/Number of maternal deaths",
+          "World/Europe/FRA/Mortality/Number of maternal deaths",
+          "World/Europe/GBR/Mortality/Number of maternal deaths",
+          "World/Europe/HRV/Mortality/Number of maternal deaths",
+          "World/Asia/IDN/Mortality/Number of maternal deaths",
+          "World/Asia/IND/Mortality/Number of maternal deaths",
+          "World/North Africa/ISR/Mortality/Number of maternal deaths",
+          "World/Asia/KOR/Mortality/Number of maternal deaths",
+          "World/North Africa/MAR/Mortality/Number of maternal deaths",
+          "World/Latam/MEX/Mortality/Number of maternal deaths",
+          "World/South Africa/MOZ/Mortality/Number of maternal deaths",
+          "World/South Africa/NGA/Mortality/Number of maternal deaths",
+          "World/Europe/NLD/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of maternal deaths",
+          "World/Latam/PAN/Mortality/Number of maternal deaths",
+          "World/Latam/PER/Mortality/Number of maternal deaths",
+          "World/Asia/PHL/Mortality/Number of maternal deaths",
+          "World/Europe/POL/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of maternal deaths",
+          "World/South Africa/SEN/Mortality/Number of maternal deaths",
+          "World/Asia/THA/Mortality/Number of maternal deaths",
+          "World/North Africa/TUR/Mortality/Number of maternal deaths",
+          "World/Pair/USA/Mortality/Number of maternal deaths",
+          "World/Asia/VNM/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/YEM/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/ARE/Mortality/Number of neonatal deaths",
+          "World/Latam/ARG/Mortality/Number of neonatal deaths",
+          "World/Europe/AUT/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of neonatal deaths",
+          "World/Asia/BGD/Mortality/Number of neonatal deaths",
+          "World/Latam/BRA/Mortality/Number of neonatal deaths",
+          "World/Latam/CHL/Mortality/Number of neonatal deaths",
+          "World/Pair/CHN/Mortality/Number of neonatal deaths",
+          "World/Latam/COL/Mortality/Number of neonatal deaths",
+          "World/Latam/CRI/Mortality/Number of neonatal deaths",
+          "World/North Africa/DZA/Mortality/Number of neonatal deaths",
+          "World/North Africa/EGY/Mortality/Number of neonatal deaths",
+          "World/Europe/FRA/Mortality/Number of neonatal deaths",
+          "World/Europe/GBR/Mortality/Number of neonatal deaths",
+          "World/South Africa/GHA/Mortality/Number of neonatal deaths",
+          "World/Europe/GRC/Mortality/Number of neonatal deaths",
+          "World/Europe/HRV/Mortality/Number of neonatal deaths",
+          "World/Asia/IDN/Mortality/Number of neonatal deaths",
+          "World/Asia/IND/Mortality/Number of neonatal deaths",
+          "World/North Africa/ISR/Mortality/Number of neonatal deaths",
+          "World/Asia/KOR/Mortality/Number of neonatal deaths",
+          "World/North Africa/MAR/Mortality/Number of neonatal deaths",
+          "World/Latam/MEX/Mortality/Number of neonatal deaths",
+          "World/South Africa/MOZ/Mortality/Number of neonatal deaths",
+          "World/South Africa/NGA/Mortality/Number of neonatal deaths",
+          "World/Europe/NLD/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of neonatal deaths",
+          "World/Latam/PAN/Mortality/Number of neonatal deaths",
+          "World/Latam/PER/Mortality/Number of neonatal deaths",
+          "World/Asia/PHL/Mortality/Number of neonatal deaths",
+          "World/Europe/POL/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/QAT/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of neonatal deaths",
+          "World/South Africa/SEN/Mortality/Number of neonatal deaths",
+          "World/Asia/THA/Mortality/Number of neonatal deaths",
+          "World/North Africa/TUR/Mortality/Number of neonatal deaths",
+          "World/Pair/USA/Mortality/Number of neonatal deaths",
+          "World/Asia/VNM/Mortality/Number of neonatal deaths",
+          "World/South Africa/ZAF/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/ARE/Mortality/Number of under-five deaths",
+          "World/Latam/ARG/Mortality/Number of under-five deaths",
+          "World/Europe/AUT/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of under-five deaths",
+          "World/Asia/BGD/Mortality/Number of under-five deaths",
+          "World/Latam/BRA/Mortality/Number of under-five deaths",
+          "World/Latam/CHL/Mortality/Number of under-five deaths",
+          "World/Pair/CHN/Mortality/Number of under-five deaths",
+          "World/South Africa/CMR/Mortality/Number of under-five deaths",
+          "World/Latam/COL/Mortality/Number of under-five deaths",
+          "World/Latam/CRI/Mortality/Number of under-five deaths",
+          "World/North Africa/EGY/Mortality/Number of under-five deaths",
+          "World/Europe/FRA/Mortality/Number of under-five deaths",
+          "World/Europe/GBR/Mortality/Number of under-five deaths",
+          "World/South Africa/GHA/Mortality/Number of under-five deaths",
+          "World/Europe/GRC/Mortality/Number of under-five deaths",
+          "World/Europe/HRV/Mortality/Number of under-five deaths",
+          "World/Asia/IDN/Mortality/Number of under-five deaths",
+          "World/Asia/IND/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/IRQ/Mortality/Number of under-five deaths",
+          "World/North Africa/ISR/Mortality/Number of under-five deaths",
+          "World/Asia/KOR/Mortality/Number of under-five deaths",
+          "World/South Africa/LBR/Mortality/Number of under-five deaths",
+          "World/North Africa/MAR/Mortality/Number of under-five deaths",
+          "World/Latam/MEX/Mortality/Number of under-five deaths",
+          "World/South Africa/MOZ/Mortality/Number of under-five deaths",
+          "World/Europe/NLD/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of under-five deaths",
+          "World/Latam/PAN/Mortality/Number of under-five deaths",
+          "World/Latam/PER/Mortality/Number of under-five deaths",
+          "World/Asia/PHL/Mortality/Number of under-five deaths",
+          "World/Europe/POL/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of under-five deaths",
+          "World/South Africa/SEN/Mortality/Number of under-five deaths",
+          "World/Europe/SWE/Mortality/Number of under-five deaths",
+          "World/Asia/THA/Mortality/Number of under-five deaths",
+          "World/North Africa/TUR/Mortality/Number of under-five deaths",
+          "World/Pair/USA/Mortality/Number of under-five deaths",
+          "World/Asia/VNM/Mortality/Number of under-five deaths",
+          "World/Latam/ARG/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/AUT/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/AZE/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/BGD/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/BRA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/CHL/Health/Nurses and midwives (per 1,000 people)",
+          "World/Pair/CHN/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/CMR/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/CRI/Health/Nurses and midwives (per 1,000 people)",
+          "World/North Africa/EGY/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/ESP/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/FRA/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/GHA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/HRV/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/IDN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/IND/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/KOR/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/LBR/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/MOZ/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/NLD/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/OMN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/PAN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/PER/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/POL/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/SAU/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/SWE/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/THA/Health/Nurses and midwives (per 1,000 people)",
+          "World/North Africa/TUR/Health/Nurses and midwives (per 1,000 people)",
+          "World/Pair/USA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/VEN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/YEM/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/ZAF/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/ARG/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/AUT/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/BGD/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/BRA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/CHL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Pair/CHN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/CMR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/COL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/CRI/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/DEU/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/DZA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/EGY/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/ESP/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/FRA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/GBR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/GHA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/GRC/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/HRV/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/IDN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/IND/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/ISR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/KOR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/LBR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/NGA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/NLD/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/PAN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/PER/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/PHL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/POL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/SEN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/SWE/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Pair/USA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/VEN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/VNM/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/AZE/Health/People practicing open defecation (% of population)",
+          "World/Asia/BGD/Health/People practicing open defecation (% of population)",
+          "World/Latam/BRA/Health/People practicing open defecation (% of population)",
+          "World/Latam/CHL/Health/People practicing open defecation (% of population)",
+          "World/Pair/CHN/Health/People practicing open defecation (% of population)",
+          "World/South Africa/CMR/Health/People practicing open defecation (% of population)",
+          "World/Latam/COL/Health/People practicing open defecation (% of population)",
+          "World/Latam/CRI/Health/People practicing open defecation (% of population)",
+          "World/North Africa/DZA/Health/People practicing open defecation (% of population)",
+          "World/North Africa/EGY/Health/People practicing open defecation (% of population)",
+          "World/South Africa/GHA/Health/People practicing open defecation (% of population)",
+          "World/Europe/GRC/Health/People practicing open defecation (% of population)",
+          "World/Asia/IDN/Health/People practicing open defecation (% of population)",
+          "World/Asia/IND/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/IRQ/Health/People practicing open defecation (% of population)",
+          "World/South Africa/LBR/Health/People practicing open defecation (% of population)",
+          "World/North Africa/MAR/Health/People practicing open defecation (% of population)",
+          "World/Latam/MEX/Health/People practicing open defecation (% of population)",
+          "World/South Africa/MOZ/Health/People practicing open defecation (% of population)",
+          "World/South Africa/NGA/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/OMN/Health/People practicing open defecation (% of population)",
+          "World/Latam/PAN/Health/People practicing open defecation (% of population)",
+          "World/Latam/PER/Health/People practicing open defecation (% of population)",
+          "World/Asia/PHL/Health/People practicing open defecation (% of population)",
+          "World/South Africa/SEN/Health/People practicing open defecation (% of population)",
+          "World/Asia/THA/Health/People practicing open defecation (% of population)",
+          "World/North Africa/TUR/Health/People practicing open defecation (% of population)",
+          "World/Latam/VEN/Health/People practicing open defecation (% of population)",
+          "World/Asia/VNM/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/YEM/Health/People practicing open defecation (% of population)",
+          "World/South Africa/ZAF/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/ARE/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/ARG/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/BGD/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/BRA/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/CHL/Health/People using at least basic drinking water services (% of population)",
+          "World/Pair/CHN/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/CMR/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/COL/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/CRI/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/DZA/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/EGY/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/ESP/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/FRA/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/GHA/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/GRC/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/HRV/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/IDN/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/IND/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/IRQ/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/KOR/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/LBR/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/MAR/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/MEX/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/MOZ/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/NGA/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/OMN/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/PAN/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/PER/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/PHL/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/POL/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/QAT/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/SEN/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/SWE/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/THA/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/TUR/Health/People using at least basic drinking water services (% of population)",
+          "World/Pair/USA/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/VEN/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/VNM/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/ZAF/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/ARE/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/ARG/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/AUT/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/BGD/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/BRA/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/CHL/Health/People using at least basic sanitation services (% of population)",
+          "World/Pair/CHN/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/CMR/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/COL/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/CRI/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/DEU/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/DZA/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/EGY/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/ESP/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/FRA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/GBR/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/GHA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/GRC/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/HRV/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/IDN/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/IND/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/IRQ/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/ISR/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/KOR/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/LBR/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/MAR/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/MEX/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/MOZ/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/NGA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/NLD/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/OMN/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/PAN/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/PER/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/PHL/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/POL/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/SEN/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/SWE/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/THA/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/TUR/Health/People using at least basic sanitation services (% of population)",
+          "World/Pair/USA/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/VEN/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/VNM/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/ZAF/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/ARE/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/ARG/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/AUT/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/BGD/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/BRA/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/CHL/Health/People using safely managed sanitation services (% of population)",
+          "World/Pair/CHN/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/COL/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/CRI/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/DEU/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/DZA/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/EGY/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/FRA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/GBR/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/GHA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/GRC/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/IND/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/ISR/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/KOR/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/MAR/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/MEX/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/NGA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/NLD/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/PER/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/PHL/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/POL/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/QAT/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/SEN/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/SWE/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/THA/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/TUR/Health/People using safely managed sanitation services (% of population)",
+          "World/Pair/USA/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/VEN/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/AUT/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/CHL/Agriculture/Permanent cropland (% of land area)",
+          "World/Pair/CHN/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/CMR/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/COL/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/CRI/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/DZA/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/FRA/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/GRC/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/IDN/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/IND/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/MAR/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/MEX/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/NLD/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/OMN/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/PER/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/PHL/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/SEN/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/THA/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/TUR/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/VNM/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/ZAF/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/ARE/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/AUT/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 0-14 (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/COL/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/GHA/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/HRV/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/IND/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/KOR/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/PER/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/POL/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/SEN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/THA/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 0-14 (% of total population)",
+          "World/Pair/USA/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 15-64 (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/COL/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/DEU/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/GBR/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/GHA/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/GRC/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/IND/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/ISR/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/PER/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/POL/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/SEN/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 15-64 (% of total population)",
+          "World/Pair/USA/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/AUT/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 65 and above (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/COL/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/DEU/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/DZA/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/GBR/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/GRC/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/HRV/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/IND/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/ISR/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/KOR/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/MOZ/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/NGA/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/PER/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/POL/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/THA/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 65 and above (% of total population)",
+          "World/Pair/USA/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population in largest city",
+          "World/Latam/ARG/Demography/Population in largest city",
+          "World/Europe/AUT/Demography/Population in largest city",
+          "World/Persian Gulf/AZE/Demography/Population in largest city",
+          "World/Asia/BGD/Demography/Population in largest city",
+          "World/Latam/BRA/Demography/Population in largest city",
+          "World/Latam/CHL/Demography/Population in largest city",
+          "World/Pair/CHN/Demography/Population in largest city",
+          "World/South Africa/CMR/Demography/Population in largest city",
+          "World/Latam/COL/Demography/Population in largest city",
+          "World/Latam/CRI/Demography/Population in largest city",
+          "World/North Africa/DZA/Demography/Population in largest city",
+          "World/North Africa/EGY/Demography/Population in largest city",
+          "World/Europe/ESP/Demography/Population in largest city",
+          "World/Europe/FRA/Demography/Population in largest city",
+          "World/Europe/GBR/Demography/Population in largest city",
+          "World/South Africa/GHA/Demography/Population in largest city",
+          "World/Europe/GRC/Demography/Population in largest city",
+          "World/Europe/HRV/Demography/Population in largest city",
+          "World/Asia/IDN/Demography/Population in largest city",
+          "World/Asia/IND/Demography/Population in largest city",
+          "World/North Africa/ISR/Demography/Population in largest city",
+          "World/North Africa/MAR/Demography/Population in largest city",
+          "World/Latam/MEX/Demography/Population in largest city",
+          "World/South Africa/MOZ/Demography/Population in largest city",
+          "World/South Africa/NGA/Demography/Population in largest city",
+          "World/Europe/NLD/Demography/Population in largest city",
+          "World/Persian Gulf/OMN/Demography/Population in largest city",
+          "World/Latam/PAN/Demography/Population in largest city",
+          "World/Latam/PER/Demography/Population in largest city",
+          "World/Asia/PHL/Demography/Population in largest city",
+          "World/Europe/POL/Demography/Population in largest city",
+          "World/Persian Gulf/QAT/Demography/Population in largest city",
+          "World/Persian Gulf/SAU/Demography/Population in largest city",
+          "World/South Africa/SEN/Demography/Population in largest city",
+          "World/Asia/THA/Demography/Population in largest city",
+          "World/North Africa/TUR/Demography/Population in largest city",
+          "World/Pair/USA/Demography/Population in largest city",
+          "World/Latam/VEN/Demography/Population in largest city",
+          "World/Asia/VNM/Demography/Population in largest city",
+          "World/Persian Gulf/YEM/Demography/Population in largest city",
+          "World/South Africa/ZAF/Demography/Population in largest city",
+          "World/Persian Gulf/ARE/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/ARG/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/AUT/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/AZE/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/BGD/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/CHL/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/CMR/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/COL/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/CRI/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/DZA/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/EGY/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/FRA/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/GBR/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/IDN/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/IND/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/ISR/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/KOR/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/MAR/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/MEX/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/MOZ/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/NGA/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/NLD/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/PAN/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/PER/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/POL/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/QAT/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/SAU/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/SEN/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/TUR/Demography/Population in the largest city (% of urban population)",
+          "World/Pair/USA/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/VEN/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/VNM/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/YEM/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/ZAF/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/ARE/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/ARG/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/AUT/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/AZE/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/BGD/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/BRA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/CHL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Pair/CHN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/CMR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/COL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/CRI/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/DEU/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/DZA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/EGY/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/ESP/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/FRA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/GBR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/GHA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/GRC/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/IDN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/IND/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/ISR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/KOR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/MAR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/MEX/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/MOZ/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/NGA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/NLD/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/OMN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/PAN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/PER/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/PHL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/POL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/SAU/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/SEN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/THA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/TUR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Pair/USA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/VEN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/VNM/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/YEM/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/ZAF/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/ARE/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/ARG/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/AUT/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/BGD/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/BRA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Pair/CHN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/CMR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/COL/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/CRI/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/DZA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/GBR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/GHA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/GRC/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/IDN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/IND/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/KOR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/MAR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/MEX/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/MOZ/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/NGA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/PAN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/PER/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/POL/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/THA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/TUR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Pair/USA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/VNM/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/ZAF/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/AZE/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/BGD/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/CHL/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Pair/CHN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/CMR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/EGY/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Europe/GBR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/GHA/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/IDN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/IND/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/LBR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/MAR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/MOZ/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/PAN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/PER/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/SEN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Europe/SWE/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/THA/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/TUR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/VNM/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Persian Gulf/YEM/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/ZAF/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Persian Gulf/ARE/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/AZE/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/BGD/Health/Pregnant women receiving prenatal care (%)",
+          "World/Pair/CHN/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/CMR/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/COL/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/CRI/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/DZA/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/EGY/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/IDN/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/IND/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/LBR/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/MAR/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/MOZ/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/NGA/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/PER/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/PHL/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/QAT/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/SAU/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/SEN/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/TUR/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/VEN/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/VNM/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/YEM/Health/Pregnant women receiving prenatal care (%)",
+          "World/Europe/AUT/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/BGD/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/BRA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Pair/CHN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/COL/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/CRI/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/DEU/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/ESP/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/GBR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/GRC/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/IND/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/MEX/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/NLD/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/PAN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/PER/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/PHL/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Pair/USA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/VEN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/VNM/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/BGD/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/BRA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/CHL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Pair/CHN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/COL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/CRI/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/ESP/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/GBR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/KOR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/MEX/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/NLD/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/PAN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/PER/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/PHL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/THA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Pair/USA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/VEN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/VNM/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/ARE/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/ARG/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/BGD/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/BRA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/CHL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Pair/CHN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/COL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/CRI/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Europe/GBR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Europe/HRV/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/IND/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/KOR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/MEX/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/PAN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/PER/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/PHL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Pair/USA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/VEN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/VNM/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/ARG/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/BGD/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/BRA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/CHL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Pair/CHN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/COL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/CRI/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/ESP/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/FRA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/GBR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/GRC/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/KOR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/MEX/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/NLD/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/PAN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/PER/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/PHL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/THA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Pair/USA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/VEN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/VNM/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/ARG/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/AUT/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/AZE/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/BGD/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/BRA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/CHL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Pair/CHN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/CMR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/COL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/CRI/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/DEU/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/DZA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/ESP/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/FRA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/GBR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/GHA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/GRC/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/HRV/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/IDN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/IND/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/ISR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/KOR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/LBR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/MAR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/MEX/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/MOZ/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/NGA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/NLD/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/OMN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/PAN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/PER/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/PHL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/POL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/QAT/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/SEN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/SWE/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/THA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/TUR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Pair/USA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/VNM/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/YEM/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/ZAF/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/ARG/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/AZE/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/BGD/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/CHL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Pair/CHN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/COL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/CRI/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/DZA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/EGY/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/GRC/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/IDN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/IND/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/KOR/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/MAR/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/MEX/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/South Africa/NGA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/NLD/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/OMN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/PAN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/PER/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/PHL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/POL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/QAT/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/SAU/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/THA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Pair/USA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/VEN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/VNM/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/YEM/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/South Africa/ZAF/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/AZE/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/BGD/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/BRA/Health/Prevalence of undernourishment (% of population)",
+          "World/Pair/CHN/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/CMR/Health/Prevalence of undernourishment (% of population)",
+          "World/North Africa/DZA/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/GHA/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/IDN/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of undernourishment (% of population)",
+          "World/North Africa/MAR/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/MEX/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/MOZ/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/NGA/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/OMN/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/PAN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/PHL/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/SEN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/THA/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/VEN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/VNM/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/YEM/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/ARE/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/AZE/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/BGD/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/BRA/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Pair/CHN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/South Africa/CMR/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/CRI/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Europe/GBR/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/IDN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/South Africa/NGA/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Europe/NLD/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/OMN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/PAN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/QAT/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/SAU/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/VEN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/VNM/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/YEM/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/AZE/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/BGD/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/BRA/Economy/Primary income payments (BoP, current US$)",
+          "World/Pair/CHN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/COL/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/CRI/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/ESP/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/GRC/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/HRV/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/IDN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/IND/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/KOR/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/MEX/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/NLD/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/PAN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/PER/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/PHL/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/POL/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/SAU/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/THA/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/TUR/Economy/Primary income payments (BoP, current US$)",
+          "World/Pair/USA/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/VEN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/VNM/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/AZE/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/CHL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Pair/CHN/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/COL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/ESP/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/GRC/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/HRV/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/IND/Economy/Primary income receipts (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/KOR/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/LBR/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/MEX/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/NLD/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/PER/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/PHL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/POL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/QAT/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/THA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Pair/USA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/VNM/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/AZE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/BRA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/CRI/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/MEX/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/VEN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/AZE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/BRA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/CRI/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/MEX/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/VEN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/ARG/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/AZE/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/CHL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Pair/CHN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/COL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/CRI/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/North Africa/EGY/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Europe/ESP/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/GHA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Europe/GRC/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/IND/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/MEX/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/MOZ/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/NGA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/PAN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/PER/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/PHL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/SAU/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/SEN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/THA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/North Africa/TUR/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Pair/USA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/VNM/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/YEM/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/AZE/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/BGD/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/BRA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/CHL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Pair/CHN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/CMR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/DZA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/EGY/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Europe/GBR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/GHA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/IND/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/ISR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/KOR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/MEX/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/MOZ/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/PAN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/PHL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Europe/POL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/SEN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/THA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/TUR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Pair/USA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/VNM/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Persian Gulf/ARE/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/ARG/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/AUT/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/BGD/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/BRA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/CHL/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/CRI/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/DEU/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/DZA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/ESP/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/FRA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/GBR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/GHA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/GRC/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/IND/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/ISR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/KOR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/MEX/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/NGA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/NLD/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/PAN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/QAT/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/SAU/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/SEN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/TUR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Pair/USA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/VNM/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/ARE/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/ARG/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/AUT/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/BGD/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/BRA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/CHL/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Pair/CHN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/CRI/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/DEU/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/DZA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/ESP/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/FRA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/GBR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/GRC/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/ISR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/KOR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/MEX/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/MOZ/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/NLD/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/OMN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/PAN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/POL/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/QAT/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/SAU/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/SEN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/TUR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Pair/USA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/VEN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/VNM/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/ZAF/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/ARE/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/ARG/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/BGD/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Pair/CHN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/CRI/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/DEU/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/EGY/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/GBR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/GHA/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/GRC/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/IDN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/IND/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/KOR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/MAR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/MEX/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/MOZ/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/NLD/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/PAN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/PHL/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/POL/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Persian Gulf/SAU/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/SWE/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/TUR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Pair/USA/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/VNM/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/ZAF/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Persian Gulf/ARE/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/ARG/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/AUT/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/AZE/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/BGD/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/BRA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/CHL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Pair/CHN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/CMR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/COL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/CRI/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/DZA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/EGY/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/ESP/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/FRA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/GBR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/GHA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/IDN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/IND/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/ISR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/KOR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/LBR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/MAR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/MEX/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/MOZ/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/NGA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/NLD/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/OMN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/PAN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/PER/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/PHL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/POL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/QAT/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/SAU/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/SEN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/THA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/TUR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Pair/USA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/VEN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/VNM/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/YEM/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/ZAF/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/AUT/R&D/Researchers in R&D (per million people)",
+          "World/Latam/BRA/R&D/Researchers in R&D (per million people)",
+          "World/Pair/CHN/R&D/Researchers in R&D (per million people)",
+          "World/Europe/DEU/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/DZA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/ESP/R&D/Researchers in R&D (per million people)",
+          "World/Europe/FRA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/GBR/R&D/Researchers in R&D (per million people)",
+          "World/South Africa/GHA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/GRC/R&D/Researchers in R&D (per million people)",
+          "World/Asia/IND/R&D/Researchers in R&D (per million people)",
+          "World/Asia/KOR/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/MAR/R&D/Researchers in R&D (per million people)",
+          "World/South Africa/MOZ/R&D/Researchers in R&D (per million people)",
+          "World/Europe/NLD/R&D/Researchers in R&D (per million people)",
+          "World/Persian Gulf/OMN/R&D/Researchers in R&D (per million people)",
+          "World/Asia/PHL/R&D/Researchers in R&D (per million people)",
+          "World/Europe/POL/R&D/Researchers in R&D (per million people)",
+          "World/Asia/THA/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/TUR/R&D/Researchers in R&D (per million people)",
+          "World/Pair/USA/R&D/Researchers in R&D (per million people)",
+          "World/Latam/VEN/R&D/Researchers in R&D (per million people)",
+          "World/Persian Gulf/AZE/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/BGD/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/BRA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/CHL/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Pair/CHN/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/CRI/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/North Africa/EGY/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Europe/ESP/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/South Africa/GHA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/IND/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/KOR/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/North Africa/MAR/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/MEX/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/PAN/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/PER/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Europe/POL/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/THA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Pair/USA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/VNM/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Persian Gulf/AZE/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/BGD/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/BRA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/CHL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Pair/CHN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/CMR/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/COL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/CRI/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/DZA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/EGY/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/GHA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/IDN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/IND/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/MAR/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/MEX/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/MOZ/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/PAN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/PER/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/PHL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Europe/POL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/SEN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/THA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/VNM/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Persian Gulf/ARE/Demography/Rural population",
+          "World/Latam/ARG/Demography/Rural population",
+          "World/Europe/AUT/Demography/Rural population",
+          "World/Persian Gulf/AZE/Demography/Rural population",
+          "World/Asia/BGD/Demography/Rural population",
+          "World/Latam/BRA/Demography/Rural population",
+          "World/Pair/CHN/Demography/Rural population",
+          "World/South Africa/CMR/Demography/Rural population",
+          "World/Latam/COL/Demography/Rural population",
+          "World/Latam/CRI/Demography/Rural population",
+          "World/North Africa/DZA/Demography/Rural population",
+          "World/North Africa/EGY/Demography/Rural population",
+          "World/Europe/FRA/Demography/Rural population",
+          "World/Europe/GBR/Demography/Rural population",
+          "World/South Africa/GHA/Demography/Rural population",
+          "World/Europe/GRC/Demography/Rural population",
+          "World/Europe/HRV/Demography/Rural population",
+          "World/Asia/IDN/Demography/Rural population",
+          "World/Asia/IND/Demography/Rural population",
+          "World/North Africa/ISR/Demography/Rural population",
+          "World/South Africa/LBR/Demography/Rural population",
+          "World/North Africa/MAR/Demography/Rural population",
+          "World/South Africa/MOZ/Demography/Rural population",
+          "World/South Africa/NGA/Demography/Rural population",
+          "World/Europe/NLD/Demography/Rural population",
+          "World/Persian Gulf/OMN/Demography/Rural population",
+          "World/Latam/PAN/Demography/Rural population",
+          "World/Asia/PHL/Demography/Rural population",
+          "World/Europe/POL/Demography/Rural population",
+          "World/Persian Gulf/QAT/Demography/Rural population",
+          "World/Persian Gulf/SAU/Demography/Rural population",
+          "World/South Africa/SEN/Demography/Rural population",
+          "World/North Africa/TUR/Demography/Rural population",
+          "World/Pair/USA/Demography/Rural population",
+          "World/Asia/VNM/Demography/Rural population",
+          "World/Persian Gulf/YEM/Demography/Rural population",
+          "World/Persian Gulf/ARE/Demography/Rural population (% of total population)",
+          "World/Latam/ARG/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Rural population (% of total population)",
+          "World/Asia/BGD/Demography/Rural population (% of total population)",
+          "World/Latam/BRA/Demography/Rural population (% of total population)",
+          "World/Latam/CHL/Demography/Rural population (% of total population)",
+          "World/Pair/CHN/Demography/Rural population (% of total population)",
+          "World/South Africa/CMR/Demography/Rural population (% of total population)",
+          "World/Latam/COL/Demography/Rural population (% of total population)",
+          "World/Latam/CRI/Demography/Rural population (% of total population)",
+          "World/Europe/DEU/Demography/Rural population (% of total population)",
+          "World/North Africa/DZA/Demography/Rural population (% of total population)",
+          "World/Europe/ESP/Demography/Rural population (% of total population)",
+          "World/Europe/FRA/Demography/Rural population (% of total population)",
+          "World/Europe/GBR/Demography/Rural population (% of total population)",
+          "World/South Africa/GHA/Demography/Rural population (% of total population)",
+          "World/Europe/GRC/Demography/Rural population (% of total population)",
+          "World/Europe/HRV/Demography/Rural population (% of total population)",
+          "World/Asia/IDN/Demography/Rural population (% of total population)",
+          "World/Asia/IND/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/IRQ/Demography/Rural population (% of total population)",
+          "World/North Africa/ISR/Demography/Rural population (% of total population)",
+          "World/South Africa/LBR/Demography/Rural population (% of total population)",
+          "World/North Africa/MAR/Demography/Rural population (% of total population)",
+          "World/Latam/MEX/Demography/Rural population (% of total population)",
+          "World/South Africa/MOZ/Demography/Rural population (% of total population)",
+          "World/South Africa/NGA/Demography/Rural population (% of total population)",
+          "World/Europe/NLD/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Rural population (% of total population)",
+          "World/Latam/PAN/Demography/Rural population (% of total population)",
+          "World/Latam/PER/Demography/Rural population (% of total population)",
+          "World/Europe/POL/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Rural population (% of total population)",
+          "World/South Africa/SEN/Demography/Rural population (% of total population)",
+          "World/Asia/THA/Demography/Rural population (% of total population)",
+          "World/North Africa/TUR/Demography/Rural population (% of total population)",
+          "World/Pair/USA/Demography/Rural population (% of total population)",
+          "World/Latam/VEN/Demography/Rural population (% of total population)",
+          "World/Asia/VNM/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Rural population (% of total population)",
+          "World/South Africa/ZAF/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/ARE/industry/Scientific and technical journal articles",
+          "World/Latam/ARG/industry/Scientific and technical journal articles",
+          "World/Europe/AUT/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/AZE/industry/Scientific and technical journal articles",
+          "World/Asia/BGD/industry/Scientific and technical journal articles",
+          "World/Latam/BRA/industry/Scientific and technical journal articles",
+          "World/Latam/CHL/industry/Scientific and technical journal articles",
+          "World/Pair/CHN/industry/Scientific and technical journal articles",
+          "World/South Africa/CMR/industry/Scientific and technical journal articles",
+          "World/Latam/COL/industry/Scientific and technical journal articles",
+          "World/Latam/CRI/industry/Scientific and technical journal articles",
+          "World/North Africa/DZA/industry/Scientific and technical journal articles",
+          "World/North Africa/EGY/industry/Scientific and technical journal articles",
+          "World/Europe/ESP/industry/Scientific and technical journal articles",
+          "World/Europe/FRA/industry/Scientific and technical journal articles",
+          "World/Europe/GBR/industry/Scientific and technical journal articles",
+          "World/South Africa/GHA/industry/Scientific and technical journal articles",
+          "World/Europe/GRC/industry/Scientific and technical journal articles",
+          "World/Europe/HRV/industry/Scientific and technical journal articles",
+          "World/Asia/IDN/industry/Scientific and technical journal articles",
+          "World/Asia/IND/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/IRQ/industry/Scientific and technical journal articles",
+          "World/North Africa/ISR/industry/Scientific and technical journal articles",
+          "World/Asia/KOR/industry/Scientific and technical journal articles",
+          "World/South Africa/LBR/industry/Scientific and technical journal articles",
+          "World/North Africa/MAR/industry/Scientific and technical journal articles",
+          "World/Latam/MEX/industry/Scientific and technical journal articles",
+          "World/South Africa/MOZ/industry/Scientific and technical journal articles",
+          "World/South Africa/NGA/industry/Scientific and technical journal articles",
+          "World/Europe/NLD/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/OMN/industry/Scientific and technical journal articles",
+          "World/Latam/PAN/industry/Scientific and technical journal articles",
+          "World/Latam/PER/industry/Scientific and technical journal articles",
+          "World/Asia/PHL/industry/Scientific and technical journal articles",
+          "World/Europe/POL/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/QAT/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/SAU/industry/Scientific and technical journal articles",
+          "World/South Africa/SEN/industry/Scientific and technical journal articles",
+          "World/Europe/SWE/industry/Scientific and technical journal articles",
+          "World/Asia/THA/industry/Scientific and technical journal articles",
+          "World/North Africa/TUR/industry/Scientific and technical journal articles",
+          "World/Pair/USA/industry/Scientific and technical journal articles",
+          "World/Latam/VEN/industry/Scientific and technical journal articles",
+          "World/Asia/VNM/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/YEM/industry/Scientific and technical journal articles",
+          "World/South Africa/ZAF/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/AZE/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/BGD/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/CHL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/CMR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/COL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/CRI/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/DEU/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/DZA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/IDN/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/IND/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/KOR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/MAR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/MEX/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/NGA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/NLD/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/PER/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/PHL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/SWE/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/THA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Pair/USA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/VNM/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/AUT/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Service exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/Service exports (BoP, current US$)",
+          "World/Latam/BRA/Exports/Service exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/Service exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/CMR/Exports/Service exports (BoP, current US$)",
+          "World/Latam/COL/Exports/Service exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/Service exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/DZA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/Service exports (BoP, current US$)",
+          "World/Europe/FRA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/GRC/Exports/Service exports (BoP, current US$)",
+          "World/Europe/HRV/Exports/Service exports (BoP, current US$)",
+          "World/Asia/IDN/Exports/Service exports (BoP, current US$)",
+          "World/Asia/IND/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Service exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Service exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Service exports (BoP, current US$)",
+          "World/Latam/PAN/Exports/Service exports (BoP, current US$)",
+          "World/Latam/PER/Exports/Service exports (BoP, current US$)",
+          "World/Asia/PHL/Exports/Service exports (BoP, current US$)",
+          "World/Europe/POL/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Service exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/Service exports (BoP, current US$)",
+          "World/Asia/THA/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Service exports (BoP, current US$)",
+          "World/Pair/USA/Exports/Service exports (BoP, current US$)",
+          "World/Asia/VNM/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Service exports (BoP, current US$)",
+          "World/Latam/ARG/Imports/Service imports (BoP, current US$)",
+          "World/Europe/AUT/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Service imports (BoP, current US$)",
+          "World/Asia/BGD/Imports/Service imports (BoP, current US$)",
+          "World/Latam/BRA/Imports/Service imports (BoP, current US$)",
+          "World/Latam/CHL/Imports/Service imports (BoP, current US$)",
+          "World/Pair/CHN/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/CMR/Imports/Service imports (BoP, current US$)",
+          "World/Latam/COL/Imports/Service imports (BoP, current US$)",
+          "World/Latam/CRI/Imports/Service imports (BoP, current US$)",
+          "World/Europe/DEU/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Service imports (BoP, current US$)",
+          "World/Europe/ESP/Imports/Service imports (BoP, current US$)",
+          "World/Europe/FRA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/GBR/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/HRV/Imports/Service imports (BoP, current US$)",
+          "World/Asia/IDN/Imports/Service imports (BoP, current US$)",
+          "World/Asia/IND/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Service imports (BoP, current US$)",
+          "World/Asia/KOR/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Service imports (BoP, current US$)",
+          "World/Latam/MEX/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/NLD/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Service imports (BoP, current US$)",
+          "World/Latam/PAN/Imports/Service imports (BoP, current US$)",
+          "World/Latam/PER/Imports/Service imports (BoP, current US$)",
+          "World/Asia/PHL/Imports/Service imports (BoP, current US$)",
+          "World/Europe/POL/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Service imports (BoP, current US$)",
+          "World/Europe/SWE/Imports/Service imports (BoP, current US$)",
+          "World/Asia/THA/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Service imports (BoP, current US$)",
+          "World/Pair/USA/Imports/Service imports (BoP, current US$)",
+          "World/Asia/VNM/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Europe/AUT/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/BGD/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/BRA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/CHL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Pair/CHN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/COL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/DZA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/EGY/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/IDN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/IND/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/MAR/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/MEX/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/South Africa/NGA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/PAN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/PHL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Europe/POL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/QAT/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/SAU/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/South Africa/SEN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/TUR/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Pair/USA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/VEN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/VNM/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/YEM/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/ARG/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/AUT/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/AZE/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/BGD/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/BRA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/CHL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/CMR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/COL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/CRI/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/DEU/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/DZA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/ESP/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/FRA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/GBR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/GHA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/GRC/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/HRV/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/IND/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/ISR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/KOR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/MAR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/MEX/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/MOZ/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/NGA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/NLD/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/PAN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/PER/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/POL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/SAU/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/SEN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/SWE/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/TUR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Pair/USA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/VEN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/VNM/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/YEM/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/ZAF/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/AZE/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/CHL/Economy/Taxes on international trade (% of revenue)",
+          "World/Pair/CHN/Economy/Taxes on international trade (% of revenue)",
+          "World/South Africa/CMR/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/COL/Economy/Taxes on international trade (% of revenue)",
+          "World/North Africa/EGY/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/ESP/Economy/Taxes on international trade (% of revenue)",
+          "World/South Africa/GHA/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/HRV/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/IND/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/KOR/Economy/Taxes on international trade (% of revenue)",
+          "World/North Africa/MAR/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/MEX/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/PAN/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/PER/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/POL/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/THA/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/ARG/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/AUT/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Asia/BGD/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/CHL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Pair/CHN/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/COL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/CRI/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/DEU/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/DZA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/ESP/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/FRA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/GBR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/GRC/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/ISR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Asia/KOR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/MAR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/MEX/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Persian Gulf/OMN/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/PER/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/POL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/SWE/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Pair/USA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Persian Gulf/ARE/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/AZE/Industry/Time required to start a business (days)",
+          "World/Latam/CHL/Industry/Time required to start a business (days)",
+          "World/Pair/CHN/Industry/Time required to start a business (days)",
+          "World/South Africa/CMR/Industry/Time required to start a business (days)",
+          "World/Latam/COL/Industry/Time required to start a business (days)",
+          "World/Latam/CRI/Industry/Time required to start a business (days)",
+          "World/Europe/DEU/Industry/Time required to start a business (days)",
+          "World/North Africa/DZA/Industry/Time required to start a business (days)",
+          "World/Europe/ESP/Industry/Time required to start a business (days)",
+          "World/Europe/FRA/Industry/Time required to start a business (days)",
+          "World/Europe/GBR/Industry/Time required to start a business (days)",
+          "World/Asia/IND/Industry/Time required to start a business (days)",
+          "World/Asia/KOR/Industry/Time required to start a business (days)",
+          "World/South Africa/LBR/Industry/Time required to start a business (days)",
+          "World/North Africa/MAR/Industry/Time required to start a business (days)",
+          "World/South Africa/MOZ/Industry/Time required to start a business (days)",
+          "World/Europe/NLD/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/OMN/Industry/Time required to start a business (days)",
+          "World/Latam/PAN/Industry/Time required to start a business (days)",
+          "World/Latam/PER/Industry/Time required to start a business (days)",
+          "World/Europe/POL/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/SAU/Industry/Time required to start a business (days)",
+          "World/South Africa/SEN/Industry/Time required to start a business (days)",
+          "World/Asia/THA/Industry/Time required to start a business (days)",
+          "World/Latam/VEN/Industry/Time required to start a business (days)",
+          "World/Asia/VNM/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/YEM/Industry/Time required to start a business (days)",
+          "World/South Africa/ZAF/Industry/Time required to start a business (days)",
+          "World/Latam/ARG/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Persian Gulf/AZE/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/BGD/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/CHL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/COL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/CRI/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/North Africa/EGY/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/GHA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/GRC/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/HRV/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/IDN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/IND/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/LBR/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/MEX/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/MOZ/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/NGA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/PAN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/PER/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/PHL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/POL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/SEN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/THA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/North Africa/TUR/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/VEN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/VNM/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Persian Gulf/YEM/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/ZAF/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/ARG/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/BGD/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/CHL/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/CMR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/COL/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/CRI/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/EGY/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/GHA/R&D/Time to obtain an electrical connection (days)",
+          "World/Europe/GRC/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/IDN/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/IND/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/MAR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/MEX/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/MOZ/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/NGA/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/PAN/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/PER/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/PHL/R&D/Time to obtain an electrical connection (days)",
+          "World/Europe/POL/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/SEN/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/TUR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/VEN/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/VNM/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/ZAF/R&D/Time to obtain an electrical connection (days)",
+          "World/Persian Gulf/ARE/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/ARG/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/AUT/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/AZE/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/BGD/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/BRA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/CHL/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Pair/CHN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/CRI/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/EGY/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/GBR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/GHA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/HRV/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/IND/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/ISR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/KOR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/MAR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/MEX/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/MOZ/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/NGA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/NLD/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/OMN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/PAN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/POL/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/QAT/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/THA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Pair/USA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/VEN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/VNM/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/YEM/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/ARE/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/AZE/Industry/Total fisheries production (metric tons)",
+          "World/Asia/BGD/Industry/Total fisheries production (metric tons)",
+          "World/Latam/BRA/Industry/Total fisheries production (metric tons)",
+          "World/Latam/CHL/Industry/Total fisheries production (metric tons)",
+          "World/Pair/CHN/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/CMR/Industry/Total fisheries production (metric tons)",
+          "World/Latam/CRI/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/EGY/Industry/Total fisheries production (metric tons)",
+          "World/Europe/HRV/Industry/Total fisheries production (metric tons)",
+          "World/Asia/IDN/Industry/Total fisheries production (metric tons)",
+          "World/Asia/IND/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/ISR/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/MAR/Industry/Total fisheries production (metric tons)",
+          "World/Latam/MEX/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/MOZ/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/NGA/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/OMN/Industry/Total fisheries production (metric tons)",
+          "World/Asia/PHL/Industry/Total fisheries production (metric tons)",
+          "World/Europe/POL/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/SAU/Industry/Total fisheries production (metric tons)",
+          "World/Asia/THA/Industry/Total fisheries production (metric tons)",
+          "World/Asia/VNM/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/ARE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/CRI/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/HRV/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/IRQ/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/ISR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/KOR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/MAR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/MEX/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/South Africa/MOZ/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/PHL/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/SWE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/THA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/TUR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/South Africa/ZAF/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/CHL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/COL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/IND/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/PER/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/THA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/BGD/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/BRA/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/CHL/Economy/Total reserves (includes gold, current US$)",
+          "World/Pair/CHN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/COL/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/CRI/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/DZA/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/ESP/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/FRA/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/GBR/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/GHA/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/IDN/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/IND/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/IRQ/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/ISR/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/KOR/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/LBR/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/MAR/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/MEX/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/MOZ/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/OMN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/PAN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/PER/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/PHL/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/POL/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/QAT/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/SAU/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/SWE/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/THA/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/TUR/Economy/Total reserves (includes gold, current US$)",
+          "World/Pair/USA/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/VEN/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/VNM/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/ZAF/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/ARE/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/BGD/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/BRA/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/CHL/Economy/Total reserves minus gold (current US$)",
+          "World/Pair/CHN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/COL/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/CRI/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/DZA/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/FRA/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/GBR/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/GHA/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/IDN/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/IND/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/ISR/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/KOR/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/LBR/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/MAR/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/MEX/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/MOZ/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/OMN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/PAN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/PER/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/PHL/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/POL/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/QAT/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/SAU/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/SWE/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/THA/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/TUR/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/VEN/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/VNM/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/ZAF/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/ARG/Health/UHC service coverage index",
+          "World/Europe/AUT/Health/UHC service coverage index",
+          "World/Persian Gulf/AZE/Health/UHC service coverage index",
+          "World/Asia/BGD/Health/UHC service coverage index",
+          "World/Latam/BRA/Health/UHC service coverage index",
+          "World/Latam/CHL/Health/UHC service coverage index",
+          "World/Pair/CHN/Health/UHC service coverage index",
+          "World/South Africa/CMR/Health/UHC service coverage index",
+          "World/Latam/COL/Health/UHC service coverage index",
+          "World/Latam/CRI/Health/UHC service coverage index",
+          "World/Europe/DEU/Health/UHC service coverage index",
+          "World/North Africa/DZA/Health/UHC service coverage index",
+          "World/North Africa/EGY/Health/UHC service coverage index",
+          "World/Europe/ESP/Health/UHC service coverage index",
+          "World/Europe/FRA/Health/UHC service coverage index",
+          "World/Europe/GBR/Health/UHC service coverage index",
+          "World/South Africa/GHA/Health/UHC service coverage index",
+          "World/Europe/GRC/Health/UHC service coverage index",
+          "World/Europe/HRV/Health/UHC service coverage index",
+          "World/Asia/IDN/Health/UHC service coverage index",
+          "World/Asia/IND/Health/UHC service coverage index",
+          "World/Persian Gulf/IRQ/Health/UHC service coverage index",
+          "World/North Africa/ISR/Health/UHC service coverage index",
+          "World/Asia/KOR/Health/UHC service coverage index",
+          "World/South Africa/LBR/Health/UHC service coverage index",
+          "World/North Africa/MAR/Health/UHC service coverage index",
+          "World/Latam/MEX/Health/UHC service coverage index",
+          "World/South Africa/MOZ/Health/UHC service coverage index",
+          "World/South Africa/NGA/Health/UHC service coverage index",
+          "World/Europe/NLD/Health/UHC service coverage index",
+          "World/Persian Gulf/OMN/Health/UHC service coverage index",
+          "World/Latam/PAN/Health/UHC service coverage index",
+          "World/Latam/PER/Health/UHC service coverage index",
+          "World/Asia/PHL/Health/UHC service coverage index",
+          "World/Europe/POL/Health/UHC service coverage index",
+          "World/Persian Gulf/QAT/Health/UHC service coverage index",
+          "World/South Africa/SEN/Health/UHC service coverage index",
+          "World/Europe/SWE/Health/UHC service coverage index",
+          "World/Asia/THA/Health/UHC service coverage index",
+          "World/North Africa/TUR/Health/UHC service coverage index",
+          "World/Pair/USA/Health/UHC service coverage index",
+          "World/Latam/VEN/Health/UHC service coverage index",
+          "World/Asia/VNM/Health/UHC service coverage index",
+          "World/Persian Gulf/YEM/Health/UHC service coverage index",
+          "World/South Africa/ZAF/Health/UHC service coverage index",
+          "World/Persian Gulf/ARE/Demography/Urban population",
+          "World/Latam/ARG/Demography/Urban population",
+          "World/Persian Gulf/AZE/Demography/Urban population",
+          "World/Asia/BGD/Demography/Urban population",
+          "World/Latam/BRA/Demography/Urban population",
+          "World/Latam/CHL/Demography/Urban population",
+          "World/Pair/CHN/Demography/Urban population",
+          "World/South Africa/CMR/Demography/Urban population",
+          "World/Latam/COL/Demography/Urban population",
+          "World/Latam/CRI/Demography/Urban population",
+          "World/North Africa/DZA/Demography/Urban population",
+          "World/North Africa/EGY/Demography/Urban population",
+          "World/Europe/ESP/Demography/Urban population",
+          "World/Europe/FRA/Demography/Urban population",
+          "World/Europe/GBR/Demography/Urban population",
+          "World/South Africa/GHA/Demography/Urban population",
+          "World/Asia/IDN/Demography/Urban population",
+          "World/Asia/IND/Demography/Urban population",
+          "World/North Africa/ISR/Demography/Urban population",
+          "World/Asia/KOR/Demography/Urban population",
+          "World/South Africa/LBR/Demography/Urban population",
+          "World/North Africa/MAR/Demography/Urban population",
+          "World/Latam/MEX/Demography/Urban population",
+          "World/South Africa/MOZ/Demography/Urban population",
+          "World/South Africa/NGA/Demography/Urban population",
+          "World/Europe/NLD/Demography/Urban population",
+          "World/Persian Gulf/OMN/Demography/Urban population",
+          "World/Latam/PAN/Demography/Urban population",
+          "World/Latam/PER/Demography/Urban population",
+          "World/Asia/PHL/Demography/Urban population",
+          "World/Europe/POL/Demography/Urban population",
+          "World/Persian Gulf/QAT/Demography/Urban population",
+          "World/Persian Gulf/SAU/Demography/Urban population",
+          "World/South Africa/SEN/Demography/Urban population",
+          "World/Asia/THA/Demography/Urban population",
+          "World/North Africa/TUR/Demography/Urban population",
+          "World/Pair/USA/Demography/Urban population",
+          "World/Latam/VEN/Demography/Urban population",
+          "World/Asia/VNM/Demography/Urban population",
+          "World/Persian Gulf/YEM/Demography/Urban population",
+          "World/South Africa/ZAF/Demography/Urban population",
+          "World/Persian Gulf/ARE/Demography/Urban population (% of total population)",
+          "World/Latam/ARG/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Urban population (% of total population)",
+          "World/Asia/BGD/Demography/Urban population (% of total population)",
+          "World/Latam/BRA/Demography/Urban population (% of total population)",
+          "World/Latam/CHL/Demography/Urban population (% of total population)",
+          "World/Pair/CHN/Demography/Urban population (% of total population)",
+          "World/South Africa/CMR/Demography/Urban population (% of total population)",
+          "World/Latam/COL/Demography/Urban population (% of total population)",
+          "World/Latam/CRI/Demography/Urban population (% of total population)",
+          "World/Europe/DEU/Demography/Urban population (% of total population)",
+          "World/North Africa/DZA/Demography/Urban population (% of total population)",
+          "World/Europe/ESP/Demography/Urban population (% of total population)",
+          "World/Europe/FRA/Demography/Urban population (% of total population)",
+          "World/Europe/GBR/Demography/Urban population (% of total population)",
+          "World/South Africa/GHA/Demography/Urban population (% of total population)",
+          "World/Europe/GRC/Demography/Urban population (% of total population)",
+          "World/Europe/HRV/Demography/Urban population (% of total population)",
+          "World/Asia/IDN/Demography/Urban population (% of total population)",
+          "World/Asia/IND/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/IRQ/Demography/Urban population (% of total population)",
+          "World/North Africa/ISR/Demography/Urban population (% of total population)",
+          "World/South Africa/LBR/Demography/Urban population (% of total population)",
+          "World/North Africa/MAR/Demography/Urban population (% of total population)",
+          "World/Latam/MEX/Demography/Urban population (% of total population)",
+          "World/South Africa/MOZ/Demography/Urban population (% of total population)",
+          "World/South Africa/NGA/Demography/Urban population (% of total population)",
+          "World/Europe/NLD/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Urban population (% of total population)",
+          "World/Latam/PAN/Demography/Urban population (% of total population)",
+          "World/Latam/PER/Demography/Urban population (% of total population)",
+          "World/Europe/POL/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Urban population (% of total population)",
+          "World/South Africa/SEN/Demography/Urban population (% of total population)",
+          "World/Asia/THA/Demography/Urban population (% of total population)",
+          "World/North Africa/TUR/Demography/Urban population (% of total population)",
+          "World/Pair/USA/Demography/Urban population (% of total population)",
+          "World/Latam/VEN/Demography/Urban population (% of total population)",
+          "World/Asia/VNM/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Urban population (% of total population)",
+          "World/South Africa/ZAF/Demography/Urban population (% of total population)",
+          "World/Europe/AUT/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Persian Gulf/AZE/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/BGD/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/CHL/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Pair/CHN/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/COL/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/CRI/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/DEU/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/DZA/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/EGY/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/GBR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/South Africa/GHA/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/HRV/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/IND/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/ISR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/KOR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/MAR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/MEX/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/South Africa/MOZ/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/South Africa/NGA/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/NLD/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/PAN/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/PER/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/PHL/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/POL/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Persian Gulf/SAU/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/SWE/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/THA/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/TUR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/VEN/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/VNM/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Persian Gulf/YEM/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/South Africa/ZAF/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Persian Gulf/ARE/A&D",
+          "World/Latam/ARG/A&D",
+          "World/Europe/AUT/A&D",
+          "World/Persian Gulf/AZE/A&D",
+          "World/Asia/BGD/A&D",
+          "World/Latam/BRA/A&D",
+          "World/Latam/CHL/A&D",
+          "World/Pair/CHN/A&D",
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+          "World/North Africa/EGY/A&D",
+          "World/Europe/GBR/A&D",
+          "World/South Africa/GHA/A&D",
+          "World/Europe/HRV/A&D",
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+          "World/North Africa/MAR/A&D",
+          "World/Latam/MEX/A&D",
+          "World/South Africa/MOZ/A&D",
+          "World/South Africa/NGA/A&D",
+          "World/Europe/NLD/A&D",
+          "World/Persian Gulf/OMN/A&D",
+          "World/Latam/PAN/A&D",
+          "World/Europe/POL/A&D",
+          "World/Persian Gulf/QAT/A&D",
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+          "World/Pair/USA/A&D",
+          "World/Latam/VEN/A&D",
+          "World/Asia/VNM/A&D",
+          "World/Persian Gulf/YEM/A&D",
+          "World/Persian Gulf/ARE/Agriculture",
+          "World/Latam/ARG/Agriculture",
+          "World/Europe/AUT/Agriculture",
+          "World/Persian Gulf/AZE/Agriculture",
+          "World/Asia/BGD/Agriculture",
+          "World/Latam/BRA/Agriculture",
+          "World/Latam/CHL/Agriculture",
+          "World/Pair/CHN/Agriculture",
+          "World/South Africa/CMR/Agriculture",
+          "World/Latam/COL/Agriculture",
+          "World/Latam/CRI/Agriculture",
+          "World/Europe/DEU/Agriculture",
+          "World/North Africa/DZA/Agriculture",
+          "World/North Africa/EGY/Agriculture",
+          "World/Europe/ESP/Agriculture",
+          "World/Europe/FRA/Agriculture",
+          "World/South Africa/GHA/Agriculture",
+          "World/Europe/GRC/Agriculture",
+          "World/Europe/HRV/Agriculture",
+          "World/Asia/IDN/Agriculture",
+          "World/Asia/IND/Agriculture",
+          "World/North Africa/ISR/Agriculture",
+          "World/Asia/KOR/Agriculture",
+          "World/South Africa/LBR/Agriculture",
+          "World/North Africa/MAR/Agriculture",
+          "World/Latam/MEX/Agriculture",
+          "World/South Africa/MOZ/Agriculture",
+          "World/South Africa/NGA/Agriculture",
+          "World/Europe/NLD/Agriculture",
+          "World/Persian Gulf/OMN/Agriculture",
+          "World/Latam/PAN/Agriculture",
+          "World/Latam/PER/Agriculture",
+          "World/Asia/PHL/Agriculture",
+          "World/Europe/POL/Agriculture",
+          "World/Persian Gulf/QAT/Agriculture",
+          "World/Persian Gulf/SAU/Agriculture",
+          "World/South Africa/SEN/Agriculture",
+          "World/Europe/SWE/Agriculture",
+          "World/Asia/THA/Agriculture",
+          "World/North Africa/TUR/Agriculture",
+          "World/Pair/USA/Agriculture",
+          "World/Latam/VEN/Agriculture",
+          "World/Asia/VNM/Agriculture",
+          "World/Persian Gulf/YEM/Agriculture",
+          "World/South Africa/ZAF/Agriculture",
+          "World/Persian Gulf/ARE/Demography",
+          "World/Latam/ARG/Demography",
+          "World/Europe/AUT/Demography",
+          "World/Persian Gulf/AZE/Demography",
+          "World/Asia/BGD/Demography",
+          "World/Latam/BRA/Demography",
+          "World/Latam/CHL/Demography",
+          "World/Pair/CHN/Demography",
+          "World/South Africa/CMR/Demography",
+          "World/Latam/COL/Demography",
+          "World/Latam/CRI/Demography",
+          "World/Europe/DEU/Demography",
+          "World/North Africa/DZA/Demography",
+          "World/North Africa/EGY/Demography",
+          "World/Europe/ESP/Demography",
+          "World/Europe/FRA/Demography",
+          "World/Europe/GBR/Demography",
+          "World/South Africa/GHA/Demography",
+          "World/Europe/GRC/Demography",
+          "World/Europe/HRV/Demography",
+          "World/Asia/IDN/Demography",
+          "World/Asia/IND/Demography",
+          "World/Persian Gulf/IRQ/Demography",
+          "World/North Africa/ISR/Demography",
+          "World/Asia/KOR/Demography",
+          "World/South Africa/LBR/Demography",
+          "World/North Africa/MAR/Demography",
+          "World/Latam/MEX/Demography",
+          "World/South Africa/MOZ/Demography",
+          "World/South Africa/NGA/Demography",
+          "World/Europe/NLD/Demography",
+          "World/Persian Gulf/OMN/Demography",
+          "World/Latam/PAN/Demography",
+          "World/Latam/PER/Demography",
+          "World/Asia/PHL/Demography",
+          "World/Europe/POL/Demography",
+          "World/Persian Gulf/QAT/Demography",
+          "World/Persian Gulf/SAU/Demography",
+          "World/South Africa/SEN/Demography",
+          "World/Europe/SWE/Demography",
+          "World/Asia/THA/Demography",
+          "World/North Africa/TUR/Demography",
+          "World/Pair/USA/Demography",
+          "World/Latam/VEN/Demography",
+          "World/Asia/VNM/Demography",
+          "World/Persian Gulf/YEM/Demography",
+          "World/South Africa/ZAF/Demography",
+          "World/Latam/ARG/Demoraphy",
+          "World/Asia/BGD/Demoraphy",
+          "World/Latam/BRA/Demoraphy",
+          "World/South Africa/CMR/Demoraphy",
+          "World/Latam/COL/Demoraphy",
+          "World/Latam/CRI/Demoraphy",
+          "World/North Africa/DZA/Demoraphy",
+          "World/North Africa/EGY/Demoraphy",
+          "World/South Africa/GHA/Demoraphy",
+          "World/Asia/IND/Demoraphy",
+          "World/South Africa/LBR/Demoraphy",
+          "World/North Africa/MAR/Demoraphy",
+          "World/Latam/MEX/Demoraphy",
+          "World/South Africa/MOZ/Demoraphy",
+          "World/South Africa/NGA/Demoraphy",
+          "World/Latam/PAN/Demoraphy",
+          "World/Latam/PER/Demoraphy",
+          "World/Asia/PHL/Demoraphy",
+          "World/North Africa/TUR/Demoraphy",
+          "World/Latam/VEN/Demoraphy",
+          "World/Asia/VNM/Demoraphy",
+          "World/Persian Gulf/ARE/Economy",
+          "World/Latam/ARG/Economy",
+          "World/Europe/AUT/Economy",
+          "World/Persian Gulf/AZE/Economy",
+          "World/Asia/BGD/Economy",
+          "World/Latam/BRA/Economy",
+          "World/Latam/CHL/Economy",
+          "World/Pair/CHN/Economy",
+          "World/South Africa/CMR/Economy",
+          "World/Latam/COL/Economy",
+          "World/Latam/CRI/Economy",
+          "World/Europe/DEU/Economy",
+          "World/North Africa/DZA/Economy",
+          "World/North Africa/EGY/Economy",
+          "World/Europe/ESP/Economy",
+          "World/Europe/FRA/Economy",
+          "World/Europe/GBR/Economy",
+          "World/South Africa/GHA/Economy",
+          "World/Europe/GRC/Economy",
+          "World/Europe/HRV/Economy",
+          "World/Asia/IDN/Economy",
+          "World/Asia/IND/Economy",
+          "World/Persian Gulf/IRQ/Economy",
+          "World/North Africa/ISR/Economy",
+          "World/Asia/KOR/Economy",
+          "World/South Africa/LBR/Economy",
+          "World/North Africa/MAR/Economy",
+          "World/Latam/MEX/Economy",
+          "World/South Africa/MOZ/Economy",
+          "World/South Africa/NGA/Economy",
+          "World/Europe/NLD/Economy",
+          "World/Persian Gulf/OMN/Economy",
+          "World/Latam/PAN/Economy",
+          "World/Latam/PER/Economy",
+          "World/Asia/PHL/Economy",
+          "World/Europe/POL/Economy",
+          "World/Persian Gulf/QAT/Economy",
+          "World/Persian Gulf/SAU/Economy",
+          "World/South Africa/SEN/Economy",
+          "World/Europe/SWE/Economy",
+          "World/Asia/THA/Economy",
+          "World/North Africa/TUR/Economy",
+          "World/Pair/USA/Economy",
+          "World/Latam/VEN/Economy",
+          "World/Asia/VNM/Economy",
+          "World/Persian Gulf/YEM/Economy",
+          "World/South Africa/ZAF/Economy",
+          "World/Persian Gulf/ARE/Employment",
+          "World/Latam/ARG/Employment",
+          "World/Europe/AUT/Employment",
+          "World/Persian Gulf/AZE/Employment",
+          "World/Asia/BGD/Employment",
+          "World/Latam/BRA/Employment",
+          "World/Latam/CHL/Employment",
+          "World/Pair/CHN/Employment",
+          "World/South Africa/CMR/Employment",
+          "World/Latam/COL/Employment",
+          "World/Latam/CRI/Employment",
+          "World/Europe/DEU/Employment",
+          "World/North Africa/DZA/Employment",
+          "World/North Africa/EGY/Employment",
+          "World/Europe/ESP/Employment",
+          "World/Europe/FRA/Employment",
+          "World/Europe/GBR/Employment",
+          "World/South Africa/GHA/Employment",
+          "World/Europe/GRC/Employment",
+          "World/Europe/HRV/Employment",
+          "World/Asia/IDN/Employment",
+          "World/Asia/IND/Employment",
+          "World/North Africa/ISR/Employment",
+          "World/Asia/KOR/Employment",
+          "World/South Africa/LBR/Employment",
+          "World/North Africa/MAR/Employment",
+          "World/Latam/MEX/Employment",
+          "World/South Africa/MOZ/Employment",
+          "World/South Africa/NGA/Employment",
+          "World/Europe/NLD/Employment",
+          "World/Persian Gulf/OMN/Employment",
+          "World/Latam/PAN/Employment",
+          "World/Latam/PER/Employment",
+          "World/Asia/PHL/Employment",
+          "World/Europe/POL/Employment",
+          "World/Persian Gulf/QAT/Employment",
+          "World/Persian Gulf/SAU/Employment",
+          "World/South Africa/SEN/Employment",
+          "World/Europe/SWE/Employment",
+          "World/Asia/THA/Employment",
+          "World/North Africa/TUR/Employment",
+          "World/Pair/USA/Employment",
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+          "World/Asia/VNM/Employment",
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+          "World/Latam/ARG/Environment",
+          "World/Europe/AUT/Environment",
+          "World/Persian Gulf/AZE/Environment",
+          "World/Asia/BGD/Environment",
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+          "World/Persian Gulf/ARE/Equality",
+          "World/Latam/ARG/Equality",
+          "World/Europe/AUT/Equality",
+          "World/Persian Gulf/AZE/Equality",
+          "World/Asia/BGD/Equality",
+          "World/Latam/BRA/Equality",
+          "World/Latam/CHL/Equality",
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+          "World/Latam/CRI/Equality",
+          "World/Europe/DEU/Equality",
+          "World/North Africa/DZA/Equality",
+          "World/North Africa/EGY/Equality",
+          "World/Europe/ESP/Equality",
+          "World/Europe/FRA/Equality",
+          "World/Europe/GBR/Equality",
+          "World/South Africa/GHA/Equality",
+          "World/Europe/GRC/Equality",
+          "World/Europe/HRV/Equality",
+          "World/Asia/IND/Equality",
+          "World/North Africa/ISR/Equality",
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+          "World/Latam",
+          "World/North Africa",
+          "World/Pair",
+          "World/Persian Gulf",
+          "World/South Africa",
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+          "Adjusted net national income per capita (current US$)",
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+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
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+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
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+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
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+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
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+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
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+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
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+          "Population ages 0-14 (% of total population)",
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+          "Population ages 0-14 (% of total population)",
+          "Population ages 15-64 (% of total population)",
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+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
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+          "Population ages 65 and above (% of total population)",
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+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
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+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
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+          "Population in largest city",
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+          "Population in largest city",
+          "Population in largest city",
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+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
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+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
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+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
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+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "A&D",
+          "A&D",
+          "A&D",
+          "A&D",
+          "A&D",
+          "A&D",
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+          "Demoraphy",
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+          "Demoraphy",
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+          "Demoraphy",
+          "Demoraphy",
+          "Demoraphy",
+          "Demoraphy",
+          "Demoraphy",
+          "Demoraphy",
+          "Economy",
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+          "Environment",
+          "Environment",
+          "Environment",
+          "Environment",
+          "Equality",
+          "Equality",
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+          "Exports",
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+          "Health",
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+          "Imports",
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+          "World/Asia/PHL/Economy/Adjusted net national income (current US$)",
+          "World/Europe/POL/Economy/Adjusted net national income (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted net national income (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted net national income (current US$)",
+          "World/Europe/SWE/Economy/Adjusted net national income (current US$)",
+          "World/Asia/THA/Economy/Adjusted net national income (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted net national income (current US$)",
+          "World/Pair/USA/Economy/Adjusted net national income (current US$)",
+          "World/Latam/VEN/Economy/Adjusted net national income (current US$)",
+          "World/Asia/VNM/Economy/Adjusted net national income (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted net national income (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted net national income (current US$)",
+          "World/Latam/ARG/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/AUT/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/BGD/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/BRA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/CHL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Pair/CHN/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/COL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/CRI/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/DEU/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/ESP/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/FRA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/GBR/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/HRV/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/IDN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/IND/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/KOR/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/MEX/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/NLD/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/PAN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/PER/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/PHL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/POL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/SWE/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/THA/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Pair/USA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/VEN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/VNM/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/ESP/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/HRV/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/SWE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/ESP/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/HRV/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/SWE/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: net national savings (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/ARE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/ARG/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/AUT/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/AZE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/BGD/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/BRA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/CHL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Pair/CHN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/CMR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/COL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/CRI/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/DEU/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/EGY/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/GBR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/GHA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/GRC/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/HRV/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/IND/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/ISR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/KOR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/LBR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/MAR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/MEX/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/MOZ/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/NGA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/NLD/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/OMN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/PAN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/PER/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/PHL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/POL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/QAT/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/SAU/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/SEN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/SWE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/TUR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Pair/USA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/VEN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/VNM/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/YEM/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/AUT/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/BGD/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/BRA/Agriculture/Agricultural land (% of land area)",
+          "World/Pair/CHN/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/CMR/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/CRI/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/DEU/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/DZA/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/EGY/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/ESP/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/FRA/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/GRC/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/IDN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/IND/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/ISR/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/KOR/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/LBR/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/NGA/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/NLD/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/PAN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/PHL/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/POL/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/SWE/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/TUR/Agriculture/Agricultural land (% of land area)",
+          "World/Pair/USA/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/VEN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/VNM/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/ARE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CHL/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/HRV/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/TUR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/AUT/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/BRA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/CHL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/COL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/DZA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/EGY/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/GRC/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/HRV/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/IDN/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/KOR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/MAR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/MEX/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/PHL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/POL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/THA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/TUR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Pair/USA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/VEN/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/VNM/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/BGD/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/BRA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/CHL/Agriculture/Aquaculture production (metric tons)",
+          "World/Pair/CHN/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/CMR/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/COL/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/CRI/Agriculture/Aquaculture production (metric tons)",
+          "World/North Africa/EGY/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/FRA/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/GHA/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/IDN/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/IND/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/KOR/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/MEX/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/NGA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/PER/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/PHL/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/POL/Agriculture/Aquaculture production (metric tons)",
+          "World/Persian Gulf/SAU/Agriculture/Aquaculture production (metric tons)",
+          "World/North Africa/TUR/Agriculture/Aquaculture production (metric tons)",
+          "World/Pair/USA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/VEN/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/VNM/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/ZAF/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/AUT/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (% of land area)",
+          "World/Asia/BGD/Agriculture/Arable land (% of land area)",
+          "World/Latam/BRA/Agriculture/Arable land (% of land area)",
+          "World/Latam/CHL/Agriculture/Arable land (% of land area)",
+          "World/Pair/CHN/Agriculture/Arable land (% of land area)",
+          "World/South Africa/CMR/Agriculture/Arable land (% of land area)",
+          "World/Latam/COL/Agriculture/Arable land (% of land area)",
+          "World/Latam/CRI/Agriculture/Arable land (% of land area)",
+          "World/South Africa/GHA/Agriculture/Arable land (% of land area)",
+          "World/Europe/GRC/Agriculture/Arable land (% of land area)",
+          "World/Asia/IDN/Agriculture/Arable land (% of land area)",
+          "World/Asia/IND/Agriculture/Arable land (% of land area)",
+          "World/Asia/KOR/Agriculture/Arable land (% of land area)",
+          "World/South Africa/LBR/Agriculture/Arable land (% of land area)",
+          "World/North Africa/MAR/Agriculture/Arable land (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Arable land (% of land area)",
+          "World/Latam/PAN/Agriculture/Arable land (% of land area)",
+          "World/Europe/POL/Agriculture/Arable land (% of land area)",
+          "World/Europe/SWE/Agriculture/Arable land (% of land area)",
+          "World/North Africa/TUR/Agriculture/Arable land (% of land area)",
+          "World/Pair/USA/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/ARE/Agriculture/Arable land (hectares per person)",
+          "World/Latam/ARG/Agriculture/Arable land (hectares per person)",
+          "World/Europe/AUT/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (hectares per person)",
+          "World/Asia/BGD/Agriculture/Arable land (hectares per person)",
+          "World/Latam/CHL/Agriculture/Arable land (hectares per person)",
+          "World/Pair/CHN/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/CMR/Agriculture/Arable land (hectares per person)",
+          "World/Latam/COL/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/DZA/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/EGY/Agriculture/Arable land (hectares per person)",
+          "World/Europe/ESP/Agriculture/Arable land (hectares per person)",
+          "World/Europe/FRA/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/GHA/Agriculture/Arable land (hectares per person)",
+          "World/Europe/GRC/Agriculture/Arable land (hectares per person)",
+          "World/Asia/IND/Agriculture/Arable land (hectares per person)",
+          "World/Asia/KOR/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/LBR/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/MAR/Agriculture/Arable land (hectares per person)",
+          "World/Latam/MEX/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/MOZ/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/NGA/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/OMN/Agriculture/Arable land (hectares per person)",
+          "World/Latam/PAN/Agriculture/Arable land (hectares per person)",
+          "World/Latam/PER/Agriculture/Arable land (hectares per person)",
+          "World/Asia/PHL/Agriculture/Arable land (hectares per person)",
+          "World/Europe/POL/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/QAT/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/SAU/Agriculture/Arable land (hectares per person)",
+          "World/Europe/SWE/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/TUR/Agriculture/Arable land (hectares per person)",
+          "World/Pair/USA/Agriculture/Arable land (hectares per person)",
+          "World/Latam/VEN/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/ZAF/Agriculture/Arable land (hectares per person)",
+          "World/Europe/AUT/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (hectares)",
+          "World/Asia/BGD/Agriculture/Arable land (hectares)",
+          "World/Latam/BRA/Agriculture/Arable land (hectares)",
+          "World/Latam/CHL/Agriculture/Arable land (hectares)",
+          "World/Pair/CHN/Agriculture/Arable land (hectares)",
+          "World/South Africa/CMR/Agriculture/Arable land (hectares)",
+          "World/Latam/COL/Agriculture/Arable land (hectares)",
+          "World/Latam/CRI/Agriculture/Arable land (hectares)",
+          "World/South Africa/GHA/Agriculture/Arable land (hectares)",
+          "World/Europe/GRC/Agriculture/Arable land (hectares)",
+          "World/Asia/IDN/Agriculture/Arable land (hectares)",
+          "World/Asia/IND/Agriculture/Arable land (hectares)",
+          "World/Asia/KOR/Agriculture/Arable land (hectares)",
+          "World/South Africa/LBR/Agriculture/Arable land (hectares)",
+          "World/North Africa/MAR/Agriculture/Arable land (hectares)",
+          "World/South Africa/MOZ/Agriculture/Arable land (hectares)",
+          "World/Latam/PAN/Agriculture/Arable land (hectares)",
+          "World/Europe/POL/Agriculture/Arable land (hectares)",
+          "World/Europe/SWE/Agriculture/Arable land (hectares)",
+          "World/North Africa/TUR/Agriculture/Arable land (hectares)",
+          "World/Pair/USA/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/ARE/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/ARG/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/AZE/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/BGD/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Pair/CHN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/CMR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/COL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/CRI/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/DEU/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/DZA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/EGY/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/FRA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/GHA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/HRV/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/IDN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/IND/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/ISR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/LBR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/MAR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/MEX/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/MOZ/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/NGA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/PAN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/PER/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/PHL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/POL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/SAU/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/SEN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/THA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/TUR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Pair/USA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/VEN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/VNM/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/YEM/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/ZAF/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/ARE/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/BGD/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Pair/CHN/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/CRI/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/DZA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/EGY/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/GHA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/IDN/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/IND/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/MAR/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/MEX/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/MOZ/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/PER/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/PHL/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/SAU/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/TUR/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Pair/USA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/VNM/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/YEM/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/ZAF/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/ARE/Economy/Broad money (% of GDP)",
+          "World/Latam/ARG/Economy/Broad money (% of GDP)",
+          "World/Persian Gulf/AZE/Economy/Broad money (% of GDP)",
+          "World/Asia/BGD/Economy/Broad money (% of GDP)",
+          "World/Latam/BRA/Economy/Broad money (% of GDP)",
+          "World/Pair/CHN/Economy/Broad money (% of GDP)",
+          "World/South Africa/CMR/Economy/Broad money (% of GDP)",
+          "World/North Africa/DZA/Economy/Broad money (% of GDP)",
+          "World/Europe/GBR/Economy/Broad money (% of GDP)",
+          "World/Europe/HRV/Economy/Broad money (% of GDP)",
+          "World/Asia/IND/Economy/Broad money (% of GDP)",
+          "World/North Africa/ISR/Economy/Broad money (% of GDP)",
+          "World/Asia/KOR/Economy/Broad money (% of GDP)",
+          "World/North Africa/MAR/Economy/Broad money (% of GDP)",
+          "World/Latam/MEX/Economy/Broad money (% of GDP)",
+          "World/South Africa/MOZ/Economy/Broad money (% of GDP)",
+          "World/Latam/PER/Economy/Broad money (% of GDP)",
+          "World/Asia/PHL/Economy/Broad money (% of GDP)",
+          "World/Europe/POL/Economy/Broad money (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Broad money (% of GDP)",
+          "World/South Africa/SEN/Economy/Broad money (% of GDP)",
+          "World/North Africa/TUR/Economy/Broad money (% of GDP)",
+          "World/Pair/USA/Economy/Broad money (% of GDP)",
+          "World/Latam/VEN/Economy/Broad money (% of GDP)",
+          "World/Asia/VNM/Economy/Broad money (% of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/PHL/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/VNM/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/PHL/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/VNM/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/ARG/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/BRA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/CHL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/COL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/ESP/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/IDN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/MAR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/PER/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/TUR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/South Africa/ZAF/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions (kt)",
+          "World/Latam/ARG/Environment/CO2 emissions (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions (kt)",
+          "World/Latam/CHL/Environment/CO2 emissions (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions (kt)",
+          "World/Latam/COL/Environment/CO2 emissions (kt)",
+          "World/Latam/CRI/Environment/CO2 emissions (kt)",
+          "World/Europe/DEU/Environment/CO2 emissions (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kt)",
+          "World/Europe/GBR/Environment/CO2 emissions (kt)",
+          "World/South Africa/GHA/Environment/CO2 emissions (kt)",
+          "World/Asia/IDN/Environment/CO2 emissions (kt)",
+          "World/Asia/IND/Environment/CO2 emissions (kt)",
+          "World/Asia/KOR/Environment/CO2 emissions (kt)",
+          "World/South Africa/LBR/Environment/CO2 emissions (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions (kt)",
+          "World/Latam/MEX/Environment/CO2 emissions (kt)",
+          "World/South Africa/MOZ/Environment/CO2 emissions (kt)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kt)",
+          "World/Latam/PAN/Environment/CO2 emissions (kt)",
+          "World/Latam/PER/Environment/CO2 emissions (kt)",
+          "World/Asia/PHL/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kt)",
+          "World/South Africa/SEN/Environment/CO2 emissions (kt)",
+          "World/Europe/SWE/Environment/CO2 emissions (kt)",
+          "World/Asia/THA/Environment/CO2 emissions (kt)",
+          "World/North Africa/TUR/Environment/CO2 emissions (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions (kt)",
+          "World/South Africa/ZAF/Environment/CO2 emissions (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/BRA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/CHL/Environment/CO2 emissions (metric tons per capita)",
+          "World/Pair/CHN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/CRI/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/DEU/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/DZA/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/EGY/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/GBR/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/GHA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/GRC/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/IDN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/IND/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/ISR/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/KOR/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/MAR/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/MOZ/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/NLD/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/PAN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/PER/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/PHL/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/SEN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/SWE/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/THA/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/TUR/Environment/CO2 emissions (metric tons per capita)",
+          "World/Pair/USA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/VNM/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/ARG/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/COL/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/ESP/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/IND/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/ISR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/KOR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/MEX/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/South Africa/NGA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/NLD/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/PER/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/POL/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/THA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/TUR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Pair/USA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/CHL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/CMR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/CRI/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/GBR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/GHA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/IDN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/IND/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/MOZ/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/NLD/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/PAN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/PER/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/PHL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/POL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/THA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Europe/AUT/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/BGD/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/BRA/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/CHL/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Pair/CHN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/CMR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/IDN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/IND/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/KOR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/North Africa/MAR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/MEX/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/NGA/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/SEN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/VNM/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/ARE/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/AZE/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/BGD/Industry/Capture fisheries production (metric tons)",
+          "World/Latam/CHL/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/CMR/Industry/Capture fisheries production (metric tons)",
+          "World/Latam/CRI/Industry/Capture fisheries production (metric tons)",
+          "World/Europe/GRC/Industry/Capture fisheries production (metric tons)",
+          "World/Europe/HRV/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/IDN/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/IND/Industry/Capture fisheries production (metric tons)",
+          "World/North Africa/ISR/Industry/Capture fisheries production (metric tons)",
+          "World/North Africa/MAR/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/MOZ/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/NGA/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/OMN/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/PHL/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/SAU/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/THA/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/VNM/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/BGD/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/BRA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/CHL/Agriculture/Cereal yield (kg per hectare)",
+          "World/Pair/CHN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/COL/Agriculture/Cereal yield (kg per hectare)",
+          "World/South Africa/GHA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/IDN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/IND/Agriculture/Cereal yield (kg per hectare)",
+          "World/North Africa/ISR/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/MEX/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/PAN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/PER/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/PHL/Agriculture/Cereal yield (kg per hectare)",
+          "World/Persian Gulf/QAT/Agriculture/Cereal yield (kg per hectare)",
+          "World/Persian Gulf/SAU/Agriculture/Cereal yield (kg per hectare)",
+          "World/North Africa/TUR/Agriculture/Cereal yield (kg per hectare)",
+          "World/Pair/USA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/VNM/Agriculture/Cereal yield (kg per hectare)",
+          "World/Europe/AUT/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/AZE/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/BGD/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Pair/CHN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/COL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/DEU/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/DZA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/GBR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/South Africa/GHA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/IDN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/IND/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/KOR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/MAR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/MEX/Environment/Combustible renewables and waste (% of total energy)",
+          "World/South Africa/MOZ/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/NLD/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/PAN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/PER/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/PHL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/POL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/SAU/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/SWE/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/TUR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Pair/USA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/VNM/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/ARE/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/BGD/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Pair/CHN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/CMR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Latam/CRI/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/DEU/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/DZA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/EGY/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/FRA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/GBR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/GHA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/IDN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/IND/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/ISR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/MAR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Latam/MEX/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/MOZ/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/NLD/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/OMN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/POL/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/QAT/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/SEN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/TUR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Pair/USA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/YEM/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/ZAF/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/AUT/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/AZE/Exports/Commercial service exports (current US$)",
+          "World/Asia/BGD/Exports/Commercial service exports (current US$)",
+          "World/Latam/BRA/Exports/Commercial service exports (current US$)",
+          "World/Latam/CHL/Exports/Commercial service exports (current US$)",
+          "World/Pair/CHN/Exports/Commercial service exports (current US$)",
+          "World/South Africa/CMR/Exports/Commercial service exports (current US$)",
+          "World/Latam/COL/Exports/Commercial service exports (current US$)",
+          "World/Latam/CRI/Exports/Commercial service exports (current US$)",
+          "World/Europe/DEU/Exports/Commercial service exports (current US$)",
+          "World/North Africa/DZA/Exports/Commercial service exports (current US$)",
+          "World/Europe/ESP/Exports/Commercial service exports (current US$)",
+          "World/Europe/FRA/Exports/Commercial service exports (current US$)",
+          "World/Europe/GBR/Exports/Commercial service exports (current US$)",
+          "World/South Africa/GHA/Exports/Commercial service exports (current US$)",
+          "World/Europe/GRC/Exports/Commercial service exports (current US$)",
+          "World/Europe/HRV/Exports/Commercial service exports (current US$)",
+          "World/Asia/IDN/Exports/Commercial service exports (current US$)",
+          "World/Asia/IND/Exports/Commercial service exports (current US$)",
+          "World/North Africa/ISR/Exports/Commercial service exports (current US$)",
+          "World/Asia/KOR/Exports/Commercial service exports (current US$)",
+          "World/North Africa/MAR/Exports/Commercial service exports (current US$)",
+          "World/Europe/NLD/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/OMN/Exports/Commercial service exports (current US$)",
+          "World/Latam/PAN/Exports/Commercial service exports (current US$)",
+          "World/Latam/PER/Exports/Commercial service exports (current US$)",
+          "World/Asia/PHL/Exports/Commercial service exports (current US$)",
+          "World/Europe/POL/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/QAT/Exports/Commercial service exports (current US$)",
+          "World/South Africa/SEN/Exports/Commercial service exports (current US$)",
+          "World/Europe/SWE/Exports/Commercial service exports (current US$)",
+          "World/Asia/THA/Exports/Commercial service exports (current US$)",
+          "World/North Africa/TUR/Exports/Commercial service exports (current US$)",
+          "World/Pair/USA/Exports/Commercial service exports (current US$)",
+          "World/Asia/VNM/Exports/Commercial service exports (current US$)",
+          "World/South Africa/ZAF/Exports/Commercial service exports (current US$)",
+          "World/Latam/ARG/Imports/Commercial service imports (current US$)",
+          "World/Europe/AUT/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/AZE/Imports/Commercial service imports (current US$)",
+          "World/Asia/BGD/Imports/Commercial service imports (current US$)",
+          "World/Latam/BRA/Imports/Commercial service imports (current US$)",
+          "World/Latam/CHL/Imports/Commercial service imports (current US$)",
+          "World/Pair/CHN/Imports/Commercial service imports (current US$)",
+          "World/South Africa/CMR/Imports/Commercial service imports (current US$)",
+          "World/Latam/COL/Imports/Commercial service imports (current US$)",
+          "World/Latam/CRI/Imports/Commercial service imports (current US$)",
+          "World/Europe/DEU/Imports/Commercial service imports (current US$)",
+          "World/North Africa/DZA/Imports/Commercial service imports (current US$)",
+          "World/North Africa/EGY/Imports/Commercial service imports (current US$)",
+          "World/Europe/ESP/Imports/Commercial service imports (current US$)",
+          "World/Europe/FRA/Imports/Commercial service imports (current US$)",
+          "World/Europe/GBR/Imports/Commercial service imports (current US$)",
+          "World/South Africa/GHA/Imports/Commercial service imports (current US$)",
+          "World/Europe/HRV/Imports/Commercial service imports (current US$)",
+          "World/Asia/IDN/Imports/Commercial service imports (current US$)",
+          "World/Asia/IND/Imports/Commercial service imports (current US$)",
+          "World/North Africa/ISR/Imports/Commercial service imports (current US$)",
+          "World/Asia/KOR/Imports/Commercial service imports (current US$)",
+          "World/North Africa/MAR/Imports/Commercial service imports (current US$)",
+          "World/Latam/MEX/Imports/Commercial service imports (current US$)",
+          "World/South Africa/MOZ/Imports/Commercial service imports (current US$)",
+          "World/South Africa/NGA/Imports/Commercial service imports (current US$)",
+          "World/Europe/NLD/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/OMN/Imports/Commercial service imports (current US$)",
+          "World/Latam/PAN/Imports/Commercial service imports (current US$)",
+          "World/Latam/PER/Imports/Commercial service imports (current US$)",
+          "World/Asia/PHL/Imports/Commercial service imports (current US$)",
+          "World/Europe/POL/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/QAT/Imports/Commercial service imports (current US$)",
+          "World/South Africa/SEN/Imports/Commercial service imports (current US$)",
+          "World/Europe/SWE/Imports/Commercial service imports (current US$)",
+          "World/Asia/THA/Imports/Commercial service imports (current US$)",
+          "World/North Africa/TUR/Imports/Commercial service imports (current US$)",
+          "World/Pair/USA/Imports/Commercial service imports (current US$)",
+          "World/Asia/VNM/Imports/Commercial service imports (current US$)",
+          "World/South Africa/ZAF/Imports/Commercial service imports (current US$)",
+          "World/Latam/ARG/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/BGD/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/BRA/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/CMR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/COL/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/CRI/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/DZA/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/EGY/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/GHA/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/IND/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/LBR/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/MAR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/MEX/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/MOZ/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/NGA/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/PAN/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/PER/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/PHL/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/TUR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/VEN/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/VNM/Demoraphy/Completeness of birth registration (%)",
+          "World/Persian Gulf/ARE/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/AUT/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/AZE/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/BGD/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/BRA/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/CHL/Economy/Consumer price index (2010 = 100)",
+          "World/Pair/CHN/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/CMR/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/COL/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/CRI/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/DEU/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/DZA/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/EGY/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/ESP/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/FRA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/GBR/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/GHA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/GRC/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/IDN/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/IND/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/ISR/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/KOR/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/LBR/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/MAR/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/MEX/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/MOZ/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/NGA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/NLD/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/OMN/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/PAN/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/PER/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/PHL/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/POL/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/QAT/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/SAU/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/SEN/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/SWE/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/THA/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/TUR/Economy/Consumer price index (2010 = 100)",
+          "World/Pair/USA/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/VEN/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/VNM/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/YEM/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/ZAF/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/ARE/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/BGD/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/CHL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Pair/CHN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/CMR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/COL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/CRI/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/DEU/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/ESP/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/FRA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/GBR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/GHA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/IDN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/IND/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/ISR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/KOR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/LBR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/MAR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/MEX/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/MOZ/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/NLD/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/OMN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/PAN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/PER/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/PHL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/POL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/QAT/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/SAU/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/SWE/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/THA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/TUR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Pair/USA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/VNM/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/ZAF/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/AZE/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/BGD/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/BRA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/CHL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Pair/CHN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/CMR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/COL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/CRI/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/IDN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/IND/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/MEX/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/PER/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/PHL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/THA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Pair/USA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/VNM/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/ARG/Health/Current health expenditure (% of GDP)",
+          "World/Europe/AUT/Health/Current health expenditure (% of GDP)",
+          "World/Latam/CHL/Health/Current health expenditure (% of GDP)",
+          "World/Pair/CHN/Health/Current health expenditure (% of GDP)",
+          "World/South Africa/CMR/Health/Current health expenditure (% of GDP)",
+          "World/Latam/COL/Health/Current health expenditure (% of GDP)",
+          "World/Latam/CRI/Health/Current health expenditure (% of GDP)",
+          "World/Europe/ESP/Health/Current health expenditure (% of GDP)",
+          "World/Europe/FRA/Health/Current health expenditure (% of GDP)",
+          "World/Europe/GBR/Health/Current health expenditure (% of GDP)",
+          "World/Europe/GRC/Health/Current health expenditure (% of GDP)",
+          "World/Asia/IDN/Health/Current health expenditure (% of GDP)",
+          "World/Asia/IND/Health/Current health expenditure (% of GDP)",
+          "World/Asia/KOR/Health/Current health expenditure (% of GDP)",
+          "World/South Africa/MOZ/Health/Current health expenditure (% of GDP)",
+          "World/Europe/NLD/Health/Current health expenditure (% of GDP)",
+          "World/Latam/PER/Health/Current health expenditure (% of GDP)",
+          "World/Europe/POL/Health/Current health expenditure (% of GDP)",
+          "World/Europe/SWE/Health/Current health expenditure (% of GDP)",
+          "World/Asia/THA/Health/Current health expenditure (% of GDP)",
+          "World/Pair/USA/Health/Current health expenditure (% of GDP)",
+          "World/Asia/VNM/Health/Current health expenditure (% of GDP)",
+          "World/Persian Gulf/YEM/Health/Current health expenditure (% of GDP)",
+          "World/Persian Gulf/ARE/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Current health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/CMR/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/GHA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/GRC/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/MEX/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/MOZ/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/NGA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/OMN/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/SEN/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Current health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/VEN/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/BGD/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/CHL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Pair/CHN/Economy/Customs and other import duties (% of tax revenue)",
+          "World/South Africa/CMR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/COL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/CRI/Economy/Customs and other import duties (% of tax revenue)",
+          "World/North Africa/EGY/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/ESP/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/HRV/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/IND/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/KOR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/North Africa/MAR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/MEX/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/PAN/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/PER/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/POL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/THA/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/BGD/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/CHL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Pair/CHN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/CMR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/COL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/CRI/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/DEU/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/DZA/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/EGY/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/FRA/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/GRC/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/HRV/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/IND/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/KOR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/LBR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/MEX/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/OMN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/PER/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/POL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/SAU/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/SEN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/SWE/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/TUR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/VEN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/VNM/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/ARE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/AZE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/BGD/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/CHL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Pair/CHN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/CMR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/COL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/CRI/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/DEU/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/DZA/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/EGY/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/GRC/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/HRV/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/IND/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/KOR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/LBR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/MAR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/MEX/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/OMN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/PAN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/PER/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/POL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/SAU/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/SEN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/SWE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/TUR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/VEN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/VNM/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/ARG/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/GRC/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/MEX/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/MOZ/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/OMN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/TUR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/CMR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/EGY/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/GHA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/NGA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/SEN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/AUT/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/BGD/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/BRA/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/CHL/Environment/Electric power consumption (kWh per capita)",
+          "World/Pair/CHN/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/CMR/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/COL/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/CRI/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/DZA/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/EGY/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/GBR/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/GRC/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/HRV/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/IDN/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/IND/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/KOR/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/MAR/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/MEX/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/MOZ/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/NGA/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/OMN/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/PAN/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/PER/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/PHL/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/POL/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/SAU/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/SEN/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/SWE/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/THA/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/TUR/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/VNM/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/YEM/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/ARE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CHL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/COL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/GHA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/LBR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/MEX/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/NGA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PAN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PER/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/PHL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/QAT/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/TUR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GRC/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/HRV/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/SAU/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Pair/USA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/ARE/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CHL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/COL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/GHA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GRC/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/HRV/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/LBR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/MEX/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/NGA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PAN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PER/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/PHL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/THA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/TUR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Pair/USA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/BGD/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Pair/CHN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/CMR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/CRI/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/DEU/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/DZA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/GBR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/GHA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/IDN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/IND/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/ISR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/KOR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/MEX/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/MOZ/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/NGA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/NLD/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Persian Gulf/OMN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/PAN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/PER/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/PHL/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/POL/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/SWE/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/THA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/TUR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Pair/USA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/VEN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/BGD/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/BRA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/CHL/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Pair/CHN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/CRI/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/DZA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/GBR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/IDN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/IND/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/KOR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/MAR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/South Africa/MOZ/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/South Africa/NGA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/NLD/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Persian Gulf/OMN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/PAN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/PER/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Persian Gulf/SAU/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/THA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/TUR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Pair/USA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/VNM/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/AUT/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/AZE/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/BGD/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Pair/CHN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/CMR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/COL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/CRI/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/DEU/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/DZA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/ESP/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/FRA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/GBR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/GHA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/HRV/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/IDN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/IND/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/ISR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/KOR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/MEX/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/MOZ/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/NGA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/NLD/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/PAN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/PHL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/POL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/SAU/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/SWE/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/TUR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Pair/USA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/ZAF/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/ARE/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/DEU/Exports/Export unit value index (2000 = 100)",
+          "World/North Africa/DZA/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/FRA/Exports/Export unit value index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/GRC/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/HRV/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/POL/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/SWE/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/THA/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/YEM/Exports/Export unit value index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Exports/Export value index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export value index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export value index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export value index (2000 = 100)",
+          "World/Latam/CHL/Exports/Export value index (2000 = 100)",
+          "World/Pair/CHN/Exports/Export value index (2000 = 100)",
+          "World/Latam/COL/Exports/Export value index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export value index (2000 = 100)",
+          "World/Europe/DEU/Exports/Export value index (2000 = 100)",
+          "World/North Africa/DZA/Exports/Export value index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export value index (2000 = 100)",
+          "World/Europe/FRA/Exports/Export value index (2000 = 100)",
+          "World/Europe/GBR/Exports/Export value index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export value index (2000 = 100)",
+          "World/Asia/IDN/Exports/Export value index (2000 = 100)",
+          "World/Asia/IND/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/IRQ/Exports/Export value index (2000 = 100)",
+          "World/Asia/KOR/Exports/Export value index (2000 = 100)",
+          "World/North Africa/MAR/Exports/Export value index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export value index (2000 = 100)",
+          "World/South Africa/MOZ/Exports/Export value index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/OMN/Exports/Export value index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export value index (2000 = 100)",
+          "World/Latam/PER/Exports/Export value index (2000 = 100)",
+          "World/Asia/PHL/Exports/Export value index (2000 = 100)",
+          "World/Europe/POL/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/SAU/Exports/Export value index (2000 = 100)",
+          "World/South Africa/SEN/Exports/Export value index (2000 = 100)",
+          "World/Europe/SWE/Exports/Export value index (2000 = 100)",
+          "World/Asia/THA/Exports/Export value index (2000 = 100)",
+          "World/North Africa/TUR/Exports/Export value index (2000 = 100)",
+          "World/Pair/USA/Exports/Export value index (2000 = 100)",
+          "World/Latam/VEN/Exports/Export value index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export value index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Exports/Export volume index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export volume index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export volume index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export volume index (2000 = 100)",
+          "World/Latam/CHL/Exports/Export volume index (2000 = 100)",
+          "World/Pair/CHN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/COL/Exports/Export volume index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export volume index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export volume index (2000 = 100)",
+          "World/Europe/GRC/Exports/Export volume index (2000 = 100)",
+          "World/Asia/IDN/Exports/Export volume index (2000 = 100)",
+          "World/Asia/IND/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/ISR/Exports/Export volume index (2000 = 100)",
+          "World/Asia/KOR/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/MAR/Exports/Export volume index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/MOZ/Exports/Export volume index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/OMN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/PER/Exports/Export volume index (2000 = 100)",
+          "World/Asia/PHL/Exports/Export volume index (2000 = 100)",
+          "World/Europe/POL/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/QAT/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/SAU/Exports/Export volume index (2000 = 100)",
+          "World/Asia/THA/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/TUR/Exports/Export volume index (2000 = 100)",
+          "World/Pair/USA/Exports/Export volume index (2000 = 100)",
+          "World/Latam/VEN/Exports/Export volume index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/YEM/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export volume index (2000 = 100)",
+          "World/Europe/AUT/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/BGD/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/BRA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/CHL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Pair/CHN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/COL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/CRI/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/DEU/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/ESP/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/FRA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/GBR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/HRV/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/IDN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/IND/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/IRQ/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/KOR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/MEX/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/MOZ/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/NLD/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/PAN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/PER/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/PHL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/POL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/SWE/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/THA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Pair/USA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/VEN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/VNM/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/ARE/Exports/Exports of goods and services (current US$)",
+          "World/Europe/AUT/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/AZE/Exports/Exports of goods and services (current US$)",
+          "World/Asia/BGD/Exports/Exports of goods and services (current US$)",
+          "World/Latam/BRA/Exports/Exports of goods and services (current US$)",
+          "World/Latam/CHL/Exports/Exports of goods and services (current US$)",
+          "World/Pair/CHN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/COL/Exports/Exports of goods and services (current US$)",
+          "World/Latam/CRI/Exports/Exports of goods and services (current US$)",
+          "World/Europe/DEU/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/DZA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/ESP/Exports/Exports of goods and services (current US$)",
+          "World/Europe/FRA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/GBR/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/GHA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/HRV/Exports/Exports of goods and services (current US$)",
+          "World/Asia/IDN/Exports/Exports of goods and services (current US$)",
+          "World/Asia/IND/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/IRQ/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/ISR/Exports/Exports of goods and services (current US$)",
+          "World/Asia/KOR/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/MAR/Exports/Exports of goods and services (current US$)",
+          "World/Latam/MEX/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/MOZ/Exports/Exports of goods and services (current US$)",
+          "World/Europe/NLD/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/OMN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/PAN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/PER/Exports/Exports of goods and services (current US$)",
+          "World/Asia/PHL/Exports/Exports of goods and services (current US$)",
+          "World/Europe/POL/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/QAT/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/SAU/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/SEN/Exports/Exports of goods and services (current US$)",
+          "World/Europe/SWE/Exports/Exports of goods and services (current US$)",
+          "World/Asia/THA/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/TUR/Exports/Exports of goods and services (current US$)",
+          "World/Pair/USA/Exports/Exports of goods and services (current US$)",
+          "World/Latam/VEN/Exports/Exports of goods and services (current US$)",
+          "World/Asia/VNM/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/ZAF/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/ARE/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/Final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/ARG/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/CHL/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/COL/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/CRI/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/EGY/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/GHA/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/GRC/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/HRV/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/IDN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/IND/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/LBR/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/MAR/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/MEX/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/MOZ/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/NGA/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/PAN/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/PER/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/PHL/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/POL/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/SEN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/THA/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/TUR/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/VEN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/VNM/R&D/Firms offering formal training (% of firms)",
+          "World/Persian Gulf/YEM/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/ZAF/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/ARG/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Persian Gulf/AZE/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/BGD/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/CHL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/CMR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/COL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/CRI/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/EGY/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/GHA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Europe/GRC/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/IDN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/IND/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/LBR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/MAR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/MEX/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/MOZ/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/NGA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/PAN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/PER/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/PHL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Europe/POL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/SEN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/THA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/TUR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/VEN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/VNM/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Persian Gulf/YEM/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/ZAF/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/ARG/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/AZE/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/BGD/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/CHL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/CMR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/COL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/CRI/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/North Africa/EGY/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/GHA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Europe/GRC/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/IDN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/LBR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/MEX/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/MOZ/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/NGA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/PAN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/PER/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/PHL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Europe/POL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/SEN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/THA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/North Africa/TUR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/VEN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/VNM/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/YEM/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/ZAF/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/ARE/Internet/Fixed broadband subscriptions",
+          "World/Latam/ARG/Internet/Fixed broadband subscriptions",
+          "World/Europe/AUT/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/AZE/Internet/Fixed broadband subscriptions",
+          "World/Asia/BGD/Internet/Fixed broadband subscriptions",
+          "World/Latam/BRA/Internet/Fixed broadband subscriptions",
+          "World/Latam/CHL/Internet/Fixed broadband subscriptions",
+          "World/Pair/CHN/Internet/Fixed broadband subscriptions",
+          "World/South Africa/CMR/Internet/Fixed broadband subscriptions",
+          "World/Latam/COL/Internet/Fixed broadband subscriptions",
+          "World/Latam/CRI/Internet/Fixed broadband subscriptions",
+          "World/Europe/DEU/Internet/Fixed broadband subscriptions",
+          "World/North Africa/DZA/Internet/Fixed broadband subscriptions",
+          "World/North Africa/EGY/Internet/Fixed broadband subscriptions",
+          "World/Europe/ESP/Internet/Fixed broadband subscriptions",
+          "World/Europe/FRA/Internet/Fixed broadband subscriptions",
+          "World/Europe/GBR/Internet/Fixed broadband subscriptions",
+          "World/South Africa/GHA/Internet/Fixed broadband subscriptions",
+          "World/Europe/GRC/Internet/Fixed broadband subscriptions",
+          "World/Europe/HRV/Internet/Fixed broadband subscriptions",
+          "World/Asia/IDN/Internet/Fixed broadband subscriptions",
+          "World/Asia/IND/Internet/Fixed broadband subscriptions",
+          "World/North Africa/ISR/Internet/Fixed broadband subscriptions",
+          "World/Asia/KOR/Internet/Fixed broadband subscriptions",
+          "World/South Africa/LBR/Internet/Fixed broadband subscriptions",
+          "World/North Africa/MAR/Internet/Fixed broadband subscriptions",
+          "World/Latam/MEX/Internet/Fixed broadband subscriptions",
+          "World/South Africa/MOZ/Internet/Fixed broadband subscriptions",
+          "World/Europe/NLD/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/OMN/Internet/Fixed broadband subscriptions",
+          "World/Latam/PAN/Internet/Fixed broadband subscriptions",
+          "World/Latam/PER/Internet/Fixed broadband subscriptions",
+          "World/Asia/PHL/Internet/Fixed broadband subscriptions",
+          "World/Europe/POL/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/QAT/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/SAU/Internet/Fixed broadband subscriptions",
+          "World/South Africa/SEN/Internet/Fixed broadband subscriptions",
+          "World/Europe/SWE/Internet/Fixed broadband subscriptions",
+          "World/Asia/THA/Internet/Fixed broadband subscriptions",
+          "World/North Africa/TUR/Internet/Fixed broadband subscriptions",
+          "World/Pair/USA/Internet/Fixed broadband subscriptions",
+          "World/Latam/VEN/Internet/Fixed broadband subscriptions",
+          "World/Asia/VNM/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/YEM/Internet/Fixed broadband subscriptions",
+          "World/South Africa/ZAF/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/ARE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/ARG/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/AUT/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/AZE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/BGD/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/BRA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/CHL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Pair/CHN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/CMR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/COL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/CRI/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/DEU/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/DZA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/EGY/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/ESP/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/FRA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/GBR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/GHA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/GRC/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/HRV/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/IDN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/IND/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/ISR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/KOR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/LBR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/MAR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/MEX/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/MOZ/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/NLD/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/OMN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/PAN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/PER/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/PHL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/POL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/QAT/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/SAU/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/SEN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/SWE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/THA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/TUR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Pair/USA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/VEN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/VNM/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/YEM/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/ZAF/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/ARE/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/AZE/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/BGD/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/BRA/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/CHL/Industry/Food production index (2014-2016 = 100)",
+          "World/Pair/CHN/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/CMR/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/COL/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/CRI/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/IDN/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/IND/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/ISR/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/LBR/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/MEX/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Industry/Food production index (2014-2016 = 100)",
+          "World/Europe/NLD/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/PAN/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/PER/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/PHL/Industry/Food production index (2014-2016 = 100)",
+          "World/Europe/POL/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/SAU/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/THA/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Industry/Food production index (2014-2016 = 100)",
+          "World/Pair/USA/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/VNM/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/ZAF/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/ARE/Demography/Forest area (% of land area)",
+          "World/Latam/ARG/Demography/Forest area (% of land area)",
+          "World/Europe/AUT/Demography/Forest area (% of land area)",
+          "World/Persian Gulf/AZE/Demography/Forest area (% of land area)",
+          "World/Asia/BGD/Demography/Forest area (% of land area)",
+          "World/Latam/BRA/Demography/Forest area (% of land area)",
+          "World/Latam/CHL/Demography/Forest area (% of land area)",
+          "World/Pair/CHN/Demography/Forest area (% of land area)",
+          "World/South Africa/CMR/Demography/Forest area (% of land area)",
+          "World/Latam/COL/Demography/Forest area (% of land area)",
+          "World/Latam/CRI/Demography/Forest area (% of land area)",
+          "World/North Africa/DZA/Demography/Forest area (% of land area)",
+          "World/North Africa/EGY/Demography/Forest area (% of land area)",
+          "World/Europe/ESP/Demography/Forest area (% of land area)",
+          "World/Europe/FRA/Demography/Forest area (% of land area)",
+          "World/Europe/GBR/Demography/Forest area (% of land area)",
+          "World/Europe/GRC/Demography/Forest area (% of land area)",
+          "World/Europe/HRV/Demography/Forest area (% of land area)",
+          "World/Asia/IDN/Demography/Forest area (% of land area)",
+          "World/Asia/IND/Demography/Forest area (% of land area)",
+          "World/Asia/KOR/Demography/Forest area (% of land area)",
+          "World/South Africa/LBR/Demography/Forest area (% of land area)",
+          "World/North Africa/MAR/Demography/Forest area (% of land area)",
+          "World/Latam/MEX/Demography/Forest area (% of land area)",
+          "World/South Africa/MOZ/Demography/Forest area (% of land area)",
+          "World/South Africa/NGA/Demography/Forest area (% of land area)",
+          "World/Europe/NLD/Demography/Forest area (% of land area)",
+          "World/Latam/PAN/Demography/Forest area (% of land area)",
+          "World/Latam/PER/Demography/Forest area (% of land area)",
+          "World/Europe/POL/Demography/Forest area (% of land area)",
+          "World/South Africa/SEN/Demography/Forest area (% of land area)",
+          "World/North Africa/TUR/Demography/Forest area (% of land area)",
+          "World/Pair/USA/Demography/Forest area (% of land area)",
+          "World/Latam/VEN/Demography/Forest area (% of land area)",
+          "World/Asia/VNM/Demography/Forest area (% of land area)",
+          "World/South Africa/ZAF/Demography/Forest area (% of land area)",
+          "World/Persian Gulf/ARE/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/AUT/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/AZE/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/BGD/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/BRA/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/CHL/Principal/GDP deflator (base year varies by country)",
+          "World/Pair/CHN/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/CMR/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/COL/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/CRI/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/DEU/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/DZA/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/EGY/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/ESP/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/FRA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/GBR/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/GHA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/GRC/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/HRV/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/IDN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/IND/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/ISR/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/KOR/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/LBR/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/MAR/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/MEX/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/MOZ/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/NGA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/NLD/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/OMN/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/PAN/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/PER/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/PHL/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/POL/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/QAT/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/SAU/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/SEN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/THA/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/TUR/Principal/GDP deflator (base year varies by country)",
+          "World/Pair/USA/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/VEN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/VNM/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/YEM/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/ZAF/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/ARE/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/AUT/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/AZE/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/BGD/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/BRA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/CHL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Pair/CHN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/CMR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/COL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/CRI/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/DEU/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/DZA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/EGY/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/ESP/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/FRA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/GBR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/GHA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/GRC/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/HRV/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/IDN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/IND/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/ISR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/KOR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/LBR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/MAR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/MEX/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/MOZ/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/NGA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/NLD/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/OMN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/PAN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/PER/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/PHL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/POL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/QAT/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/SAU/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/SEN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/THA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/TUR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Pair/USA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/VEN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/VNM/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/YEM/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/ZAF/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/ARE/Principal/GDP per capita (current US$)",
+          "World/Latam/ARG/Principal/GDP per capita (current US$)",
+          "World/Europe/AUT/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/AZE/Principal/GDP per capita (current US$)",
+          "World/Asia/BGD/Principal/GDP per capita (current US$)",
+          "World/Latam/BRA/Principal/GDP per capita (current US$)",
+          "World/Latam/CHL/Principal/GDP per capita (current US$)",
+          "World/Pair/CHN/Principal/GDP per capita (current US$)",
+          "World/South Africa/CMR/Principal/GDP per capita (current US$)",
+          "World/Latam/COL/Principal/GDP per capita (current US$)",
+          "World/Latam/CRI/Principal/GDP per capita (current US$)",
+          "World/Europe/DEU/Principal/GDP per capita (current US$)",
+          "World/North Africa/DZA/Principal/GDP per capita (current US$)",
+          "World/North Africa/EGY/Principal/GDP per capita (current US$)",
+          "World/Europe/ESP/Principal/GDP per capita (current US$)",
+          "World/Europe/FRA/Principal/GDP per capita (current US$)",
+          "World/Europe/GBR/Principal/GDP per capita (current US$)",
+          "World/South Africa/GHA/Principal/GDP per capita (current US$)",
+          "World/Europe/GRC/Principal/GDP per capita (current US$)",
+          "World/Europe/HRV/Principal/GDP per capita (current US$)",
+          "World/Asia/IDN/Principal/GDP per capita (current US$)",
+          "World/Asia/IND/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/IRQ/Principal/GDP per capita (current US$)",
+          "World/North Africa/ISR/Principal/GDP per capita (current US$)",
+          "World/Asia/KOR/Principal/GDP per capita (current US$)",
+          "World/North Africa/MAR/Principal/GDP per capita (current US$)",
+          "World/Latam/MEX/Principal/GDP per capita (current US$)",
+          "World/South Africa/MOZ/Principal/GDP per capita (current US$)",
+          "World/South Africa/NGA/Principal/GDP per capita (current US$)",
+          "World/Europe/NLD/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/OMN/Principal/GDP per capita (current US$)",
+          "World/Latam/PAN/Principal/GDP per capita (current US$)",
+          "World/Latam/PER/Principal/GDP per capita (current US$)",
+          "World/Asia/PHL/Principal/GDP per capita (current US$)",
+          "World/Europe/POL/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/QAT/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/SAU/Principal/GDP per capita (current US$)",
+          "World/South Africa/SEN/Principal/GDP per capita (current US$)",
+          "World/Europe/SWE/Principal/GDP per capita (current US$)",
+          "World/Asia/THA/Principal/GDP per capita (current US$)",
+          "World/North Africa/TUR/Principal/GDP per capita (current US$)",
+          "World/Pair/USA/Principal/GDP per capita (current US$)",
+          "World/Latam/VEN/Principal/GDP per capita (current US$)",
+          "World/Asia/VNM/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/YEM/Principal/GDP per capita (current US$)",
+          "World/South Africa/ZAF/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/ARE/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/ARG/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/AZE/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/BGD/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/BRA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/CHL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Pair/CHN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/CMR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/COL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/CRI/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/EGY/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/FRA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/GBR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/GHA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/GRC/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/IDN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/IND/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/ISR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/KOR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/MAR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/MOZ/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/NGA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/OMN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/PAN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/PER/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/PHL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/POL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/QAT/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/SAU/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/SEN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/THA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/TUR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Pair/USA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/VNM/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/ZAF/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/ARG/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/AUT/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/AZE/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/BGD/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/BRA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/CHL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Pair/CHN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/CMR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/COL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/CRI/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/DEU/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/EGY/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/ESP/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/FRA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/GBR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/GHA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/HRV/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/IDN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/IND/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/ISR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/KOR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/MAR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/MEX/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/MOZ/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/NGA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/NLD/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/PAN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/PHL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/POL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/SWE/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/THA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/TUR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Pair/USA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/VNM/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/ZAF/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/AUT/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/AZE/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/BGD/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Pair/CHN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/CMR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/COL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/CRI/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/DEU/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/DZA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/ESP/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/FRA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/GBR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/GHA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/HRV/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/IDN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/IND/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/ISR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/KOR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/MEX/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/MOZ/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/NGA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/NLD/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/PAN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/PHL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/POL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/SAU/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/SWE/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/TUR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Pair/USA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/ZAF/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/ARE/Economy/GNI (current US$)",
+          "World/Latam/ARG/Economy/GNI (current US$)",
+          "World/Europe/AUT/Economy/GNI (current US$)",
+          "World/Persian Gulf/AZE/Economy/GNI (current US$)",
+          "World/Asia/BGD/Economy/GNI (current US$)",
+          "World/Latam/BRA/Economy/GNI (current US$)",
+          "World/Latam/CHL/Economy/GNI (current US$)",
+          "World/Pair/CHN/Economy/GNI (current US$)",
+          "World/South Africa/CMR/Economy/GNI (current US$)",
+          "World/Latam/COL/Economy/GNI (current US$)",
+          "World/Latam/CRI/Economy/GNI (current US$)",
+          "World/Europe/DEU/Economy/GNI (current US$)",
+          "World/North Africa/DZA/Economy/GNI (current US$)",
+          "World/North Africa/EGY/Economy/GNI (current US$)",
+          "World/Europe/ESP/Economy/GNI (current US$)",
+          "World/Europe/FRA/Economy/GNI (current US$)",
+          "World/Europe/GBR/Economy/GNI (current US$)",
+          "World/South Africa/GHA/Economy/GNI (current US$)",
+          "World/Europe/HRV/Economy/GNI (current US$)",
+          "World/Asia/IDN/Economy/GNI (current US$)",
+          "World/Asia/IND/Economy/GNI (current US$)",
+          "World/Persian Gulf/IRQ/Economy/GNI (current US$)",
+          "World/North Africa/ISR/Economy/GNI (current US$)",
+          "World/Asia/KOR/Economy/GNI (current US$)",
+          "World/South Africa/LBR/Economy/GNI (current US$)",
+          "World/North Africa/MAR/Economy/GNI (current US$)",
+          "World/Latam/MEX/Economy/GNI (current US$)",
+          "World/South Africa/MOZ/Economy/GNI (current US$)",
+          "World/South Africa/NGA/Economy/GNI (current US$)",
+          "World/Europe/NLD/Economy/GNI (current US$)",
+          "World/Persian Gulf/OMN/Economy/GNI (current US$)",
+          "World/Latam/PAN/Economy/GNI (current US$)",
+          "World/Latam/PER/Economy/GNI (current US$)",
+          "World/Asia/PHL/Economy/GNI (current US$)",
+          "World/Europe/POL/Economy/GNI (current US$)",
+          "World/Persian Gulf/QAT/Economy/GNI (current US$)",
+          "World/Persian Gulf/SAU/Economy/GNI (current US$)",
+          "World/South Africa/SEN/Economy/GNI (current US$)",
+          "World/Europe/SWE/Economy/GNI (current US$)",
+          "World/Asia/THA/Economy/GNI (current US$)",
+          "World/North Africa/TUR/Economy/GNI (current US$)",
+          "World/Pair/USA/Economy/GNI (current US$)",
+          "World/Latam/VEN/Economy/GNI (current US$)",
+          "World/Asia/VNM/Economy/GNI (current US$)",
+          "World/Persian Gulf/YEM/Economy/GNI (current US$)",
+          "World/South Africa/ZAF/Economy/GNI (current US$)",
+          "World/Persian Gulf/ARE/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/General government final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/General government final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/AUT/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/BRA/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/Goods exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/COL/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/FRA/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/IDN/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/IND/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/IRQ/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/LBR/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/MEX/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/MOZ/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/PER/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/PHL/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/POL/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/THA/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Goods exports (BoP, current US$)",
+          "World/Pair/USA/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/VEN/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/VNM/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/ARG/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/AUT/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/BGD/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/BRA/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/CHL/Imports/Goods imports (BoP, current US$)",
+          "World/Pair/CHN/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/COL/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/CRI/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/DEU/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/ESP/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/FRA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/GBR/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/GRC/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/HRV/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/IDN/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/IND/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/KOR/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/LBR/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/MEX/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/NLD/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/PER/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/PHL/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/POL/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/SWE/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/THA/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Goods imports (BoP, current US$)",
+          "World/Pair/USA/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/VNM/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/YEM/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross capital formation (current US$)",
+          "World/Latam/ARG/Economy/Gross capital formation (current US$)",
+          "World/Europe/AUT/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross capital formation (current US$)",
+          "World/Asia/BGD/Economy/Gross capital formation (current US$)",
+          "World/Latam/BRA/Economy/Gross capital formation (current US$)",
+          "World/Latam/CHL/Economy/Gross capital formation (current US$)",
+          "World/Pair/CHN/Economy/Gross capital formation (current US$)",
+          "World/South Africa/CMR/Economy/Gross capital formation (current US$)",
+          "World/Latam/COL/Economy/Gross capital formation (current US$)",
+          "World/Latam/CRI/Economy/Gross capital formation (current US$)",
+          "World/Europe/DEU/Economy/Gross capital formation (current US$)",
+          "World/North Africa/DZA/Economy/Gross capital formation (current US$)",
+          "World/North Africa/EGY/Economy/Gross capital formation (current US$)",
+          "World/Europe/ESP/Economy/Gross capital formation (current US$)",
+          "World/Europe/FRA/Economy/Gross capital formation (current US$)",
+          "World/Europe/GBR/Economy/Gross capital formation (current US$)",
+          "World/South Africa/GHA/Economy/Gross capital formation (current US$)",
+          "World/Europe/HRV/Economy/Gross capital formation (current US$)",
+          "World/Asia/IDN/Economy/Gross capital formation (current US$)",
+          "World/Asia/IND/Economy/Gross capital formation (current US$)",
+          "World/North Africa/ISR/Economy/Gross capital formation (current US$)",
+          "World/Asia/KOR/Economy/Gross capital formation (current US$)",
+          "World/North Africa/MAR/Economy/Gross capital formation (current US$)",
+          "World/Latam/MEX/Economy/Gross capital formation (current US$)",
+          "World/South Africa/MOZ/Economy/Gross capital formation (current US$)",
+          "World/South Africa/NGA/Economy/Gross capital formation (current US$)",
+          "World/Europe/NLD/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross capital formation (current US$)",
+          "World/Latam/PAN/Economy/Gross capital formation (current US$)",
+          "World/Latam/PER/Economy/Gross capital formation (current US$)",
+          "World/Asia/PHL/Economy/Gross capital formation (current US$)",
+          "World/Europe/POL/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross capital formation (current US$)",
+          "World/South Africa/SEN/Economy/Gross capital formation (current US$)",
+          "World/Europe/SWE/Economy/Gross capital formation (current US$)",
+          "World/Asia/THA/Economy/Gross capital formation (current US$)",
+          "World/North Africa/TUR/Economy/Gross capital formation (current US$)",
+          "World/Pair/USA/Economy/Gross capital formation (current US$)",
+          "World/Latam/VEN/Economy/Gross capital formation (current US$)",
+          "World/Asia/VNM/Economy/Gross capital formation (current US$)",
+          "World/South Africa/ZAF/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross domestic savings (current US$)",
+          "World/Latam/ARG/Economy/Gross domestic savings (current US$)",
+          "World/Europe/AUT/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross domestic savings (current US$)",
+          "World/Asia/BGD/Economy/Gross domestic savings (current US$)",
+          "World/Latam/BRA/Economy/Gross domestic savings (current US$)",
+          "World/Latam/CHL/Economy/Gross domestic savings (current US$)",
+          "World/Pair/CHN/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/CMR/Economy/Gross domestic savings (current US$)",
+          "World/Latam/COL/Economy/Gross domestic savings (current US$)",
+          "World/Latam/CRI/Economy/Gross domestic savings (current US$)",
+          "World/Europe/DEU/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/DZA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/ESP/Economy/Gross domestic savings (current US$)",
+          "World/Europe/FRA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/GBR/Economy/Gross domestic savings (current US$)",
+          "World/Europe/HRV/Economy/Gross domestic savings (current US$)",
+          "World/Asia/IDN/Economy/Gross domestic savings (current US$)",
+          "World/Asia/IND/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/ISR/Economy/Gross domestic savings (current US$)",
+          "World/Asia/KOR/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/MAR/Economy/Gross domestic savings (current US$)",
+          "World/Latam/MEX/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/NGA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/NLD/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross domestic savings (current US$)",
+          "World/Latam/PAN/Economy/Gross domestic savings (current US$)",
+          "World/Latam/PER/Economy/Gross domestic savings (current US$)",
+          "World/Asia/PHL/Economy/Gross domestic savings (current US$)",
+          "World/Europe/POL/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/SEN/Economy/Gross domestic savings (current US$)",
+          "World/Europe/SWE/Economy/Gross domestic savings (current US$)",
+          "World/Asia/THA/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/TUR/Economy/Gross domestic savings (current US$)",
+          "World/Pair/USA/Economy/Gross domestic savings (current US$)",
+          "World/Asia/VNM/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/ZAF/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/ARG/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/AUT/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/BGD/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/BRA/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/CHL/Economy/Gross fixed capital formation (current US$)",
+          "World/Pair/CHN/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/CMR/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/COL/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/CRI/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/DEU/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/DZA/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/EGY/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/FRA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/GBR/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/GHA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/HRV/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/IDN/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/IND/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/ISR/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/KOR/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/MAR/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/MEX/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/MOZ/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/NGA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/NLD/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/PAN/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/PER/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/PHL/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/POL/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/SEN/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/SWE/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/THA/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/TUR/Economy/Gross fixed capital formation (current US$)",
+          "World/Pair/USA/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/VEN/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/VNM/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/ZAF/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross national expenditure (current US$)",
+          "World/Latam/ARG/Economy/Gross national expenditure (current US$)",
+          "World/Europe/AUT/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross national expenditure (current US$)",
+          "World/Asia/BGD/Economy/Gross national expenditure (current US$)",
+          "World/Latam/BRA/Economy/Gross national expenditure (current US$)",
+          "World/Latam/CHL/Economy/Gross national expenditure (current US$)",
+          "World/Pair/CHN/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Gross national expenditure (current US$)",
+          "World/Latam/COL/Economy/Gross national expenditure (current US$)",
+          "World/Latam/CRI/Economy/Gross national expenditure (current US$)",
+          "World/Europe/DEU/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Gross national expenditure (current US$)",
+          "World/Europe/ESP/Economy/Gross national expenditure (current US$)",
+          "World/Europe/FRA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/GBR/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/GRC/Economy/Gross national expenditure (current US$)",
+          "World/Europe/HRV/Economy/Gross national expenditure (current US$)",
+          "World/Asia/IDN/Economy/Gross national expenditure (current US$)",
+          "World/Asia/IND/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Gross national expenditure (current US$)",
+          "World/Asia/KOR/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Gross national expenditure (current US$)",
+          "World/Latam/MEX/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/NLD/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross national expenditure (current US$)",
+          "World/Latam/PAN/Economy/Gross national expenditure (current US$)",
+          "World/Latam/PER/Economy/Gross national expenditure (current US$)",
+          "World/Asia/PHL/Economy/Gross national expenditure (current US$)",
+          "World/Europe/POL/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Gross national expenditure (current US$)",
+          "World/Europe/SWE/Economy/Gross national expenditure (current US$)",
+          "World/Asia/THA/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Gross national expenditure (current US$)",
+          "World/Pair/USA/Economy/Gross national expenditure (current US$)",
+          "World/Latam/VEN/Economy/Gross national expenditure (current US$)",
+          "World/Asia/VNM/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/AUT/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/AZE/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/BGD/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/BRA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/CHL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Pair/CHN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/CMR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/COL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/CRI/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/DEU/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/DZA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/EGY/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/ESP/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/FRA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/GBR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/GHA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/GRC/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/IND/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/ISR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/KOR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/MAR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/MEX/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/MOZ/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/NGA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/NLD/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/OMN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/PAN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/PER/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/PHL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/POL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/SAU/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Pair/USA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/VEN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/VNM/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/ZAF/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/ARG/Economy/Gross savings (current US$)",
+          "World/Europe/AUT/Economy/Gross savings (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross savings (current US$)",
+          "World/Asia/BGD/Economy/Gross savings (current US$)",
+          "World/Latam/BRA/Economy/Gross savings (current US$)",
+          "World/Latam/CHL/Economy/Gross savings (current US$)",
+          "World/Pair/CHN/Economy/Gross savings (current US$)",
+          "World/South Africa/CMR/Economy/Gross savings (current US$)",
+          "World/Latam/COL/Economy/Gross savings (current US$)",
+          "World/Latam/CRI/Economy/Gross savings (current US$)",
+          "World/Europe/DEU/Economy/Gross savings (current US$)",
+          "World/North Africa/EGY/Economy/Gross savings (current US$)",
+          "World/Europe/ESP/Economy/Gross savings (current US$)",
+          "World/Europe/FRA/Economy/Gross savings (current US$)",
+          "World/Europe/GBR/Economy/Gross savings (current US$)",
+          "World/South Africa/GHA/Economy/Gross savings (current US$)",
+          "World/Europe/HRV/Economy/Gross savings (current US$)",
+          "World/Asia/IDN/Economy/Gross savings (current US$)",
+          "World/Asia/IND/Economy/Gross savings (current US$)",
+          "World/North Africa/ISR/Economy/Gross savings (current US$)",
+          "World/Asia/KOR/Economy/Gross savings (current US$)",
+          "World/North Africa/MAR/Economy/Gross savings (current US$)",
+          "World/Latam/MEX/Economy/Gross savings (current US$)",
+          "World/South Africa/MOZ/Economy/Gross savings (current US$)",
+          "World/South Africa/NGA/Economy/Gross savings (current US$)",
+          "World/Europe/NLD/Economy/Gross savings (current US$)",
+          "World/Latam/PAN/Economy/Gross savings (current US$)",
+          "World/Latam/PER/Economy/Gross savings (current US$)",
+          "World/Asia/PHL/Economy/Gross savings (current US$)",
+          "World/Europe/POL/Economy/Gross savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross savings (current US$)",
+          "World/South Africa/SEN/Economy/Gross savings (current US$)",
+          "World/Europe/SWE/Economy/Gross savings (current US$)",
+          "World/Asia/THA/Economy/Gross savings (current US$)",
+          "World/North Africa/TUR/Economy/Gross savings (current US$)",
+          "World/Pair/USA/Economy/Gross savings (current US$)",
+          "World/Asia/VNM/Economy/Gross savings (current US$)",
+          "World/South Africa/ZAF/Economy/Gross savings (current US$)",
+          "World/Latam/ARG/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/AUT/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/BGD/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/BRA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/CHL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/CMR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/COL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/CRI/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/DEU/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/DZA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/EGY/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/ESP/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/FRA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/GBR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/GHA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/GRC/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/HRV/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/IDN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/IND/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/ISR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/KOR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/MAR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/MEX/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/MOZ/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/NGA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/NLD/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/PAN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/PER/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/POL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/SEN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/SWE/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/TUR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Pair/USA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/VEN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/VNM/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/YEM/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/ZAF/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/AUT/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/DEU/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/ESP/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/POL/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/VEN/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/ARG/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/AUT/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/AZE/Health/Hospital beds (per 1,000 people)",
+          "World/Asia/BGD/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/BRA/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/CHL/Health/Hospital beds (per 1,000 people)",
+          "World/Pair/CHN/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/CMR/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/COL/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/CRI/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/DEU/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/EGY/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/ESP/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/FRA/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/GBR/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/ISR/Health/Hospital beds (per 1,000 people)",
+          "World/Asia/KOR/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/LBR/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/MEX/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/MOZ/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/NGA/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/NLD/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/OMN/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/SAU/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/TUR/Health/Hospital beds (per 1,000 people)",
+          "World/Pair/USA/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/ZAF/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/ARE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/ICT service exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/CMR/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/DZA/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/GRC/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/HRV/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/IND/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/MEX/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/NGA/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/PAN/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/POL/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/THA/Exports/ICT service exports (BoP, current US$)",
+          "World/Pair/USA/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import unit value index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import unit value index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/ESP/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/FRA/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/GRC/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/HRV/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import unit value index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/POL/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/SWE/Imports/Import unit value index (2000 = 100)",
+          "World/Asia/THA/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Imports/Import value index (2000 = 100)",
+          "World/Latam/ARG/Imports/Import value index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import value index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import value index (2000 = 100)",
+          "World/Latam/BRA/Imports/Import value index (2000 = 100)",
+          "World/Latam/CHL/Imports/Import value index (2000 = 100)",
+          "World/Pair/CHN/Imports/Import value index (2000 = 100)",
+          "World/Latam/COL/Imports/Import value index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import value index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import value index (2000 = 100)",
+          "World/North Africa/DZA/Imports/Import value index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import value index (2000 = 100)",
+          "World/Europe/ESP/Imports/Import value index (2000 = 100)",
+          "World/Europe/FRA/Imports/Import value index (2000 = 100)",
+          "World/Europe/GBR/Imports/Import value index (2000 = 100)",
+          "World/Europe/GRC/Imports/Import value index (2000 = 100)",
+          "World/Europe/HRV/Imports/Import value index (2000 = 100)",
+          "World/Asia/IDN/Imports/Import value index (2000 = 100)",
+          "World/Asia/IND/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/IRQ/Imports/Import value index (2000 = 100)",
+          "World/North Africa/ISR/Imports/Import value index (2000 = 100)",
+          "World/Asia/KOR/Imports/Import value index (2000 = 100)",
+          "World/North Africa/MAR/Imports/Import value index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import value index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import value index (2000 = 100)",
+          "World/South Africa/NGA/Imports/Import value index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/OMN/Imports/Import value index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import value index (2000 = 100)",
+          "World/Latam/PER/Imports/Import value index (2000 = 100)",
+          "World/Asia/PHL/Imports/Import value index (2000 = 100)",
+          "World/Europe/POL/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/SAU/Imports/Import value index (2000 = 100)",
+          "World/South Africa/SEN/Imports/Import value index (2000 = 100)",
+          "World/Europe/SWE/Imports/Import value index (2000 = 100)",
+          "World/Asia/THA/Imports/Import value index (2000 = 100)",
+          "World/North Africa/TUR/Imports/Import value index (2000 = 100)",
+          "World/Pair/USA/Imports/Import value index (2000 = 100)",
+          "World/Asia/VNM/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/YEM/Imports/Import value index (2000 = 100)",
+          "World/South Africa/ZAF/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Imports/Import volume index (2000 = 100)",
+          "World/Latam/ARG/Imports/Import volume index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import volume index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import volume index (2000 = 100)",
+          "World/Latam/BRA/Imports/Import volume index (2000 = 100)",
+          "World/Latam/CHL/Imports/Import volume index (2000 = 100)",
+          "World/Pair/CHN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/COL/Imports/Import volume index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import volume index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/DZA/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import volume index (2000 = 100)",
+          "World/Europe/GBR/Imports/Import volume index (2000 = 100)",
+          "World/Asia/IDN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/IND/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/ISR/Imports/Import volume index (2000 = 100)",
+          "World/Asia/KOR/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/MAR/Imports/Import volume index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/NGA/Imports/Import volume index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/OMN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/PER/Imports/Import volume index (2000 = 100)",
+          "World/Asia/PHL/Imports/Import volume index (2000 = 100)",
+          "World/Europe/POL/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/SAU/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/SEN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/THA/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/TUR/Imports/Import volume index (2000 = 100)",
+          "World/Pair/USA/Imports/Import volume index (2000 = 100)",
+          "World/Latam/VEN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/VNM/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/YEM/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/ZAF/Imports/Import volume index (2000 = 100)",
+          "World/Latam/ARG/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/AUT/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/BGD/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/BRA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/CHL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Pair/CHN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/CMR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/COL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/CRI/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/DEU/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/ESP/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/FRA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/GBR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/GRC/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/HRV/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/IDN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/IND/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/KOR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/MEX/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/NLD/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/PAN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/PER/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/PHL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/POL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/SAU/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/SWE/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/THA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Pair/USA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/VNM/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/YEM/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/ARE/Imports/Imports of goods and services (current US$)",
+          "World/Latam/ARG/Imports/Imports of goods and services (current US$)",
+          "World/Europe/AUT/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/AZE/Imports/Imports of goods and services (current US$)",
+          "World/Asia/BGD/Imports/Imports of goods and services (current US$)",
+          "World/Latam/BRA/Imports/Imports of goods and services (current US$)",
+          "World/Latam/CHL/Imports/Imports of goods and services (current US$)",
+          "World/Pair/CHN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/COL/Imports/Imports of goods and services (current US$)",
+          "World/Latam/CRI/Imports/Imports of goods and services (current US$)",
+          "World/Europe/DEU/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/DZA/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/EGY/Imports/Imports of goods and services (current US$)",
+          "World/Europe/ESP/Imports/Imports of goods and services (current US$)",
+          "World/Europe/FRA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/GBR/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/GHA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/GRC/Imports/Imports of goods and services (current US$)",
+          "World/Europe/HRV/Imports/Imports of goods and services (current US$)",
+          "World/Asia/IDN/Imports/Imports of goods and services (current US$)",
+          "World/Asia/IND/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/IRQ/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/ISR/Imports/Imports of goods and services (current US$)",
+          "World/Asia/KOR/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/MAR/Imports/Imports of goods and services (current US$)",
+          "World/Latam/MEX/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/MOZ/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/NGA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/NLD/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/OMN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/PAN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/PER/Imports/Imports of goods and services (current US$)",
+          "World/Asia/PHL/Imports/Imports of goods and services (current US$)",
+          "World/Europe/POL/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/QAT/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/SAU/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/SEN/Imports/Imports of goods and services (current US$)",
+          "World/Europe/SWE/Imports/Imports of goods and services (current US$)",
+          "World/Asia/THA/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/TUR/Imports/Imports of goods and services (current US$)",
+          "World/Pair/USA/Imports/Imports of goods and services (current US$)",
+          "World/Latam/VEN/Imports/Imports of goods and services (current US$)",
+          "World/Asia/VNM/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/ZAF/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/ARE/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/AUT/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/AZE/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/BRA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Pair/CHN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/CMR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/CRI/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/EGY/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/ESP/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/GBR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/GHA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/HRV/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/IDN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/IND/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/IRQ/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/ISR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/LBR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/MOZ/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/OMN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/PER/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/POL/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/QAT/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/SAU/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/SEN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/THA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/TUR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Pair/USA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/VNM/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/YEM/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/ARE/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/ARG/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/AUT/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/AZE/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/BGD/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/BRA/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/CHL/Economy/Industry (including construction), value added (current US$)",
+          "World/Pair/CHN/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/CMR/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/COL/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/CRI/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/DEU/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/DZA/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/EGY/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/ESP/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/FRA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/GBR/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/GHA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/GRC/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/HRV/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/IDN/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/IND/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/ISR/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/KOR/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/LBR/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/MAR/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/MEX/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/MOZ/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/NGA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/NLD/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/OMN/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/PAN/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/PER/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/PHL/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/POL/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/SAU/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/SEN/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/SWE/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/THA/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/TUR/Economy/Industry (including construction), value added (current US$)",
+          "World/Pair/USA/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/VEN/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/VNM/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/YEM/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/ZAF/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/BRA/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Pair/CHN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/COL/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/CRI/Mortality/Intentional homicides (per 100,000 people)",
+          "World/North Africa/EGY/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/ESP/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/GBR/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/IDN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/IND/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/IRQ/Mortality/Intentional homicides (per 100,000 people)",
+          "World/North Africa/ISR/Mortality/Intentional homicides (per 100,000 people)",
+          "World/South Africa/MOZ/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/NLD/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/PAN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/PER/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/POL/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/THA/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/VNM/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/YEM/Mortality/Intentional homicides (per 100,000 people)",
+          "World/South Africa/ZAF/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/ARE/Migration/International migrant stock (% of population)",
+          "World/Europe/AUT/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/AZE/Migration/International migrant stock (% of population)",
+          "World/Latam/CHL/Migration/International migrant stock (% of population)",
+          "World/Pair/CHN/Migration/International migrant stock (% of population)",
+          "World/Europe/DEU/Migration/International migrant stock (% of population)",
+          "World/North Africa/DZA/Migration/International migrant stock (% of population)",
+          "World/Europe/ESP/Migration/International migrant stock (% of population)",
+          "World/Europe/FRA/Migration/International migrant stock (% of population)",
+          "World/Europe/GBR/Migration/International migrant stock (% of population)",
+          "World/Europe/GRC/Migration/International migrant stock (% of population)",
+          "World/Europe/HRV/Migration/International migrant stock (% of population)",
+          "World/Asia/IND/Migration/International migrant stock (% of population)",
+          "World/North Africa/ISR/Migration/International migrant stock (% of population)",
+          "World/Asia/KOR/Migration/International migrant stock (% of population)",
+          "World/North Africa/MAR/Migration/International migrant stock (% of population)",
+          "World/Latam/MEX/Migration/International migrant stock (% of population)",
+          "World/South Africa/MOZ/Migration/International migrant stock (% of population)",
+          "World/South Africa/NGA/Migration/International migrant stock (% of population)",
+          "World/Europe/NLD/Migration/International migrant stock (% of population)",
+          "World/Latam/PAN/Migration/International migrant stock (% of population)",
+          "World/Latam/PER/Migration/International migrant stock (% of population)",
+          "World/Europe/POL/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/SAU/Migration/International migrant stock (% of population)",
+          "World/South Africa/SEN/Migration/International migrant stock (% of population)",
+          "World/Europe/SWE/Migration/International migrant stock (% of population)",
+          "World/Asia/THA/Migration/International migrant stock (% of population)",
+          "World/North Africa/TUR/Migration/International migrant stock (% of population)",
+          "World/Pair/USA/Migration/International migrant stock (% of population)",
+          "World/Asia/VNM/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/YEM/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/ARE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/AUT/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/AZE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/BRA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/CHL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Pair/CHN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/COL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/CRI/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/DEU/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/DZA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/EGY/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/GHA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/GRC/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/IDN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/IND/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/IRQ/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/ISR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/KOR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/LBR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/MEX/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/MOZ/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/NGA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/NLD/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/OMN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/PAN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/PER/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/PHL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/POL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/QAT/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/SWE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/TUR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Pair/USA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/VEN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/VNM/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/YEM/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/ZAF/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/ARE/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/ARG/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/AZE/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/BGD/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/CHL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Pair/CHN/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/CMR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/COL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/CRI/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/EGY/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/ESP/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/GBR/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/GHA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/GRC/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/HRV/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/IDN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/IND/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/ISR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/KOR/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/LBR/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/MAR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/MEX/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/MOZ/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/NGA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/NLD/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/PAN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/PER/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/PHL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/POL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/QAT/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/SAU/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/SEN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/THA/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/TUR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Pair/USA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/VNM/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/YEM/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/ARE/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/ARG/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/AZE/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/BGD/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/CHL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Pair/CHN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/CMR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/COL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/CRI/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/EGY/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/ESP/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/GBR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/GHA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/GRC/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/HRV/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/IDN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/IND/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/ISR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/KOR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/LBR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/MAR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/MEX/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/MOZ/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/NGA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/NLD/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/PAN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/PER/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/PHL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/POL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/QAT/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/SAU/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/SEN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/THA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/TUR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Pair/USA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/VEN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/VNM/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/YEM/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/ARE/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/ARG/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/AUT/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/AZE/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/BGD/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/BRA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Pair/CHN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/COL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/CRI/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/GBR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/IDN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/IND/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/ISR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/KOR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/LBR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/MEX/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/NLD/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/PAN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/PER/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/PHL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/POL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/SAU/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/THA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Pair/USA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/VEN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/VNM/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/ZAF/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/ARE/Health/Low-birthweight babies (% of births)",
+          "World/Asia/BGD/Health/Low-birthweight babies (% of births)",
+          "World/Latam/BRA/Health/Low-birthweight babies (% of births)",
+          "World/Latam/CHL/Health/Low-birthweight babies (% of births)",
+          "World/Pair/CHN/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/CMR/Health/Low-birthweight babies (% of births)",
+          "World/Latam/COL/Health/Low-birthweight babies (% of births)",
+          "World/Latam/CRI/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/DZA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/ESP/Health/Low-birthweight babies (% of births)",
+          "World/Europe/FRA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/GBR/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/GHA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/HRV/Health/Low-birthweight babies (% of births)",
+          "World/Asia/IDN/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/ISR/Health/Low-birthweight babies (% of births)",
+          "World/Asia/KOR/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/MAR/Health/Low-birthweight babies (% of births)",
+          "World/Latam/MEX/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/MOZ/Health/Low-birthweight babies (% of births)",
+          "World/Europe/NLD/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/OMN/Health/Low-birthweight babies (% of births)",
+          "World/Latam/PAN/Health/Low-birthweight babies (% of births)",
+          "World/Latam/PER/Health/Low-birthweight babies (% of births)",
+          "World/Asia/PHL/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/QAT/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/SEN/Health/Low-birthweight babies (% of births)",
+          "World/Asia/THA/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/TUR/Health/Low-birthweight babies (% of births)",
+          "World/Asia/VNM/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/ZAF/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/ARE/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/BGD/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/CHL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Pair/CHN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/COL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/South Africa/GHA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Europe/HRV/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/IDN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/IND/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/North Africa/ISR/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/KOR/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/MEX/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/OMN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/PAN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Europe/POL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/QAT/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/SAU/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/THA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Pair/USA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/VNM/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/South Africa/ZAF/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/ARE/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/ARG/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/AZE/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/BGD/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/CHL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Pair/CHN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/CMR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/CRI/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/DZA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/EGY/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/ESP/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/GBR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/GHA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/HRV/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/IDN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/IND/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/ISR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/KOR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/LBR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/MAR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/MEX/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/MOZ/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/NGA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/NLD/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/PAN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/PER/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/PHL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/POL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/QAT/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/SAU/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/SEN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/THA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/TUR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Pair/USA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/VNM/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/YEM/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports (current US$)",
+          "World/Europe/AUT/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/AZE/Exports/Merchandise exports (current US$)",
+          "World/Asia/BGD/Exports/Merchandise exports (current US$)",
+          "World/Latam/BRA/Exports/Merchandise exports (current US$)",
+          "World/Latam/CHL/Exports/Merchandise exports (current US$)",
+          "World/Pair/CHN/Exports/Merchandise exports (current US$)",
+          "World/Latam/COL/Exports/Merchandise exports (current US$)",
+          "World/Latam/CRI/Exports/Merchandise exports (current US$)",
+          "World/Europe/DEU/Exports/Merchandise exports (current US$)",
+          "World/North Africa/EGY/Exports/Merchandise exports (current US$)",
+          "World/Europe/ESP/Exports/Merchandise exports (current US$)",
+          "World/Europe/FRA/Exports/Merchandise exports (current US$)",
+          "World/Europe/GBR/Exports/Merchandise exports (current US$)",
+          "World/South Africa/GHA/Exports/Merchandise exports (current US$)",
+          "World/Asia/IDN/Exports/Merchandise exports (current US$)",
+          "World/Asia/IND/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports (current US$)",
+          "World/North Africa/ISR/Exports/Merchandise exports (current US$)",
+          "World/Asia/KOR/Exports/Merchandise exports (current US$)",
+          "World/North Africa/MAR/Exports/Merchandise exports (current US$)",
+          "World/Latam/MEX/Exports/Merchandise exports (current US$)",
+          "World/South Africa/MOZ/Exports/Merchandise exports (current US$)",
+          "World/Europe/NLD/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/OMN/Exports/Merchandise exports (current US$)",
+          "World/Latam/PAN/Exports/Merchandise exports (current US$)",
+          "World/Latam/PER/Exports/Merchandise exports (current US$)",
+          "World/Asia/PHL/Exports/Merchandise exports (current US$)",
+          "World/Europe/POL/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/SAU/Exports/Merchandise exports (current US$)",
+          "World/South Africa/SEN/Exports/Merchandise exports (current US$)",
+          "World/Europe/SWE/Exports/Merchandise exports (current US$)",
+          "World/Asia/THA/Exports/Merchandise exports (current US$)",
+          "World/North Africa/TUR/Exports/Merchandise exports (current US$)",
+          "World/Pair/USA/Exports/Merchandise exports (current US$)",
+          "World/Latam/VEN/Exports/Merchandise exports (current US$)",
+          "World/Asia/VNM/Exports/Merchandise exports (current US$)",
+          "World/South Africa/ZAF/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/AUT/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/AZE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/BGD/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/BRA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/CHL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Pair/CHN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/COL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/CRI/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/DEU/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/DZA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/EGY/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/ESP/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/FRA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/GBR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/GHA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/IDN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/IND/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/KOR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/MAR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/MEX/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/MOZ/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/NGA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/NLD/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/OMN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/PER/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/PHL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/POL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/SEN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/SWE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/THA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/TUR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Pair/USA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/VEN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/VNM/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/ZAF/Exports/Merchandise exports by the reporting economy (current US$)",
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+          "World/Persian Gulf/AZE/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/BGD/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/CHL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Pair/CHN/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/COL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/DEU/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/ESP/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/GBR/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/GRC/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/IDN/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/North Africa/ISR/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/KOR/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/South Africa/LBR/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/MEX/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/South Africa/MOZ/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/South Africa/NGA/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/NLD/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/PAN/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
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+          "World/Asia/PHL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/POL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/SWE/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Asia/THA/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Pair/USA/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Latam/VEN/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/South Africa/ZAF/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/AUT/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/BRA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/CHL/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Pair/CHN/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/COL/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/DEU/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/ESP/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/FRA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/GRC/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Asia/IDN/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Asia/IND/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/North Africa/ISR/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Asia/KOR/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/MEX/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/South Africa/MOZ/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/South Africa/NGA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Europe/NLD/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/OMN/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/PAN/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Latam/PER/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Asia/PHL/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Persian Gulf/SAU/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/Pair/USA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "World/South Africa/ZAF/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
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+          "World/Latam/ARG/Imports/Merchandise imports (current US$)",
+          "World/Europe/AUT/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/AZE/Imports/Merchandise imports (current US$)",
+          "World/Asia/BGD/Imports/Merchandise imports (current US$)",
+          "World/Latam/BRA/Imports/Merchandise imports (current US$)",
+          "World/Latam/CHL/Imports/Merchandise imports (current US$)",
+          "World/Pair/CHN/Imports/Merchandise imports (current US$)",
+          "World/Latam/COL/Imports/Merchandise imports (current US$)",
+          "World/Latam/CRI/Imports/Merchandise imports (current US$)",
+          "World/Europe/DEU/Imports/Merchandise imports (current US$)",
+          "World/North Africa/DZA/Imports/Merchandise imports (current US$)",
+          "World/North Africa/EGY/Imports/Merchandise imports (current US$)",
+          "World/Europe/ESP/Imports/Merchandise imports (current US$)",
+          "World/Europe/FRA/Imports/Merchandise imports (current US$)",
+          "World/Europe/GBR/Imports/Merchandise imports (current US$)",
+          "World/Europe/GRC/Imports/Merchandise imports (current US$)",
+          "World/Europe/HRV/Imports/Merchandise imports (current US$)",
+          "World/Asia/IDN/Imports/Merchandise imports (current US$)",
+          "World/Asia/IND/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/IRQ/Imports/Merchandise imports (current US$)",
+          "World/North Africa/ISR/Imports/Merchandise imports (current US$)",
+          "World/Asia/KOR/Imports/Merchandise imports (current US$)",
+          "World/North Africa/MAR/Imports/Merchandise imports (current US$)",
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+          "World/South Africa/MOZ/Imports/Merchandise imports (current US$)",
+          "World/South Africa/NGA/Imports/Merchandise imports (current US$)",
+          "World/Europe/NLD/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports (current US$)",
+          "World/Latam/PAN/Imports/Merchandise imports (current US$)",
+          "World/Latam/PER/Imports/Merchandise imports (current US$)",
+          "World/Asia/PHL/Imports/Merchandise imports (current US$)",
+          "World/Europe/POL/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports (current US$)",
+          "World/South Africa/SEN/Imports/Merchandise imports (current US$)",
+          "World/Europe/SWE/Imports/Merchandise imports (current US$)",
+          "World/Asia/THA/Imports/Merchandise imports (current US$)",
+          "World/North Africa/TUR/Imports/Merchandise imports (current US$)",
+          "World/Pair/USA/Imports/Merchandise imports (current US$)",
+          "World/Asia/VNM/Imports/Merchandise imports (current US$)",
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+          "World/South Africa/ZAF/Imports/Merchandise imports (current US$)",
+          "World/Persian Gulf/ARE/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Latam/ARG/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/AUT/Imports/Merchandise imports by the reporting economy (current US$)",
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+          "World/Asia/BGD/Imports/Merchandise imports by the reporting economy (current US$)",
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+          "World/Latam/CRI/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/DEU/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/North Africa/DZA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/North Africa/EGY/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/ESP/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/FRA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/GBR/Imports/Merchandise imports by the reporting economy (current US$)",
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+          "World/Europe/GRC/Imports/Merchandise imports by the reporting economy (current US$)",
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+          "World/Asia/KOR/Imports/Merchandise imports by the reporting economy (current US$)",
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+          "World/South Africa/MOZ/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/NGA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/NLD/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports by the reporting economy (current US$)",
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+          "World/Europe/POL/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/SEN/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Europe/SWE/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/Asia/THA/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/North Africa/TUR/Imports/Merchandise imports by the reporting economy (current US$)",
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+          "World/Asia/VNM/Imports/Merchandise imports by the reporting economy (current US$)",
+          "World/South Africa/ZAF/Imports/Merchandise imports by the reporting economy (current US$)",
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+          "World/Latam/BRA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Pair/CHN/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Europe/GRC/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/IDN/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/KOR/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/South Africa/NGA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/PHL/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/THA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Latam/VEN/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Asia/VNM/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Persian Gulf/YEM/Imports/Merchandise imports from high-income economies (% of total merchandise imports)",
+          "World/Europe/AUT/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Persian Gulf/AZE/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/BGD/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/BRA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
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+          "World/Pair/CHN/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/South Africa/CMR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/DEU/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/EGY/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/ESP/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/FRA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/GBR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/South Africa/GHA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/GRC/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/IDN/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
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+          "World/North Africa/ISR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/KOR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/MAR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/South Africa/MOZ/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/South Africa/NGA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/NLD/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/PAN/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/PER/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/PHL/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/POL/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Europe/SWE/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/THA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/VEN/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Asia/VNM/Imports/Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "World/Latam/ARG/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/BRA/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/CHL/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/EGY/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/ESP/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/GBR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/ISR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/South Africa/LBR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/MAR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/PAN/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/PER/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/POL/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/SWE/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/VEN/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/South Africa/ZAF/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/AUT/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/AZE/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/BGD/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Pair/CHN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/CMR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/EGY/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/ESP/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/FRA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/GBR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/GHA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/GRC/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/IND/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/ISR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/KOR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/MAR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/MOZ/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/NLD/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/PER/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/POL/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/SEN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/YEM/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/AUT/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Methane emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/COL/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/DEU/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/ESP/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/FRA/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Methane emissions (% change from 1990)",
+          "World/South Africa/GHA/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/KOR/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/MAR/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/MEX/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/PER/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/PHL/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/POL/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/SAU/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/TUR/Environment/Methane emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/ARG/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/COL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/IND/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/PER/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/POL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Pair/USA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/COL/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/IRQ/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/POL/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/THA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Military/Military expenditure (current USD)",
+          "World/Europe/AUT/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/AZE/Military/Military expenditure (current USD)",
+          "World/Asia/BGD/Military/Military expenditure (current USD)",
+          "World/Latam/BRA/Military/Military expenditure (current USD)",
+          "World/Latam/CHL/Military/Military expenditure (current USD)",
+          "World/Pair/CHN/Military/Military expenditure (current USD)",
+          "World/South Africa/CMR/Military/Military expenditure (current USD)",
+          "World/Latam/COL/Military/Military expenditure (current USD)",
+          "World/North Africa/DZA/Military/Military expenditure (current USD)",
+          "World/North Africa/EGY/Military/Military expenditure (current USD)",
+          "World/Europe/ESP/Military/Military expenditure (current USD)",
+          "World/Europe/FRA/Military/Military expenditure (current USD)",
+          "World/South Africa/GHA/Military/Military expenditure (current USD)",
+          "World/Europe/GRC/Military/Military expenditure (current USD)",
+          "World/Asia/IDN/Military/Military expenditure (current USD)",
+          "World/Asia/IND/Military/Military expenditure (current USD)",
+          "World/North Africa/ISR/Military/Military expenditure (current USD)",
+          "World/Asia/KOR/Military/Military expenditure (current USD)",
+          "World/North Africa/MAR/Military/Military expenditure (current USD)",
+          "World/Latam/MEX/Military/Military expenditure (current USD)",
+          "World/South Africa/MOZ/Military/Military expenditure (current USD)",
+          "World/South Africa/NGA/Military/Military expenditure (current USD)",
+          "World/Europe/NLD/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/OMN/Military/Military expenditure (current USD)",
+          "World/Latam/PER/Military/Military expenditure (current USD)",
+          "World/Asia/PHL/Military/Military expenditure (current USD)",
+          "World/Europe/POL/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/SAU/Military/Military expenditure (current USD)",
+          "World/South Africa/SEN/Military/Military expenditure (current USD)",
+          "World/Asia/THA/Military/Military expenditure (current USD)",
+          "World/North Africa/TUR/Military/Military expenditure (current USD)",
+          "World/Pair/USA/Military/Military expenditure (current USD)",
+          "World/Asia/VNM/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/YEM/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/ARE/Internet/Mobile cellular subscriptions",
+          "World/Latam/BRA/Internet/Mobile cellular subscriptions",
+          "World/Latam/CHL/Internet/Mobile cellular subscriptions",
+          "World/Pair/CHN/Internet/Mobile cellular subscriptions",
+          "World/Europe/DEU/Internet/Mobile cellular subscriptions",
+          "World/North Africa/DZA/Internet/Mobile cellular subscriptions",
+          "World/North Africa/EGY/Internet/Mobile cellular subscriptions",
+          "World/Europe/FRA/Internet/Mobile cellular subscriptions",
+          "World/Europe/GBR/Internet/Mobile cellular subscriptions",
+          "World/Europe/HRV/Internet/Mobile cellular subscriptions",
+          "World/Asia/IDN/Internet/Mobile cellular subscriptions",
+          "World/North Africa/ISR/Internet/Mobile cellular subscriptions",
+          "World/Asia/KOR/Internet/Mobile cellular subscriptions",
+          "World/North Africa/MAR/Internet/Mobile cellular subscriptions",
+          "World/Latam/MEX/Internet/Mobile cellular subscriptions",
+          "World/Europe/NLD/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/OMN/Internet/Mobile cellular subscriptions",
+          "World/Latam/PER/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/QAT/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/SAU/Internet/Mobile cellular subscriptions",
+          "World/Europe/SWE/Internet/Mobile cellular subscriptions",
+          "World/Asia/THA/Internet/Mobile cellular subscriptions",
+          "World/North Africa/TUR/Internet/Mobile cellular subscriptions",
+          "World/Pair/USA/Internet/Mobile cellular subscriptions",
+          "World/South Africa/ZAF/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/ARE/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/BRA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/CHL/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Pair/CHN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/DEU/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/DZA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/EGY/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/FRA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/HRV/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/IDN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/ISR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/KOR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/MAR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/MEX/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/NLD/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/OMN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/PER/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/QAT/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/SAU/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/SWE/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/THA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/TUR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Pair/USA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/VEN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/South Africa/ZAF/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/ARE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/AZE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/BGD/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/CHL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Pair/CHN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/CMR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/COL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/CRI/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/DEU/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/DZA/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/EGY/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/FRA/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/GRC/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/HRV/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/IND/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/KOR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/LBR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/MAR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/MEX/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/OMN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/PAN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/PER/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/POL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/QAT/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/SAU/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/SEN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/SWE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/TUR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/VEN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/VNM/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/AUT/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/AZE/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/BGD/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Pair/CHN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/CMR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/DEU/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/DZA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/ESP/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/GBR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/GRC/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/HRV/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/IDN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/ISR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/KOR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/MAR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Latam/MEX/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/MOZ/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/NGA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/NLD/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/OMN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/PHL/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/POL/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/QAT/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/SAU/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/SWE/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/THA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/TUR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Latam/VEN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/VNM/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/YEM/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/ARE/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/AUT/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Latam/BRA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Latam/COL/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/North Africa/DZA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/North Africa/EGY/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/ESP/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/GHA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/KOR/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/LBR/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/NLD/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/SWE/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/VNM/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/ZAF/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/BGD/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/BRA/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/COL/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/CRI/Economy/Net primary income (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Net primary income (BoP, current US$)",
+          "World/Europe/FRA/Economy/Net primary income (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/IDN/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/IND/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/KOR/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/MEX/Economy/Net primary income (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/PAN/Economy/Net primary income (BoP, current US$)",
+          "World/Europe/POL/Economy/Net primary income (BoP, current US$)",
+          "World/Persian Gulf/QAT/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/THA/Economy/Net primary income (BoP, current US$)",
+          "World/North Africa/TUR/Economy/Net primary income (BoP, current US$)",
+          "World/Pair/USA/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/VEN/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/VNM/Economy/Net primary income (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/BGD/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/BRA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/COL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/CRI/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/North Africa/EGY/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Europe/FRA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/IND/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/KOR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/LBR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/MEX/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/NGA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/PAN/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/PHL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Europe/POL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/THA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/North Africa/TUR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Pair/USA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/VNM/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/ZAF/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/BGD/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/COL/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/CRI/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/DEU/Economy/Net secondary income (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/ESP/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/FRA/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/GBR/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/IND/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/KOR/Economy/Net secondary income (BoP, current US$)",
+          "World/North Africa/MAR/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/MEX/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/NGA/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/PER/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/PHL/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/POL/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/SAU/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/SWE/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/THA/Economy/Net secondary income (BoP, current US$)",
+          "World/Pair/USA/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/VEN/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/VNM/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/ARE/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/AUT/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/COL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/CRI/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/DZA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/EGY/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/ESP/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/FRA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/GBR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/GHA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/IND/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/ISR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/MAR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/MEX/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/NGA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/PAN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/PER/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/PHL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/POL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/QAT/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/SEN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/SWE/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/THA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Pair/USA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/VNM/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/YEM/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/ZAF/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/AUT/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/COL/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/CRI/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/ESP/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/GRC/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/SWE/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/ZAF/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CHL/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/THA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/VEN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/BRA/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 10-14 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/IDN/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/IRQ/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 10-14 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/ARE/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/ARG/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 15-19 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/PHL/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 15-19 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/VEN/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 20-24 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/PHL/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/VEN/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/ARE/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/ARG/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/BRA/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 5-9 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/EGY/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/GHA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/IDN/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 5-9 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/ARE/Mortality/Number of infant deaths",
+          "World/Latam/ARG/Mortality/Number of infant deaths",
+          "World/Europe/AUT/Mortality/Number of infant deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of infant deaths",
+          "World/Asia/BGD/Mortality/Number of infant deaths",
+          "World/Latam/BRA/Mortality/Number of infant deaths",
+          "World/Latam/CHL/Mortality/Number of infant deaths",
+          "World/Pair/CHN/Mortality/Number of infant deaths",
+          "World/South Africa/CMR/Mortality/Number of infant deaths",
+          "World/Latam/COL/Mortality/Number of infant deaths",
+          "World/Latam/CRI/Mortality/Number of infant deaths",
+          "World/North Africa/EGY/Mortality/Number of infant deaths",
+          "World/Europe/FRA/Mortality/Number of infant deaths",
+          "World/Europe/GBR/Mortality/Number of infant deaths",
+          "World/South Africa/GHA/Mortality/Number of infant deaths",
+          "World/Europe/GRC/Mortality/Number of infant deaths",
+          "World/Europe/HRV/Mortality/Number of infant deaths",
+          "World/Asia/IDN/Mortality/Number of infant deaths",
+          "World/Asia/IND/Mortality/Number of infant deaths",
+          "World/Persian Gulf/IRQ/Mortality/Number of infant deaths",
+          "World/North Africa/ISR/Mortality/Number of infant deaths",
+          "World/Asia/KOR/Mortality/Number of infant deaths",
+          "World/South Africa/LBR/Mortality/Number of infant deaths",
+          "World/North Africa/MAR/Mortality/Number of infant deaths",
+          "World/Latam/MEX/Mortality/Number of infant deaths",
+          "World/South Africa/MOZ/Mortality/Number of infant deaths",
+          "World/Europe/NLD/Mortality/Number of infant deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of infant deaths",
+          "World/Latam/PAN/Mortality/Number of infant deaths",
+          "World/Latam/PER/Mortality/Number of infant deaths",
+          "World/Asia/PHL/Mortality/Number of infant deaths",
+          "World/Europe/POL/Mortality/Number of infant deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of infant deaths",
+          "World/South Africa/SEN/Mortality/Number of infant deaths",
+          "World/Europe/SWE/Mortality/Number of infant deaths",
+          "World/Asia/THA/Mortality/Number of infant deaths",
+          "World/North Africa/TUR/Mortality/Number of infant deaths",
+          "World/Pair/USA/Mortality/Number of infant deaths",
+          "World/Asia/VNM/Mortality/Number of infant deaths",
+          "World/Latam/ARG/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of maternal deaths",
+          "World/Asia/BGD/Mortality/Number of maternal deaths",
+          "World/Latam/BRA/Mortality/Number of maternal deaths",
+          "World/Latam/CHL/Mortality/Number of maternal deaths",
+          "World/Pair/CHN/Mortality/Number of maternal deaths",
+          "World/South Africa/CMR/Mortality/Number of maternal deaths",
+          "World/Latam/COL/Mortality/Number of maternal deaths",
+          "World/Latam/CRI/Mortality/Number of maternal deaths",
+          "World/North Africa/DZA/Mortality/Number of maternal deaths",
+          "World/North Africa/EGY/Mortality/Number of maternal deaths",
+          "World/Europe/FRA/Mortality/Number of maternal deaths",
+          "World/Europe/GBR/Mortality/Number of maternal deaths",
+          "World/Europe/HRV/Mortality/Number of maternal deaths",
+          "World/Asia/IDN/Mortality/Number of maternal deaths",
+          "World/Asia/IND/Mortality/Number of maternal deaths",
+          "World/North Africa/ISR/Mortality/Number of maternal deaths",
+          "World/Asia/KOR/Mortality/Number of maternal deaths",
+          "World/North Africa/MAR/Mortality/Number of maternal deaths",
+          "World/Latam/MEX/Mortality/Number of maternal deaths",
+          "World/South Africa/MOZ/Mortality/Number of maternal deaths",
+          "World/South Africa/NGA/Mortality/Number of maternal deaths",
+          "World/Europe/NLD/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of maternal deaths",
+          "World/Latam/PAN/Mortality/Number of maternal deaths",
+          "World/Latam/PER/Mortality/Number of maternal deaths",
+          "World/Asia/PHL/Mortality/Number of maternal deaths",
+          "World/Europe/POL/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of maternal deaths",
+          "World/South Africa/SEN/Mortality/Number of maternal deaths",
+          "World/Asia/THA/Mortality/Number of maternal deaths",
+          "World/North Africa/TUR/Mortality/Number of maternal deaths",
+          "World/Pair/USA/Mortality/Number of maternal deaths",
+          "World/Asia/VNM/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/YEM/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/ARE/Mortality/Number of neonatal deaths",
+          "World/Latam/ARG/Mortality/Number of neonatal deaths",
+          "World/Europe/AUT/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of neonatal deaths",
+          "World/Asia/BGD/Mortality/Number of neonatal deaths",
+          "World/Latam/BRA/Mortality/Number of neonatal deaths",
+          "World/Latam/CHL/Mortality/Number of neonatal deaths",
+          "World/Pair/CHN/Mortality/Number of neonatal deaths",
+          "World/Latam/COL/Mortality/Number of neonatal deaths",
+          "World/Latam/CRI/Mortality/Number of neonatal deaths",
+          "World/North Africa/DZA/Mortality/Number of neonatal deaths",
+          "World/North Africa/EGY/Mortality/Number of neonatal deaths",
+          "World/Europe/FRA/Mortality/Number of neonatal deaths",
+          "World/Europe/GBR/Mortality/Number of neonatal deaths",
+          "World/South Africa/GHA/Mortality/Number of neonatal deaths",
+          "World/Europe/GRC/Mortality/Number of neonatal deaths",
+          "World/Europe/HRV/Mortality/Number of neonatal deaths",
+          "World/Asia/IDN/Mortality/Number of neonatal deaths",
+          "World/Asia/IND/Mortality/Number of neonatal deaths",
+          "World/North Africa/ISR/Mortality/Number of neonatal deaths",
+          "World/Asia/KOR/Mortality/Number of neonatal deaths",
+          "World/North Africa/MAR/Mortality/Number of neonatal deaths",
+          "World/Latam/MEX/Mortality/Number of neonatal deaths",
+          "World/South Africa/MOZ/Mortality/Number of neonatal deaths",
+          "World/South Africa/NGA/Mortality/Number of neonatal deaths",
+          "World/Europe/NLD/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of neonatal deaths",
+          "World/Latam/PAN/Mortality/Number of neonatal deaths",
+          "World/Latam/PER/Mortality/Number of neonatal deaths",
+          "World/Asia/PHL/Mortality/Number of neonatal deaths",
+          "World/Europe/POL/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/QAT/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of neonatal deaths",
+          "World/South Africa/SEN/Mortality/Number of neonatal deaths",
+          "World/Asia/THA/Mortality/Number of neonatal deaths",
+          "World/North Africa/TUR/Mortality/Number of neonatal deaths",
+          "World/Pair/USA/Mortality/Number of neonatal deaths",
+          "World/Asia/VNM/Mortality/Number of neonatal deaths",
+          "World/South Africa/ZAF/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/ARE/Mortality/Number of under-five deaths",
+          "World/Latam/ARG/Mortality/Number of under-five deaths",
+          "World/Europe/AUT/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of under-five deaths",
+          "World/Asia/BGD/Mortality/Number of under-five deaths",
+          "World/Latam/BRA/Mortality/Number of under-five deaths",
+          "World/Latam/CHL/Mortality/Number of under-five deaths",
+          "World/Pair/CHN/Mortality/Number of under-five deaths",
+          "World/South Africa/CMR/Mortality/Number of under-five deaths",
+          "World/Latam/COL/Mortality/Number of under-five deaths",
+          "World/Latam/CRI/Mortality/Number of under-five deaths",
+          "World/North Africa/EGY/Mortality/Number of under-five deaths",
+          "World/Europe/FRA/Mortality/Number of under-five deaths",
+          "World/Europe/GBR/Mortality/Number of under-five deaths",
+          "World/South Africa/GHA/Mortality/Number of under-five deaths",
+          "World/Europe/GRC/Mortality/Number of under-five deaths",
+          "World/Europe/HRV/Mortality/Number of under-five deaths",
+          "World/Asia/IDN/Mortality/Number of under-five deaths",
+          "World/Asia/IND/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/IRQ/Mortality/Number of under-five deaths",
+          "World/North Africa/ISR/Mortality/Number of under-five deaths",
+          "World/Asia/KOR/Mortality/Number of under-five deaths",
+          "World/South Africa/LBR/Mortality/Number of under-five deaths",
+          "World/North Africa/MAR/Mortality/Number of under-five deaths",
+          "World/Latam/MEX/Mortality/Number of under-five deaths",
+          "World/South Africa/MOZ/Mortality/Number of under-five deaths",
+          "World/Europe/NLD/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of under-five deaths",
+          "World/Latam/PAN/Mortality/Number of under-five deaths",
+          "World/Latam/PER/Mortality/Number of under-five deaths",
+          "World/Asia/PHL/Mortality/Number of under-five deaths",
+          "World/Europe/POL/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of under-five deaths",
+          "World/South Africa/SEN/Mortality/Number of under-five deaths",
+          "World/Europe/SWE/Mortality/Number of under-five deaths",
+          "World/Asia/THA/Mortality/Number of under-five deaths",
+          "World/North Africa/TUR/Mortality/Number of under-five deaths",
+          "World/Pair/USA/Mortality/Number of under-five deaths",
+          "World/Asia/VNM/Mortality/Number of under-five deaths",
+          "World/Latam/ARG/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/AUT/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/AZE/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/BGD/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/BRA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/CHL/Health/Nurses and midwives (per 1,000 people)",
+          "World/Pair/CHN/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/CMR/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/CRI/Health/Nurses and midwives (per 1,000 people)",
+          "World/North Africa/EGY/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/ESP/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/FRA/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/GHA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/HRV/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/IDN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/IND/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/KOR/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/LBR/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/MOZ/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/NLD/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/OMN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/PAN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/PER/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/POL/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/SAU/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/SWE/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/THA/Health/Nurses and midwives (per 1,000 people)",
+          "World/North Africa/TUR/Health/Nurses and midwives (per 1,000 people)",
+          "World/Pair/USA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/VEN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/YEM/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/ZAF/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/ARG/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/AUT/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/BGD/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/BRA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/CHL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Pair/CHN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/CMR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/COL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/CRI/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/DEU/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/DZA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/EGY/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/ESP/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/FRA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/GBR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/GHA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/GRC/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/HRV/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/IDN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/IND/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/ISR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/KOR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/LBR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/NGA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/NLD/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/PAN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/PER/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/PHL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/POL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/SEN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/SWE/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Pair/USA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/VEN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/VNM/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/AZE/Health/People practicing open defecation (% of population)",
+          "World/Asia/BGD/Health/People practicing open defecation (% of population)",
+          "World/Latam/BRA/Health/People practicing open defecation (% of population)",
+          "World/Latam/CHL/Health/People practicing open defecation (% of population)",
+          "World/Pair/CHN/Health/People practicing open defecation (% of population)",
+          "World/South Africa/CMR/Health/People practicing open defecation (% of population)",
+          "World/Latam/COL/Health/People practicing open defecation (% of population)",
+          "World/Latam/CRI/Health/People practicing open defecation (% of population)",
+          "World/North Africa/DZA/Health/People practicing open defecation (% of population)",
+          "World/North Africa/EGY/Health/People practicing open defecation (% of population)",
+          "World/South Africa/GHA/Health/People practicing open defecation (% of population)",
+          "World/Europe/GRC/Health/People practicing open defecation (% of population)",
+          "World/Asia/IDN/Health/People practicing open defecation (% of population)",
+          "World/Asia/IND/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/IRQ/Health/People practicing open defecation (% of population)",
+          "World/South Africa/LBR/Health/People practicing open defecation (% of population)",
+          "World/North Africa/MAR/Health/People practicing open defecation (% of population)",
+          "World/Latam/MEX/Health/People practicing open defecation (% of population)",
+          "World/South Africa/MOZ/Health/People practicing open defecation (% of population)",
+          "World/South Africa/NGA/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/OMN/Health/People practicing open defecation (% of population)",
+          "World/Latam/PAN/Health/People practicing open defecation (% of population)",
+          "World/Latam/PER/Health/People practicing open defecation (% of population)",
+          "World/Asia/PHL/Health/People practicing open defecation (% of population)",
+          "World/South Africa/SEN/Health/People practicing open defecation (% of population)",
+          "World/Asia/THA/Health/People practicing open defecation (% of population)",
+          "World/North Africa/TUR/Health/People practicing open defecation (% of population)",
+          "World/Latam/VEN/Health/People practicing open defecation (% of population)",
+          "World/Asia/VNM/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/YEM/Health/People practicing open defecation (% of population)",
+          "World/South Africa/ZAF/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/ARE/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/ARG/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/BGD/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/BRA/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/CHL/Health/People using at least basic drinking water services (% of population)",
+          "World/Pair/CHN/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/CMR/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/COL/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/CRI/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/DZA/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/EGY/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/ESP/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/FRA/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/GHA/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/GRC/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/HRV/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/IDN/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/IND/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/IRQ/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/KOR/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/LBR/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/MAR/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/MEX/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/MOZ/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/NGA/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/OMN/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/PAN/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/PER/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/PHL/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/POL/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/QAT/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/SEN/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/SWE/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/THA/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/TUR/Health/People using at least basic drinking water services (% of population)",
+          "World/Pair/USA/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/VEN/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/VNM/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/ZAF/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/ARE/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/ARG/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/AUT/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/BGD/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/BRA/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/CHL/Health/People using at least basic sanitation services (% of population)",
+          "World/Pair/CHN/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/CMR/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/COL/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/CRI/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/DEU/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/DZA/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/EGY/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/ESP/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/FRA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/GBR/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/GHA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/GRC/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/HRV/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/IDN/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/IND/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/IRQ/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/ISR/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/KOR/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/LBR/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/MAR/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/MEX/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/MOZ/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/NGA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/NLD/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/OMN/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/PAN/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/PER/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/PHL/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/POL/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/SEN/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/SWE/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/THA/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/TUR/Health/People using at least basic sanitation services (% of population)",
+          "World/Pair/USA/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/VEN/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/VNM/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/ZAF/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/ARE/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/ARG/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/AUT/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/BGD/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/BRA/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/CHL/Health/People using safely managed sanitation services (% of population)",
+          "World/Pair/CHN/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/COL/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/CRI/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/DEU/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/DZA/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/EGY/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/FRA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/GBR/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/GHA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/GRC/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/IND/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/ISR/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/KOR/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/MAR/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/MEX/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/NGA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/NLD/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/PER/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/PHL/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/POL/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/QAT/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/SEN/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/SWE/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/THA/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/TUR/Health/People using safely managed sanitation services (% of population)",
+          "World/Pair/USA/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/VEN/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/AUT/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/CHL/Agriculture/Permanent cropland (% of land area)",
+          "World/Pair/CHN/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/CMR/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/COL/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/CRI/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/DZA/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/FRA/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/GRC/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/IDN/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/IND/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/MAR/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/MEX/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/NLD/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/OMN/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/PER/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/PHL/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/SEN/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/THA/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/TUR/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/VNM/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/ZAF/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/ARE/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/AUT/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 0-14 (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/COL/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/GHA/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/HRV/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/IND/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/KOR/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/PER/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/POL/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/SEN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/THA/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 0-14 (% of total population)",
+          "World/Pair/USA/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 15-64 (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/COL/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/DEU/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/GBR/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/GHA/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/GRC/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/IND/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/ISR/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/PER/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/POL/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/SEN/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 15-64 (% of total population)",
+          "World/Pair/USA/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/AUT/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 65 and above (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/COL/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/DEU/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/DZA/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/GBR/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/GRC/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/HRV/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/IND/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/ISR/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/KOR/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/MOZ/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/NGA/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/PER/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/POL/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/THA/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 65 and above (% of total population)",
+          "World/Pair/USA/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population in largest city",
+          "World/Latam/ARG/Demography/Population in largest city",
+          "World/Europe/AUT/Demography/Population in largest city",
+          "World/Persian Gulf/AZE/Demography/Population in largest city",
+          "World/Asia/BGD/Demography/Population in largest city",
+          "World/Latam/BRA/Demography/Population in largest city",
+          "World/Latam/CHL/Demography/Population in largest city",
+          "World/Pair/CHN/Demography/Population in largest city",
+          "World/South Africa/CMR/Demography/Population in largest city",
+          "World/Latam/COL/Demography/Population in largest city",
+          "World/Latam/CRI/Demography/Population in largest city",
+          "World/North Africa/DZA/Demography/Population in largest city",
+          "World/North Africa/EGY/Demography/Population in largest city",
+          "World/Europe/ESP/Demography/Population in largest city",
+          "World/Europe/FRA/Demography/Population in largest city",
+          "World/Europe/GBR/Demography/Population in largest city",
+          "World/South Africa/GHA/Demography/Population in largest city",
+          "World/Europe/GRC/Demography/Population in largest city",
+          "World/Europe/HRV/Demography/Population in largest city",
+          "World/Asia/IDN/Demography/Population in largest city",
+          "World/Asia/IND/Demography/Population in largest city",
+          "World/North Africa/ISR/Demography/Population in largest city",
+          "World/North Africa/MAR/Demography/Population in largest city",
+          "World/Latam/MEX/Demography/Population in largest city",
+          "World/South Africa/MOZ/Demography/Population in largest city",
+          "World/South Africa/NGA/Demography/Population in largest city",
+          "World/Europe/NLD/Demography/Population in largest city",
+          "World/Persian Gulf/OMN/Demography/Population in largest city",
+          "World/Latam/PAN/Demography/Population in largest city",
+          "World/Latam/PER/Demography/Population in largest city",
+          "World/Asia/PHL/Demography/Population in largest city",
+          "World/Europe/POL/Demography/Population in largest city",
+          "World/Persian Gulf/QAT/Demography/Population in largest city",
+          "World/Persian Gulf/SAU/Demography/Population in largest city",
+          "World/South Africa/SEN/Demography/Population in largest city",
+          "World/Asia/THA/Demography/Population in largest city",
+          "World/North Africa/TUR/Demography/Population in largest city",
+          "World/Pair/USA/Demography/Population in largest city",
+          "World/Latam/VEN/Demography/Population in largest city",
+          "World/Asia/VNM/Demography/Population in largest city",
+          "World/Persian Gulf/YEM/Demography/Population in largest city",
+          "World/South Africa/ZAF/Demography/Population in largest city",
+          "World/Persian Gulf/ARE/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/ARG/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/AUT/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/AZE/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/BGD/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/CHL/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/CMR/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/COL/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/CRI/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/DZA/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/EGY/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/FRA/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/GBR/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/IDN/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/IND/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/ISR/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/KOR/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/MAR/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/MEX/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/MOZ/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/NGA/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/NLD/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/PAN/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/PER/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/POL/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/QAT/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/SAU/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/SEN/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/TUR/Demography/Population in the largest city (% of urban population)",
+          "World/Pair/USA/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/VEN/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/VNM/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/YEM/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/ZAF/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/ARE/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/ARG/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/AUT/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/AZE/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/BGD/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/BRA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/CHL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Pair/CHN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/CMR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/COL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/CRI/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/DEU/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/DZA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/EGY/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/ESP/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/FRA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/GBR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/GHA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/GRC/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/IDN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/IND/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/ISR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/KOR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/MAR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/MEX/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/MOZ/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/NGA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/NLD/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/OMN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/PAN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/PER/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/PHL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/POL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/SAU/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/SEN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/THA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/TUR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Pair/USA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/VEN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/VNM/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/YEM/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/ZAF/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/ARE/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/ARG/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/AUT/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/BGD/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/BRA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Pair/CHN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/CMR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/COL/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/CRI/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/DZA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/GBR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/GHA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/GRC/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/IDN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/IND/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/KOR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/MAR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/MEX/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/MOZ/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/NGA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/PAN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/PER/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/POL/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/THA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/TUR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Pair/USA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/VNM/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/ZAF/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/AZE/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/BGD/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/CHL/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Pair/CHN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/CMR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/EGY/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Europe/GBR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/GHA/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/IDN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/IND/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/LBR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/MAR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/MOZ/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/PAN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/PER/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/SEN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Europe/SWE/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/THA/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/TUR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/VNM/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Persian Gulf/YEM/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/ZAF/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Persian Gulf/ARE/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/AZE/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/BGD/Health/Pregnant women receiving prenatal care (%)",
+          "World/Pair/CHN/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/CMR/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/COL/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/CRI/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/DZA/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/EGY/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/IDN/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/IND/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/LBR/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/MAR/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/MOZ/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/NGA/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/PER/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/PHL/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/QAT/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/SAU/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/SEN/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/TUR/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/VEN/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/VNM/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/YEM/Health/Pregnant women receiving prenatal care (%)",
+          "World/Europe/AUT/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/BGD/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/BRA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Pair/CHN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/COL/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/CRI/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/DEU/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/ESP/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/GBR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/GRC/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/IND/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/MEX/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/NLD/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/PAN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/PER/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/PHL/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Pair/USA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/VEN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/VNM/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/BGD/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/BRA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/CHL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Pair/CHN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/COL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/CRI/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/ESP/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/GBR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/KOR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/MEX/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/NLD/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/PAN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/PER/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/PHL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/THA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Pair/USA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/VEN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/VNM/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/ARE/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/ARG/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/BGD/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/BRA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/CHL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Pair/CHN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/COL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/CRI/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Europe/GBR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Europe/HRV/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/IND/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/KOR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/MEX/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/PAN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/PER/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/PHL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Pair/USA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/VEN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/VNM/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/ARG/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/BGD/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/BRA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/CHL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Pair/CHN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/COL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/CRI/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/ESP/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/FRA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/GBR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/GRC/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/KOR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/MEX/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/NLD/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/PAN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/PER/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/PHL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/THA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Pair/USA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/VEN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/VNM/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/ARG/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/AUT/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/AZE/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/BGD/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/BRA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/CHL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Pair/CHN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/CMR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/COL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/CRI/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/DEU/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/DZA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/ESP/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/FRA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/GBR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/GHA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/GRC/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/HRV/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/IDN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/IND/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/ISR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/KOR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/LBR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/MAR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/MEX/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/MOZ/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/NGA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/NLD/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/OMN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/PAN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/PER/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/PHL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/POL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/QAT/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/SEN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/SWE/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/THA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/TUR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Pair/USA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/VNM/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/YEM/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/ZAF/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/ARG/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/AZE/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/BGD/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/CHL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Pair/CHN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/COL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/CRI/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/DZA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/EGY/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/GRC/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/IDN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/IND/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/KOR/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/MAR/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/MEX/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/South Africa/NGA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/NLD/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/OMN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/PAN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/PER/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/PHL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/POL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/QAT/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/SAU/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/THA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Pair/USA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/VEN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/VNM/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/YEM/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/South Africa/ZAF/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/AZE/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/BGD/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/BRA/Health/Prevalence of undernourishment (% of population)",
+          "World/Pair/CHN/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/CMR/Health/Prevalence of undernourishment (% of population)",
+          "World/North Africa/DZA/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/GHA/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/IDN/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of undernourishment (% of population)",
+          "World/North Africa/MAR/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/MEX/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/MOZ/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/NGA/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/OMN/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/PAN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/PHL/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/SEN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/THA/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/VEN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/VNM/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/YEM/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/ARE/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/AZE/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/BGD/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/BRA/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Pair/CHN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/South Africa/CMR/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/CRI/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Europe/GBR/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/IDN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/South Africa/NGA/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Europe/NLD/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/OMN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/PAN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/QAT/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/SAU/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/VEN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/VNM/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/YEM/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/AZE/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/BGD/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/BRA/Economy/Primary income payments (BoP, current US$)",
+          "World/Pair/CHN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/COL/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/CRI/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/ESP/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/GRC/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/HRV/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/IDN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/IND/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/KOR/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/MEX/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/NLD/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/PAN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/PER/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/PHL/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/POL/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/SAU/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/THA/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/TUR/Economy/Primary income payments (BoP, current US$)",
+          "World/Pair/USA/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/VEN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/VNM/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/AZE/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/CHL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Pair/CHN/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/COL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/ESP/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/GRC/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/HRV/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/IND/Economy/Primary income receipts (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/KOR/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/LBR/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/MEX/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/NLD/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/PER/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/PHL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/POL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/QAT/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/THA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Pair/USA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/VNM/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/AZE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/BRA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/CRI/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/MEX/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/VEN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/AZE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/BRA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/CRI/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/MEX/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/VEN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/ARG/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/AZE/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/CHL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Pair/CHN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/COL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/CRI/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/North Africa/EGY/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Europe/ESP/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/GHA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Europe/GRC/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/IND/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/MEX/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/MOZ/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/NGA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/PAN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/PER/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/PHL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/SAU/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/SEN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/THA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/North Africa/TUR/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Pair/USA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/VNM/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/YEM/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/AZE/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/BGD/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/BRA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/CHL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Pair/CHN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/CMR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/DZA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/EGY/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Europe/GBR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/GHA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/IND/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/ISR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/KOR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/MEX/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/MOZ/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/PAN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/PHL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Europe/POL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/SEN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/THA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/TUR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Pair/USA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/VNM/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Persian Gulf/ARE/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/ARG/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/AUT/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/BGD/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/BRA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/CHL/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/CRI/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/DEU/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/DZA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/ESP/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/FRA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/GBR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/GHA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/GRC/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/IND/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/ISR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/KOR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/MEX/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/NGA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/NLD/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/PAN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/QAT/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/SAU/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/SEN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/TUR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Pair/USA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/VNM/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/ARE/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/ARG/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/AUT/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/BGD/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/BRA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/CHL/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Pair/CHN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/CRI/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/DEU/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/DZA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/ESP/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/FRA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/GBR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/GRC/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/ISR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/KOR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/MEX/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/MOZ/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/NLD/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/OMN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/PAN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/POL/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/QAT/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/SAU/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/SEN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/TUR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Pair/USA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/VEN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/VNM/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/ZAF/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/ARE/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/ARG/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/BGD/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Pair/CHN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/CRI/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/DEU/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/EGY/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/GBR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/GHA/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/GRC/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/IDN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/IND/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/KOR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/MAR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/MEX/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/MOZ/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/NLD/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/PAN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/PHL/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/POL/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Persian Gulf/SAU/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/SWE/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/TUR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Pair/USA/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/VNM/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/ZAF/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Persian Gulf/ARE/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/ARG/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/AUT/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/AZE/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/BGD/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/BRA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/CHL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Pair/CHN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/CMR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/COL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/CRI/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/DZA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/EGY/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/ESP/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/FRA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/GBR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/GHA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/IDN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/IND/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/ISR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/KOR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/LBR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/MAR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/MEX/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/MOZ/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/NGA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/NLD/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/OMN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/PAN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/PER/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/PHL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/POL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/QAT/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/SAU/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/SEN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/THA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/TUR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Pair/USA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/VEN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/VNM/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/YEM/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/ZAF/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/AUT/R&D/Researchers in R&D (per million people)",
+          "World/Latam/BRA/R&D/Researchers in R&D (per million people)",
+          "World/Pair/CHN/R&D/Researchers in R&D (per million people)",
+          "World/Europe/DEU/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/DZA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/ESP/R&D/Researchers in R&D (per million people)",
+          "World/Europe/FRA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/GBR/R&D/Researchers in R&D (per million people)",
+          "World/South Africa/GHA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/GRC/R&D/Researchers in R&D (per million people)",
+          "World/Asia/IND/R&D/Researchers in R&D (per million people)",
+          "World/Asia/KOR/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/MAR/R&D/Researchers in R&D (per million people)",
+          "World/South Africa/MOZ/R&D/Researchers in R&D (per million people)",
+          "World/Europe/NLD/R&D/Researchers in R&D (per million people)",
+          "World/Persian Gulf/OMN/R&D/Researchers in R&D (per million people)",
+          "World/Asia/PHL/R&D/Researchers in R&D (per million people)",
+          "World/Europe/POL/R&D/Researchers in R&D (per million people)",
+          "World/Asia/THA/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/TUR/R&D/Researchers in R&D (per million people)",
+          "World/Pair/USA/R&D/Researchers in R&D (per million people)",
+          "World/Latam/VEN/R&D/Researchers in R&D (per million people)",
+          "World/Persian Gulf/AZE/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/BGD/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/BRA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/CHL/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Pair/CHN/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/CRI/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/North Africa/EGY/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Europe/ESP/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/South Africa/GHA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/IND/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/KOR/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/North Africa/MAR/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/MEX/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/PAN/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/PER/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Europe/POL/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/THA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Pair/USA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/VNM/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Persian Gulf/AZE/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/BGD/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/BRA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/CHL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Pair/CHN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/CMR/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/COL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/CRI/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/DZA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/EGY/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/GHA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/IDN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/IND/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/MAR/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/MEX/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/MOZ/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/PAN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/PER/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/PHL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Europe/POL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/SEN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/THA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/VNM/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Persian Gulf/ARE/Demography/Rural population",
+          "World/Latam/ARG/Demography/Rural population",
+          "World/Europe/AUT/Demography/Rural population",
+          "World/Persian Gulf/AZE/Demography/Rural population",
+          "World/Asia/BGD/Demography/Rural population",
+          "World/Latam/BRA/Demography/Rural population",
+          "World/Pair/CHN/Demography/Rural population",
+          "World/South Africa/CMR/Demography/Rural population",
+          "World/Latam/COL/Demography/Rural population",
+          "World/Latam/CRI/Demography/Rural population",
+          "World/North Africa/DZA/Demography/Rural population",
+          "World/North Africa/EGY/Demography/Rural population",
+          "World/Europe/FRA/Demography/Rural population",
+          "World/Europe/GBR/Demography/Rural population",
+          "World/South Africa/GHA/Demography/Rural population",
+          "World/Europe/GRC/Demography/Rural population",
+          "World/Europe/HRV/Demography/Rural population",
+          "World/Asia/IDN/Demography/Rural population",
+          "World/Asia/IND/Demography/Rural population",
+          "World/North Africa/ISR/Demography/Rural population",
+          "World/South Africa/LBR/Demography/Rural population",
+          "World/North Africa/MAR/Demography/Rural population",
+          "World/South Africa/MOZ/Demography/Rural population",
+          "World/South Africa/NGA/Demography/Rural population",
+          "World/Europe/NLD/Demography/Rural population",
+          "World/Persian Gulf/OMN/Demography/Rural population",
+          "World/Latam/PAN/Demography/Rural population",
+          "World/Asia/PHL/Demography/Rural population",
+          "World/Europe/POL/Demography/Rural population",
+          "World/Persian Gulf/QAT/Demography/Rural population",
+          "World/Persian Gulf/SAU/Demography/Rural population",
+          "World/South Africa/SEN/Demography/Rural population",
+          "World/North Africa/TUR/Demography/Rural population",
+          "World/Pair/USA/Demography/Rural population",
+          "World/Asia/VNM/Demography/Rural population",
+          "World/Persian Gulf/YEM/Demography/Rural population",
+          "World/Persian Gulf/ARE/Demography/Rural population (% of total population)",
+          "World/Latam/ARG/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Rural population (% of total population)",
+          "World/Asia/BGD/Demography/Rural population (% of total population)",
+          "World/Latam/BRA/Demography/Rural population (% of total population)",
+          "World/Latam/CHL/Demography/Rural population (% of total population)",
+          "World/Pair/CHN/Demography/Rural population (% of total population)",
+          "World/South Africa/CMR/Demography/Rural population (% of total population)",
+          "World/Latam/COL/Demography/Rural population (% of total population)",
+          "World/Latam/CRI/Demography/Rural population (% of total population)",
+          "World/Europe/DEU/Demography/Rural population (% of total population)",
+          "World/North Africa/DZA/Demography/Rural population (% of total population)",
+          "World/Europe/ESP/Demography/Rural population (% of total population)",
+          "World/Europe/FRA/Demography/Rural population (% of total population)",
+          "World/Europe/GBR/Demography/Rural population (% of total population)",
+          "World/South Africa/GHA/Demography/Rural population (% of total population)",
+          "World/Europe/GRC/Demography/Rural population (% of total population)",
+          "World/Europe/HRV/Demography/Rural population (% of total population)",
+          "World/Asia/IDN/Demography/Rural population (% of total population)",
+          "World/Asia/IND/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/IRQ/Demography/Rural population (% of total population)",
+          "World/North Africa/ISR/Demography/Rural population (% of total population)",
+          "World/South Africa/LBR/Demography/Rural population (% of total population)",
+          "World/North Africa/MAR/Demography/Rural population (% of total population)",
+          "World/Latam/MEX/Demography/Rural population (% of total population)",
+          "World/South Africa/MOZ/Demography/Rural population (% of total population)",
+          "World/South Africa/NGA/Demography/Rural population (% of total population)",
+          "World/Europe/NLD/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Rural population (% of total population)",
+          "World/Latam/PAN/Demography/Rural population (% of total population)",
+          "World/Latam/PER/Demography/Rural population (% of total population)",
+          "World/Europe/POL/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Rural population (% of total population)",
+          "World/South Africa/SEN/Demography/Rural population (% of total population)",
+          "World/Asia/THA/Demography/Rural population (% of total population)",
+          "World/North Africa/TUR/Demography/Rural population (% of total population)",
+          "World/Pair/USA/Demography/Rural population (% of total population)",
+          "World/Latam/VEN/Demography/Rural population (% of total population)",
+          "World/Asia/VNM/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Rural population (% of total population)",
+          "World/South Africa/ZAF/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/ARE/industry/Scientific and technical journal articles",
+          "World/Latam/ARG/industry/Scientific and technical journal articles",
+          "World/Europe/AUT/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/AZE/industry/Scientific and technical journal articles",
+          "World/Asia/BGD/industry/Scientific and technical journal articles",
+          "World/Latam/BRA/industry/Scientific and technical journal articles",
+          "World/Latam/CHL/industry/Scientific and technical journal articles",
+          "World/Pair/CHN/industry/Scientific and technical journal articles",
+          "World/South Africa/CMR/industry/Scientific and technical journal articles",
+          "World/Latam/COL/industry/Scientific and technical journal articles",
+          "World/Latam/CRI/industry/Scientific and technical journal articles",
+          "World/North Africa/DZA/industry/Scientific and technical journal articles",
+          "World/North Africa/EGY/industry/Scientific and technical journal articles",
+          "World/Europe/ESP/industry/Scientific and technical journal articles",
+          "World/Europe/FRA/industry/Scientific and technical journal articles",
+          "World/Europe/GBR/industry/Scientific and technical journal articles",
+          "World/South Africa/GHA/industry/Scientific and technical journal articles",
+          "World/Europe/GRC/industry/Scientific and technical journal articles",
+          "World/Europe/HRV/industry/Scientific and technical journal articles",
+          "World/Asia/IDN/industry/Scientific and technical journal articles",
+          "World/Asia/IND/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/IRQ/industry/Scientific and technical journal articles",
+          "World/North Africa/ISR/industry/Scientific and technical journal articles",
+          "World/Asia/KOR/industry/Scientific and technical journal articles",
+          "World/South Africa/LBR/industry/Scientific and technical journal articles",
+          "World/North Africa/MAR/industry/Scientific and technical journal articles",
+          "World/Latam/MEX/industry/Scientific and technical journal articles",
+          "World/South Africa/MOZ/industry/Scientific and technical journal articles",
+          "World/South Africa/NGA/industry/Scientific and technical journal articles",
+          "World/Europe/NLD/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/OMN/industry/Scientific and technical journal articles",
+          "World/Latam/PAN/industry/Scientific and technical journal articles",
+          "World/Latam/PER/industry/Scientific and technical journal articles",
+          "World/Asia/PHL/industry/Scientific and technical journal articles",
+          "World/Europe/POL/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/QAT/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/SAU/industry/Scientific and technical journal articles",
+          "World/South Africa/SEN/industry/Scientific and technical journal articles",
+          "World/Europe/SWE/industry/Scientific and technical journal articles",
+          "World/Asia/THA/industry/Scientific and technical journal articles",
+          "World/North Africa/TUR/industry/Scientific and technical journal articles",
+          "World/Pair/USA/industry/Scientific and technical journal articles",
+          "World/Latam/VEN/industry/Scientific and technical journal articles",
+          "World/Asia/VNM/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/YEM/industry/Scientific and technical journal articles",
+          "World/South Africa/ZAF/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/AZE/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/BGD/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/CHL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/CMR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/COL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/CRI/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/DEU/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/DZA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/IDN/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/IND/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/KOR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/MAR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/MEX/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/NGA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/NLD/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/PER/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/PHL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/SWE/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/THA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Pair/USA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/VNM/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/AUT/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Service exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/Service exports (BoP, current US$)",
+          "World/Latam/BRA/Exports/Service exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/Service exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/CMR/Exports/Service exports (BoP, current US$)",
+          "World/Latam/COL/Exports/Service exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/Service exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/DZA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/Service exports (BoP, current US$)",
+          "World/Europe/FRA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/GRC/Exports/Service exports (BoP, current US$)",
+          "World/Europe/HRV/Exports/Service exports (BoP, current US$)",
+          "World/Asia/IDN/Exports/Service exports (BoP, current US$)",
+          "World/Asia/IND/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Service exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Service exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Service exports (BoP, current US$)",
+          "World/Latam/PAN/Exports/Service exports (BoP, current US$)",
+          "World/Latam/PER/Exports/Service exports (BoP, current US$)",
+          "World/Asia/PHL/Exports/Service exports (BoP, current US$)",
+          "World/Europe/POL/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Service exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/Service exports (BoP, current US$)",
+          "World/Asia/THA/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Service exports (BoP, current US$)",
+          "World/Pair/USA/Exports/Service exports (BoP, current US$)",
+          "World/Asia/VNM/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Service exports (BoP, current US$)",
+          "World/Latam/ARG/Imports/Service imports (BoP, current US$)",
+          "World/Europe/AUT/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Service imports (BoP, current US$)",
+          "World/Asia/BGD/Imports/Service imports (BoP, current US$)",
+          "World/Latam/BRA/Imports/Service imports (BoP, current US$)",
+          "World/Latam/CHL/Imports/Service imports (BoP, current US$)",
+          "World/Pair/CHN/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/CMR/Imports/Service imports (BoP, current US$)",
+          "World/Latam/COL/Imports/Service imports (BoP, current US$)",
+          "World/Latam/CRI/Imports/Service imports (BoP, current US$)",
+          "World/Europe/DEU/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Service imports (BoP, current US$)",
+          "World/Europe/ESP/Imports/Service imports (BoP, current US$)",
+          "World/Europe/FRA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/GBR/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/HRV/Imports/Service imports (BoP, current US$)",
+          "World/Asia/IDN/Imports/Service imports (BoP, current US$)",
+          "World/Asia/IND/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Service imports (BoP, current US$)",
+          "World/Asia/KOR/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Service imports (BoP, current US$)",
+          "World/Latam/MEX/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/NLD/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Service imports (BoP, current US$)",
+          "World/Latam/PAN/Imports/Service imports (BoP, current US$)",
+          "World/Latam/PER/Imports/Service imports (BoP, current US$)",
+          "World/Asia/PHL/Imports/Service imports (BoP, current US$)",
+          "World/Europe/POL/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Service imports (BoP, current US$)",
+          "World/Europe/SWE/Imports/Service imports (BoP, current US$)",
+          "World/Asia/THA/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Service imports (BoP, current US$)",
+          "World/Pair/USA/Imports/Service imports (BoP, current US$)",
+          "World/Asia/VNM/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Europe/AUT/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/BGD/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/BRA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/CHL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Pair/CHN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/COL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/DZA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/EGY/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/IDN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/IND/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/MAR/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/MEX/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/South Africa/NGA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/PAN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/PHL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Europe/POL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/QAT/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/SAU/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/South Africa/SEN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/TUR/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Pair/USA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/VEN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/VNM/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/YEM/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/ARG/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/AUT/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/AZE/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/BGD/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/BRA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/CHL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/CMR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/COL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/CRI/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/DEU/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/DZA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/ESP/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/FRA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/GBR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/GHA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/GRC/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/HRV/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/IND/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/ISR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/KOR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/MAR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/MEX/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/MOZ/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/NGA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/NLD/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/PAN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/PER/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/POL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/SAU/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/SEN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/SWE/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/TUR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Pair/USA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/VEN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/VNM/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/YEM/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/ZAF/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/AZE/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/CHL/Economy/Taxes on international trade (% of revenue)",
+          "World/Pair/CHN/Economy/Taxes on international trade (% of revenue)",
+          "World/South Africa/CMR/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/COL/Economy/Taxes on international trade (% of revenue)",
+          "World/North Africa/EGY/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/ESP/Economy/Taxes on international trade (% of revenue)",
+          "World/South Africa/GHA/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/HRV/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/IND/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/KOR/Economy/Taxes on international trade (% of revenue)",
+          "World/North Africa/MAR/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/MEX/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/PAN/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/PER/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/POL/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/THA/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/ARG/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/AUT/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Asia/BGD/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/CHL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Pair/CHN/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/COL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/CRI/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/DEU/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/DZA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/ESP/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/FRA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/GBR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/GRC/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/ISR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Asia/KOR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/MAR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/MEX/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Persian Gulf/OMN/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/PER/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/POL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/SWE/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Pair/USA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Persian Gulf/ARE/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/AZE/Industry/Time required to start a business (days)",
+          "World/Latam/CHL/Industry/Time required to start a business (days)",
+          "World/Pair/CHN/Industry/Time required to start a business (days)",
+          "World/South Africa/CMR/Industry/Time required to start a business (days)",
+          "World/Latam/COL/Industry/Time required to start a business (days)",
+          "World/Latam/CRI/Industry/Time required to start a business (days)",
+          "World/Europe/DEU/Industry/Time required to start a business (days)",
+          "World/North Africa/DZA/Industry/Time required to start a business (days)",
+          "World/Europe/ESP/Industry/Time required to start a business (days)",
+          "World/Europe/FRA/Industry/Time required to start a business (days)",
+          "World/Europe/GBR/Industry/Time required to start a business (days)",
+          "World/Asia/IND/Industry/Time required to start a business (days)",
+          "World/Asia/KOR/Industry/Time required to start a business (days)",
+          "World/South Africa/LBR/Industry/Time required to start a business (days)",
+          "World/North Africa/MAR/Industry/Time required to start a business (days)",
+          "World/South Africa/MOZ/Industry/Time required to start a business (days)",
+          "World/Europe/NLD/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/OMN/Industry/Time required to start a business (days)",
+          "World/Latam/PAN/Industry/Time required to start a business (days)",
+          "World/Latam/PER/Industry/Time required to start a business (days)",
+          "World/Europe/POL/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/SAU/Industry/Time required to start a business (days)",
+          "World/South Africa/SEN/Industry/Time required to start a business (days)",
+          "World/Asia/THA/Industry/Time required to start a business (days)",
+          "World/Latam/VEN/Industry/Time required to start a business (days)",
+          "World/Asia/VNM/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/YEM/Industry/Time required to start a business (days)",
+          "World/South Africa/ZAF/Industry/Time required to start a business (days)",
+          "World/Latam/ARG/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Persian Gulf/AZE/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/BGD/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/CHL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/COL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/CRI/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/North Africa/EGY/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/GHA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/GRC/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/HRV/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/IDN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/IND/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/LBR/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/MEX/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/MOZ/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/NGA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/PAN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/PER/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/PHL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/POL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/SEN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/THA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/North Africa/TUR/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/VEN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/VNM/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Persian Gulf/YEM/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/ZAF/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/ARG/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/BGD/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/CHL/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/CMR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/COL/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/CRI/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/EGY/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/GHA/R&D/Time to obtain an electrical connection (days)",
+          "World/Europe/GRC/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/IDN/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/IND/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/MAR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/MEX/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/MOZ/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/NGA/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/PAN/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/PER/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/PHL/R&D/Time to obtain an electrical connection (days)",
+          "World/Europe/POL/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/SEN/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/TUR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/VEN/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/VNM/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/ZAF/R&D/Time to obtain an electrical connection (days)",
+          "World/Persian Gulf/ARE/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/ARG/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/AUT/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/AZE/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/BGD/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/BRA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/CHL/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Pair/CHN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/CRI/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/EGY/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/GBR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/GHA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/HRV/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/IND/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/ISR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/KOR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/MAR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/MEX/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/MOZ/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/NGA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/NLD/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/OMN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/PAN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/POL/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/QAT/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/THA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Pair/USA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/VEN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/VNM/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/YEM/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/ARE/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/AZE/Industry/Total fisheries production (metric tons)",
+          "World/Asia/BGD/Industry/Total fisheries production (metric tons)",
+          "World/Latam/BRA/Industry/Total fisheries production (metric tons)",
+          "World/Latam/CHL/Industry/Total fisheries production (metric tons)",
+          "World/Pair/CHN/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/CMR/Industry/Total fisheries production (metric tons)",
+          "World/Latam/CRI/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/EGY/Industry/Total fisheries production (metric tons)",
+          "World/Europe/HRV/Industry/Total fisheries production (metric tons)",
+          "World/Asia/IDN/Industry/Total fisheries production (metric tons)",
+          "World/Asia/IND/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/ISR/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/MAR/Industry/Total fisheries production (metric tons)",
+          "World/Latam/MEX/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/MOZ/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/NGA/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/OMN/Industry/Total fisheries production (metric tons)",
+          "World/Asia/PHL/Industry/Total fisheries production (metric tons)",
+          "World/Europe/POL/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/SAU/Industry/Total fisheries production (metric tons)",
+          "World/Asia/THA/Industry/Total fisheries production (metric tons)",
+          "World/Asia/VNM/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/ARE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/CRI/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/HRV/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/IRQ/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/ISR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/KOR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/MAR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/MEX/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/South Africa/MOZ/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/PHL/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/SWE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/THA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/TUR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/South Africa/ZAF/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/CHL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/COL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/IND/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/PER/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/THA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/BGD/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/BRA/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/CHL/Economy/Total reserves (includes gold, current US$)",
+          "World/Pair/CHN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/COL/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/CRI/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/DZA/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/ESP/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/FRA/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/GBR/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/GHA/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/IDN/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/IND/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/IRQ/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/ISR/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/KOR/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/LBR/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/MAR/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/MEX/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/MOZ/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/OMN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/PAN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/PER/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/PHL/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/POL/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/QAT/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/SAU/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/SWE/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/THA/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/TUR/Economy/Total reserves (includes gold, current US$)",
+          "World/Pair/USA/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/VEN/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/VNM/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/ZAF/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/ARE/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/BGD/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/BRA/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/CHL/Economy/Total reserves minus gold (current US$)",
+          "World/Pair/CHN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/COL/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/CRI/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/DZA/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/FRA/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/GBR/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/GHA/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/IDN/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/IND/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/ISR/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/KOR/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/LBR/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/MAR/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/MEX/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/MOZ/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/OMN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/PAN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/PER/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/PHL/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/POL/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/QAT/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/SAU/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/SWE/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/THA/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/TUR/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/VEN/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/VNM/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/ZAF/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/ARG/Health/UHC service coverage index",
+          "World/Europe/AUT/Health/UHC service coverage index",
+          "World/Persian Gulf/AZE/Health/UHC service coverage index",
+          "World/Asia/BGD/Health/UHC service coverage index",
+          "World/Latam/BRA/Health/UHC service coverage index",
+          "World/Latam/CHL/Health/UHC service coverage index",
+          "World/Pair/CHN/Health/UHC service coverage index",
+          "World/South Africa/CMR/Health/UHC service coverage index",
+          "World/Latam/COL/Health/UHC service coverage index",
+          "World/Latam/CRI/Health/UHC service coverage index",
+          "World/Europe/DEU/Health/UHC service coverage index",
+          "World/North Africa/DZA/Health/UHC service coverage index",
+          "World/North Africa/EGY/Health/UHC service coverage index",
+          "World/Europe/ESP/Health/UHC service coverage index",
+          "World/Europe/FRA/Health/UHC service coverage index",
+          "World/Europe/GBR/Health/UHC service coverage index",
+          "World/South Africa/GHA/Health/UHC service coverage index",
+          "World/Europe/GRC/Health/UHC service coverage index",
+          "World/Europe/HRV/Health/UHC service coverage index",
+          "World/Asia/IDN/Health/UHC service coverage index",
+          "World/Asia/IND/Health/UHC service coverage index",
+          "World/Persian Gulf/IRQ/Health/UHC service coverage index",
+          "World/North Africa/ISR/Health/UHC service coverage index",
+          "World/Asia/KOR/Health/UHC service coverage index",
+          "World/South Africa/LBR/Health/UHC service coverage index",
+          "World/North Africa/MAR/Health/UHC service coverage index",
+          "World/Latam/MEX/Health/UHC service coverage index",
+          "World/South Africa/MOZ/Health/UHC service coverage index",
+          "World/South Africa/NGA/Health/UHC service coverage index",
+          "World/Europe/NLD/Health/UHC service coverage index",
+          "World/Persian Gulf/OMN/Health/UHC service coverage index",
+          "World/Latam/PAN/Health/UHC service coverage index",
+          "World/Latam/PER/Health/UHC service coverage index",
+          "World/Asia/PHL/Health/UHC service coverage index",
+          "World/Europe/POL/Health/UHC service coverage index",
+          "World/Persian Gulf/QAT/Health/UHC service coverage index",
+          "World/South Africa/SEN/Health/UHC service coverage index",
+          "World/Europe/SWE/Health/UHC service coverage index",
+          "World/Asia/THA/Health/UHC service coverage index",
+          "World/North Africa/TUR/Health/UHC service coverage index",
+          "World/Pair/USA/Health/UHC service coverage index",
+          "World/Latam/VEN/Health/UHC service coverage index",
+          "World/Asia/VNM/Health/UHC service coverage index",
+          "World/Persian Gulf/YEM/Health/UHC service coverage index",
+          "World/South Africa/ZAF/Health/UHC service coverage index",
+          "World/Persian Gulf/ARE/Demography/Urban population",
+          "World/Latam/ARG/Demography/Urban population",
+          "World/Persian Gulf/AZE/Demography/Urban population",
+          "World/Asia/BGD/Demography/Urban population",
+          "World/Latam/BRA/Demography/Urban population",
+          "World/Latam/CHL/Demography/Urban population",
+          "World/Pair/CHN/Demography/Urban population",
+          "World/South Africa/CMR/Demography/Urban population",
+          "World/Latam/COL/Demography/Urban population",
+          "World/Latam/CRI/Demography/Urban population",
+          "World/North Africa/DZA/Demography/Urban population",
+          "World/North Africa/EGY/Demography/Urban population",
+          "World/Europe/ESP/Demography/Urban population",
+          "World/Europe/FRA/Demography/Urban population",
+          "World/Europe/GBR/Demography/Urban population",
+          "World/South Africa/GHA/Demography/Urban population",
+          "World/Asia/IDN/Demography/Urban population",
+          "World/Asia/IND/Demography/Urban population",
+          "World/North Africa/ISR/Demography/Urban population",
+          "World/Asia/KOR/Demography/Urban population",
+          "World/South Africa/LBR/Demography/Urban population",
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+          "World/Europe/SWE",
+          "World/Asia/THA",
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+          "World/Latam",
+          "World/North Africa",
+          "World/Pair",
+          "World/Persian Gulf",
+          "World/South Africa",
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+         ],
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+          "Adjusted net national income per capita (current US$)",
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+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
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+          "Adjusted savings: carbon dioxide damage (current US$)",
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+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
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+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
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+          "People using at least basic sanitation services (% of population)",
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+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
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+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
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+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
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+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
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+          "Population ages 15-64 (% of total population)",
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+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
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+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in the largest city (% of urban population)",
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+          "Population in the largest city (% of urban population)",
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+          "Population in the largest city (% of urban population)",
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+          "Population in the largest city (% of urban population)",
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+          "Population in the largest city (% of urban population)",
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+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
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+          "Population in urban agglomerations of more than 1 million",
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+          "Population in urban agglomerations of more than 1 million",
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+          "Population in urban agglomerations of more than 1 million",
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+          "Population in urban agglomerations of more than 1 million",
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+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
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+          "Rural population",
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+          "Rural population",
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+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
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+          "World/Latam/MEX/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/NLD/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/PAN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/PER/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/PHL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/POL/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Europe/SWE/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/THA/Economy/Adjusted net national income per capita (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted net national income per capita (current US$)",
+          "World/Pair/USA/Economy/Adjusted net national income per capita (current US$)",
+          "World/Latam/VEN/Economy/Adjusted net national income per capita (current US$)",
+          "World/Asia/VNM/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted net national income per capita (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted net national income per capita (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: carbon dioxide damage (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/ESP/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/HRV/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Europe/SWE/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: consumption of fixed capital (current US$)",
+          "World/Latam/ARG/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/BRA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/ESP/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/HRV/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/MEX/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Europe/SWE/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Pair/USA/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Adjusted savings: education expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/DEU/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/DZA/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/South Africa/LBR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/NLD/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Latam/PER/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Europe/POL/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: net national savings (current US$)",
+          "World/South Africa/SEN/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: net national savings (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: net national savings (current US$)",
+          "World/Persian Gulf/ARE/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/AUT/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/BGD/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/CHL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Pair/CHN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/CMR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/COL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/CRI/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/EGY/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/FRA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/GBR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/GHA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Europe/GRC/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/IDN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/IND/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/ISR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/KOR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/MAR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/MOZ/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/South Africa/NGA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/PAN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/PHL/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/QAT/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/SAU/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/THA/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/North Africa/TUR/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Latam/VEN/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Asia/VNM/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/YEM/Economy/Adjusted savings: particulate emission damage (current US$)",
+          "World/Persian Gulf/ARE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/ARG/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/AUT/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/AZE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/BGD/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/BRA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/CHL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Pair/CHN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/CMR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/COL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/CRI/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/DEU/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/EGY/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/GBR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/GHA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/GRC/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/HRV/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/IND/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/ISR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/KOR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/LBR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/MAR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/MEX/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/MOZ/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/NGA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/NLD/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/OMN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/PAN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/PER/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/PHL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/POL/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/QAT/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/SAU/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/South Africa/SEN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/SWE/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/North Africa/TUR/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Pair/USA/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Latam/VEN/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Asia/VNM/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Persian Gulf/YEM/Demography/Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "World/Europe/AUT/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/BGD/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/BRA/Agriculture/Agricultural land (% of land area)",
+          "World/Pair/CHN/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/CMR/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/CRI/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/DEU/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/DZA/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/EGY/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/ESP/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/FRA/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/GRC/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/IDN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/IND/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/ISR/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/KOR/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/LBR/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Agricultural land (% of land area)",
+          "World/South Africa/NGA/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/NLD/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/PAN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/PHL/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/POL/Agriculture/Agricultural land (% of land area)",
+          "World/Europe/SWE/Agriculture/Agricultural land (% of land area)",
+          "World/North Africa/TUR/Agriculture/Agricultural land (% of land area)",
+          "World/Pair/USA/Agriculture/Agricultural land (% of land area)",
+          "World/Latam/VEN/Agriculture/Agricultural land (% of land area)",
+          "World/Asia/VNM/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural land (% of land area)",
+          "World/Persian Gulf/ARE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CHL/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/HRV/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/TUR/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Agriculture/Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/AUT/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/BRA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/CHL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/COL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/DZA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/EGY/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/GRC/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/HRV/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/IDN/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/KOR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/MAR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/MEX/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/PHL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Europe/POL/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/THA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/North Africa/TUR/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Pair/USA/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Latam/VEN/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/VNM/Agriculture/Agricultural raw materials imports (% of merchandise imports)",
+          "World/Asia/BGD/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/BRA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/CHL/Agriculture/Aquaculture production (metric tons)",
+          "World/Pair/CHN/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/CMR/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/COL/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/CRI/Agriculture/Aquaculture production (metric tons)",
+          "World/North Africa/EGY/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/FRA/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/GHA/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/IDN/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/IND/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/KOR/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/MEX/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/NGA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/PER/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/PHL/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/POL/Agriculture/Aquaculture production (metric tons)",
+          "World/Persian Gulf/SAU/Agriculture/Aquaculture production (metric tons)",
+          "World/North Africa/TUR/Agriculture/Aquaculture production (metric tons)",
+          "World/Pair/USA/Agriculture/Aquaculture production (metric tons)",
+          "World/Latam/VEN/Agriculture/Aquaculture production (metric tons)",
+          "World/Asia/VNM/Agriculture/Aquaculture production (metric tons)",
+          "World/South Africa/ZAF/Agriculture/Aquaculture production (metric tons)",
+          "World/Europe/AUT/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (% of land area)",
+          "World/Asia/BGD/Agriculture/Arable land (% of land area)",
+          "World/Latam/BRA/Agriculture/Arable land (% of land area)",
+          "World/Latam/CHL/Agriculture/Arable land (% of land area)",
+          "World/Pair/CHN/Agriculture/Arable land (% of land area)",
+          "World/South Africa/CMR/Agriculture/Arable land (% of land area)",
+          "World/Latam/COL/Agriculture/Arable land (% of land area)",
+          "World/Latam/CRI/Agriculture/Arable land (% of land area)",
+          "World/South Africa/GHA/Agriculture/Arable land (% of land area)",
+          "World/Europe/GRC/Agriculture/Arable land (% of land area)",
+          "World/Asia/IDN/Agriculture/Arable land (% of land area)",
+          "World/Asia/IND/Agriculture/Arable land (% of land area)",
+          "World/Asia/KOR/Agriculture/Arable land (% of land area)",
+          "World/South Africa/LBR/Agriculture/Arable land (% of land area)",
+          "World/North Africa/MAR/Agriculture/Arable land (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Arable land (% of land area)",
+          "World/Latam/PAN/Agriculture/Arable land (% of land area)",
+          "World/Europe/POL/Agriculture/Arable land (% of land area)",
+          "World/Europe/SWE/Agriculture/Arable land (% of land area)",
+          "World/North Africa/TUR/Agriculture/Arable land (% of land area)",
+          "World/Pair/USA/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (% of land area)",
+          "World/Persian Gulf/ARE/Agriculture/Arable land (hectares per person)",
+          "World/Latam/ARG/Agriculture/Arable land (hectares per person)",
+          "World/Europe/AUT/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (hectares per person)",
+          "World/Asia/BGD/Agriculture/Arable land (hectares per person)",
+          "World/Latam/CHL/Agriculture/Arable land (hectares per person)",
+          "World/Pair/CHN/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/CMR/Agriculture/Arable land (hectares per person)",
+          "World/Latam/COL/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/DZA/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/EGY/Agriculture/Arable land (hectares per person)",
+          "World/Europe/ESP/Agriculture/Arable land (hectares per person)",
+          "World/Europe/FRA/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/GHA/Agriculture/Arable land (hectares per person)",
+          "World/Europe/GRC/Agriculture/Arable land (hectares per person)",
+          "World/Asia/IND/Agriculture/Arable land (hectares per person)",
+          "World/Asia/KOR/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/LBR/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/MAR/Agriculture/Arable land (hectares per person)",
+          "World/Latam/MEX/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/MOZ/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/NGA/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/OMN/Agriculture/Arable land (hectares per person)",
+          "World/Latam/PAN/Agriculture/Arable land (hectares per person)",
+          "World/Latam/PER/Agriculture/Arable land (hectares per person)",
+          "World/Asia/PHL/Agriculture/Arable land (hectares per person)",
+          "World/Europe/POL/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/QAT/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/SAU/Agriculture/Arable land (hectares per person)",
+          "World/Europe/SWE/Agriculture/Arable land (hectares per person)",
+          "World/North Africa/TUR/Agriculture/Arable land (hectares per person)",
+          "World/Pair/USA/Agriculture/Arable land (hectares per person)",
+          "World/Latam/VEN/Agriculture/Arable land (hectares per person)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (hectares per person)",
+          "World/South Africa/ZAF/Agriculture/Arable land (hectares per person)",
+          "World/Europe/AUT/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/AZE/Agriculture/Arable land (hectares)",
+          "World/Asia/BGD/Agriculture/Arable land (hectares)",
+          "World/Latam/BRA/Agriculture/Arable land (hectares)",
+          "World/Latam/CHL/Agriculture/Arable land (hectares)",
+          "World/Pair/CHN/Agriculture/Arable land (hectares)",
+          "World/South Africa/CMR/Agriculture/Arable land (hectares)",
+          "World/Latam/COL/Agriculture/Arable land (hectares)",
+          "World/Latam/CRI/Agriculture/Arable land (hectares)",
+          "World/South Africa/GHA/Agriculture/Arable land (hectares)",
+          "World/Europe/GRC/Agriculture/Arable land (hectares)",
+          "World/Asia/IDN/Agriculture/Arable land (hectares)",
+          "World/Asia/IND/Agriculture/Arable land (hectares)",
+          "World/Asia/KOR/Agriculture/Arable land (hectares)",
+          "World/South Africa/LBR/Agriculture/Arable land (hectares)",
+          "World/North Africa/MAR/Agriculture/Arable land (hectares)",
+          "World/South Africa/MOZ/Agriculture/Arable land (hectares)",
+          "World/Latam/PAN/Agriculture/Arable land (hectares)",
+          "World/Europe/POL/Agriculture/Arable land (hectares)",
+          "World/Europe/SWE/Agriculture/Arable land (hectares)",
+          "World/North Africa/TUR/Agriculture/Arable land (hectares)",
+          "World/Pair/USA/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/YEM/Agriculture/Arable land (hectares)",
+          "World/Persian Gulf/ARE/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/ARG/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/AZE/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/BGD/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Pair/CHN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/CMR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/COL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/CRI/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/DEU/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/DZA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/EGY/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/FRA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/GHA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/HRV/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/IDN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/IND/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/ISR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/LBR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/MAR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/MEX/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/MOZ/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/NGA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/PAN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/PER/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/PHL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Europe/POL/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/SAU/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/SEN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/THA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/North Africa/TUR/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Pair/USA/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Latam/VEN/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Asia/VNM/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/YEM/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/South Africa/ZAF/Industry/Automated teller machines (ATMs) (per 100,000 adults)",
+          "World/Persian Gulf/ARE/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/BGD/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Pair/CHN/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/CRI/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/DZA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/EGY/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/GHA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/IDN/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/IND/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/MAR/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/MEX/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/MOZ/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Latam/PER/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/PHL/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/SAU/Mortality/Births attended by skilled health staff (% of total)",
+          "World/North Africa/TUR/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Pair/USA/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Asia/VNM/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/YEM/Mortality/Births attended by skilled health staff (% of total)",
+          "World/South Africa/ZAF/Mortality/Births attended by skilled health staff (% of total)",
+          "World/Persian Gulf/ARE/Economy/Broad money (% of GDP)",
+          "World/Latam/ARG/Economy/Broad money (% of GDP)",
+          "World/Persian Gulf/AZE/Economy/Broad money (% of GDP)",
+          "World/Asia/BGD/Economy/Broad money (% of GDP)",
+          "World/Latam/BRA/Economy/Broad money (% of GDP)",
+          "World/Pair/CHN/Economy/Broad money (% of GDP)",
+          "World/South Africa/CMR/Economy/Broad money (% of GDP)",
+          "World/North Africa/DZA/Economy/Broad money (% of GDP)",
+          "World/Europe/GBR/Economy/Broad money (% of GDP)",
+          "World/Europe/HRV/Economy/Broad money (% of GDP)",
+          "World/Asia/IND/Economy/Broad money (% of GDP)",
+          "World/North Africa/ISR/Economy/Broad money (% of GDP)",
+          "World/Asia/KOR/Economy/Broad money (% of GDP)",
+          "World/North Africa/MAR/Economy/Broad money (% of GDP)",
+          "World/Latam/MEX/Economy/Broad money (% of GDP)",
+          "World/South Africa/MOZ/Economy/Broad money (% of GDP)",
+          "World/Latam/PER/Economy/Broad money (% of GDP)",
+          "World/Asia/PHL/Economy/Broad money (% of GDP)",
+          "World/Europe/POL/Economy/Broad money (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Broad money (% of GDP)",
+          "World/South Africa/SEN/Economy/Broad money (% of GDP)",
+          "World/North Africa/TUR/Economy/Broad money (% of GDP)",
+          "World/Pair/USA/Economy/Broad money (% of GDP)",
+          "World/Latam/VEN/Economy/Broad money (% of GDP)",
+          "World/Asia/VNM/Economy/Broad money (% of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/PHL/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Asia/VNM/Environment/CO2 emissions (kg per 2015 US$ of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/PHL/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Asia/VNM/Environment/CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "World/Latam/ARG/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/AUT/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/BGD/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/BRA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/CHL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Pair/CHN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/COL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/CRI/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/DEU/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/ESP/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/FRA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/GBR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/GRC/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/HRV/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/IDN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/IND/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/ISR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/KOR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/MAR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/MEX/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/NLD/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/PAN/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Latam/PER/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/POL/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Europe/SWE/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Asia/THA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/North Africa/TUR/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Pair/USA/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/South Africa/ZAF/Environment/CO2 emissions (kg per PPP $ of GDP)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions (kt)",
+          "World/Latam/ARG/Environment/CO2 emissions (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions (kt)",
+          "World/Latam/CHL/Environment/CO2 emissions (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions (kt)",
+          "World/Latam/COL/Environment/CO2 emissions (kt)",
+          "World/Latam/CRI/Environment/CO2 emissions (kt)",
+          "World/Europe/DEU/Environment/CO2 emissions (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions (kt)",
+          "World/Europe/GBR/Environment/CO2 emissions (kt)",
+          "World/South Africa/GHA/Environment/CO2 emissions (kt)",
+          "World/Asia/IDN/Environment/CO2 emissions (kt)",
+          "World/Asia/IND/Environment/CO2 emissions (kt)",
+          "World/Asia/KOR/Environment/CO2 emissions (kt)",
+          "World/South Africa/LBR/Environment/CO2 emissions (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions (kt)",
+          "World/Latam/MEX/Environment/CO2 emissions (kt)",
+          "World/South Africa/MOZ/Environment/CO2 emissions (kt)",
+          "World/South Africa/NGA/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (kt)",
+          "World/Latam/PAN/Environment/CO2 emissions (kt)",
+          "World/Latam/PER/Environment/CO2 emissions (kt)",
+          "World/Asia/PHL/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (kt)",
+          "World/South Africa/SEN/Environment/CO2 emissions (kt)",
+          "World/Europe/SWE/Environment/CO2 emissions (kt)",
+          "World/Asia/THA/Environment/CO2 emissions (kt)",
+          "World/North Africa/TUR/Environment/CO2 emissions (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions (kt)",
+          "World/South Africa/ZAF/Environment/CO2 emissions (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/BRA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/CHL/Environment/CO2 emissions (metric tons per capita)",
+          "World/Pair/CHN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/CRI/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/DEU/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/DZA/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/EGY/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/GBR/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/GHA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/GRC/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/IDN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/IND/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/ISR/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/KOR/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/MAR/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/MOZ/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/NLD/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/PAN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Latam/PER/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/PHL/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions (metric tons per capita)",
+          "World/South Africa/SEN/Environment/CO2 emissions (metric tons per capita)",
+          "World/Europe/SWE/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/THA/Environment/CO2 emissions (metric tons per capita)",
+          "World/North Africa/TUR/Environment/CO2 emissions (metric tons per capita)",
+          "World/Pair/USA/Environment/CO2 emissions (metric tons per capita)",
+          "World/Asia/VNM/Environment/CO2 emissions (metric tons per capita)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/ARG/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/AZE/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/COL/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/ESP/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/IND/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/ISR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/KOR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/MEX/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/South Africa/NGA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/NLD/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Latam/PER/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Europe/POL/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/THA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/North Africa/TUR/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Pair/USA/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions from gaseous fuel consumption (kt)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/BGD/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/BRA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/CHL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Pair/CHN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/CMR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/CRI/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/DZA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/EGY/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/GBR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/GHA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/IDN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/IND/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/North Africa/MAR/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/South Africa/MOZ/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/NLD/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/PAN/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Latam/PER/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/PHL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Europe/POL/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/THA/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Asia/VNM/Environment/CO2 emissions from liquid fuel consumption (kt)",
+          "World/Persian Gulf/ARE/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Europe/AUT/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/BGD/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/BRA/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/CHL/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Pair/CHN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/CMR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/IDN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/IND/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/KOR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/North Africa/MAR/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Latam/MEX/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/NGA/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/OMN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/QAT/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/SAU/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/South Africa/SEN/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Asia/VNM/Environment/CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "World/Persian Gulf/ARE/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/AZE/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/BGD/Industry/Capture fisheries production (metric tons)",
+          "World/Latam/CHL/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/CMR/Industry/Capture fisheries production (metric tons)",
+          "World/Latam/CRI/Industry/Capture fisheries production (metric tons)",
+          "World/Europe/GRC/Industry/Capture fisheries production (metric tons)",
+          "World/Europe/HRV/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/IDN/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/IND/Industry/Capture fisheries production (metric tons)",
+          "World/North Africa/ISR/Industry/Capture fisheries production (metric tons)",
+          "World/North Africa/MAR/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/MOZ/Industry/Capture fisheries production (metric tons)",
+          "World/South Africa/NGA/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/OMN/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/PHL/Industry/Capture fisheries production (metric tons)",
+          "World/Persian Gulf/SAU/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/THA/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/VNM/Industry/Capture fisheries production (metric tons)",
+          "World/Asia/BGD/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/BRA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/CHL/Agriculture/Cereal yield (kg per hectare)",
+          "World/Pair/CHN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/COL/Agriculture/Cereal yield (kg per hectare)",
+          "World/South Africa/GHA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/IDN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/IND/Agriculture/Cereal yield (kg per hectare)",
+          "World/North Africa/ISR/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/MEX/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/PAN/Agriculture/Cereal yield (kg per hectare)",
+          "World/Latam/PER/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/PHL/Agriculture/Cereal yield (kg per hectare)",
+          "World/Persian Gulf/QAT/Agriculture/Cereal yield (kg per hectare)",
+          "World/Persian Gulf/SAU/Agriculture/Cereal yield (kg per hectare)",
+          "World/North Africa/TUR/Agriculture/Cereal yield (kg per hectare)",
+          "World/Pair/USA/Agriculture/Cereal yield (kg per hectare)",
+          "World/Asia/VNM/Agriculture/Cereal yield (kg per hectare)",
+          "World/Europe/AUT/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/AZE/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/BGD/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Pair/CHN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/COL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/DEU/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/DZA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/GBR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/South Africa/GHA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/IDN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/IND/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/KOR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/MAR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/MEX/Environment/Combustible renewables and waste (% of total energy)",
+          "World/South Africa/MOZ/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/NLD/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/PAN/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Latam/PER/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/PHL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/POL/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/SAU/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Europe/SWE/Environment/Combustible renewables and waste (% of total energy)",
+          "World/North Africa/TUR/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Pair/USA/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Asia/VNM/Environment/Combustible renewables and waste (% of total energy)",
+          "World/Persian Gulf/ARE/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/BGD/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Pair/CHN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/CMR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Latam/CRI/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/DEU/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/DZA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/EGY/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/FRA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/GBR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/GHA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/IDN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Asia/IND/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/ISR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/MAR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Latam/MEX/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/MOZ/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/NLD/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/OMN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/POL/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/QAT/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/SEN/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/North Africa/TUR/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Pair/USA/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Persian Gulf/YEM/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/South Africa/ZAF/Economy/Commercial bank branches (per 100,000 adults)",
+          "World/Europe/AUT/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/AZE/Exports/Commercial service exports (current US$)",
+          "World/Asia/BGD/Exports/Commercial service exports (current US$)",
+          "World/Latam/BRA/Exports/Commercial service exports (current US$)",
+          "World/Latam/CHL/Exports/Commercial service exports (current US$)",
+          "World/Pair/CHN/Exports/Commercial service exports (current US$)",
+          "World/South Africa/CMR/Exports/Commercial service exports (current US$)",
+          "World/Latam/COL/Exports/Commercial service exports (current US$)",
+          "World/Latam/CRI/Exports/Commercial service exports (current US$)",
+          "World/Europe/DEU/Exports/Commercial service exports (current US$)",
+          "World/North Africa/DZA/Exports/Commercial service exports (current US$)",
+          "World/Europe/ESP/Exports/Commercial service exports (current US$)",
+          "World/Europe/FRA/Exports/Commercial service exports (current US$)",
+          "World/Europe/GBR/Exports/Commercial service exports (current US$)",
+          "World/South Africa/GHA/Exports/Commercial service exports (current US$)",
+          "World/Europe/GRC/Exports/Commercial service exports (current US$)",
+          "World/Europe/HRV/Exports/Commercial service exports (current US$)",
+          "World/Asia/IDN/Exports/Commercial service exports (current US$)",
+          "World/Asia/IND/Exports/Commercial service exports (current US$)",
+          "World/North Africa/ISR/Exports/Commercial service exports (current US$)",
+          "World/Asia/KOR/Exports/Commercial service exports (current US$)",
+          "World/North Africa/MAR/Exports/Commercial service exports (current US$)",
+          "World/Europe/NLD/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/OMN/Exports/Commercial service exports (current US$)",
+          "World/Latam/PAN/Exports/Commercial service exports (current US$)",
+          "World/Latam/PER/Exports/Commercial service exports (current US$)",
+          "World/Asia/PHL/Exports/Commercial service exports (current US$)",
+          "World/Europe/POL/Exports/Commercial service exports (current US$)",
+          "World/Persian Gulf/QAT/Exports/Commercial service exports (current US$)",
+          "World/South Africa/SEN/Exports/Commercial service exports (current US$)",
+          "World/Europe/SWE/Exports/Commercial service exports (current US$)",
+          "World/Asia/THA/Exports/Commercial service exports (current US$)",
+          "World/North Africa/TUR/Exports/Commercial service exports (current US$)",
+          "World/Pair/USA/Exports/Commercial service exports (current US$)",
+          "World/Asia/VNM/Exports/Commercial service exports (current US$)",
+          "World/South Africa/ZAF/Exports/Commercial service exports (current US$)",
+          "World/Latam/ARG/Imports/Commercial service imports (current US$)",
+          "World/Europe/AUT/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/AZE/Imports/Commercial service imports (current US$)",
+          "World/Asia/BGD/Imports/Commercial service imports (current US$)",
+          "World/Latam/BRA/Imports/Commercial service imports (current US$)",
+          "World/Latam/CHL/Imports/Commercial service imports (current US$)",
+          "World/Pair/CHN/Imports/Commercial service imports (current US$)",
+          "World/South Africa/CMR/Imports/Commercial service imports (current US$)",
+          "World/Latam/COL/Imports/Commercial service imports (current US$)",
+          "World/Latam/CRI/Imports/Commercial service imports (current US$)",
+          "World/Europe/DEU/Imports/Commercial service imports (current US$)",
+          "World/North Africa/DZA/Imports/Commercial service imports (current US$)",
+          "World/North Africa/EGY/Imports/Commercial service imports (current US$)",
+          "World/Europe/ESP/Imports/Commercial service imports (current US$)",
+          "World/Europe/FRA/Imports/Commercial service imports (current US$)",
+          "World/Europe/GBR/Imports/Commercial service imports (current US$)",
+          "World/South Africa/GHA/Imports/Commercial service imports (current US$)",
+          "World/Europe/HRV/Imports/Commercial service imports (current US$)",
+          "World/Asia/IDN/Imports/Commercial service imports (current US$)",
+          "World/Asia/IND/Imports/Commercial service imports (current US$)",
+          "World/North Africa/ISR/Imports/Commercial service imports (current US$)",
+          "World/Asia/KOR/Imports/Commercial service imports (current US$)",
+          "World/North Africa/MAR/Imports/Commercial service imports (current US$)",
+          "World/Latam/MEX/Imports/Commercial service imports (current US$)",
+          "World/South Africa/MOZ/Imports/Commercial service imports (current US$)",
+          "World/South Africa/NGA/Imports/Commercial service imports (current US$)",
+          "World/Europe/NLD/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/OMN/Imports/Commercial service imports (current US$)",
+          "World/Latam/PAN/Imports/Commercial service imports (current US$)",
+          "World/Latam/PER/Imports/Commercial service imports (current US$)",
+          "World/Asia/PHL/Imports/Commercial service imports (current US$)",
+          "World/Europe/POL/Imports/Commercial service imports (current US$)",
+          "World/Persian Gulf/QAT/Imports/Commercial service imports (current US$)",
+          "World/South Africa/SEN/Imports/Commercial service imports (current US$)",
+          "World/Europe/SWE/Imports/Commercial service imports (current US$)",
+          "World/Asia/THA/Imports/Commercial service imports (current US$)",
+          "World/North Africa/TUR/Imports/Commercial service imports (current US$)",
+          "World/Pair/USA/Imports/Commercial service imports (current US$)",
+          "World/Asia/VNM/Imports/Commercial service imports (current US$)",
+          "World/South Africa/ZAF/Imports/Commercial service imports (current US$)",
+          "World/Latam/ARG/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/BGD/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/BRA/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/CMR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/COL/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/CRI/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/DZA/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/EGY/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/GHA/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/IND/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/LBR/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/MAR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/MEX/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/MOZ/Demoraphy/Completeness of birth registration (%)",
+          "World/South Africa/NGA/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/PAN/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/PER/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/PHL/Demoraphy/Completeness of birth registration (%)",
+          "World/North Africa/TUR/Demoraphy/Completeness of birth registration (%)",
+          "World/Latam/VEN/Demoraphy/Completeness of birth registration (%)",
+          "World/Asia/VNM/Demoraphy/Completeness of birth registration (%)",
+          "World/Persian Gulf/ARE/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/AUT/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/AZE/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/BGD/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/BRA/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/CHL/Economy/Consumer price index (2010 = 100)",
+          "World/Pair/CHN/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/CMR/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/COL/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/CRI/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/DEU/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/DZA/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/EGY/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/ESP/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/FRA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/GBR/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/GHA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/GRC/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/IDN/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/IND/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/ISR/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/KOR/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/LBR/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/MAR/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/MEX/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/MOZ/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/NGA/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/NLD/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/OMN/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/PAN/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/PER/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/PHL/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/POL/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/QAT/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/SAU/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/SEN/Economy/Consumer price index (2010 = 100)",
+          "World/Europe/SWE/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/THA/Economy/Consumer price index (2010 = 100)",
+          "World/North Africa/TUR/Economy/Consumer price index (2010 = 100)",
+          "World/Pair/USA/Economy/Consumer price index (2010 = 100)",
+          "World/Latam/VEN/Economy/Consumer price index (2010 = 100)",
+          "World/Asia/VNM/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/YEM/Economy/Consumer price index (2010 = 100)",
+          "World/South Africa/ZAF/Economy/Consumer price index (2010 = 100)",
+          "World/Persian Gulf/ARE/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/BGD/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/CHL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Pair/CHN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/CMR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/COL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/CRI/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/DEU/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/ESP/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/FRA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/GBR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/GHA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/IDN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/IND/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/ISR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/KOR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/LBR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/MAR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/MEX/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/MOZ/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/NLD/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/OMN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/PAN/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Latam/PER/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/PHL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/POL/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/QAT/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/SAU/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Europe/SWE/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/THA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/North Africa/TUR/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Pair/USA/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Asia/VNM/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/South Africa/ZAF/Exports/Container port traffic (TEU: 20 foot equivalent units)",
+          "World/Persian Gulf/AZE/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/BGD/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/BRA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/CHL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Pair/CHN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/CMR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/COL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/CRI/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/IDN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/IND/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/MEX/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/PER/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/PHL/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/THA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Pair/USA/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Asia/VNM/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Agriculture/Crop production index (2014-2016 = 100)",
+          "World/Latam/ARG/Health/Current health expenditure (% of GDP)",
+          "World/Europe/AUT/Health/Current health expenditure (% of GDP)",
+          "World/Latam/CHL/Health/Current health expenditure (% of GDP)",
+          "World/Pair/CHN/Health/Current health expenditure (% of GDP)",
+          "World/South Africa/CMR/Health/Current health expenditure (% of GDP)",
+          "World/Latam/COL/Health/Current health expenditure (% of GDP)",
+          "World/Latam/CRI/Health/Current health expenditure (% of GDP)",
+          "World/Europe/ESP/Health/Current health expenditure (% of GDP)",
+          "World/Europe/FRA/Health/Current health expenditure (% of GDP)",
+          "World/Europe/GBR/Health/Current health expenditure (% of GDP)",
+          "World/Europe/GRC/Health/Current health expenditure (% of GDP)",
+          "World/Asia/IDN/Health/Current health expenditure (% of GDP)",
+          "World/Asia/IND/Health/Current health expenditure (% of GDP)",
+          "World/Asia/KOR/Health/Current health expenditure (% of GDP)",
+          "World/South Africa/MOZ/Health/Current health expenditure (% of GDP)",
+          "World/Europe/NLD/Health/Current health expenditure (% of GDP)",
+          "World/Latam/PER/Health/Current health expenditure (% of GDP)",
+          "World/Europe/POL/Health/Current health expenditure (% of GDP)",
+          "World/Europe/SWE/Health/Current health expenditure (% of GDP)",
+          "World/Asia/THA/Health/Current health expenditure (% of GDP)",
+          "World/Pair/USA/Health/Current health expenditure (% of GDP)",
+          "World/Asia/VNM/Health/Current health expenditure (% of GDP)",
+          "World/Persian Gulf/YEM/Health/Current health expenditure (% of GDP)",
+          "World/Persian Gulf/ARE/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Current health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/CMR/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/GHA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/GRC/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Current health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/MEX/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/MOZ/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/NGA/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/OMN/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/SEN/Health/Current health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Current health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Current health expenditure per capita (current US$)",
+          "World/Latam/VEN/Health/Current health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Health/Current health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Current health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/BGD/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/CHL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Pair/CHN/Economy/Customs and other import duties (% of tax revenue)",
+          "World/South Africa/CMR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/COL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/CRI/Economy/Customs and other import duties (% of tax revenue)",
+          "World/North Africa/EGY/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/ESP/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/HRV/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/IND/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/KOR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/North Africa/MAR/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/MEX/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/PAN/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Latam/PER/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Europe/POL/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/THA/Economy/Customs and other import duties (% of tax revenue)",
+          "World/Asia/BGD/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/CHL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Pair/CHN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/CMR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/COL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/CRI/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/DEU/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/DZA/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/EGY/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/FRA/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/GRC/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/HRV/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/IND/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/KOR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/LBR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/MEX/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/OMN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/PER/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/POL/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/SAU/Economy/Domestic credit to private sector (% of GDP)",
+          "World/South Africa/SEN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Europe/SWE/Economy/Domestic credit to private sector (% of GDP)",
+          "World/North Africa/TUR/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Latam/VEN/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Asia/VNM/Economy/Domestic credit to private sector (% of GDP)",
+          "World/Persian Gulf/ARE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/AZE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/BGD/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/CHL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Pair/CHN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/CMR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/COL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/CRI/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/DEU/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/DZA/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/EGY/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/GRC/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/HRV/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/IND/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/KOR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/LBR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/MAR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/MEX/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/OMN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/PAN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/PER/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/POL/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/QAT/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Persian Gulf/SAU/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/South Africa/SEN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Europe/SWE/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/North Africa/TUR/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/VEN/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Asia/VNM/Economy/Domestic credit to private sector by banks (% of GDP)",
+          "World/Latam/ARG/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/GRC/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/MEX/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/MOZ/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/OMN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/North Africa/TUR/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Domestic general government health expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/AUT/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/BGD/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/BRA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/CHL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Pair/CHN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/CMR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/COL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/CRI/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/DEU/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/DZA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/EGY/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/ESP/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/FRA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/GBR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/GHA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/HRV/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/IDN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/IND/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/ISR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/KOR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/LBR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/North Africa/MAR/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/NGA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/NLD/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/PAN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Latam/PER/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/PHL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/POL/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/QAT/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/SEN/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/SWE/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/THA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Pair/USA/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Asia/VNM/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Health/Domestic private health expenditure per capita (current US$)",
+          "World/South Africa/ZAF/Health/Domestic private health expenditure per capita (current US$)",
+          "World/Europe/AUT/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/BGD/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/BRA/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/CHL/Environment/Electric power consumption (kWh per capita)",
+          "World/Pair/CHN/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/CMR/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/COL/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/CRI/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/DZA/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/EGY/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/GBR/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/GRC/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/HRV/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/IDN/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/IND/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/KOR/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/MAR/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/MEX/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/MOZ/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/NGA/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/OMN/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/PAN/Environment/Electric power consumption (kWh per capita)",
+          "World/Latam/PER/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/PHL/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/POL/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/SAU/Environment/Electric power consumption (kWh per capita)",
+          "World/South Africa/SEN/Environment/Electric power consumption (kWh per capita)",
+          "World/Europe/SWE/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/THA/Environment/Electric power consumption (kWh per capita)",
+          "World/North Africa/TUR/Environment/Electric power consumption (kWh per capita)",
+          "World/Asia/VNM/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/YEM/Environment/Electric power consumption (kWh per capita)",
+          "World/Persian Gulf/ARE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CHL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/COL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/GHA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/LBR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/MEX/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/NGA/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PAN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PER/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/PHL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/QAT/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/TUR/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GRC/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/HRV/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/SAU/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Pair/USA/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in industry (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/ARE/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/ARG/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/AUT/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/BGD/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/BRA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CHL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Pair/CHN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/CMR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/COL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/CRI/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/DEU/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/DZA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/EGY/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/ESP/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/FRA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GBR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/GHA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/GRC/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/HRV/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IDN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/IND/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/ISR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/KOR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/LBR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/MAR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/MEX/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/NGA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/NLD/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PAN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/PER/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/PHL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/POL/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/SEN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Europe/SWE/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/THA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/North Africa/TUR/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Pair/USA/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Latam/VEN/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Asia/VNM/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Employment/Employment in services (% of total employment) (modeled ILO estimate)",
+          "World/Persian Gulf/AZE/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/BGD/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Pair/CHN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/CMR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/CRI/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/DEU/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/DZA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/GBR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/GHA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/IDN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/IND/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/ISR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/KOR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/MEX/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/MOZ/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/South Africa/NGA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/NLD/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Persian Gulf/OMN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/PAN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/PER/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/PHL/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/POL/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Europe/SWE/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/THA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/North Africa/TUR/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Pair/USA/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Latam/VEN/Environment/Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "World/Asia/BGD/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/BRA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/CHL/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Pair/CHN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/CRI/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/DZA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/GBR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/IDN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/IND/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/KOR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/MAR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/South Africa/MOZ/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/South Africa/NGA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/NLD/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Persian Gulf/OMN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/PAN/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Latam/PER/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Persian Gulf/SAU/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/THA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/North Africa/TUR/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Pair/USA/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Asia/VNM/Environment/Energy use (kg of oil equivalent per capita)",
+          "World/Europe/AUT/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/AZE/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/BGD/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Pair/CHN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/CMR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/COL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/CRI/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/DEU/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/DZA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/ESP/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/FRA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/GBR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/GHA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/HRV/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/IDN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/IND/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/ISR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/KOR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/MEX/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/MOZ/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/NGA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/NLD/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Latam/PAN/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Asia/PHL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/POL/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/SAU/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Europe/SWE/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/North Africa/TUR/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Pair/USA/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/South Africa/ZAF/Environment/Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "World/Persian Gulf/ARE/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/DEU/Exports/Export unit value index (2000 = 100)",
+          "World/North Africa/DZA/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/FRA/Exports/Export unit value index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/GRC/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/HRV/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export unit value index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/POL/Exports/Export unit value index (2000 = 100)",
+          "World/Europe/SWE/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/THA/Exports/Export unit value index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/YEM/Exports/Export unit value index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export unit value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Exports/Export value index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export value index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export value index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export value index (2000 = 100)",
+          "World/Latam/CHL/Exports/Export value index (2000 = 100)",
+          "World/Pair/CHN/Exports/Export value index (2000 = 100)",
+          "World/Latam/COL/Exports/Export value index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export value index (2000 = 100)",
+          "World/Europe/DEU/Exports/Export value index (2000 = 100)",
+          "World/North Africa/DZA/Exports/Export value index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export value index (2000 = 100)",
+          "World/Europe/FRA/Exports/Export value index (2000 = 100)",
+          "World/Europe/GBR/Exports/Export value index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export value index (2000 = 100)",
+          "World/Asia/IDN/Exports/Export value index (2000 = 100)",
+          "World/Asia/IND/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/IRQ/Exports/Export value index (2000 = 100)",
+          "World/Asia/KOR/Exports/Export value index (2000 = 100)",
+          "World/North Africa/MAR/Exports/Export value index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export value index (2000 = 100)",
+          "World/South Africa/MOZ/Exports/Export value index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/OMN/Exports/Export value index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export value index (2000 = 100)",
+          "World/Latam/PER/Exports/Export value index (2000 = 100)",
+          "World/Asia/PHL/Exports/Export value index (2000 = 100)",
+          "World/Europe/POL/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/SAU/Exports/Export value index (2000 = 100)",
+          "World/South Africa/SEN/Exports/Export value index (2000 = 100)",
+          "World/Europe/SWE/Exports/Export value index (2000 = 100)",
+          "World/Asia/THA/Exports/Export value index (2000 = 100)",
+          "World/North Africa/TUR/Exports/Export value index (2000 = 100)",
+          "World/Pair/USA/Exports/Export value index (2000 = 100)",
+          "World/Latam/VEN/Exports/Export value index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export value index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Exports/Export volume index (2000 = 100)",
+          "World/Europe/AUT/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/AZE/Exports/Export volume index (2000 = 100)",
+          "World/Asia/BGD/Exports/Export volume index (2000 = 100)",
+          "World/Latam/BRA/Exports/Export volume index (2000 = 100)",
+          "World/Latam/CHL/Exports/Export volume index (2000 = 100)",
+          "World/Pair/CHN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/COL/Exports/Export volume index (2000 = 100)",
+          "World/Latam/CRI/Exports/Export volume index (2000 = 100)",
+          "World/Europe/ESP/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/GHA/Exports/Export volume index (2000 = 100)",
+          "World/Europe/GRC/Exports/Export volume index (2000 = 100)",
+          "World/Asia/IDN/Exports/Export volume index (2000 = 100)",
+          "World/Asia/IND/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/ISR/Exports/Export volume index (2000 = 100)",
+          "World/Asia/KOR/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/MAR/Exports/Export volume index (2000 = 100)",
+          "World/Latam/MEX/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/MOZ/Exports/Export volume index (2000 = 100)",
+          "World/Europe/NLD/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/OMN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/PAN/Exports/Export volume index (2000 = 100)",
+          "World/Latam/PER/Exports/Export volume index (2000 = 100)",
+          "World/Asia/PHL/Exports/Export volume index (2000 = 100)",
+          "World/Europe/POL/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/QAT/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/SAU/Exports/Export volume index (2000 = 100)",
+          "World/Asia/THA/Exports/Export volume index (2000 = 100)",
+          "World/North Africa/TUR/Exports/Export volume index (2000 = 100)",
+          "World/Pair/USA/Exports/Export volume index (2000 = 100)",
+          "World/Latam/VEN/Exports/Export volume index (2000 = 100)",
+          "World/Asia/VNM/Exports/Export volume index (2000 = 100)",
+          "World/Persian Gulf/YEM/Exports/Export volume index (2000 = 100)",
+          "World/South Africa/ZAF/Exports/Export volume index (2000 = 100)",
+          "World/Europe/AUT/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/BGD/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/BRA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/CHL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Pair/CHN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/COL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/CRI/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/DEU/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/ESP/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/FRA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/GBR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/HRV/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/IDN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/IND/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/IRQ/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/KOR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/MEX/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/MOZ/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/NLD/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/PAN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/PER/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/PHL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/POL/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Europe/SWE/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/THA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Pair/USA/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Latam/VEN/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Asia/VNM/Exports/Exports of goods and services (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Exports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/ARE/Exports/Exports of goods and services (current US$)",
+          "World/Europe/AUT/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/AZE/Exports/Exports of goods and services (current US$)",
+          "World/Asia/BGD/Exports/Exports of goods and services (current US$)",
+          "World/Latam/BRA/Exports/Exports of goods and services (current US$)",
+          "World/Latam/CHL/Exports/Exports of goods and services (current US$)",
+          "World/Pair/CHN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/COL/Exports/Exports of goods and services (current US$)",
+          "World/Latam/CRI/Exports/Exports of goods and services (current US$)",
+          "World/Europe/DEU/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/DZA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/ESP/Exports/Exports of goods and services (current US$)",
+          "World/Europe/FRA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/GBR/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/GHA/Exports/Exports of goods and services (current US$)",
+          "World/Europe/HRV/Exports/Exports of goods and services (current US$)",
+          "World/Asia/IDN/Exports/Exports of goods and services (current US$)",
+          "World/Asia/IND/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/IRQ/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/ISR/Exports/Exports of goods and services (current US$)",
+          "World/Asia/KOR/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/MAR/Exports/Exports of goods and services (current US$)",
+          "World/Latam/MEX/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/MOZ/Exports/Exports of goods and services (current US$)",
+          "World/Europe/NLD/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/OMN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/PAN/Exports/Exports of goods and services (current US$)",
+          "World/Latam/PER/Exports/Exports of goods and services (current US$)",
+          "World/Asia/PHL/Exports/Exports of goods and services (current US$)",
+          "World/Europe/POL/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/QAT/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/SAU/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/SEN/Exports/Exports of goods and services (current US$)",
+          "World/Europe/SWE/Exports/Exports of goods and services (current US$)",
+          "World/Asia/THA/Exports/Exports of goods and services (current US$)",
+          "World/North Africa/TUR/Exports/Exports of goods and services (current US$)",
+          "World/Pair/USA/Exports/Exports of goods and services (current US$)",
+          "World/Latam/VEN/Exports/Exports of goods and services (current US$)",
+          "World/Asia/VNM/Exports/Exports of goods and services (current US$)",
+          "World/South Africa/ZAF/Exports/Exports of goods and services (current US$)",
+          "World/Persian Gulf/ARE/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/Final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/Final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/Final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/Final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Final consumption expenditure (current US$)",
+          "World/Latam/ARG/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/CHL/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/COL/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/CRI/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/EGY/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/GHA/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/GRC/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/HRV/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/IDN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/IND/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/LBR/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/MAR/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/MEX/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/MOZ/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/NGA/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/PAN/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/PER/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/PHL/R&D/Firms offering formal training (% of firms)",
+          "World/Europe/POL/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/SEN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/THA/R&D/Firms offering formal training (% of firms)",
+          "World/North Africa/TUR/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/VEN/R&D/Firms offering formal training (% of firms)",
+          "World/Asia/VNM/R&D/Firms offering formal training (% of firms)",
+          "World/Persian Gulf/YEM/R&D/Firms offering formal training (% of firms)",
+          "World/South Africa/ZAF/R&D/Firms offering formal training (% of firms)",
+          "World/Latam/ARG/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Persian Gulf/AZE/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/BGD/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/CHL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/CMR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/COL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/CRI/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/EGY/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/GHA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Europe/GRC/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/IDN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/IND/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/LBR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/MAR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/MEX/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/MOZ/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/NGA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/PAN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/PER/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/PHL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Europe/POL/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/SEN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/THA/Economy/Firms using banks to finance investment (% of firms)",
+          "World/North Africa/TUR/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/VEN/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Asia/VNM/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Persian Gulf/YEM/Economy/Firms using banks to finance investment (% of firms)",
+          "World/South Africa/ZAF/Economy/Firms using banks to finance investment (% of firms)",
+          "World/Latam/ARG/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/AZE/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/BGD/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/CHL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/CMR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/COL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/CRI/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/North Africa/EGY/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/GHA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Europe/GRC/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/IDN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/LBR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/MEX/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/MOZ/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/NGA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/PAN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/PER/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/PHL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Europe/POL/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/SEN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/THA/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/North Africa/TUR/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Latam/VEN/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Asia/VNM/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/YEM/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/South Africa/ZAF/Economy/Firms using banks to finance working capital (% of firms)",
+          "World/Persian Gulf/ARE/Internet/Fixed broadband subscriptions",
+          "World/Latam/ARG/Internet/Fixed broadband subscriptions",
+          "World/Europe/AUT/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/AZE/Internet/Fixed broadband subscriptions",
+          "World/Asia/BGD/Internet/Fixed broadband subscriptions",
+          "World/Latam/BRA/Internet/Fixed broadband subscriptions",
+          "World/Latam/CHL/Internet/Fixed broadband subscriptions",
+          "World/Pair/CHN/Internet/Fixed broadband subscriptions",
+          "World/South Africa/CMR/Internet/Fixed broadband subscriptions",
+          "World/Latam/COL/Internet/Fixed broadband subscriptions",
+          "World/Latam/CRI/Internet/Fixed broadband subscriptions",
+          "World/Europe/DEU/Internet/Fixed broadband subscriptions",
+          "World/North Africa/DZA/Internet/Fixed broadband subscriptions",
+          "World/North Africa/EGY/Internet/Fixed broadband subscriptions",
+          "World/Europe/ESP/Internet/Fixed broadband subscriptions",
+          "World/Europe/FRA/Internet/Fixed broadband subscriptions",
+          "World/Europe/GBR/Internet/Fixed broadband subscriptions",
+          "World/South Africa/GHA/Internet/Fixed broadband subscriptions",
+          "World/Europe/GRC/Internet/Fixed broadband subscriptions",
+          "World/Europe/HRV/Internet/Fixed broadband subscriptions",
+          "World/Asia/IDN/Internet/Fixed broadband subscriptions",
+          "World/Asia/IND/Internet/Fixed broadband subscriptions",
+          "World/North Africa/ISR/Internet/Fixed broadband subscriptions",
+          "World/Asia/KOR/Internet/Fixed broadband subscriptions",
+          "World/South Africa/LBR/Internet/Fixed broadband subscriptions",
+          "World/North Africa/MAR/Internet/Fixed broadband subscriptions",
+          "World/Latam/MEX/Internet/Fixed broadband subscriptions",
+          "World/South Africa/MOZ/Internet/Fixed broadband subscriptions",
+          "World/Europe/NLD/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/OMN/Internet/Fixed broadband subscriptions",
+          "World/Latam/PAN/Internet/Fixed broadband subscriptions",
+          "World/Latam/PER/Internet/Fixed broadband subscriptions",
+          "World/Asia/PHL/Internet/Fixed broadband subscriptions",
+          "World/Europe/POL/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/QAT/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/SAU/Internet/Fixed broadband subscriptions",
+          "World/South Africa/SEN/Internet/Fixed broadband subscriptions",
+          "World/Europe/SWE/Internet/Fixed broadband subscriptions",
+          "World/Asia/THA/Internet/Fixed broadband subscriptions",
+          "World/North Africa/TUR/Internet/Fixed broadband subscriptions",
+          "World/Pair/USA/Internet/Fixed broadband subscriptions",
+          "World/Latam/VEN/Internet/Fixed broadband subscriptions",
+          "World/Asia/VNM/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/YEM/Internet/Fixed broadband subscriptions",
+          "World/South Africa/ZAF/Internet/Fixed broadband subscriptions",
+          "World/Persian Gulf/ARE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/ARG/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/AUT/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/AZE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/BGD/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/BRA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/CHL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Pair/CHN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/CMR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/COL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/CRI/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/DEU/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/DZA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/EGY/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/ESP/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/FRA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/GBR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/GHA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/GRC/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/HRV/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/IDN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/IND/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/ISR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/KOR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/LBR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/MAR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/MEX/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/MOZ/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/NLD/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/OMN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/PAN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/PER/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/PHL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/POL/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/QAT/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/SAU/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/SEN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Europe/SWE/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/THA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/North Africa/TUR/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Pair/USA/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Latam/VEN/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Asia/VNM/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/YEM/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/South Africa/ZAF/Internet/Fixed broadband subscriptions (per 100 people)",
+          "World/Persian Gulf/ARE/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/AZE/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/BGD/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/BRA/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/CHL/Industry/Food production index (2014-2016 = 100)",
+          "World/Pair/CHN/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/CMR/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/COL/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/CRI/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/IDN/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/IND/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/ISR/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/LBR/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/MEX/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Industry/Food production index (2014-2016 = 100)",
+          "World/Europe/NLD/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/PAN/Industry/Food production index (2014-2016 = 100)",
+          "World/Latam/PER/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/PHL/Industry/Food production index (2014-2016 = 100)",
+          "World/Europe/POL/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/SAU/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/THA/Industry/Food production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Industry/Food production index (2014-2016 = 100)",
+          "World/Pair/USA/Industry/Food production index (2014-2016 = 100)",
+          "World/Asia/VNM/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Industry/Food production index (2014-2016 = 100)",
+          "World/South Africa/ZAF/Industry/Food production index (2014-2016 = 100)",
+          "World/Persian Gulf/ARE/Demography/Forest area (% of land area)",
+          "World/Latam/ARG/Demography/Forest area (% of land area)",
+          "World/Europe/AUT/Demography/Forest area (% of land area)",
+          "World/Persian Gulf/AZE/Demography/Forest area (% of land area)",
+          "World/Asia/BGD/Demography/Forest area (% of land area)",
+          "World/Latam/BRA/Demography/Forest area (% of land area)",
+          "World/Latam/CHL/Demography/Forest area (% of land area)",
+          "World/Pair/CHN/Demography/Forest area (% of land area)",
+          "World/South Africa/CMR/Demography/Forest area (% of land area)",
+          "World/Latam/COL/Demography/Forest area (% of land area)",
+          "World/Latam/CRI/Demography/Forest area (% of land area)",
+          "World/North Africa/DZA/Demography/Forest area (% of land area)",
+          "World/North Africa/EGY/Demography/Forest area (% of land area)",
+          "World/Europe/ESP/Demography/Forest area (% of land area)",
+          "World/Europe/FRA/Demography/Forest area (% of land area)",
+          "World/Europe/GBR/Demography/Forest area (% of land area)",
+          "World/Europe/GRC/Demography/Forest area (% of land area)",
+          "World/Europe/HRV/Demography/Forest area (% of land area)",
+          "World/Asia/IDN/Demography/Forest area (% of land area)",
+          "World/Asia/IND/Demography/Forest area (% of land area)",
+          "World/Asia/KOR/Demography/Forest area (% of land area)",
+          "World/South Africa/LBR/Demography/Forest area (% of land area)",
+          "World/North Africa/MAR/Demography/Forest area (% of land area)",
+          "World/Latam/MEX/Demography/Forest area (% of land area)",
+          "World/South Africa/MOZ/Demography/Forest area (% of land area)",
+          "World/South Africa/NGA/Demography/Forest area (% of land area)",
+          "World/Europe/NLD/Demography/Forest area (% of land area)",
+          "World/Latam/PAN/Demography/Forest area (% of land area)",
+          "World/Latam/PER/Demography/Forest area (% of land area)",
+          "World/Europe/POL/Demography/Forest area (% of land area)",
+          "World/South Africa/SEN/Demography/Forest area (% of land area)",
+          "World/North Africa/TUR/Demography/Forest area (% of land area)",
+          "World/Pair/USA/Demography/Forest area (% of land area)",
+          "World/Latam/VEN/Demography/Forest area (% of land area)",
+          "World/Asia/VNM/Demography/Forest area (% of land area)",
+          "World/South Africa/ZAF/Demography/Forest area (% of land area)",
+          "World/Persian Gulf/ARE/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/AUT/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/AZE/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/BGD/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/BRA/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/CHL/Principal/GDP deflator (base year varies by country)",
+          "World/Pair/CHN/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/CMR/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/COL/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/CRI/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/DEU/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/DZA/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/EGY/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/ESP/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/FRA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/GBR/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/GHA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/GRC/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/HRV/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/IDN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/IND/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/ISR/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/KOR/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/LBR/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/MAR/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/MEX/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/MOZ/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/NGA/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/NLD/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/OMN/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/PAN/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/PER/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/PHL/Principal/GDP deflator (base year varies by country)",
+          "World/Europe/POL/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/QAT/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/SAU/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/SEN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/THA/Principal/GDP deflator (base year varies by country)",
+          "World/North Africa/TUR/Principal/GDP deflator (base year varies by country)",
+          "World/Pair/USA/Principal/GDP deflator (base year varies by country)",
+          "World/Latam/VEN/Principal/GDP deflator (base year varies by country)",
+          "World/Asia/VNM/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/YEM/Principal/GDP deflator (base year varies by country)",
+          "World/South Africa/ZAF/Principal/GDP deflator (base year varies by country)",
+          "World/Persian Gulf/ARE/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/AUT/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/AZE/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/BGD/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/BRA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/CHL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Pair/CHN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/CMR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/COL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/CRI/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/DEU/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/DZA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/EGY/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/ESP/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/FRA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/GBR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/GHA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/GRC/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/HRV/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/IDN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/IND/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/ISR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/KOR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/LBR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/MAR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/MEX/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/MOZ/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/NGA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/NLD/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/OMN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/PAN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/PER/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/PHL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Europe/POL/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/QAT/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/SAU/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/SEN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/THA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/North Africa/TUR/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Pair/USA/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Latam/VEN/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Asia/VNM/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/YEM/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/South Africa/ZAF/Principal/GDP deflator: linked series (base year varies by country)",
+          "World/Persian Gulf/ARE/Principal/GDP per capita (current US$)",
+          "World/Latam/ARG/Principal/GDP per capita (current US$)",
+          "World/Europe/AUT/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/AZE/Principal/GDP per capita (current US$)",
+          "World/Asia/BGD/Principal/GDP per capita (current US$)",
+          "World/Latam/BRA/Principal/GDP per capita (current US$)",
+          "World/Latam/CHL/Principal/GDP per capita (current US$)",
+          "World/Pair/CHN/Principal/GDP per capita (current US$)",
+          "World/South Africa/CMR/Principal/GDP per capita (current US$)",
+          "World/Latam/COL/Principal/GDP per capita (current US$)",
+          "World/Latam/CRI/Principal/GDP per capita (current US$)",
+          "World/Europe/DEU/Principal/GDP per capita (current US$)",
+          "World/North Africa/DZA/Principal/GDP per capita (current US$)",
+          "World/North Africa/EGY/Principal/GDP per capita (current US$)",
+          "World/Europe/ESP/Principal/GDP per capita (current US$)",
+          "World/Europe/FRA/Principal/GDP per capita (current US$)",
+          "World/Europe/GBR/Principal/GDP per capita (current US$)",
+          "World/South Africa/GHA/Principal/GDP per capita (current US$)",
+          "World/Europe/GRC/Principal/GDP per capita (current US$)",
+          "World/Europe/HRV/Principal/GDP per capita (current US$)",
+          "World/Asia/IDN/Principal/GDP per capita (current US$)",
+          "World/Asia/IND/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/IRQ/Principal/GDP per capita (current US$)",
+          "World/North Africa/ISR/Principal/GDP per capita (current US$)",
+          "World/Asia/KOR/Principal/GDP per capita (current US$)",
+          "World/North Africa/MAR/Principal/GDP per capita (current US$)",
+          "World/Latam/MEX/Principal/GDP per capita (current US$)",
+          "World/South Africa/MOZ/Principal/GDP per capita (current US$)",
+          "World/South Africa/NGA/Principal/GDP per capita (current US$)",
+          "World/Europe/NLD/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/OMN/Principal/GDP per capita (current US$)",
+          "World/Latam/PAN/Principal/GDP per capita (current US$)",
+          "World/Latam/PER/Principal/GDP per capita (current US$)",
+          "World/Asia/PHL/Principal/GDP per capita (current US$)",
+          "World/Europe/POL/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/QAT/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/SAU/Principal/GDP per capita (current US$)",
+          "World/South Africa/SEN/Principal/GDP per capita (current US$)",
+          "World/Europe/SWE/Principal/GDP per capita (current US$)",
+          "World/Asia/THA/Principal/GDP per capita (current US$)",
+          "World/North Africa/TUR/Principal/GDP per capita (current US$)",
+          "World/Pair/USA/Principal/GDP per capita (current US$)",
+          "World/Latam/VEN/Principal/GDP per capita (current US$)",
+          "World/Asia/VNM/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/YEM/Principal/GDP per capita (current US$)",
+          "World/South Africa/ZAF/Principal/GDP per capita (current US$)",
+          "World/Persian Gulf/ARE/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/ARG/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/AZE/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/BGD/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/BRA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/CHL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Pair/CHN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/CMR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/COL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/CRI/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/EGY/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/FRA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/GBR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/GHA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/GRC/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/IDN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/IND/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/ISR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/KOR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/MAR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/MOZ/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/NGA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/OMN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/PAN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/PER/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/PHL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Europe/POL/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/QAT/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Persian Gulf/SAU/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/SEN/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/THA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/North Africa/TUR/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Pair/USA/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Asia/VNM/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/South Africa/ZAF/Principal/GDP per person employed (constant 2017 PPP $)",
+          "World/Latam/ARG/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/AUT/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/AZE/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/BGD/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/BRA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/CHL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Pair/CHN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/CMR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/COL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/CRI/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/DEU/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/EGY/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/ESP/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/FRA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/GBR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/GHA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/HRV/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/IDN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/IND/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/ISR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/KOR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/MAR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/MEX/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/MOZ/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/NGA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/NLD/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Latam/PAN/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/PHL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/POL/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/SWE/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/THA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/North Africa/TUR/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Pair/USA/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Asia/VNM/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/South Africa/ZAF/Principal/GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "World/Europe/AUT/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/AZE/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/BGD/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Pair/CHN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/CMR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/COL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/CRI/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/DEU/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/DZA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/ESP/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/FRA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/GBR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/GHA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/HRV/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/IDN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/IND/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/ISR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/KOR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/MEX/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/MOZ/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/NGA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/NLD/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Latam/PAN/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Asia/PHL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/POL/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/SAU/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Europe/SWE/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/North Africa/TUR/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Pair/USA/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/South Africa/ZAF/principal/GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "World/Persian Gulf/ARE/Economy/GNI (current US$)",
+          "World/Latam/ARG/Economy/GNI (current US$)",
+          "World/Europe/AUT/Economy/GNI (current US$)",
+          "World/Persian Gulf/AZE/Economy/GNI (current US$)",
+          "World/Asia/BGD/Economy/GNI (current US$)",
+          "World/Latam/BRA/Economy/GNI (current US$)",
+          "World/Latam/CHL/Economy/GNI (current US$)",
+          "World/Pair/CHN/Economy/GNI (current US$)",
+          "World/South Africa/CMR/Economy/GNI (current US$)",
+          "World/Latam/COL/Economy/GNI (current US$)",
+          "World/Latam/CRI/Economy/GNI (current US$)",
+          "World/Europe/DEU/Economy/GNI (current US$)",
+          "World/North Africa/DZA/Economy/GNI (current US$)",
+          "World/North Africa/EGY/Economy/GNI (current US$)",
+          "World/Europe/ESP/Economy/GNI (current US$)",
+          "World/Europe/FRA/Economy/GNI (current US$)",
+          "World/Europe/GBR/Economy/GNI (current US$)",
+          "World/South Africa/GHA/Economy/GNI (current US$)",
+          "World/Europe/HRV/Economy/GNI (current US$)",
+          "World/Asia/IDN/Economy/GNI (current US$)",
+          "World/Asia/IND/Economy/GNI (current US$)",
+          "World/Persian Gulf/IRQ/Economy/GNI (current US$)",
+          "World/North Africa/ISR/Economy/GNI (current US$)",
+          "World/Asia/KOR/Economy/GNI (current US$)",
+          "World/South Africa/LBR/Economy/GNI (current US$)",
+          "World/North Africa/MAR/Economy/GNI (current US$)",
+          "World/Latam/MEX/Economy/GNI (current US$)",
+          "World/South Africa/MOZ/Economy/GNI (current US$)",
+          "World/South Africa/NGA/Economy/GNI (current US$)",
+          "World/Europe/NLD/Economy/GNI (current US$)",
+          "World/Persian Gulf/OMN/Economy/GNI (current US$)",
+          "World/Latam/PAN/Economy/GNI (current US$)",
+          "World/Latam/PER/Economy/GNI (current US$)",
+          "World/Asia/PHL/Economy/GNI (current US$)",
+          "World/Europe/POL/Economy/GNI (current US$)",
+          "World/Persian Gulf/QAT/Economy/GNI (current US$)",
+          "World/Persian Gulf/SAU/Economy/GNI (current US$)",
+          "World/South Africa/SEN/Economy/GNI (current US$)",
+          "World/Europe/SWE/Economy/GNI (current US$)",
+          "World/Asia/THA/Economy/GNI (current US$)",
+          "World/North Africa/TUR/Economy/GNI (current US$)",
+          "World/Pair/USA/Economy/GNI (current US$)",
+          "World/Latam/VEN/Economy/GNI (current US$)",
+          "World/Asia/VNM/Economy/GNI (current US$)",
+          "World/Persian Gulf/YEM/Economy/GNI (current US$)",
+          "World/South Africa/ZAF/Economy/GNI (current US$)",
+          "World/Persian Gulf/ARE/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/General government final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/General government final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/General government final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/General government final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/General government final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/General government final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/General government final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/General government final consumption expenditure (current US$)",
+          "World/Europe/AUT/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/BRA/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/Goods exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/COL/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/FRA/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/IDN/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/IND/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/IRQ/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/LBR/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/MEX/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/MOZ/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/PER/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/PHL/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/POL/Exports/Goods exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Goods exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/THA/Exports/Goods exports (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Goods exports (BoP, current US$)",
+          "World/Pair/USA/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/VEN/Exports/Goods exports (BoP, current US$)",
+          "World/Asia/VNM/Exports/Goods exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Goods exports (BoP, current US$)",
+          "World/Latam/ARG/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/AUT/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/BGD/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/BRA/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/CHL/Imports/Goods imports (BoP, current US$)",
+          "World/Pair/CHN/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/COL/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/CRI/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/DEU/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/ESP/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/FRA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/GBR/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/GRC/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/HRV/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/IDN/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/IND/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/KOR/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/LBR/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/MEX/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/NLD/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Goods imports (BoP, current US$)",
+          "World/Latam/PER/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/PHL/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/POL/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Goods imports (BoP, current US$)",
+          "World/Europe/SWE/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/THA/Imports/Goods imports (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Goods imports (BoP, current US$)",
+          "World/Pair/USA/Imports/Goods imports (BoP, current US$)",
+          "World/Asia/VNM/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/YEM/Imports/Goods imports (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Goods imports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross capital formation (current US$)",
+          "World/Latam/ARG/Economy/Gross capital formation (current US$)",
+          "World/Europe/AUT/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross capital formation (current US$)",
+          "World/Asia/BGD/Economy/Gross capital formation (current US$)",
+          "World/Latam/BRA/Economy/Gross capital formation (current US$)",
+          "World/Latam/CHL/Economy/Gross capital formation (current US$)",
+          "World/Pair/CHN/Economy/Gross capital formation (current US$)",
+          "World/South Africa/CMR/Economy/Gross capital formation (current US$)",
+          "World/Latam/COL/Economy/Gross capital formation (current US$)",
+          "World/Latam/CRI/Economy/Gross capital formation (current US$)",
+          "World/Europe/DEU/Economy/Gross capital formation (current US$)",
+          "World/North Africa/DZA/Economy/Gross capital formation (current US$)",
+          "World/North Africa/EGY/Economy/Gross capital formation (current US$)",
+          "World/Europe/ESP/Economy/Gross capital formation (current US$)",
+          "World/Europe/FRA/Economy/Gross capital formation (current US$)",
+          "World/Europe/GBR/Economy/Gross capital formation (current US$)",
+          "World/South Africa/GHA/Economy/Gross capital formation (current US$)",
+          "World/Europe/HRV/Economy/Gross capital formation (current US$)",
+          "World/Asia/IDN/Economy/Gross capital formation (current US$)",
+          "World/Asia/IND/Economy/Gross capital formation (current US$)",
+          "World/North Africa/ISR/Economy/Gross capital formation (current US$)",
+          "World/Asia/KOR/Economy/Gross capital formation (current US$)",
+          "World/North Africa/MAR/Economy/Gross capital formation (current US$)",
+          "World/Latam/MEX/Economy/Gross capital formation (current US$)",
+          "World/South Africa/MOZ/Economy/Gross capital formation (current US$)",
+          "World/South Africa/NGA/Economy/Gross capital formation (current US$)",
+          "World/Europe/NLD/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross capital formation (current US$)",
+          "World/Latam/PAN/Economy/Gross capital formation (current US$)",
+          "World/Latam/PER/Economy/Gross capital formation (current US$)",
+          "World/Asia/PHL/Economy/Gross capital formation (current US$)",
+          "World/Europe/POL/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross capital formation (current US$)",
+          "World/South Africa/SEN/Economy/Gross capital formation (current US$)",
+          "World/Europe/SWE/Economy/Gross capital formation (current US$)",
+          "World/Asia/THA/Economy/Gross capital formation (current US$)",
+          "World/North Africa/TUR/Economy/Gross capital formation (current US$)",
+          "World/Pair/USA/Economy/Gross capital formation (current US$)",
+          "World/Latam/VEN/Economy/Gross capital formation (current US$)",
+          "World/Asia/VNM/Economy/Gross capital formation (current US$)",
+          "World/South Africa/ZAF/Economy/Gross capital formation (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross domestic savings (current US$)",
+          "World/Latam/ARG/Economy/Gross domestic savings (current US$)",
+          "World/Europe/AUT/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross domestic savings (current US$)",
+          "World/Asia/BGD/Economy/Gross domestic savings (current US$)",
+          "World/Latam/BRA/Economy/Gross domestic savings (current US$)",
+          "World/Latam/CHL/Economy/Gross domestic savings (current US$)",
+          "World/Pair/CHN/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/CMR/Economy/Gross domestic savings (current US$)",
+          "World/Latam/COL/Economy/Gross domestic savings (current US$)",
+          "World/Latam/CRI/Economy/Gross domestic savings (current US$)",
+          "World/Europe/DEU/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/DZA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/ESP/Economy/Gross domestic savings (current US$)",
+          "World/Europe/FRA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/GBR/Economy/Gross domestic savings (current US$)",
+          "World/Europe/HRV/Economy/Gross domestic savings (current US$)",
+          "World/Asia/IDN/Economy/Gross domestic savings (current US$)",
+          "World/Asia/IND/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/ISR/Economy/Gross domestic savings (current US$)",
+          "World/Asia/KOR/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/MAR/Economy/Gross domestic savings (current US$)",
+          "World/Latam/MEX/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/NGA/Economy/Gross domestic savings (current US$)",
+          "World/Europe/NLD/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross domestic savings (current US$)",
+          "World/Latam/PAN/Economy/Gross domestic savings (current US$)",
+          "World/Latam/PER/Economy/Gross domestic savings (current US$)",
+          "World/Asia/PHL/Economy/Gross domestic savings (current US$)",
+          "World/Europe/POL/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/SEN/Economy/Gross domestic savings (current US$)",
+          "World/Europe/SWE/Economy/Gross domestic savings (current US$)",
+          "World/Asia/THA/Economy/Gross domestic savings (current US$)",
+          "World/North Africa/TUR/Economy/Gross domestic savings (current US$)",
+          "World/Pair/USA/Economy/Gross domestic savings (current US$)",
+          "World/Asia/VNM/Economy/Gross domestic savings (current US$)",
+          "World/South Africa/ZAF/Economy/Gross domestic savings (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/ARG/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/AUT/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/BGD/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/BRA/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/CHL/Economy/Gross fixed capital formation (current US$)",
+          "World/Pair/CHN/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/CMR/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/COL/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/CRI/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/DEU/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/DZA/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/EGY/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/FRA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/GBR/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/GHA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/HRV/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/IDN/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/IND/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/ISR/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/KOR/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/MAR/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/MEX/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/MOZ/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/NGA/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/NLD/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/PAN/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/PER/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/PHL/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/POL/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/SEN/Economy/Gross fixed capital formation (current US$)",
+          "World/Europe/SWE/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/THA/Economy/Gross fixed capital formation (current US$)",
+          "World/North Africa/TUR/Economy/Gross fixed capital formation (current US$)",
+          "World/Pair/USA/Economy/Gross fixed capital formation (current US$)",
+          "World/Latam/VEN/Economy/Gross fixed capital formation (current US$)",
+          "World/Asia/VNM/Economy/Gross fixed capital formation (current US$)",
+          "World/South Africa/ZAF/Economy/Gross fixed capital formation (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross national expenditure (current US$)",
+          "World/Latam/ARG/Economy/Gross national expenditure (current US$)",
+          "World/Europe/AUT/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross national expenditure (current US$)",
+          "World/Asia/BGD/Economy/Gross national expenditure (current US$)",
+          "World/Latam/BRA/Economy/Gross national expenditure (current US$)",
+          "World/Latam/CHL/Economy/Gross national expenditure (current US$)",
+          "World/Pair/CHN/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Gross national expenditure (current US$)",
+          "World/Latam/COL/Economy/Gross national expenditure (current US$)",
+          "World/Latam/CRI/Economy/Gross national expenditure (current US$)",
+          "World/Europe/DEU/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Gross national expenditure (current US$)",
+          "World/Europe/ESP/Economy/Gross national expenditure (current US$)",
+          "World/Europe/FRA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/GBR/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/GRC/Economy/Gross national expenditure (current US$)",
+          "World/Europe/HRV/Economy/Gross national expenditure (current US$)",
+          "World/Asia/IDN/Economy/Gross national expenditure (current US$)",
+          "World/Asia/IND/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Gross national expenditure (current US$)",
+          "World/Asia/KOR/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Gross national expenditure (current US$)",
+          "World/Latam/MEX/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Gross national expenditure (current US$)",
+          "World/Europe/NLD/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross national expenditure (current US$)",
+          "World/Latam/PAN/Economy/Gross national expenditure (current US$)",
+          "World/Latam/PER/Economy/Gross national expenditure (current US$)",
+          "World/Asia/PHL/Economy/Gross national expenditure (current US$)",
+          "World/Europe/POL/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Gross national expenditure (current US$)",
+          "World/Europe/SWE/Economy/Gross national expenditure (current US$)",
+          "World/Asia/THA/Economy/Gross national expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Gross national expenditure (current US$)",
+          "World/Pair/USA/Economy/Gross national expenditure (current US$)",
+          "World/Latam/VEN/Economy/Gross national expenditure (current US$)",
+          "World/Asia/VNM/Economy/Gross national expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Gross national expenditure (current US$)",
+          "World/Persian Gulf/ARE/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/AUT/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/AZE/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/BGD/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/BRA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/CHL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Pair/CHN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/CMR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/COL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/CRI/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/DEU/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/DZA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/EGY/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/ESP/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/FRA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/GBR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/GHA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/GRC/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/IND/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/ISR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/KOR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/North Africa/MAR/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/MEX/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/MOZ/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/NGA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/NLD/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/OMN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/PAN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/PER/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/PHL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Europe/POL/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Persian Gulf/SAU/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Pair/USA/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/VEN/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Asia/VNM/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/South Africa/ZAF/Economy/Gross national expenditure deflator (base year varies by country)",
+          "World/Latam/ARG/Economy/Gross savings (current US$)",
+          "World/Europe/AUT/Economy/Gross savings (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross savings (current US$)",
+          "World/Asia/BGD/Economy/Gross savings (current US$)",
+          "World/Latam/BRA/Economy/Gross savings (current US$)",
+          "World/Latam/CHL/Economy/Gross savings (current US$)",
+          "World/Pair/CHN/Economy/Gross savings (current US$)",
+          "World/South Africa/CMR/Economy/Gross savings (current US$)",
+          "World/Latam/COL/Economy/Gross savings (current US$)",
+          "World/Latam/CRI/Economy/Gross savings (current US$)",
+          "World/Europe/DEU/Economy/Gross savings (current US$)",
+          "World/North Africa/EGY/Economy/Gross savings (current US$)",
+          "World/Europe/ESP/Economy/Gross savings (current US$)",
+          "World/Europe/FRA/Economy/Gross savings (current US$)",
+          "World/Europe/GBR/Economy/Gross savings (current US$)",
+          "World/South Africa/GHA/Economy/Gross savings (current US$)",
+          "World/Europe/HRV/Economy/Gross savings (current US$)",
+          "World/Asia/IDN/Economy/Gross savings (current US$)",
+          "World/Asia/IND/Economy/Gross savings (current US$)",
+          "World/North Africa/ISR/Economy/Gross savings (current US$)",
+          "World/Asia/KOR/Economy/Gross savings (current US$)",
+          "World/North Africa/MAR/Economy/Gross savings (current US$)",
+          "World/Latam/MEX/Economy/Gross savings (current US$)",
+          "World/South Africa/MOZ/Economy/Gross savings (current US$)",
+          "World/South Africa/NGA/Economy/Gross savings (current US$)",
+          "World/Europe/NLD/Economy/Gross savings (current US$)",
+          "World/Latam/PAN/Economy/Gross savings (current US$)",
+          "World/Latam/PER/Economy/Gross savings (current US$)",
+          "World/Asia/PHL/Economy/Gross savings (current US$)",
+          "World/Europe/POL/Economy/Gross savings (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross savings (current US$)",
+          "World/South Africa/SEN/Economy/Gross savings (current US$)",
+          "World/Europe/SWE/Economy/Gross savings (current US$)",
+          "World/Asia/THA/Economy/Gross savings (current US$)",
+          "World/North Africa/TUR/Economy/Gross savings (current US$)",
+          "World/Pair/USA/Economy/Gross savings (current US$)",
+          "World/Asia/VNM/Economy/Gross savings (current US$)",
+          "World/South Africa/ZAF/Economy/Gross savings (current US$)",
+          "World/Latam/ARG/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/AUT/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/AZE/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/BGD/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/BRA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/CHL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/CMR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/COL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/CRI/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/DEU/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/DZA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/EGY/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/ESP/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/FRA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/GBR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/GHA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/GRC/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/HRV/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/IDN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/IND/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/ISR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/KOR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/MAR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/MEX/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/MOZ/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/NGA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/NLD/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/PAN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/PER/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/POL/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/SEN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/SWE/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/North Africa/TUR/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Pair/USA/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Latam/VEN/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Asia/VNM/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Persian Gulf/YEM/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/South Africa/ZAF/Economy/Gross value added at basic prices (GVA) (current US$)",
+          "World/Europe/AUT/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/DEU/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/ESP/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/POL/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/VEN/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/ARG/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/AUT/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/AZE/Health/Hospital beds (per 1,000 people)",
+          "World/Asia/BGD/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/BRA/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/CHL/Health/Hospital beds (per 1,000 people)",
+          "World/Pair/CHN/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/CMR/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/COL/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/CRI/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/DEU/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/EGY/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/ESP/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/FRA/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/GBR/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/ISR/Health/Hospital beds (per 1,000 people)",
+          "World/Asia/KOR/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/LBR/Health/Hospital beds (per 1,000 people)",
+          "World/Latam/MEX/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/MOZ/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/NGA/Health/Hospital beds (per 1,000 people)",
+          "World/Europe/NLD/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/OMN/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/SAU/Health/Hospital beds (per 1,000 people)",
+          "World/North Africa/TUR/Health/Hospital beds (per 1,000 people)",
+          "World/Pair/USA/Health/Hospital beds (per 1,000 people)",
+          "World/South Africa/ZAF/Health/Hospital beds (per 1,000 people)",
+          "World/Persian Gulf/ARE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/ARG/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/AZE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/BGD/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/BRA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/CHL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Pair/CHN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/CMR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/COL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/CRI/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/DEU/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/DZA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/EGY/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/ESP/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/FRA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/GBR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/GHA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/GRC/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/HRV/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/IDN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/IND/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/ISR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/KOR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/MAR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/MEX/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/MOZ/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/NGA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/NLD/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/OMN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/PAN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/PER/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/PHL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/POL/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/QAT/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Persian Gulf/SAU/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/SEN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/SWE/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/THA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/North Africa/TUR/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Pair/USA/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Latam/VEN/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Asia/VNM/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/South Africa/ZAF/Economy/Households and NPISHs Final consumption expenditure (current US$)",
+          "World/Europe/AUT/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/ICT service exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/CMR/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/DZA/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/GRC/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/HRV/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/IND/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/ICT service exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/MEX/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/NGA/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/ICT service exports (BoP, current US$)",
+          "World/Latam/PAN/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/POL/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/ICT service exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/ICT service exports (BoP, current US$)",
+          "World/Asia/THA/Exports/ICT service exports (BoP, current US$)",
+          "World/Pair/USA/Exports/ICT service exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/ICT service exports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import unit value index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import unit value index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/ESP/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/FRA/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/GRC/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/HRV/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import unit value index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import unit value index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/POL/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import unit value index (2000 = 100)",
+          "World/Europe/SWE/Imports/Import unit value index (2000 = 100)",
+          "World/Asia/THA/Imports/Import unit value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Imports/Import value index (2000 = 100)",
+          "World/Latam/ARG/Imports/Import value index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import value index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import value index (2000 = 100)",
+          "World/Latam/BRA/Imports/Import value index (2000 = 100)",
+          "World/Latam/CHL/Imports/Import value index (2000 = 100)",
+          "World/Pair/CHN/Imports/Import value index (2000 = 100)",
+          "World/Latam/COL/Imports/Import value index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import value index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import value index (2000 = 100)",
+          "World/North Africa/DZA/Imports/Import value index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import value index (2000 = 100)",
+          "World/Europe/ESP/Imports/Import value index (2000 = 100)",
+          "World/Europe/FRA/Imports/Import value index (2000 = 100)",
+          "World/Europe/GBR/Imports/Import value index (2000 = 100)",
+          "World/Europe/GRC/Imports/Import value index (2000 = 100)",
+          "World/Europe/HRV/Imports/Import value index (2000 = 100)",
+          "World/Asia/IDN/Imports/Import value index (2000 = 100)",
+          "World/Asia/IND/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/IRQ/Imports/Import value index (2000 = 100)",
+          "World/North Africa/ISR/Imports/Import value index (2000 = 100)",
+          "World/Asia/KOR/Imports/Import value index (2000 = 100)",
+          "World/North Africa/MAR/Imports/Import value index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import value index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import value index (2000 = 100)",
+          "World/South Africa/NGA/Imports/Import value index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/OMN/Imports/Import value index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import value index (2000 = 100)",
+          "World/Latam/PER/Imports/Import value index (2000 = 100)",
+          "World/Asia/PHL/Imports/Import value index (2000 = 100)",
+          "World/Europe/POL/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/SAU/Imports/Import value index (2000 = 100)",
+          "World/South Africa/SEN/Imports/Import value index (2000 = 100)",
+          "World/Europe/SWE/Imports/Import value index (2000 = 100)",
+          "World/Asia/THA/Imports/Import value index (2000 = 100)",
+          "World/North Africa/TUR/Imports/Import value index (2000 = 100)",
+          "World/Pair/USA/Imports/Import value index (2000 = 100)",
+          "World/Asia/VNM/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/YEM/Imports/Import value index (2000 = 100)",
+          "World/South Africa/ZAF/Imports/Import value index (2000 = 100)",
+          "World/Persian Gulf/ARE/Imports/Import volume index (2000 = 100)",
+          "World/Latam/ARG/Imports/Import volume index (2000 = 100)",
+          "World/Europe/AUT/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/AZE/Imports/Import volume index (2000 = 100)",
+          "World/Asia/BGD/Imports/Import volume index (2000 = 100)",
+          "World/Latam/BRA/Imports/Import volume index (2000 = 100)",
+          "World/Latam/CHL/Imports/Import volume index (2000 = 100)",
+          "World/Pair/CHN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/COL/Imports/Import volume index (2000 = 100)",
+          "World/Latam/CRI/Imports/Import volume index (2000 = 100)",
+          "World/Europe/DEU/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/DZA/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/EGY/Imports/Import volume index (2000 = 100)",
+          "World/Europe/GBR/Imports/Import volume index (2000 = 100)",
+          "World/Asia/IDN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/IND/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/ISR/Imports/Import volume index (2000 = 100)",
+          "World/Asia/KOR/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/MAR/Imports/Import volume index (2000 = 100)",
+          "World/Latam/MEX/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/MOZ/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/NGA/Imports/Import volume index (2000 = 100)",
+          "World/Europe/NLD/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/OMN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/PAN/Imports/Import volume index (2000 = 100)",
+          "World/Latam/PER/Imports/Import volume index (2000 = 100)",
+          "World/Asia/PHL/Imports/Import volume index (2000 = 100)",
+          "World/Europe/POL/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/QAT/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/SAU/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/SEN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/THA/Imports/Import volume index (2000 = 100)",
+          "World/North Africa/TUR/Imports/Import volume index (2000 = 100)",
+          "World/Pair/USA/Imports/Import volume index (2000 = 100)",
+          "World/Latam/VEN/Imports/Import volume index (2000 = 100)",
+          "World/Asia/VNM/Imports/Import volume index (2000 = 100)",
+          "World/Persian Gulf/YEM/Imports/Import volume index (2000 = 100)",
+          "World/South Africa/ZAF/Imports/Import volume index (2000 = 100)",
+          "World/Latam/ARG/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/AUT/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/BGD/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/BRA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/CHL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Pair/CHN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/CMR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/COL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/CRI/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/DEU/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/ESP/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/FRA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/GBR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/GRC/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/HRV/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/IDN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/IND/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/KOR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/MEX/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/NLD/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/PAN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Latam/PER/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/PHL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/POL/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/SAU/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Europe/SWE/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/THA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Pair/USA/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Asia/VNM/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/YEM/Imports/Imports of goods and services (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Imports of goods and services (BoP, current US$)",
+          "World/Persian Gulf/ARE/Imports/Imports of goods and services (current US$)",
+          "World/Latam/ARG/Imports/Imports of goods and services (current US$)",
+          "World/Europe/AUT/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/AZE/Imports/Imports of goods and services (current US$)",
+          "World/Asia/BGD/Imports/Imports of goods and services (current US$)",
+          "World/Latam/BRA/Imports/Imports of goods and services (current US$)",
+          "World/Latam/CHL/Imports/Imports of goods and services (current US$)",
+          "World/Pair/CHN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/COL/Imports/Imports of goods and services (current US$)",
+          "World/Latam/CRI/Imports/Imports of goods and services (current US$)",
+          "World/Europe/DEU/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/DZA/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/EGY/Imports/Imports of goods and services (current US$)",
+          "World/Europe/ESP/Imports/Imports of goods and services (current US$)",
+          "World/Europe/FRA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/GBR/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/GHA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/GRC/Imports/Imports of goods and services (current US$)",
+          "World/Europe/HRV/Imports/Imports of goods and services (current US$)",
+          "World/Asia/IDN/Imports/Imports of goods and services (current US$)",
+          "World/Asia/IND/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/IRQ/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/ISR/Imports/Imports of goods and services (current US$)",
+          "World/Asia/KOR/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/MAR/Imports/Imports of goods and services (current US$)",
+          "World/Latam/MEX/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/MOZ/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/NGA/Imports/Imports of goods and services (current US$)",
+          "World/Europe/NLD/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/OMN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/PAN/Imports/Imports of goods and services (current US$)",
+          "World/Latam/PER/Imports/Imports of goods and services (current US$)",
+          "World/Asia/PHL/Imports/Imports of goods and services (current US$)",
+          "World/Europe/POL/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/QAT/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/SAU/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/SEN/Imports/Imports of goods and services (current US$)",
+          "World/Europe/SWE/Imports/Imports of goods and services (current US$)",
+          "World/Asia/THA/Imports/Imports of goods and services (current US$)",
+          "World/North Africa/TUR/Imports/Imports of goods and services (current US$)",
+          "World/Pair/USA/Imports/Imports of goods and services (current US$)",
+          "World/Latam/VEN/Imports/Imports of goods and services (current US$)",
+          "World/Asia/VNM/Imports/Imports of goods and services (current US$)",
+          "World/South Africa/ZAF/Imports/Imports of goods and services (current US$)",
+          "World/Persian Gulf/ARE/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/AUT/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/AZE/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/BRA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Pair/CHN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/CMR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/CRI/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/EGY/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/ESP/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/GBR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/GHA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/HRV/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/IDN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/IND/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/IRQ/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/ISR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/LBR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/MOZ/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/OMN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Latam/PER/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Europe/POL/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/QAT/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/SAU/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/South Africa/SEN/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/THA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/North Africa/TUR/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Pair/USA/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Asia/VNM/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/YEM/Health/Incidence of tuberculosis (per 100,000 people)",
+          "World/Persian Gulf/ARE/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/ARG/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/AUT/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/AZE/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/BGD/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/BRA/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/CHL/Economy/Industry (including construction), value added (current US$)",
+          "World/Pair/CHN/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/CMR/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/COL/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/CRI/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/DEU/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/DZA/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/EGY/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/ESP/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/FRA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/GBR/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/GHA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/GRC/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/HRV/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/IDN/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/IND/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/ISR/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/KOR/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/LBR/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/MAR/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/MEX/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/MOZ/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/NGA/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/NLD/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/OMN/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/PAN/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/PER/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/PHL/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/POL/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/SAU/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/SEN/Economy/Industry (including construction), value added (current US$)",
+          "World/Europe/SWE/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/THA/Economy/Industry (including construction), value added (current US$)",
+          "World/North Africa/TUR/Economy/Industry (including construction), value added (current US$)",
+          "World/Pair/USA/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/VEN/Economy/Industry (including construction), value added (current US$)",
+          "World/Asia/VNM/Economy/Industry (including construction), value added (current US$)",
+          "World/Persian Gulf/YEM/Economy/Industry (including construction), value added (current US$)",
+          "World/South Africa/ZAF/Economy/Industry (including construction), value added (current US$)",
+          "World/Latam/BRA/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Pair/CHN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/COL/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/CRI/Mortality/Intentional homicides (per 100,000 people)",
+          "World/North Africa/EGY/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/ESP/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/GBR/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/IDN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/IND/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/IRQ/Mortality/Intentional homicides (per 100,000 people)",
+          "World/North Africa/ISR/Mortality/Intentional homicides (per 100,000 people)",
+          "World/South Africa/MOZ/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/NLD/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/PAN/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Latam/PER/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Europe/POL/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/THA/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Asia/VNM/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/YEM/Mortality/Intentional homicides (per 100,000 people)",
+          "World/South Africa/ZAF/Mortality/Intentional homicides (per 100,000 people)",
+          "World/Persian Gulf/ARE/Migration/International migrant stock (% of population)",
+          "World/Europe/AUT/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/AZE/Migration/International migrant stock (% of population)",
+          "World/Latam/CHL/Migration/International migrant stock (% of population)",
+          "World/Pair/CHN/Migration/International migrant stock (% of population)",
+          "World/Europe/DEU/Migration/International migrant stock (% of population)",
+          "World/North Africa/DZA/Migration/International migrant stock (% of population)",
+          "World/Europe/ESP/Migration/International migrant stock (% of population)",
+          "World/Europe/FRA/Migration/International migrant stock (% of population)",
+          "World/Europe/GBR/Migration/International migrant stock (% of population)",
+          "World/Europe/GRC/Migration/International migrant stock (% of population)",
+          "World/Europe/HRV/Migration/International migrant stock (% of population)",
+          "World/Asia/IND/Migration/International migrant stock (% of population)",
+          "World/North Africa/ISR/Migration/International migrant stock (% of population)",
+          "World/Asia/KOR/Migration/International migrant stock (% of population)",
+          "World/North Africa/MAR/Migration/International migrant stock (% of population)",
+          "World/Latam/MEX/Migration/International migrant stock (% of population)",
+          "World/South Africa/MOZ/Migration/International migrant stock (% of population)",
+          "World/South Africa/NGA/Migration/International migrant stock (% of population)",
+          "World/Europe/NLD/Migration/International migrant stock (% of population)",
+          "World/Latam/PAN/Migration/International migrant stock (% of population)",
+          "World/Latam/PER/Migration/International migrant stock (% of population)",
+          "World/Europe/POL/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/SAU/Migration/International migrant stock (% of population)",
+          "World/South Africa/SEN/Migration/International migrant stock (% of population)",
+          "World/Europe/SWE/Migration/International migrant stock (% of population)",
+          "World/Asia/THA/Migration/International migrant stock (% of population)",
+          "World/North Africa/TUR/Migration/International migrant stock (% of population)",
+          "World/Pair/USA/Migration/International migrant stock (% of population)",
+          "World/Asia/VNM/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/YEM/Migration/International migrant stock (% of population)",
+          "World/Persian Gulf/ARE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/AUT/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/AZE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/BRA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/CHL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Pair/CHN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/COL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/CRI/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/DEU/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/DZA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/EGY/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/GHA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/GRC/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/IDN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/IND/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/IRQ/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/ISR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/KOR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/LBR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/MEX/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/MOZ/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/NGA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/NLD/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/OMN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/PAN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/PER/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/PHL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/POL/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/QAT/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Europe/SWE/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/North Africa/TUR/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Pair/USA/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Latam/VEN/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Asia/VNM/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/YEM/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/South Africa/ZAF/Health/Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "World/Persian Gulf/ARE/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/ARG/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/AZE/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/BGD/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/CHL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Pair/CHN/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/CMR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/COL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/CRI/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/EGY/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/ESP/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/GBR/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/GHA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/GRC/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/HRV/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/IDN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/IND/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/ISR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/KOR/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/LBR/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/MAR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/MEX/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/MOZ/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/NGA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/NLD/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/PAN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Latam/PER/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/PHL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Europe/POL/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/QAT/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/SAU/Mortality/Lifetime risk of maternal death (%)",
+          "World/South Africa/SEN/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/THA/Mortality/Lifetime risk of maternal death (%)",
+          "World/North Africa/TUR/Mortality/Lifetime risk of maternal death (%)",
+          "World/Pair/USA/Mortality/Lifetime risk of maternal death (%)",
+          "World/Asia/VNM/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/YEM/Mortality/Lifetime risk of maternal death (%)",
+          "World/Persian Gulf/ARE/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/ARG/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/AZE/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/BGD/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/CHL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Pair/CHN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/CMR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/COL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/CRI/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/EGY/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/ESP/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/GBR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/GHA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/GRC/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/HRV/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/IDN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/IND/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/ISR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/KOR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/LBR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/MAR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/MEX/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/MOZ/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/NGA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/NLD/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/PAN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/PER/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/PHL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Europe/POL/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/QAT/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/SAU/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/South Africa/SEN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/THA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/North Africa/TUR/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Pair/USA/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Latam/VEN/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Asia/VNM/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/YEM/Health/Lifetime risk of maternal death (1 in: rate varies by country)",
+          "World/Persian Gulf/ARE/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/ARG/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/AUT/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/AZE/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/BGD/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/BRA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Pair/CHN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/COL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/CRI/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/DZA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/EGY/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/GBR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/GHA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/IDN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/IND/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/ISR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/KOR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/LBR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/MAR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/MEX/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/MOZ/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/NGA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/NLD/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/OMN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/PAN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/PER/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/PHL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Europe/POL/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/QAT/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/SAU/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/SEN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/THA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/North Africa/TUR/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Pair/USA/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Latam/VEN/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Asia/VNM/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/YEM/Industry/Livestock production index (2014-2016 = 100)",
+          "World/South Africa/ZAF/Industry/Livestock production index (2014-2016 = 100)",
+          "World/Persian Gulf/ARE/Health/Low-birthweight babies (% of births)",
+          "World/Asia/BGD/Health/Low-birthweight babies (% of births)",
+          "World/Latam/BRA/Health/Low-birthweight babies (% of births)",
+          "World/Latam/CHL/Health/Low-birthweight babies (% of births)",
+          "World/Pair/CHN/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/CMR/Health/Low-birthweight babies (% of births)",
+          "World/Latam/COL/Health/Low-birthweight babies (% of births)",
+          "World/Latam/CRI/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/DZA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/ESP/Health/Low-birthweight babies (% of births)",
+          "World/Europe/FRA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/GBR/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/GHA/Health/Low-birthweight babies (% of births)",
+          "World/Europe/HRV/Health/Low-birthweight babies (% of births)",
+          "World/Asia/IDN/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/ISR/Health/Low-birthweight babies (% of births)",
+          "World/Asia/KOR/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/MAR/Health/Low-birthweight babies (% of births)",
+          "World/Latam/MEX/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/MOZ/Health/Low-birthweight babies (% of births)",
+          "World/Europe/NLD/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/OMN/Health/Low-birthweight babies (% of births)",
+          "World/Latam/PAN/Health/Low-birthweight babies (% of births)",
+          "World/Latam/PER/Health/Low-birthweight babies (% of births)",
+          "World/Asia/PHL/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/QAT/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/SEN/Health/Low-birthweight babies (% of births)",
+          "World/Asia/THA/Health/Low-birthweight babies (% of births)",
+          "World/North Africa/TUR/Health/Low-birthweight babies (% of births)",
+          "World/Asia/VNM/Health/Low-birthweight babies (% of births)",
+          "World/South Africa/ZAF/Health/Low-birthweight babies (% of births)",
+          "World/Persian Gulf/ARE/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/BGD/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/CHL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Pair/CHN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/COL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/South Africa/GHA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Europe/HRV/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/IDN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/IND/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/North Africa/ISR/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/KOR/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/MEX/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/OMN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Latam/PAN/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Europe/POL/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/QAT/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/SAU/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/THA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Pair/USA/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Asia/VNM/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/South Africa/ZAF/Economy/Market capitalization of listed domestic companies (current US$)",
+          "World/Persian Gulf/ARE/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/ARG/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/AZE/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/BGD/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/CHL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Pair/CHN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/CMR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/CRI/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/DZA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/EGY/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/ESP/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/GBR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/GHA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/HRV/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/IDN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/IND/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/ISR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/KOR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/LBR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/MAR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/MEX/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/MOZ/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/NGA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/NLD/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/PAN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Latam/PER/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/PHL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Europe/POL/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/QAT/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/SAU/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/South Africa/SEN/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/THA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/North Africa/TUR/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Pair/USA/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Asia/VNM/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/YEM/Mortality/Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports (current US$)",
+          "World/Europe/AUT/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/AZE/Exports/Merchandise exports (current US$)",
+          "World/Asia/BGD/Exports/Merchandise exports (current US$)",
+          "World/Latam/BRA/Exports/Merchandise exports (current US$)",
+          "World/Latam/CHL/Exports/Merchandise exports (current US$)",
+          "World/Pair/CHN/Exports/Merchandise exports (current US$)",
+          "World/Latam/COL/Exports/Merchandise exports (current US$)",
+          "World/Latam/CRI/Exports/Merchandise exports (current US$)",
+          "World/Europe/DEU/Exports/Merchandise exports (current US$)",
+          "World/North Africa/EGY/Exports/Merchandise exports (current US$)",
+          "World/Europe/ESP/Exports/Merchandise exports (current US$)",
+          "World/Europe/FRA/Exports/Merchandise exports (current US$)",
+          "World/Europe/GBR/Exports/Merchandise exports (current US$)",
+          "World/South Africa/GHA/Exports/Merchandise exports (current US$)",
+          "World/Asia/IDN/Exports/Merchandise exports (current US$)",
+          "World/Asia/IND/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/IRQ/Exports/Merchandise exports (current US$)",
+          "World/North Africa/ISR/Exports/Merchandise exports (current US$)",
+          "World/Asia/KOR/Exports/Merchandise exports (current US$)",
+          "World/North Africa/MAR/Exports/Merchandise exports (current US$)",
+          "World/Latam/MEX/Exports/Merchandise exports (current US$)",
+          "World/South Africa/MOZ/Exports/Merchandise exports (current US$)",
+          "World/Europe/NLD/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/OMN/Exports/Merchandise exports (current US$)",
+          "World/Latam/PAN/Exports/Merchandise exports (current US$)",
+          "World/Latam/PER/Exports/Merchandise exports (current US$)",
+          "World/Asia/PHL/Exports/Merchandise exports (current US$)",
+          "World/Europe/POL/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/SAU/Exports/Merchandise exports (current US$)",
+          "World/South Africa/SEN/Exports/Merchandise exports (current US$)",
+          "World/Europe/SWE/Exports/Merchandise exports (current US$)",
+          "World/Asia/THA/Exports/Merchandise exports (current US$)",
+          "World/North Africa/TUR/Exports/Merchandise exports (current US$)",
+          "World/Pair/USA/Exports/Merchandise exports (current US$)",
+          "World/Latam/VEN/Exports/Merchandise exports (current US$)",
+          "World/Asia/VNM/Exports/Merchandise exports (current US$)",
+          "World/South Africa/ZAF/Exports/Merchandise exports (current US$)",
+          "World/Persian Gulf/ARE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/AUT/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/AZE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/BGD/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/BRA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/CHL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Pair/CHN/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/COL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Latam/CRI/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/DEU/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/DZA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/EGY/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/ESP/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/FRA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/GBR/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/South Africa/GHA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/IDN/Exports/Merchandise exports by the reporting economy (current US$)",
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+          "World/Europe/NLD/Exports/Merchandise exports by the reporting economy (current US$)",
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+          "World/Asia/PHL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Europe/POL/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Persian Gulf/QAT/Exports/Merchandise exports by the reporting economy (current US$)",
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+          "World/Europe/SWE/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/Asia/THA/Exports/Merchandise exports by the reporting economy (current US$)",
+          "World/North Africa/TUR/Exports/Merchandise exports by the reporting economy (current US$)",
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+          "World/South Africa/ZAF/Exports/Merchandise exports by the reporting economy (current US$)",
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+          "World/Latam/COL/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/DEU/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "World/Europe/ESP/Exports/Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
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+          "World/Latam/BRA/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
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+          "World/Europe/DEU/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
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+          "World/Europe/NLD/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
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+          "World/South Africa/ZAF/Exports/Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
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+          "World/Pair/USA/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Latam/VEN/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/South Africa/ZAF/Imports/Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "World/Europe/AUT/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/AZE/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/BGD/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Pair/CHN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/CMR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/COL/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/CRI/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/DZA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/EGY/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/ESP/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/FRA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/GBR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/GHA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/GRC/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/IND/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/ISR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Asia/KOR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/MAR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/MEX/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/MOZ/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/NLD/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/OMN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Latam/PER/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Europe/POL/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/QAT/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/SAU/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/South Africa/SEN/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/North Africa/TUR/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Pair/USA/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/YEM/Imports/Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/AUT/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Methane emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/COL/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/DEU/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/ESP/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/FRA/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Methane emissions (% change from 1990)",
+          "World/South Africa/GHA/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/KOR/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/MAR/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/MEX/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Methane emissions (% change from 1990)",
+          "World/Latam/PER/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/PHL/Environment/Methane emissions (% change from 1990)",
+          "World/Europe/POL/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/SAU/Environment/Methane emissions (% change from 1990)",
+          "World/North Africa/TUR/Environment/Methane emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Methane emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/ARG/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/COL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/IND/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Latam/PER/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Europe/POL/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Pair/USA/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/COL/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/IRQ/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/POL/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/THA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Military/Military expenditure (current USD)",
+          "World/Europe/AUT/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/AZE/Military/Military expenditure (current USD)",
+          "World/Asia/BGD/Military/Military expenditure (current USD)",
+          "World/Latam/BRA/Military/Military expenditure (current USD)",
+          "World/Latam/CHL/Military/Military expenditure (current USD)",
+          "World/Pair/CHN/Military/Military expenditure (current USD)",
+          "World/South Africa/CMR/Military/Military expenditure (current USD)",
+          "World/Latam/COL/Military/Military expenditure (current USD)",
+          "World/North Africa/DZA/Military/Military expenditure (current USD)",
+          "World/North Africa/EGY/Military/Military expenditure (current USD)",
+          "World/Europe/ESP/Military/Military expenditure (current USD)",
+          "World/Europe/FRA/Military/Military expenditure (current USD)",
+          "World/South Africa/GHA/Military/Military expenditure (current USD)",
+          "World/Europe/GRC/Military/Military expenditure (current USD)",
+          "World/Asia/IDN/Military/Military expenditure (current USD)",
+          "World/Asia/IND/Military/Military expenditure (current USD)",
+          "World/North Africa/ISR/Military/Military expenditure (current USD)",
+          "World/Asia/KOR/Military/Military expenditure (current USD)",
+          "World/North Africa/MAR/Military/Military expenditure (current USD)",
+          "World/Latam/MEX/Military/Military expenditure (current USD)",
+          "World/South Africa/MOZ/Military/Military expenditure (current USD)",
+          "World/South Africa/NGA/Military/Military expenditure (current USD)",
+          "World/Europe/NLD/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/OMN/Military/Military expenditure (current USD)",
+          "World/Latam/PER/Military/Military expenditure (current USD)",
+          "World/Asia/PHL/Military/Military expenditure (current USD)",
+          "World/Europe/POL/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/SAU/Military/Military expenditure (current USD)",
+          "World/South Africa/SEN/Military/Military expenditure (current USD)",
+          "World/Asia/THA/Military/Military expenditure (current USD)",
+          "World/North Africa/TUR/Military/Military expenditure (current USD)",
+          "World/Pair/USA/Military/Military expenditure (current USD)",
+          "World/Asia/VNM/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/YEM/Military/Military expenditure (current USD)",
+          "World/Persian Gulf/ARE/Internet/Mobile cellular subscriptions",
+          "World/Latam/BRA/Internet/Mobile cellular subscriptions",
+          "World/Latam/CHL/Internet/Mobile cellular subscriptions",
+          "World/Pair/CHN/Internet/Mobile cellular subscriptions",
+          "World/Europe/DEU/Internet/Mobile cellular subscriptions",
+          "World/North Africa/DZA/Internet/Mobile cellular subscriptions",
+          "World/North Africa/EGY/Internet/Mobile cellular subscriptions",
+          "World/Europe/FRA/Internet/Mobile cellular subscriptions",
+          "World/Europe/GBR/Internet/Mobile cellular subscriptions",
+          "World/Europe/HRV/Internet/Mobile cellular subscriptions",
+          "World/Asia/IDN/Internet/Mobile cellular subscriptions",
+          "World/North Africa/ISR/Internet/Mobile cellular subscriptions",
+          "World/Asia/KOR/Internet/Mobile cellular subscriptions",
+          "World/North Africa/MAR/Internet/Mobile cellular subscriptions",
+          "World/Latam/MEX/Internet/Mobile cellular subscriptions",
+          "World/Europe/NLD/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/OMN/Internet/Mobile cellular subscriptions",
+          "World/Latam/PER/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/QAT/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/SAU/Internet/Mobile cellular subscriptions",
+          "World/Europe/SWE/Internet/Mobile cellular subscriptions",
+          "World/Asia/THA/Internet/Mobile cellular subscriptions",
+          "World/North Africa/TUR/Internet/Mobile cellular subscriptions",
+          "World/Pair/USA/Internet/Mobile cellular subscriptions",
+          "World/South Africa/ZAF/Internet/Mobile cellular subscriptions",
+          "World/Persian Gulf/ARE/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/BRA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/CHL/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Pair/CHN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/DEU/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/DZA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/EGY/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/FRA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/HRV/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/IDN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/ISR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/KOR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/MAR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/MEX/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/NLD/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/OMN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/PER/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/QAT/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/SAU/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Europe/SWE/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Asia/THA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/North Africa/TUR/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Pair/USA/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Latam/VEN/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/South Africa/ZAF/Internet/Mobile cellular subscriptions (per 100 people)",
+          "World/Persian Gulf/ARE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/AZE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/BGD/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/CHL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Pair/CHN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/CMR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/COL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/CRI/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/DEU/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/DZA/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/EGY/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/FRA/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/GRC/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/HRV/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/IND/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/KOR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/LBR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/MAR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/MEX/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/OMN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/PAN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/PER/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/POL/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/QAT/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Persian Gulf/SAU/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/South Africa/SEN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/SWE/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/North Africa/TUR/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Latam/VEN/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Asia/VNM/Economy/Monetary Sector credit to private sector (% GDP)",
+          "World/Europe/AUT/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/AZE/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/BGD/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Pair/CHN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/CMR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/DEU/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/DZA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/ESP/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/GBR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/GRC/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/HRV/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/IDN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/ISR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/KOR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/MAR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Latam/MEX/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/MOZ/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/South Africa/NGA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/NLD/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/OMN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/PHL/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/POL/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/QAT/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/SAU/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Europe/SWE/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/THA/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/North Africa/TUR/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Latam/VEN/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Asia/VNM/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/YEM/Mortality/Mortality caused by road traffic injury (per 100,000 population)",
+          "World/Persian Gulf/ARE/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/AUT/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Latam/BRA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Latam/COL/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/North Africa/DZA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/North Africa/EGY/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/ESP/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/GHA/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/KOR/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/LBR/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/NLD/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Europe/SWE/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/VNM/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/South Africa/ZAF/Economy/Net barter terms of trade index (2000 = 100)",
+          "World/Asia/BGD/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/BRA/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/COL/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/CRI/Economy/Net primary income (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Net primary income (BoP, current US$)",
+          "World/Europe/FRA/Economy/Net primary income (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/IDN/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/IND/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/KOR/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/MEX/Economy/Net primary income (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/PAN/Economy/Net primary income (BoP, current US$)",
+          "World/Europe/POL/Economy/Net primary income (BoP, current US$)",
+          "World/Persian Gulf/QAT/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/THA/Economy/Net primary income (BoP, current US$)",
+          "World/North Africa/TUR/Economy/Net primary income (BoP, current US$)",
+          "World/Pair/USA/Economy/Net primary income (BoP, current US$)",
+          "World/Latam/VEN/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/VNM/Economy/Net primary income (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Net primary income (BoP, current US$)",
+          "World/Asia/BGD/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/BRA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/COL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/CRI/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/North Africa/EGY/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Europe/FRA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/IND/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/KOR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/LBR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/MEX/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/NGA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Latam/PAN/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/PHL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Europe/POL/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/THA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/North Africa/TUR/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Pair/USA/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/VNM/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/South Africa/ZAF/Economy/Net primary income (Net income from abroad) (current US$)",
+          "World/Asia/BGD/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/COL/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/CRI/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/DEU/Economy/Net secondary income (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/ESP/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/FRA/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/GBR/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/IND/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/KOR/Economy/Net secondary income (BoP, current US$)",
+          "World/North Africa/MAR/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/MEX/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/NGA/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/PER/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/PHL/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/POL/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/SAU/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Net secondary income (BoP, current US$)",
+          "World/Europe/SWE/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/THA/Economy/Net secondary income (BoP, current US$)",
+          "World/Pair/USA/Economy/Net secondary income (BoP, current US$)",
+          "World/Latam/VEN/Economy/Net secondary income (BoP, current US$)",
+          "World/Asia/VNM/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Net secondary income (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Net secondary income (BoP, current US$)",
+          "World/Persian Gulf/ARE/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/AUT/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/COL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/CRI/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/DZA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/EGY/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/ESP/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/FRA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/GBR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/GHA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/IND/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/ISR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/North Africa/MAR/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/MEX/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/NGA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/OMN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/PAN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Latam/PER/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/PHL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/POL/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/QAT/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/SAU/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/SEN/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/SWE/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/THA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Pair/USA/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Asia/VNM/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Persian Gulf/YEM/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/South Africa/ZAF/Economy/Net secondary income (Net current transfers from abroad) (current US$)",
+          "World/Europe/AUT/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/COL/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/CRI/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/ESP/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/GRC/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Europe/SWE/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/South Africa/ZAF/Environment/Nitrous oxide emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/AUT/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/CHL/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/FRA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/IND/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/PER/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/THA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Pair/USA/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/VEN/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Persian Gulf/YEM/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "World/Latam/ARG/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/BRA/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 10-14 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/IDN/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/IRQ/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 10-14 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 10-14 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 10-14 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 10-14 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 10-14 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 10-14 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 10-14 years",
+          "World/Persian Gulf/ARE/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/ARG/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 15-19 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/PHL/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 15-19 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 15-19 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 15-19 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 15-19 years",
+          "World/Latam/VEN/Mortality/Number of deaths ages 15-19 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 15-19 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 15-19 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 20-24 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/LBR/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/PHL/Mortality/Number of deaths ages 20-24 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 20-24 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 20-24 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 20-24 years",
+          "World/Latam/VEN/Mortality/Number of deaths ages 20-24 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 20-24 years",
+          "World/Persian Gulf/ARE/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/ARG/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/AUT/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/AZE/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/BGD/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/BRA/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/CHL/Mortality/Number of deaths ages 5-9 years",
+          "World/Pair/CHN/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/CMR/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/COL/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/CRI/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/DEU/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/DZA/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/EGY/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/ESP/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/FRA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/GBR/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/GHA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/GRC/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/HRV/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/IDN/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/IND/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/ISR/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/KOR/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/MAR/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/MEX/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/MOZ/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/NGA/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/NLD/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/OMN/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/PAN/Mortality/Number of deaths ages 5-9 years",
+          "World/Latam/PER/Mortality/Number of deaths ages 5-9 years",
+          "World/Europe/POL/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/QAT/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/SAU/Mortality/Number of deaths ages 5-9 years",
+          "World/South Africa/SEN/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/THA/Mortality/Number of deaths ages 5-9 years",
+          "World/North Africa/TUR/Mortality/Number of deaths ages 5-9 years",
+          "World/Pair/USA/Mortality/Number of deaths ages 5-9 years",
+          "World/Asia/VNM/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/YEM/Mortality/Number of deaths ages 5-9 years",
+          "World/Persian Gulf/ARE/Mortality/Number of infant deaths",
+          "World/Latam/ARG/Mortality/Number of infant deaths",
+          "World/Europe/AUT/Mortality/Number of infant deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of infant deaths",
+          "World/Asia/BGD/Mortality/Number of infant deaths",
+          "World/Latam/BRA/Mortality/Number of infant deaths",
+          "World/Latam/CHL/Mortality/Number of infant deaths",
+          "World/Pair/CHN/Mortality/Number of infant deaths",
+          "World/South Africa/CMR/Mortality/Number of infant deaths",
+          "World/Latam/COL/Mortality/Number of infant deaths",
+          "World/Latam/CRI/Mortality/Number of infant deaths",
+          "World/North Africa/EGY/Mortality/Number of infant deaths",
+          "World/Europe/FRA/Mortality/Number of infant deaths",
+          "World/Europe/GBR/Mortality/Number of infant deaths",
+          "World/South Africa/GHA/Mortality/Number of infant deaths",
+          "World/Europe/GRC/Mortality/Number of infant deaths",
+          "World/Europe/HRV/Mortality/Number of infant deaths",
+          "World/Asia/IDN/Mortality/Number of infant deaths",
+          "World/Asia/IND/Mortality/Number of infant deaths",
+          "World/Persian Gulf/IRQ/Mortality/Number of infant deaths",
+          "World/North Africa/ISR/Mortality/Number of infant deaths",
+          "World/Asia/KOR/Mortality/Number of infant deaths",
+          "World/South Africa/LBR/Mortality/Number of infant deaths",
+          "World/North Africa/MAR/Mortality/Number of infant deaths",
+          "World/Latam/MEX/Mortality/Number of infant deaths",
+          "World/South Africa/MOZ/Mortality/Number of infant deaths",
+          "World/Europe/NLD/Mortality/Number of infant deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of infant deaths",
+          "World/Latam/PAN/Mortality/Number of infant deaths",
+          "World/Latam/PER/Mortality/Number of infant deaths",
+          "World/Asia/PHL/Mortality/Number of infant deaths",
+          "World/Europe/POL/Mortality/Number of infant deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of infant deaths",
+          "World/South Africa/SEN/Mortality/Number of infant deaths",
+          "World/Europe/SWE/Mortality/Number of infant deaths",
+          "World/Asia/THA/Mortality/Number of infant deaths",
+          "World/North Africa/TUR/Mortality/Number of infant deaths",
+          "World/Pair/USA/Mortality/Number of infant deaths",
+          "World/Asia/VNM/Mortality/Number of infant deaths",
+          "World/Latam/ARG/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of maternal deaths",
+          "World/Asia/BGD/Mortality/Number of maternal deaths",
+          "World/Latam/BRA/Mortality/Number of maternal deaths",
+          "World/Latam/CHL/Mortality/Number of maternal deaths",
+          "World/Pair/CHN/Mortality/Number of maternal deaths",
+          "World/South Africa/CMR/Mortality/Number of maternal deaths",
+          "World/Latam/COL/Mortality/Number of maternal deaths",
+          "World/Latam/CRI/Mortality/Number of maternal deaths",
+          "World/North Africa/DZA/Mortality/Number of maternal deaths",
+          "World/North Africa/EGY/Mortality/Number of maternal deaths",
+          "World/Europe/FRA/Mortality/Number of maternal deaths",
+          "World/Europe/GBR/Mortality/Number of maternal deaths",
+          "World/Europe/HRV/Mortality/Number of maternal deaths",
+          "World/Asia/IDN/Mortality/Number of maternal deaths",
+          "World/Asia/IND/Mortality/Number of maternal deaths",
+          "World/North Africa/ISR/Mortality/Number of maternal deaths",
+          "World/Asia/KOR/Mortality/Number of maternal deaths",
+          "World/North Africa/MAR/Mortality/Number of maternal deaths",
+          "World/Latam/MEX/Mortality/Number of maternal deaths",
+          "World/South Africa/MOZ/Mortality/Number of maternal deaths",
+          "World/South Africa/NGA/Mortality/Number of maternal deaths",
+          "World/Europe/NLD/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of maternal deaths",
+          "World/Latam/PAN/Mortality/Number of maternal deaths",
+          "World/Latam/PER/Mortality/Number of maternal deaths",
+          "World/Asia/PHL/Mortality/Number of maternal deaths",
+          "World/Europe/POL/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of maternal deaths",
+          "World/South Africa/SEN/Mortality/Number of maternal deaths",
+          "World/Asia/THA/Mortality/Number of maternal deaths",
+          "World/North Africa/TUR/Mortality/Number of maternal deaths",
+          "World/Pair/USA/Mortality/Number of maternal deaths",
+          "World/Asia/VNM/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/YEM/Mortality/Number of maternal deaths",
+          "World/Persian Gulf/ARE/Mortality/Number of neonatal deaths",
+          "World/Latam/ARG/Mortality/Number of neonatal deaths",
+          "World/Europe/AUT/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of neonatal deaths",
+          "World/Asia/BGD/Mortality/Number of neonatal deaths",
+          "World/Latam/BRA/Mortality/Number of neonatal deaths",
+          "World/Latam/CHL/Mortality/Number of neonatal deaths",
+          "World/Pair/CHN/Mortality/Number of neonatal deaths",
+          "World/Latam/COL/Mortality/Number of neonatal deaths",
+          "World/Latam/CRI/Mortality/Number of neonatal deaths",
+          "World/North Africa/DZA/Mortality/Number of neonatal deaths",
+          "World/North Africa/EGY/Mortality/Number of neonatal deaths",
+          "World/Europe/FRA/Mortality/Number of neonatal deaths",
+          "World/Europe/GBR/Mortality/Number of neonatal deaths",
+          "World/South Africa/GHA/Mortality/Number of neonatal deaths",
+          "World/Europe/GRC/Mortality/Number of neonatal deaths",
+          "World/Europe/HRV/Mortality/Number of neonatal deaths",
+          "World/Asia/IDN/Mortality/Number of neonatal deaths",
+          "World/Asia/IND/Mortality/Number of neonatal deaths",
+          "World/North Africa/ISR/Mortality/Number of neonatal deaths",
+          "World/Asia/KOR/Mortality/Number of neonatal deaths",
+          "World/North Africa/MAR/Mortality/Number of neonatal deaths",
+          "World/Latam/MEX/Mortality/Number of neonatal deaths",
+          "World/South Africa/MOZ/Mortality/Number of neonatal deaths",
+          "World/South Africa/NGA/Mortality/Number of neonatal deaths",
+          "World/Europe/NLD/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of neonatal deaths",
+          "World/Latam/PAN/Mortality/Number of neonatal deaths",
+          "World/Latam/PER/Mortality/Number of neonatal deaths",
+          "World/Asia/PHL/Mortality/Number of neonatal deaths",
+          "World/Europe/POL/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/QAT/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of neonatal deaths",
+          "World/South Africa/SEN/Mortality/Number of neonatal deaths",
+          "World/Asia/THA/Mortality/Number of neonatal deaths",
+          "World/North Africa/TUR/Mortality/Number of neonatal deaths",
+          "World/Pair/USA/Mortality/Number of neonatal deaths",
+          "World/Asia/VNM/Mortality/Number of neonatal deaths",
+          "World/South Africa/ZAF/Mortality/Number of neonatal deaths",
+          "World/Persian Gulf/ARE/Mortality/Number of under-five deaths",
+          "World/Latam/ARG/Mortality/Number of under-five deaths",
+          "World/Europe/AUT/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/AZE/Mortality/Number of under-five deaths",
+          "World/Asia/BGD/Mortality/Number of under-five deaths",
+          "World/Latam/BRA/Mortality/Number of under-five deaths",
+          "World/Latam/CHL/Mortality/Number of under-five deaths",
+          "World/Pair/CHN/Mortality/Number of under-five deaths",
+          "World/South Africa/CMR/Mortality/Number of under-five deaths",
+          "World/Latam/COL/Mortality/Number of under-five deaths",
+          "World/Latam/CRI/Mortality/Number of under-five deaths",
+          "World/North Africa/EGY/Mortality/Number of under-five deaths",
+          "World/Europe/FRA/Mortality/Number of under-five deaths",
+          "World/Europe/GBR/Mortality/Number of under-five deaths",
+          "World/South Africa/GHA/Mortality/Number of under-five deaths",
+          "World/Europe/GRC/Mortality/Number of under-five deaths",
+          "World/Europe/HRV/Mortality/Number of under-five deaths",
+          "World/Asia/IDN/Mortality/Number of under-five deaths",
+          "World/Asia/IND/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/IRQ/Mortality/Number of under-five deaths",
+          "World/North Africa/ISR/Mortality/Number of under-five deaths",
+          "World/Asia/KOR/Mortality/Number of under-five deaths",
+          "World/South Africa/LBR/Mortality/Number of under-five deaths",
+          "World/North Africa/MAR/Mortality/Number of under-five deaths",
+          "World/Latam/MEX/Mortality/Number of under-five deaths",
+          "World/South Africa/MOZ/Mortality/Number of under-five deaths",
+          "World/Europe/NLD/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/OMN/Mortality/Number of under-five deaths",
+          "World/Latam/PAN/Mortality/Number of under-five deaths",
+          "World/Latam/PER/Mortality/Number of under-five deaths",
+          "World/Asia/PHL/Mortality/Number of under-five deaths",
+          "World/Europe/POL/Mortality/Number of under-five deaths",
+          "World/Persian Gulf/SAU/Mortality/Number of under-five deaths",
+          "World/South Africa/SEN/Mortality/Number of under-five deaths",
+          "World/Europe/SWE/Mortality/Number of under-five deaths",
+          "World/Asia/THA/Mortality/Number of under-five deaths",
+          "World/North Africa/TUR/Mortality/Number of under-five deaths",
+          "World/Pair/USA/Mortality/Number of under-five deaths",
+          "World/Asia/VNM/Mortality/Number of under-five deaths",
+          "World/Latam/ARG/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/AUT/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/AZE/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/BGD/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/BRA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/CHL/Health/Nurses and midwives (per 1,000 people)",
+          "World/Pair/CHN/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/CMR/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/CRI/Health/Nurses and midwives (per 1,000 people)",
+          "World/North Africa/EGY/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/ESP/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/FRA/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/GHA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/HRV/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/IDN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/IND/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/KOR/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/LBR/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/MOZ/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/NLD/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/OMN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/PAN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/PER/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/POL/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/SAU/Health/Nurses and midwives (per 1,000 people)",
+          "World/Europe/SWE/Health/Nurses and midwives (per 1,000 people)",
+          "World/Asia/THA/Health/Nurses and midwives (per 1,000 people)",
+          "World/North Africa/TUR/Health/Nurses and midwives (per 1,000 people)",
+          "World/Pair/USA/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/VEN/Health/Nurses and midwives (per 1,000 people)",
+          "World/Persian Gulf/YEM/Health/Nurses and midwives (per 1,000 people)",
+          "World/South Africa/ZAF/Health/Nurses and midwives (per 1,000 people)",
+          "World/Latam/ARG/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/AUT/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/AZE/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/BGD/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/BRA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/CHL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Pair/CHN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/CMR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/COL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/CRI/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/DEU/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/DZA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/EGY/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/ESP/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/FRA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/GBR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/GHA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/GRC/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/HRV/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/IDN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/IND/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/North Africa/ISR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/KOR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/LBR/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/NGA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/NLD/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/PAN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/PER/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/PHL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/POL/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/SAU/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/South Africa/SEN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Europe/SWE/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Pair/USA/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/VEN/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Asia/VNM/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Persian Gulf/YEM/Economy/Out-of-pocket expenditure per capita (current US$)",
+          "World/Latam/ARG/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/AZE/Health/People practicing open defecation (% of population)",
+          "World/Asia/BGD/Health/People practicing open defecation (% of population)",
+          "World/Latam/BRA/Health/People practicing open defecation (% of population)",
+          "World/Latam/CHL/Health/People practicing open defecation (% of population)",
+          "World/Pair/CHN/Health/People practicing open defecation (% of population)",
+          "World/South Africa/CMR/Health/People practicing open defecation (% of population)",
+          "World/Latam/COL/Health/People practicing open defecation (% of population)",
+          "World/Latam/CRI/Health/People practicing open defecation (% of population)",
+          "World/North Africa/DZA/Health/People practicing open defecation (% of population)",
+          "World/North Africa/EGY/Health/People practicing open defecation (% of population)",
+          "World/South Africa/GHA/Health/People practicing open defecation (% of population)",
+          "World/Europe/GRC/Health/People practicing open defecation (% of population)",
+          "World/Asia/IDN/Health/People practicing open defecation (% of population)",
+          "World/Asia/IND/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/IRQ/Health/People practicing open defecation (% of population)",
+          "World/South Africa/LBR/Health/People practicing open defecation (% of population)",
+          "World/North Africa/MAR/Health/People practicing open defecation (% of population)",
+          "World/Latam/MEX/Health/People practicing open defecation (% of population)",
+          "World/South Africa/MOZ/Health/People practicing open defecation (% of population)",
+          "World/South Africa/NGA/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/OMN/Health/People practicing open defecation (% of population)",
+          "World/Latam/PAN/Health/People practicing open defecation (% of population)",
+          "World/Latam/PER/Health/People practicing open defecation (% of population)",
+          "World/Asia/PHL/Health/People practicing open defecation (% of population)",
+          "World/South Africa/SEN/Health/People practicing open defecation (% of population)",
+          "World/Asia/THA/Health/People practicing open defecation (% of population)",
+          "World/North Africa/TUR/Health/People practicing open defecation (% of population)",
+          "World/Latam/VEN/Health/People practicing open defecation (% of population)",
+          "World/Asia/VNM/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/YEM/Health/People practicing open defecation (% of population)",
+          "World/South Africa/ZAF/Health/People practicing open defecation (% of population)",
+          "World/Persian Gulf/ARE/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/ARG/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/BGD/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/BRA/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/CHL/Health/People using at least basic drinking water services (% of population)",
+          "World/Pair/CHN/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/CMR/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/COL/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/CRI/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/DZA/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/EGY/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/ESP/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/FRA/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/GHA/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/GRC/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/HRV/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/IDN/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/IND/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/IRQ/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/KOR/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/LBR/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/MAR/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/MEX/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/MOZ/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/NGA/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/OMN/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/PAN/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/PER/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/PHL/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/POL/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/QAT/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/SEN/Health/People using at least basic drinking water services (% of population)",
+          "World/Europe/SWE/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/THA/Health/People using at least basic drinking water services (% of population)",
+          "World/North Africa/TUR/Health/People using at least basic drinking water services (% of population)",
+          "World/Pair/USA/Health/People using at least basic drinking water services (% of population)",
+          "World/Latam/VEN/Health/People using at least basic drinking water services (% of population)",
+          "World/Asia/VNM/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using at least basic drinking water services (% of population)",
+          "World/South Africa/ZAF/Health/People using at least basic drinking water services (% of population)",
+          "World/Persian Gulf/ARE/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/ARG/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/AUT/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/BGD/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/BRA/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/CHL/Health/People using at least basic sanitation services (% of population)",
+          "World/Pair/CHN/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/CMR/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/COL/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/CRI/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/DEU/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/DZA/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/EGY/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/ESP/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/FRA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/GBR/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/GHA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/GRC/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/HRV/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/IDN/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/IND/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/IRQ/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/ISR/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/KOR/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/LBR/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/MAR/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/MEX/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/MOZ/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/NGA/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/NLD/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/OMN/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/PAN/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/PER/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/PHL/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/POL/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/SEN/Health/People using at least basic sanitation services (% of population)",
+          "World/Europe/SWE/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/THA/Health/People using at least basic sanitation services (% of population)",
+          "World/North Africa/TUR/Health/People using at least basic sanitation services (% of population)",
+          "World/Pair/USA/Health/People using at least basic sanitation services (% of population)",
+          "World/Latam/VEN/Health/People using at least basic sanitation services (% of population)",
+          "World/Asia/VNM/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using at least basic sanitation services (% of population)",
+          "World/South Africa/ZAF/Health/People using at least basic sanitation services (% of population)",
+          "World/Persian Gulf/ARE/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/ARG/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/AUT/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/AZE/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/BGD/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/BRA/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/CHL/Health/People using safely managed sanitation services (% of population)",
+          "World/Pair/CHN/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/COL/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/CRI/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/DEU/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/DZA/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/EGY/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/FRA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/GBR/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/GHA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/GRC/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/IND/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/ISR/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/KOR/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/MAR/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/MEX/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/NGA/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/NLD/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/PER/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/PHL/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/POL/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/QAT/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/SAU/Health/People using safely managed sanitation services (% of population)",
+          "World/South Africa/SEN/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/SWE/Health/People using safely managed sanitation services (% of population)",
+          "World/Asia/THA/Health/People using safely managed sanitation services (% of population)",
+          "World/North Africa/TUR/Health/People using safely managed sanitation services (% of population)",
+          "World/Pair/USA/Health/People using safely managed sanitation services (% of population)",
+          "World/Latam/VEN/Health/People using safely managed sanitation services (% of population)",
+          "World/Persian Gulf/YEM/Health/People using safely managed sanitation services (% of population)",
+          "World/Europe/AUT/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/AZE/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/CHL/Agriculture/Permanent cropland (% of land area)",
+          "World/Pair/CHN/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/CMR/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/COL/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/CRI/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/DZA/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/FRA/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/GRC/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/IDN/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/IND/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/MAR/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/MEX/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/MOZ/Agriculture/Permanent cropland (% of land area)",
+          "World/Europe/NLD/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/OMN/Agriculture/Permanent cropland (% of land area)",
+          "World/Latam/PER/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/PHL/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/SEN/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/THA/Agriculture/Permanent cropland (% of land area)",
+          "World/North Africa/TUR/Agriculture/Permanent cropland (% of land area)",
+          "World/Asia/VNM/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/YEM/Agriculture/Permanent cropland (% of land area)",
+          "World/South Africa/ZAF/Agriculture/Permanent cropland (% of land area)",
+          "World/Persian Gulf/ARE/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/AUT/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 0-14 (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/COL/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/GHA/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/HRV/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/IND/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/KOR/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/PER/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 0-14 (% of total population)",
+          "World/Europe/POL/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/SEN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/THA/Demography/Population ages 0-14 (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 0-14 (% of total population)",
+          "World/Pair/USA/Demography/Population ages 0-14 (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 0-14 (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 0-14 (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 0-14 (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 15-64 (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/COL/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/DEU/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/GBR/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/GHA/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/GRC/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/IND/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/ISR/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/PER/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 15-64 (% of total population)",
+          "World/Europe/POL/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/SEN/Demography/Population ages 15-64 (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 15-64 (% of total population)",
+          "World/Pair/USA/Demography/Population ages 15-64 (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 15-64 (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 15-64 (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 15-64 (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/ARG/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/AUT/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/BGD/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/BRA/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/CHL/Demography/Population ages 65 and above (% of total population)",
+          "World/Pair/CHN/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/CMR/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/COL/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/CRI/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/DEU/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/DZA/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/EGY/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/FRA/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/GBR/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/GRC/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/HRV/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/IDN/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/IND/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/ISR/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/KOR/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/MAR/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/MEX/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/MOZ/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/NGA/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/NLD/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/PAN/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/PER/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/PHL/Demography/Population ages 65 and above (% of total population)",
+          "World/Europe/POL/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/THA/Demography/Population ages 65 and above (% of total population)",
+          "World/North Africa/TUR/Demography/Population ages 65 and above (% of total population)",
+          "World/Pair/USA/Demography/Population ages 65 and above (% of total population)",
+          "World/Latam/VEN/Demography/Population ages 65 and above (% of total population)",
+          "World/Asia/VNM/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population ages 65 and above (% of total population)",
+          "World/South Africa/ZAF/Demography/Population ages 65 and above (% of total population)",
+          "World/Persian Gulf/ARE/Demography/Population in largest city",
+          "World/Latam/ARG/Demography/Population in largest city",
+          "World/Europe/AUT/Demography/Population in largest city",
+          "World/Persian Gulf/AZE/Demography/Population in largest city",
+          "World/Asia/BGD/Demography/Population in largest city",
+          "World/Latam/BRA/Demography/Population in largest city",
+          "World/Latam/CHL/Demography/Population in largest city",
+          "World/Pair/CHN/Demography/Population in largest city",
+          "World/South Africa/CMR/Demography/Population in largest city",
+          "World/Latam/COL/Demography/Population in largest city",
+          "World/Latam/CRI/Demography/Population in largest city",
+          "World/North Africa/DZA/Demography/Population in largest city",
+          "World/North Africa/EGY/Demography/Population in largest city",
+          "World/Europe/ESP/Demography/Population in largest city",
+          "World/Europe/FRA/Demography/Population in largest city",
+          "World/Europe/GBR/Demography/Population in largest city",
+          "World/South Africa/GHA/Demography/Population in largest city",
+          "World/Europe/GRC/Demography/Population in largest city",
+          "World/Europe/HRV/Demography/Population in largest city",
+          "World/Asia/IDN/Demography/Population in largest city",
+          "World/Asia/IND/Demography/Population in largest city",
+          "World/North Africa/ISR/Demography/Population in largest city",
+          "World/North Africa/MAR/Demography/Population in largest city",
+          "World/Latam/MEX/Demography/Population in largest city",
+          "World/South Africa/MOZ/Demography/Population in largest city",
+          "World/South Africa/NGA/Demography/Population in largest city",
+          "World/Europe/NLD/Demography/Population in largest city",
+          "World/Persian Gulf/OMN/Demography/Population in largest city",
+          "World/Latam/PAN/Demography/Population in largest city",
+          "World/Latam/PER/Demography/Population in largest city",
+          "World/Asia/PHL/Demography/Population in largest city",
+          "World/Europe/POL/Demography/Population in largest city",
+          "World/Persian Gulf/QAT/Demography/Population in largest city",
+          "World/Persian Gulf/SAU/Demography/Population in largest city",
+          "World/South Africa/SEN/Demography/Population in largest city",
+          "World/Asia/THA/Demography/Population in largest city",
+          "World/North Africa/TUR/Demography/Population in largest city",
+          "World/Pair/USA/Demography/Population in largest city",
+          "World/Latam/VEN/Demography/Population in largest city",
+          "World/Asia/VNM/Demography/Population in largest city",
+          "World/Persian Gulf/YEM/Demography/Population in largest city",
+          "World/South Africa/ZAF/Demography/Population in largest city",
+          "World/Persian Gulf/ARE/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/ARG/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/AUT/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/AZE/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/BGD/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/CHL/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/CMR/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/COL/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/CRI/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/DZA/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/EGY/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/FRA/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/GBR/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/IDN/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/IND/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/ISR/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/KOR/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/MAR/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/MEX/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/MOZ/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/NGA/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/NLD/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/PAN/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/PER/Demography/Population in the largest city (% of urban population)",
+          "World/Europe/POL/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/QAT/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/SAU/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/SEN/Demography/Population in the largest city (% of urban population)",
+          "World/North Africa/TUR/Demography/Population in the largest city (% of urban population)",
+          "World/Pair/USA/Demography/Population in the largest city (% of urban population)",
+          "World/Latam/VEN/Demography/Population in the largest city (% of urban population)",
+          "World/Asia/VNM/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/YEM/Demography/Population in the largest city (% of urban population)",
+          "World/South Africa/ZAF/Demography/Population in the largest city (% of urban population)",
+          "World/Persian Gulf/ARE/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/ARG/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/AUT/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/AZE/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/BGD/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/BRA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/CHL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Pair/CHN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/CMR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/COL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/CRI/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/DEU/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/DZA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/EGY/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/ESP/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/FRA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/GBR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/GHA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/GRC/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/IDN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/IND/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/ISR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/KOR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/MAR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/MEX/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/MOZ/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/NGA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/NLD/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/OMN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/PAN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/PER/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/PHL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Europe/POL/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/SAU/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/SEN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/THA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/North Africa/TUR/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Pair/USA/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Latam/VEN/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Asia/VNM/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/YEM/Demography/Population in urban agglomerations of more than 1 million",
+          "World/South Africa/ZAF/Demography/Population in urban agglomerations of more than 1 million",
+          "World/Persian Gulf/ARE/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/ARG/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/AUT/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/BGD/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/BRA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Pair/CHN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/CMR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/COL/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/CRI/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/DZA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/GBR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/GHA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/GRC/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/IDN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/IND/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/KOR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/MAR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/MEX/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/MOZ/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/NGA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/PAN/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Latam/PER/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Europe/POL/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/THA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/North Africa/TUR/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Pair/USA/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Asia/VNM/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/South Africa/ZAF/Demography/Population in urban agglomerations of more than 1 million (% of total population)",
+          "World/Persian Gulf/AZE/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/BGD/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/CHL/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Pair/CHN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/CMR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/EGY/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Europe/GBR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/GHA/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/IDN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/IND/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/LBR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/MAR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/MOZ/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/PAN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Latam/PER/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/SEN/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Europe/SWE/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/THA/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/North Africa/TUR/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Asia/VNM/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Persian Gulf/YEM/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/South Africa/ZAF/Economy/Poverty headcount ratio at national poverty lines (% of population)",
+          "World/Persian Gulf/ARE/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/AZE/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/BGD/Health/Pregnant women receiving prenatal care (%)",
+          "World/Pair/CHN/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/CMR/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/COL/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/CRI/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/DZA/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/EGY/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/IDN/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/IND/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/LBR/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/MAR/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/MOZ/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/NGA/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/PER/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/PHL/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/QAT/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/SAU/Health/Pregnant women receiving prenatal care (%)",
+          "World/South Africa/SEN/Health/Pregnant women receiving prenatal care (%)",
+          "World/North Africa/TUR/Health/Pregnant women receiving prenatal care (%)",
+          "World/Latam/VEN/Health/Pregnant women receiving prenatal care (%)",
+          "World/Asia/VNM/Health/Pregnant women receiving prenatal care (%)",
+          "World/Persian Gulf/YEM/Health/Pregnant women receiving prenatal care (%)",
+          "World/Europe/AUT/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/BGD/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/BRA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Pair/CHN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/COL/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/CRI/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/DEU/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/ESP/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/GBR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/GRC/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/IND/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/MEX/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Europe/NLD/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/PAN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/PER/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/PHL/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Pair/USA/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Latam/VEN/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Asia/VNM/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among children (% of children ages 6-59 months)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/BGD/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/BRA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/CHL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Pair/CHN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/COL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/CRI/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/ESP/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/GBR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/KOR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/MEX/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Europe/NLD/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/PAN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/PER/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/PHL/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/THA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Pair/USA/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Latam/VEN/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Asia/VNM/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "World/Persian Gulf/ARE/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/ARG/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/BGD/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/BRA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/CHL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Pair/CHN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/COL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/CRI/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Europe/GBR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Europe/HRV/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/IND/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/KOR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/MEX/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/PAN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/PER/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/PHL/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Pair/USA/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/VEN/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Asia/VNM/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among pregnant women (%)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among pregnant women (%)",
+          "World/Latam/ARG/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/AZE/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/BGD/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/BRA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/CHL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Pair/CHN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/CMR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/COL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/CRI/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/DZA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/EGY/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/ESP/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/FRA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/GBR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/GHA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/GRC/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/ISR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/KOR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/LBR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/North Africa/MAR/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/MEX/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/MOZ/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/NGA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Europe/NLD/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/OMN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/PAN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/PER/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/PHL/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/QAT/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/SAU/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/SEN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/THA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Pair/USA/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/VEN/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Asia/VNM/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Persian Gulf/YEM/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/South Africa/ZAF/Health/Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "World/Latam/ARG/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/AUT/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/AZE/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/BGD/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/BRA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/CHL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Pair/CHN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/CMR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/COL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/CRI/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/DEU/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/DZA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/ESP/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/FRA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/GBR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/GHA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/GRC/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/HRV/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/IDN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/IND/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/ISR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/KOR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/LBR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/MAR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/MEX/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/MOZ/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/NGA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/NLD/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/OMN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/PAN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/PER/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/PHL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/POL/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/QAT/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/SEN/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Europe/SWE/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/THA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/North Africa/TUR/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Pair/USA/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Asia/VNM/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Persian Gulf/YEM/Health/Prevalence of current tobacco use (% of adults)",
+          "World/South Africa/ZAF/Health/Prevalence of current tobacco use (% of adults)",
+          "World/Latam/ARG/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/AZE/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/BGD/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/CHL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Pair/CHN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/COL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/CRI/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/DZA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/EGY/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/GRC/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/IDN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/IND/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/KOR/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/North Africa/MAR/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/MEX/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/South Africa/NGA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/NLD/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/OMN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/PAN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/PER/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/PHL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Europe/POL/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/QAT/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/SAU/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/THA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Pair/USA/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Latam/VEN/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Asia/VNM/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/YEM/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/South Africa/ZAF/Health/Prevalence of overweight (modeled estimate, % of children under 5)",
+          "World/Persian Gulf/AZE/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/BGD/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/BRA/Health/Prevalence of undernourishment (% of population)",
+          "World/Pair/CHN/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/CMR/Health/Prevalence of undernourishment (% of population)",
+          "World/North Africa/DZA/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/GHA/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/IDN/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/IRQ/Health/Prevalence of undernourishment (% of population)",
+          "World/North Africa/MAR/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/MEX/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/MOZ/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/NGA/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/OMN/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/PAN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/PHL/Health/Prevalence of undernourishment (% of population)",
+          "World/South Africa/SEN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/THA/Health/Prevalence of undernourishment (% of population)",
+          "World/Latam/VEN/Health/Prevalence of undernourishment (% of population)",
+          "World/Asia/VNM/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/YEM/Health/Prevalence of undernourishment (% of population)",
+          "World/Persian Gulf/ARE/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/AZE/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/BGD/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/BRA/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Pair/CHN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/South Africa/CMR/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/CRI/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Europe/GBR/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/IDN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/South Africa/NGA/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Europe/NLD/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/OMN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/PAN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/QAT/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/SAU/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Latam/VEN/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Asia/VNM/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/YEM/Economy/Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "World/Persian Gulf/AZE/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/BGD/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/BRA/Economy/Primary income payments (BoP, current US$)",
+          "World/Pair/CHN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/COL/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/CRI/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/ESP/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/GRC/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/HRV/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/IDN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/IND/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/KOR/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/MEX/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/NLD/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/OMN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/PAN/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/PER/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/PHL/Economy/Primary income payments (BoP, current US$)",
+          "World/Europe/POL/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/SAU/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/THA/Economy/Primary income payments (BoP, current US$)",
+          "World/North Africa/TUR/Economy/Primary income payments (BoP, current US$)",
+          "World/Pair/USA/Economy/Primary income payments (BoP, current US$)",
+          "World/Latam/VEN/Economy/Primary income payments (BoP, current US$)",
+          "World/Asia/VNM/Economy/Primary income payments (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Primary income payments (BoP, current US$)",
+          "World/Persian Gulf/AZE/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/CHL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Pair/CHN/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/COL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/ESP/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/GRC/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/HRV/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/IND/Economy/Primary income receipts (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/KOR/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/LBR/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/MEX/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/NLD/Economy/Primary income receipts (BoP, current US$)",
+          "World/Latam/PER/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/PHL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Europe/POL/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/QAT/Economy/Primary income receipts (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/THA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Pair/USA/Economy/Primary income receipts (BoP, current US$)",
+          "World/Asia/VNM/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Primary income receipts (BoP, current US$)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/AZE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/BRA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/CRI/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/MEX/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Latam/VEN/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/AZE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/BRA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/CRI/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/MEX/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/MOZ/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Europe/SWE/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Pair/USA/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among children ages 5-9 years (per 1,000)",
+          "World/Persian Gulf/ARE/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/ARG/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/AUT/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/BGD/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/CHL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Pair/CHN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/CMR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/COL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/DEU/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/DZA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/EGY/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/ESP/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/FRA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/GBR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/GHA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/GRC/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/HRV/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/IDN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/IND/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/ISR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/KOR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/LBR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/MAR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/NGA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/NLD/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/OMN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/PAN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/PER/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/PHL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Europe/POL/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/QAT/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/SAU/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/South Africa/SEN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/THA/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/North Africa/TUR/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/VEN/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Asia/VNM/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Persian Gulf/YEM/Mortality/Probability of dying among youth ages 20-24 years (per 1,000)",
+          "World/Latam/ARG/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/AZE/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/CHL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Pair/CHN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/COL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/CRI/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/North Africa/EGY/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Europe/ESP/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/GHA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Europe/GRC/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/IND/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/MEX/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/MOZ/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/NGA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/PAN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Latam/PER/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/PHL/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/SAU/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/South Africa/SEN/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/THA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/North Africa/TUR/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Pair/USA/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Asia/VNM/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/YEM/Economy/Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "World/Persian Gulf/AZE/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/BGD/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/BRA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/CHL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Pair/CHN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/CMR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/DZA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/EGY/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Europe/GBR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/GHA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/IND/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/ISR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/KOR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/MEX/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/MOZ/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Latam/PAN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/PHL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Europe/POL/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/South Africa/SEN/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/THA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/North Africa/TUR/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Pair/USA/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Asia/VNM/Equality/Proportion of seats held by women in national parliaments (%)",
+          "World/Persian Gulf/ARE/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/ARG/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/AUT/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/BGD/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/BRA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/CHL/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/CRI/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/DEU/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/DZA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/ESP/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/FRA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/GBR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/GHA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/GRC/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/IND/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/ISR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/KOR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/MEX/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/MOZ/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/NGA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Europe/NLD/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/OMN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Latam/PAN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/QAT/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/SAU/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/SEN/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/North Africa/TUR/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Pair/USA/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Asia/VNM/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/YEM/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/South Africa/ZAF/Equality/Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "World/Persian Gulf/ARE/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/ARG/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/AUT/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/BGD/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/BRA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/CHL/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Pair/CHN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/CRI/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/DEU/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/DZA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/ESP/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/FRA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/GBR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/GRC/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/ISR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/KOR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/MEX/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/MOZ/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/NLD/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/OMN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/PAN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Europe/POL/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/QAT/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/SAU/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/SEN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/North Africa/TUR/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Pair/USA/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Latam/VEN/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Asia/VNM/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/South Africa/ZAF/Equality/Ratio of female to male labor force participation rate (%) (national estimate)",
+          "World/Persian Gulf/ARE/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/ARG/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/BGD/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Pair/CHN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/CRI/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/DEU/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/EGY/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/GBR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/GHA/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/GRC/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/IDN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/IND/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/KOR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/MAR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/MEX/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/MOZ/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/NLD/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Latam/PAN/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/PHL/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/POL/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Persian Gulf/SAU/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Europe/SWE/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/North Africa/TUR/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Pair/USA/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Asia/VNM/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/South Africa/ZAF/Environment/Renewable energy consumption (% of total final energy consumption)",
+          "World/Persian Gulf/ARE/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/ARG/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/AUT/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/AZE/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/BGD/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/BRA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/CHL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Pair/CHN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/CMR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/COL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/CRI/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/DZA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/EGY/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/ESP/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/FRA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/GBR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/GHA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/IDN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/IND/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/ISR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/KOR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/LBR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/MAR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/MEX/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/MOZ/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/NGA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/NLD/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/OMN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/PAN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/PER/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/PHL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/POL/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/QAT/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/SAU/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/SEN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/THA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/North Africa/TUR/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Pair/USA/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Latam/VEN/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Asia/VNM/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Persian Gulf/YEM/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/South Africa/ZAF/Environment/Renewable internal freshwater resources per capita (cubic meters)",
+          "World/Europe/AUT/R&D/Researchers in R&D (per million people)",
+          "World/Latam/BRA/R&D/Researchers in R&D (per million people)",
+          "World/Pair/CHN/R&D/Researchers in R&D (per million people)",
+          "World/Europe/DEU/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/DZA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/ESP/R&D/Researchers in R&D (per million people)",
+          "World/Europe/FRA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/GBR/R&D/Researchers in R&D (per million people)",
+          "World/South Africa/GHA/R&D/Researchers in R&D (per million people)",
+          "World/Europe/GRC/R&D/Researchers in R&D (per million people)",
+          "World/Asia/IND/R&D/Researchers in R&D (per million people)",
+          "World/Asia/KOR/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/MAR/R&D/Researchers in R&D (per million people)",
+          "World/South Africa/MOZ/R&D/Researchers in R&D (per million people)",
+          "World/Europe/NLD/R&D/Researchers in R&D (per million people)",
+          "World/Persian Gulf/OMN/R&D/Researchers in R&D (per million people)",
+          "World/Asia/PHL/R&D/Researchers in R&D (per million people)",
+          "World/Europe/POL/R&D/Researchers in R&D (per million people)",
+          "World/Asia/THA/R&D/Researchers in R&D (per million people)",
+          "World/North Africa/TUR/R&D/Researchers in R&D (per million people)",
+          "World/Pair/USA/R&D/Researchers in R&D (per million people)",
+          "World/Latam/VEN/R&D/Researchers in R&D (per million people)",
+          "World/Persian Gulf/AZE/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/BGD/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/BRA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/CHL/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Pair/CHN/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/CRI/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/North Africa/EGY/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Europe/ESP/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/South Africa/GHA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/IND/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/KOR/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/North Africa/MAR/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/MEX/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/PAN/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Latam/PER/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Europe/POL/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/THA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Pair/USA/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Asia/VNM/Health/Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "World/Persian Gulf/AZE/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/BGD/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/BRA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/CHL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Pair/CHN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/CMR/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/COL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/CRI/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/DZA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/EGY/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/GHA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/IDN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/IND/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/North Africa/MAR/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/MEX/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/MOZ/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/PAN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Latam/PER/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/PHL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Europe/POL/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/South Africa/SEN/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/THA/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Asia/VNM/Health/Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "World/Persian Gulf/ARE/Demography/Rural population",
+          "World/Latam/ARG/Demography/Rural population",
+          "World/Europe/AUT/Demography/Rural population",
+          "World/Persian Gulf/AZE/Demography/Rural population",
+          "World/Asia/BGD/Demography/Rural population",
+          "World/Latam/BRA/Demography/Rural population",
+          "World/Pair/CHN/Demography/Rural population",
+          "World/South Africa/CMR/Demography/Rural population",
+          "World/Latam/COL/Demography/Rural population",
+          "World/Latam/CRI/Demography/Rural population",
+          "World/North Africa/DZA/Demography/Rural population",
+          "World/North Africa/EGY/Demography/Rural population",
+          "World/Europe/FRA/Demography/Rural population",
+          "World/Europe/GBR/Demography/Rural population",
+          "World/South Africa/GHA/Demography/Rural population",
+          "World/Europe/GRC/Demography/Rural population",
+          "World/Europe/HRV/Demography/Rural population",
+          "World/Asia/IDN/Demography/Rural population",
+          "World/Asia/IND/Demography/Rural population",
+          "World/North Africa/ISR/Demography/Rural population",
+          "World/South Africa/LBR/Demography/Rural population",
+          "World/North Africa/MAR/Demography/Rural population",
+          "World/South Africa/MOZ/Demography/Rural population",
+          "World/South Africa/NGA/Demography/Rural population",
+          "World/Europe/NLD/Demography/Rural population",
+          "World/Persian Gulf/OMN/Demography/Rural population",
+          "World/Latam/PAN/Demography/Rural population",
+          "World/Asia/PHL/Demography/Rural population",
+          "World/Europe/POL/Demography/Rural population",
+          "World/Persian Gulf/QAT/Demography/Rural population",
+          "World/Persian Gulf/SAU/Demography/Rural population",
+          "World/South Africa/SEN/Demography/Rural population",
+          "World/North Africa/TUR/Demography/Rural population",
+          "World/Pair/USA/Demography/Rural population",
+          "World/Asia/VNM/Demography/Rural population",
+          "World/Persian Gulf/YEM/Demography/Rural population",
+          "World/Persian Gulf/ARE/Demography/Rural population (% of total population)",
+          "World/Latam/ARG/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Rural population (% of total population)",
+          "World/Asia/BGD/Demography/Rural population (% of total population)",
+          "World/Latam/BRA/Demography/Rural population (% of total population)",
+          "World/Latam/CHL/Demography/Rural population (% of total population)",
+          "World/Pair/CHN/Demography/Rural population (% of total population)",
+          "World/South Africa/CMR/Demography/Rural population (% of total population)",
+          "World/Latam/COL/Demography/Rural population (% of total population)",
+          "World/Latam/CRI/Demography/Rural population (% of total population)",
+          "World/Europe/DEU/Demography/Rural population (% of total population)",
+          "World/North Africa/DZA/Demography/Rural population (% of total population)",
+          "World/Europe/ESP/Demography/Rural population (% of total population)",
+          "World/Europe/FRA/Demography/Rural population (% of total population)",
+          "World/Europe/GBR/Demography/Rural population (% of total population)",
+          "World/South Africa/GHA/Demography/Rural population (% of total population)",
+          "World/Europe/GRC/Demography/Rural population (% of total population)",
+          "World/Europe/HRV/Demography/Rural population (% of total population)",
+          "World/Asia/IDN/Demography/Rural population (% of total population)",
+          "World/Asia/IND/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/IRQ/Demography/Rural population (% of total population)",
+          "World/North Africa/ISR/Demography/Rural population (% of total population)",
+          "World/South Africa/LBR/Demography/Rural population (% of total population)",
+          "World/North Africa/MAR/Demography/Rural population (% of total population)",
+          "World/Latam/MEX/Demography/Rural population (% of total population)",
+          "World/South Africa/MOZ/Demography/Rural population (% of total population)",
+          "World/South Africa/NGA/Demography/Rural population (% of total population)",
+          "World/Europe/NLD/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Rural population (% of total population)",
+          "World/Latam/PAN/Demography/Rural population (% of total population)",
+          "World/Latam/PER/Demography/Rural population (% of total population)",
+          "World/Europe/POL/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/SAU/Demography/Rural population (% of total population)",
+          "World/South Africa/SEN/Demography/Rural population (% of total population)",
+          "World/Asia/THA/Demography/Rural population (% of total population)",
+          "World/North Africa/TUR/Demography/Rural population (% of total population)",
+          "World/Pair/USA/Demography/Rural population (% of total population)",
+          "World/Latam/VEN/Demography/Rural population (% of total population)",
+          "World/Asia/VNM/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/YEM/Demography/Rural population (% of total population)",
+          "World/South Africa/ZAF/Demography/Rural population (% of total population)",
+          "World/Persian Gulf/ARE/industry/Scientific and technical journal articles",
+          "World/Latam/ARG/industry/Scientific and technical journal articles",
+          "World/Europe/AUT/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/AZE/industry/Scientific and technical journal articles",
+          "World/Asia/BGD/industry/Scientific and technical journal articles",
+          "World/Latam/BRA/industry/Scientific and technical journal articles",
+          "World/Latam/CHL/industry/Scientific and technical journal articles",
+          "World/Pair/CHN/industry/Scientific and technical journal articles",
+          "World/South Africa/CMR/industry/Scientific and technical journal articles",
+          "World/Latam/COL/industry/Scientific and technical journal articles",
+          "World/Latam/CRI/industry/Scientific and technical journal articles",
+          "World/North Africa/DZA/industry/Scientific and technical journal articles",
+          "World/North Africa/EGY/industry/Scientific and technical journal articles",
+          "World/Europe/ESP/industry/Scientific and technical journal articles",
+          "World/Europe/FRA/industry/Scientific and technical journal articles",
+          "World/Europe/GBR/industry/Scientific and technical journal articles",
+          "World/South Africa/GHA/industry/Scientific and technical journal articles",
+          "World/Europe/GRC/industry/Scientific and technical journal articles",
+          "World/Europe/HRV/industry/Scientific and technical journal articles",
+          "World/Asia/IDN/industry/Scientific and technical journal articles",
+          "World/Asia/IND/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/IRQ/industry/Scientific and technical journal articles",
+          "World/North Africa/ISR/industry/Scientific and technical journal articles",
+          "World/Asia/KOR/industry/Scientific and technical journal articles",
+          "World/South Africa/LBR/industry/Scientific and technical journal articles",
+          "World/North Africa/MAR/industry/Scientific and technical journal articles",
+          "World/Latam/MEX/industry/Scientific and technical journal articles",
+          "World/South Africa/MOZ/industry/Scientific and technical journal articles",
+          "World/South Africa/NGA/industry/Scientific and technical journal articles",
+          "World/Europe/NLD/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/OMN/industry/Scientific and technical journal articles",
+          "World/Latam/PAN/industry/Scientific and technical journal articles",
+          "World/Latam/PER/industry/Scientific and technical journal articles",
+          "World/Asia/PHL/industry/Scientific and technical journal articles",
+          "World/Europe/POL/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/QAT/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/SAU/industry/Scientific and technical journal articles",
+          "World/South Africa/SEN/industry/Scientific and technical journal articles",
+          "World/Europe/SWE/industry/Scientific and technical journal articles",
+          "World/Asia/THA/industry/Scientific and technical journal articles",
+          "World/North Africa/TUR/industry/Scientific and technical journal articles",
+          "World/Pair/USA/industry/Scientific and technical journal articles",
+          "World/Latam/VEN/industry/Scientific and technical journal articles",
+          "World/Asia/VNM/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/YEM/industry/Scientific and technical journal articles",
+          "World/South Africa/ZAF/industry/Scientific and technical journal articles",
+          "World/Persian Gulf/AZE/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/BGD/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/CHL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/CMR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/COL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/CRI/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/DEU/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/DZA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/EGY/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/GHA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/IDN/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/IND/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/ISR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/KOR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/North Africa/MAR/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/MEX/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/NGA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/NLD/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Latam/PER/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/PHL/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/SEN/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/SWE/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/THA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Pair/USA/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Asia/VNM/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Persian Gulf/YEM/Economy/Secondary income receipts (BoP, current US$)",
+          "World/South Africa/ZAF/Economy/Secondary income receipts (BoP, current US$)",
+          "World/Europe/AUT/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Exports/Service exports (BoP, current US$)",
+          "World/Asia/BGD/Exports/Service exports (BoP, current US$)",
+          "World/Latam/BRA/Exports/Service exports (BoP, current US$)",
+          "World/Latam/CHL/Exports/Service exports (BoP, current US$)",
+          "World/Pair/CHN/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/CMR/Exports/Service exports (BoP, current US$)",
+          "World/Latam/COL/Exports/Service exports (BoP, current US$)",
+          "World/Latam/CRI/Exports/Service exports (BoP, current US$)",
+          "World/Europe/DEU/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/DZA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/ESP/Exports/Service exports (BoP, current US$)",
+          "World/Europe/FRA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/GBR/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/GHA/Exports/Service exports (BoP, current US$)",
+          "World/Europe/GRC/Exports/Service exports (BoP, current US$)",
+          "World/Europe/HRV/Exports/Service exports (BoP, current US$)",
+          "World/Asia/IDN/Exports/Service exports (BoP, current US$)",
+          "World/Asia/IND/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/ISR/Exports/Service exports (BoP, current US$)",
+          "World/Asia/KOR/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/MAR/Exports/Service exports (BoP, current US$)",
+          "World/Europe/NLD/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Exports/Service exports (BoP, current US$)",
+          "World/Latam/PAN/Exports/Service exports (BoP, current US$)",
+          "World/Latam/PER/Exports/Service exports (BoP, current US$)",
+          "World/Asia/PHL/Exports/Service exports (BoP, current US$)",
+          "World/Europe/POL/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Exports/Service exports (BoP, current US$)",
+          "World/Persian Gulf/SAU/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/SEN/Exports/Service exports (BoP, current US$)",
+          "World/Europe/SWE/Exports/Service exports (BoP, current US$)",
+          "World/Asia/THA/Exports/Service exports (BoP, current US$)",
+          "World/North Africa/TUR/Exports/Service exports (BoP, current US$)",
+          "World/Pair/USA/Exports/Service exports (BoP, current US$)",
+          "World/Asia/VNM/Exports/Service exports (BoP, current US$)",
+          "World/South Africa/ZAF/Exports/Service exports (BoP, current US$)",
+          "World/Latam/ARG/Imports/Service imports (BoP, current US$)",
+          "World/Europe/AUT/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/AZE/Imports/Service imports (BoP, current US$)",
+          "World/Asia/BGD/Imports/Service imports (BoP, current US$)",
+          "World/Latam/BRA/Imports/Service imports (BoP, current US$)",
+          "World/Latam/CHL/Imports/Service imports (BoP, current US$)",
+          "World/Pair/CHN/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/CMR/Imports/Service imports (BoP, current US$)",
+          "World/Latam/COL/Imports/Service imports (BoP, current US$)",
+          "World/Latam/CRI/Imports/Service imports (BoP, current US$)",
+          "World/Europe/DEU/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/DZA/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/EGY/Imports/Service imports (BoP, current US$)",
+          "World/Europe/ESP/Imports/Service imports (BoP, current US$)",
+          "World/Europe/FRA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/GBR/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/GHA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/HRV/Imports/Service imports (BoP, current US$)",
+          "World/Asia/IDN/Imports/Service imports (BoP, current US$)",
+          "World/Asia/IND/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/ISR/Imports/Service imports (BoP, current US$)",
+          "World/Asia/KOR/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/MAR/Imports/Service imports (BoP, current US$)",
+          "World/Latam/MEX/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/MOZ/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/NGA/Imports/Service imports (BoP, current US$)",
+          "World/Europe/NLD/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/OMN/Imports/Service imports (BoP, current US$)",
+          "World/Latam/PAN/Imports/Service imports (BoP, current US$)",
+          "World/Latam/PER/Imports/Service imports (BoP, current US$)",
+          "World/Asia/PHL/Imports/Service imports (BoP, current US$)",
+          "World/Europe/POL/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/QAT/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/SEN/Imports/Service imports (BoP, current US$)",
+          "World/Europe/SWE/Imports/Service imports (BoP, current US$)",
+          "World/Asia/THA/Imports/Service imports (BoP, current US$)",
+          "World/North Africa/TUR/Imports/Service imports (BoP, current US$)",
+          "World/Pair/USA/Imports/Service imports (BoP, current US$)",
+          "World/Asia/VNM/Imports/Service imports (BoP, current US$)",
+          "World/South Africa/ZAF/Imports/Service imports (BoP, current US$)",
+          "World/Persian Gulf/ARE/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Europe/AUT/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/BGD/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/BRA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/CHL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Pair/CHN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/COL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/DZA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/EGY/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/IDN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/IND/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/MAR/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/MEX/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/South Africa/NGA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/PAN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/PHL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Europe/POL/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/QAT/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/SAU/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/South Africa/SEN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/North Africa/TUR/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Pair/USA/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/VEN/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Asia/VNM/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Persian Gulf/YEM/Mortality/Suicide mortality rate (per 100,000 population)",
+          "World/Latam/ARG/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/AUT/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/AZE/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/BGD/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/BRA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/CHL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/CMR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/COL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/CRI/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/DEU/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/DZA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/ESP/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/FRA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/GBR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/GHA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/GRC/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/HRV/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/IND/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/ISR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/KOR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/MAR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/MEX/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/MOZ/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/NGA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/NLD/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/PAN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/PER/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/POL/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/SAU/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/SEN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Europe/SWE/Taxes/Taxes less subsidies on products (current US$)",
+          "World/North Africa/TUR/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Pair/USA/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Latam/VEN/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Asia/VNM/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/YEM/Taxes/Taxes less subsidies on products (current US$)",
+          "World/South Africa/ZAF/Taxes/Taxes less subsidies on products (current US$)",
+          "World/Persian Gulf/AZE/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/CHL/Economy/Taxes on international trade (% of revenue)",
+          "World/Pair/CHN/Economy/Taxes on international trade (% of revenue)",
+          "World/South Africa/CMR/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/COL/Economy/Taxes on international trade (% of revenue)",
+          "World/North Africa/EGY/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/ESP/Economy/Taxes on international trade (% of revenue)",
+          "World/South Africa/GHA/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/HRV/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/IND/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/KOR/Economy/Taxes on international trade (% of revenue)",
+          "World/North Africa/MAR/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/MEX/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/PAN/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/PER/Economy/Taxes on international trade (% of revenue)",
+          "World/Europe/POL/Economy/Taxes on international trade (% of revenue)",
+          "World/Asia/THA/Economy/Taxes on international trade (% of revenue)",
+          "World/Latam/ARG/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/AUT/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Asia/BGD/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/CHL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Pair/CHN/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/COL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/CRI/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/DEU/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/DZA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/ESP/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/FRA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/GBR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/GRC/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/ISR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Asia/KOR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/North Africa/MAR/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/MEX/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Persian Gulf/OMN/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Latam/PER/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/POL/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Europe/SWE/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Pair/USA/Industry/Textiles and clothing (% of value added in manufacturing)",
+          "World/Persian Gulf/ARE/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/AZE/Industry/Time required to start a business (days)",
+          "World/Latam/CHL/Industry/Time required to start a business (days)",
+          "World/Pair/CHN/Industry/Time required to start a business (days)",
+          "World/South Africa/CMR/Industry/Time required to start a business (days)",
+          "World/Latam/COL/Industry/Time required to start a business (days)",
+          "World/Latam/CRI/Industry/Time required to start a business (days)",
+          "World/Europe/DEU/Industry/Time required to start a business (days)",
+          "World/North Africa/DZA/Industry/Time required to start a business (days)",
+          "World/Europe/ESP/Industry/Time required to start a business (days)",
+          "World/Europe/FRA/Industry/Time required to start a business (days)",
+          "World/Europe/GBR/Industry/Time required to start a business (days)",
+          "World/Asia/IND/Industry/Time required to start a business (days)",
+          "World/Asia/KOR/Industry/Time required to start a business (days)",
+          "World/South Africa/LBR/Industry/Time required to start a business (days)",
+          "World/North Africa/MAR/Industry/Time required to start a business (days)",
+          "World/South Africa/MOZ/Industry/Time required to start a business (days)",
+          "World/Europe/NLD/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/OMN/Industry/Time required to start a business (days)",
+          "World/Latam/PAN/Industry/Time required to start a business (days)",
+          "World/Latam/PER/Industry/Time required to start a business (days)",
+          "World/Europe/POL/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/SAU/Industry/Time required to start a business (days)",
+          "World/South Africa/SEN/Industry/Time required to start a business (days)",
+          "World/Asia/THA/Industry/Time required to start a business (days)",
+          "World/Latam/VEN/Industry/Time required to start a business (days)",
+          "World/Asia/VNM/Industry/Time required to start a business (days)",
+          "World/Persian Gulf/YEM/Industry/Time required to start a business (days)",
+          "World/South Africa/ZAF/Industry/Time required to start a business (days)",
+          "World/Latam/ARG/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Persian Gulf/AZE/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/BGD/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/CHL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/COL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/CRI/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/North Africa/EGY/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/GHA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/GRC/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/HRV/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/IDN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/IND/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/LBR/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/MEX/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/MOZ/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/NGA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/PAN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/PER/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/PHL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Europe/POL/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/SEN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/THA/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/North Africa/TUR/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/VEN/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Asia/VNM/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Persian Gulf/YEM/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/South Africa/ZAF/R&D/Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "World/Latam/ARG/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/BGD/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/CHL/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/CMR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/COL/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/CRI/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/EGY/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/GHA/R&D/Time to obtain an electrical connection (days)",
+          "World/Europe/GRC/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/IDN/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/IND/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/MAR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/MEX/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/MOZ/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/NGA/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/PAN/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/PER/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/PHL/R&D/Time to obtain an electrical connection (days)",
+          "World/Europe/POL/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/SEN/R&D/Time to obtain an electrical connection (days)",
+          "World/North Africa/TUR/R&D/Time to obtain an electrical connection (days)",
+          "World/Latam/VEN/R&D/Time to obtain an electrical connection (days)",
+          "World/Asia/VNM/R&D/Time to obtain an electrical connection (days)",
+          "World/South Africa/ZAF/R&D/Time to obtain an electrical connection (days)",
+          "World/Persian Gulf/ARE/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/ARG/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/AUT/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/AZE/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/BGD/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/BRA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/CHL/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Pair/CHN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/CRI/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/EGY/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/GBR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/GHA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/HRV/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/IND/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/ISR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/KOR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/North Africa/MAR/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/MEX/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/MOZ/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/South Africa/NGA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/NLD/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/OMN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/PAN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Europe/POL/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/QAT/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/THA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Pair/USA/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Latam/VEN/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Asia/VNM/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/YEM/A&D/Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "World/Persian Gulf/ARE/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/AZE/Industry/Total fisheries production (metric tons)",
+          "World/Asia/BGD/Industry/Total fisheries production (metric tons)",
+          "World/Latam/BRA/Industry/Total fisheries production (metric tons)",
+          "World/Latam/CHL/Industry/Total fisheries production (metric tons)",
+          "World/Pair/CHN/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/CMR/Industry/Total fisheries production (metric tons)",
+          "World/Latam/CRI/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/EGY/Industry/Total fisheries production (metric tons)",
+          "World/Europe/HRV/Industry/Total fisheries production (metric tons)",
+          "World/Asia/IDN/Industry/Total fisheries production (metric tons)",
+          "World/Asia/IND/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/ISR/Industry/Total fisheries production (metric tons)",
+          "World/North Africa/MAR/Industry/Total fisheries production (metric tons)",
+          "World/Latam/MEX/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/MOZ/Industry/Total fisheries production (metric tons)",
+          "World/South Africa/NGA/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/OMN/Industry/Total fisheries production (metric tons)",
+          "World/Asia/PHL/Industry/Total fisheries production (metric tons)",
+          "World/Europe/POL/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/SAU/Industry/Total fisheries production (metric tons)",
+          "World/Asia/THA/Industry/Total fisheries production (metric tons)",
+          "World/Asia/VNM/Industry/Total fisheries production (metric tons)",
+          "World/Persian Gulf/ARE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/AZE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/BGD/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/CHL/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Pair/CHN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/CRI/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/DZA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/EGY/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/GBR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/HRV/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/IND/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/IRQ/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/ISR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/KOR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/MAR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/MEX/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/South Africa/MOZ/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/NLD/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/OMN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Latam/PAN/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/PHL/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/QAT/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Europe/SWE/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/THA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/North Africa/TUR/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Pair/USA/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Asia/VNM/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/YEM/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/South Africa/ZAF/Environment/Total greenhouse gas emissions (% change from 1990)",
+          "World/Persian Gulf/ARE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/AZE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/BGD/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/BRA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/CHL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Pair/CHN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/CMR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/COL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/CRI/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/DEU/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/DZA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/EGY/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/GBR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/GHA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/GRC/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/IDN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/IND/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/ISR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/KOR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/LBR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/MAR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/MEX/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/MOZ/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/NGA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/NLD/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/OMN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/PAN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Latam/PER/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/PHL/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/QAT/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/SAU/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/SEN/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Europe/SWE/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/THA/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/North Africa/TUR/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Asia/VNM/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/South Africa/ZAF/Environment/Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "World/Persian Gulf/ARE/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/BGD/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/BRA/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/CHL/Economy/Total reserves (includes gold, current US$)",
+          "World/Pair/CHN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/COL/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/CRI/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/DZA/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/ESP/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/FRA/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/GBR/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/GHA/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/IDN/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/IND/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/IRQ/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/ISR/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/KOR/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/LBR/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/MAR/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/MEX/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/MOZ/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/OMN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/PAN/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/PER/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/PHL/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/POL/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/QAT/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/SAU/Economy/Total reserves (includes gold, current US$)",
+          "World/Europe/SWE/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/THA/Economy/Total reserves (includes gold, current US$)",
+          "World/North Africa/TUR/Economy/Total reserves (includes gold, current US$)",
+          "World/Pair/USA/Economy/Total reserves (includes gold, current US$)",
+          "World/Latam/VEN/Economy/Total reserves (includes gold, current US$)",
+          "World/Asia/VNM/Economy/Total reserves (includes gold, current US$)",
+          "World/South Africa/ZAF/Economy/Total reserves (includes gold, current US$)",
+          "World/Persian Gulf/ARE/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/BGD/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/BRA/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/CHL/Economy/Total reserves minus gold (current US$)",
+          "World/Pair/CHN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/COL/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/CRI/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/DZA/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/FRA/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/GBR/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/GHA/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/IDN/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/IND/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/IRQ/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/ISR/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/KOR/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/LBR/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/MAR/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/MEX/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/MOZ/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/OMN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/PAN/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/PER/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/PHL/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/POL/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/QAT/Economy/Total reserves minus gold (current US$)",
+          "World/Persian Gulf/SAU/Economy/Total reserves minus gold (current US$)",
+          "World/Europe/SWE/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/THA/Economy/Total reserves minus gold (current US$)",
+          "World/North Africa/TUR/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/VEN/Economy/Total reserves minus gold (current US$)",
+          "World/Asia/VNM/Economy/Total reserves minus gold (current US$)",
+          "World/South Africa/ZAF/Economy/Total reserves minus gold (current US$)",
+          "World/Latam/ARG/Health/UHC service coverage index",
+          "World/Europe/AUT/Health/UHC service coverage index",
+          "World/Persian Gulf/AZE/Health/UHC service coverage index",
+          "World/Asia/BGD/Health/UHC service coverage index",
+          "World/Latam/BRA/Health/UHC service coverage index",
+          "World/Latam/CHL/Health/UHC service coverage index",
+          "World/Pair/CHN/Health/UHC service coverage index",
+          "World/South Africa/CMR/Health/UHC service coverage index",
+          "World/Latam/COL/Health/UHC service coverage index",
+          "World/Latam/CRI/Health/UHC service coverage index",
+          "World/Europe/DEU/Health/UHC service coverage index",
+          "World/North Africa/DZA/Health/UHC service coverage index",
+          "World/North Africa/EGY/Health/UHC service coverage index",
+          "World/Europe/ESP/Health/UHC service coverage index",
+          "World/Europe/FRA/Health/UHC service coverage index",
+          "World/Europe/GBR/Health/UHC service coverage index",
+          "World/South Africa/GHA/Health/UHC service coverage index",
+          "World/Europe/GRC/Health/UHC service coverage index",
+          "World/Europe/HRV/Health/UHC service coverage index",
+          "World/Asia/IDN/Health/UHC service coverage index",
+          "World/Asia/IND/Health/UHC service coverage index",
+          "World/Persian Gulf/IRQ/Health/UHC service coverage index",
+          "World/North Africa/ISR/Health/UHC service coverage index",
+          "World/Asia/KOR/Health/UHC service coverage index",
+          "World/South Africa/LBR/Health/UHC service coverage index",
+          "World/North Africa/MAR/Health/UHC service coverage index",
+          "World/Latam/MEX/Health/UHC service coverage index",
+          "World/South Africa/MOZ/Health/UHC service coverage index",
+          "World/South Africa/NGA/Health/UHC service coverage index",
+          "World/Europe/NLD/Health/UHC service coverage index",
+          "World/Persian Gulf/OMN/Health/UHC service coverage index",
+          "World/Latam/PAN/Health/UHC service coverage index",
+          "World/Latam/PER/Health/UHC service coverage index",
+          "World/Asia/PHL/Health/UHC service coverage index",
+          "World/Europe/POL/Health/UHC service coverage index",
+          "World/Persian Gulf/QAT/Health/UHC service coverage index",
+          "World/South Africa/SEN/Health/UHC service coverage index",
+          "World/Europe/SWE/Health/UHC service coverage index",
+          "World/Asia/THA/Health/UHC service coverage index",
+          "World/North Africa/TUR/Health/UHC service coverage index",
+          "World/Pair/USA/Health/UHC service coverage index",
+          "World/Latam/VEN/Health/UHC service coverage index",
+          "World/Asia/VNM/Health/UHC service coverage index",
+          "World/Persian Gulf/YEM/Health/UHC service coverage index",
+          "World/South Africa/ZAF/Health/UHC service coverage index",
+          "World/Persian Gulf/ARE/Demography/Urban population",
+          "World/Latam/ARG/Demography/Urban population",
+          "World/Persian Gulf/AZE/Demography/Urban population",
+          "World/Asia/BGD/Demography/Urban population",
+          "World/Latam/BRA/Demography/Urban population",
+          "World/Latam/CHL/Demography/Urban population",
+          "World/Pair/CHN/Demography/Urban population",
+          "World/South Africa/CMR/Demography/Urban population",
+          "World/Latam/COL/Demography/Urban population",
+          "World/Latam/CRI/Demography/Urban population",
+          "World/North Africa/DZA/Demography/Urban population",
+          "World/North Africa/EGY/Demography/Urban population",
+          "World/Europe/ESP/Demography/Urban population",
+          "World/Europe/FRA/Demography/Urban population",
+          "World/Europe/GBR/Demography/Urban population",
+          "World/South Africa/GHA/Demography/Urban population",
+          "World/Asia/IDN/Demography/Urban population",
+          "World/Asia/IND/Demography/Urban population",
+          "World/North Africa/ISR/Demography/Urban population",
+          "World/Asia/KOR/Demography/Urban population",
+          "World/South Africa/LBR/Demography/Urban population",
+          "World/North Africa/MAR/Demography/Urban population",
+          "World/Latam/MEX/Demography/Urban population",
+          "World/South Africa/MOZ/Demography/Urban population",
+          "World/South Africa/NGA/Demography/Urban population",
+          "World/Europe/NLD/Demography/Urban population",
+          "World/Persian Gulf/OMN/Demography/Urban population",
+          "World/Latam/PAN/Demography/Urban population",
+          "World/Latam/PER/Demography/Urban population",
+          "World/Asia/PHL/Demography/Urban population",
+          "World/Europe/POL/Demography/Urban population",
+          "World/Persian Gulf/QAT/Demography/Urban population",
+          "World/Persian Gulf/SAU/Demography/Urban population",
+          "World/South Africa/SEN/Demography/Urban population",
+          "World/Asia/THA/Demography/Urban population",
+          "World/North Africa/TUR/Demography/Urban population",
+          "World/Pair/USA/Demography/Urban population",
+          "World/Latam/VEN/Demography/Urban population",
+          "World/Asia/VNM/Demography/Urban population",
+          "World/Persian Gulf/YEM/Demography/Urban population",
+          "World/South Africa/ZAF/Demography/Urban population",
+          "World/Persian Gulf/ARE/Demography/Urban population (% of total population)",
+          "World/Latam/ARG/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/AZE/Demography/Urban population (% of total population)",
+          "World/Asia/BGD/Demography/Urban population (% of total population)",
+          "World/Latam/BRA/Demography/Urban population (% of total population)",
+          "World/Latam/CHL/Demography/Urban population (% of total population)",
+          "World/Pair/CHN/Demography/Urban population (% of total population)",
+          "World/South Africa/CMR/Demography/Urban population (% of total population)",
+          "World/Latam/COL/Demography/Urban population (% of total population)",
+          "World/Latam/CRI/Demography/Urban population (% of total population)",
+          "World/Europe/DEU/Demography/Urban population (% of total population)",
+          "World/North Africa/DZA/Demography/Urban population (% of total population)",
+          "World/Europe/ESP/Demography/Urban population (% of total population)",
+          "World/Europe/FRA/Demography/Urban population (% of total population)",
+          "World/Europe/GBR/Demography/Urban population (% of total population)",
+          "World/South Africa/GHA/Demography/Urban population (% of total population)",
+          "World/Europe/GRC/Demography/Urban population (% of total population)",
+          "World/Europe/HRV/Demography/Urban population (% of total population)",
+          "World/Asia/IDN/Demography/Urban population (% of total population)",
+          "World/Asia/IND/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/IRQ/Demography/Urban population (% of total population)",
+          "World/North Africa/ISR/Demography/Urban population (% of total population)",
+          "World/South Africa/LBR/Demography/Urban population (% of total population)",
+          "World/North Africa/MAR/Demography/Urban population (% of total population)",
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+          "World/South Africa/MOZ/Demography/Urban population (% of total population)",
+          "World/South Africa/NGA/Demography/Urban population (% of total population)",
+          "World/Europe/NLD/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/OMN/Demography/Urban population (% of total population)",
+          "World/Latam/PAN/Demography/Urban population (% of total population)",
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+          "World/Europe/POL/Demography/Urban population (% of total population)",
+          "World/Persian Gulf/QAT/Demography/Urban population (% of total population)",
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+          "World/Europe/AUT/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Persian Gulf/AZE/Equality/Women Business and the Law Index Score (scale 1-100)",
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+          "World/Latam/CRI/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/DEU/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/DZA/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/EGY/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/GBR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/South Africa/GHA/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/HRV/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/IND/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/ISR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/KOR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/MAR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/MEX/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/South Africa/MOZ/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/South Africa/NGA/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/NLD/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/PAN/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/PER/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/PHL/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/POL/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Persian Gulf/SAU/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Europe/SWE/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/THA/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/North Africa/TUR/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Latam/VEN/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Asia/VNM/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/Persian Gulf/YEM/Equality/Women Business and the Law Index Score (scale 1-100)",
+          "World/South Africa/ZAF/Equality/Women Business and the Law Index Score (scale 1-100)",
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+          "World/Europe/AUT/A&D",
+          "World/Persian Gulf/AZE/A&D",
+          "World/Asia/BGD/A&D",
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+          "World/South Africa/NGA/A&D",
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+          "World/Europe/NLD/Agriculture",
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+          "World/Persian Gulf/ARE/Demography",
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+          "World/South Africa/MOZ/Economy",
+          "World/South Africa/NGA/Economy",
+          "World/Europe/NLD/Economy",
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+          "World/Europe/SWE/Economy",
+          "World/Asia/THA/Economy",
+          "World/North Africa/TUR/Economy",
+          "World/Pair/USA/Economy",
+          "World/Latam/VEN/Economy",
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+          "World/Persian Gulf/YEM/Economy",
+          "World/South Africa/ZAF/Economy",
+          "World/Persian Gulf/ARE/Employment",
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+          "World/Europe/AUT/Employment",
+          "World/Persian Gulf/AZE/Employment",
+          "World/Asia/BGD/Employment",
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+          "World/Latam/CRI/Employment",
+          "World/Europe/DEU/Employment",
+          "World/North Africa/DZA/Employment",
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+          "World/Europe/ESP/Employment",
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+          "World/Europe/GBR/Employment",
+          "World/South Africa/GHA/Employment",
+          "World/Europe/GRC/Employment",
+          "World/Europe/HRV/Employment",
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+          "World/North Africa/ISR/Employment",
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+          "World/South Africa/LBR/Employment",
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+          "World/Latam/MEX/Employment",
+          "World/South Africa/MOZ/Employment",
+          "World/South Africa/NGA/Employment",
+          "World/Europe/NLD/Employment",
+          "World/Persian Gulf/OMN/Employment",
+          "World/Latam/PAN/Employment",
+          "World/Latam/PER/Employment",
+          "World/Asia/PHL/Employment",
+          "World/Europe/POL/Employment",
+          "World/Persian Gulf/QAT/Employment",
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+          "World/Pair/USA/Employment",
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+          "World/Persian Gulf/YEM/Employment",
+          "World/South Africa/ZAF/Employment",
+          "World/Persian Gulf/ARE/Environment",
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+          "World/Persian Gulf/ARE/Equality",
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+          "World/Latam/CRI/Equality",
+          "World/Europe/DEU/Equality",
+          "World/North Africa/DZA/Equality",
+          "World/North Africa/EGY/Equality",
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+          "World/South Africa/GHA/Equality",
+          "World/Europe/GRC/Equality",
+          "World/Europe/HRV/Equality",
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+          "World/Asia/KOR/Equality",
+          "World/North Africa/MAR/Equality",
+          "World/Latam/MEX/Equality",
+          "World/South Africa/MOZ/Equality",
+          "World/South Africa/NGA/Equality",
+          "World/Europe/NLD/Equality",
+          "World/Persian Gulf/OMN/Equality",
+          "World/Latam/PAN/Equality",
+          "World/Latam/PER/Equality",
+          "World/Asia/PHL/Equality",
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+          "World/Persian Gulf/QAT/Equality",
+          "World/Persian Gulf/SAU/Equality",
+          "World/South Africa/SEN/Equality",
+          "World/Europe/SWE/Equality",
+          "World/Asia/THA/Equality",
+          "World/North Africa/TUR/Equality",
+          "World/Pair/USA/Equality",
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+          "World/Asia/VNM/Equality",
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+          "World/Persian Gulf/ARE/Exports",
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+          "Access to electricity (% of population)",
+          "Access to electricity (% of population)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted net national income per capita (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: carbon dioxide damage (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: consumption of fixed capital (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: education expenditure (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: net national savings (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adjusted savings: particulate emission damage (current US$)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Adolescent fertility rate (births per 1,000 women ages 15-19)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural land (% of land area)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural methane emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Agricultural raw materials imports (% of merchandise imports)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Aquaculture production (metric tons)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (% of land area)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares per person)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Arable land (hectares)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Automated teller machines (ATMs) (per 100,000 adults)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Births attended by skilled health staff (% of total)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "Broad money (% of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2015 US$ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per 2017 PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kg per PPP $ of GDP)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (kt)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions (metric tons per capita)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from gaseous fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from liquid fuel consumption (kt)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Capture fisheries production (metric tons)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Cereal yield (kg per hectare)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Combustible renewables and waste (% of total energy)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial bank branches (per 100,000 adults)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service exports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Commercial service imports (current US$)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Completeness of birth registration (%)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Consumer price index (2010 = 100)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Container port traffic (TEU: 20 foot equivalent units)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Crop production index (2014-2016 = 100)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure (% of GDP)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Current health expenditure per capita (current US$)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Customs and other import duties (% of tax revenue)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic credit to private sector by banks (% of GDP)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic general government health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Domestic private health expenditure per capita (current US$)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Electric power consumption (kWh per capita)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in agriculture (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in industry (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Employment in services (% of total employment) (modeled ILO estimate)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy intensity level of primary energy (MJ/$2017 PPP GDP)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent per capita)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export unit value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export value index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Export volume index (2000 = 100)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (BoP, current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Exports of goods and services (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Final consumption expenditure (current US$)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms offering formal training (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance investment (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Firms using banks to finance working capital (% of firms)",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Fixed broadband subscriptions (per 100 people)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Food production index (2014-2016 = 100)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "Forest area (% of land area)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP deflator: linked series (base year varies by country)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per capita (current US$)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per person employed (constant 2017 PPP $)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "GNI (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "General government final consumption expenditure (current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods exports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Goods imports (BoP, current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross capital formation (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross domestic savings (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross fixed capital formation (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure (current US$)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross national expenditure deflator (base year varies by country)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross savings (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "Gross value added at basic prices (GVA) (current US$)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "HFC gas emissions (thousand metric tons of CO2 equivalent)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Hospital beds (per 1,000 people)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "Households and NPISHs Final consumption expenditure (current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "ICT service exports (BoP, current US$)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import unit value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import value index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Import volume index (2000 = 100)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (BoP, current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Imports of goods and services (current US$)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Incidence of tuberculosis (per 100,000 people)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Industry (including construction), value added (current US$)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "Intentional homicides (per 100,000 people)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "International migrant stock (% of population)",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (%)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Lifetime risk of maternal death (1 in: rate varies by country)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Livestock production index (2014-2016 = 100)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Low-birthweight babies (% of births)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Market capitalization of listed domestic companies (current US$)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Maternal mortality ratio (modeled estimate, per 100,000 live births)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports by the reporting economy (current US$)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports by the reporting economy (current US$)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from high-income economies (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (% change from 1990)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions (kt of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Methane emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Military expenditure (current USD)",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Mobile cellular subscriptions (per 100 people)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Monetary Sector credit to private sector (% GDP)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Mortality caused by road traffic injury (per 100,000 population)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net barter terms of trade index (2000 = 100)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (BoP, current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net primary income (Net income from abroad) (current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (BoP, current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Net secondary income (Net current transfers from abroad) (current US$)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (% change from 1990)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 10-14 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 15-19 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 20-24 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of deaths ages 5-9 years",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of infant deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of maternal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of neonatal deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Number of under-five deaths",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Nurses and midwives (per 1,000 people)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "Out-of-pocket expenditure per capita (current US$)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People practicing open defecation (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic drinking water services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using at least basic sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "People using safely managed sanitation services (% of population)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Permanent cropland (% of land area)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
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+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
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+          "Population ages 0-14 (% of total population)",
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+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 0-14 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 15-64 (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population ages 65 and above (% of total population)",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in largest city",
+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in largest city",
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+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
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+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in the largest city (% of urban population)",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Population in urban agglomerations of more than 1 million (% of total population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Poverty headcount ratio at national poverty lines (% of population)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Pregnant women receiving prenatal care (%)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among children (% of children ages 6-59 months)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among non-pregnant women (% of women ages 15-49)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among pregnant women (%)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of anemia among women of reproductive age (% of women ages 15-49)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of current tobacco use (% of adults)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of overweight (modeled estimate, % of children under 5)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Prevalence of undernourishment (% of population)",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Price level ratio of PPP conversion factor (GDP) to market exchange rate",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income payments (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Primary income receipts (BoP, current US$)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 10-14 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among adolescents ages 15-19 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among children ages 5-9 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Probability of dying among youth ages 20-24 years (per 1,000)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Proportion of seats held by women in national parliaments (%)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Ratio of female to male labor force participation rate (%) (national estimate)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable energy consumption (% of total final energy consumption)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Renewable internal freshwater resources per capita (cubic meters)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Researchers in R&D (per million people)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of catastrophic expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Risk of impoverishing expenditure for surgical care (% of people at risk)",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Rural population (% of total population)",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Scientific and technical journal articles",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Secondary income receipts (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service exports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Service imports (BoP, current US$)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Suicide mortality rate (per 100,000 population)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes less subsidies on products (current US$)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Taxes on international trade (% of revenue)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Textiles and clothing (% of value added in manufacturing)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time required to start a business (days)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time spent dealing with the requirements of government regulations (% of senior management time)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Time to obtain an electrical connection (days)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total fisheries production (metric tons)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (% change from 1990)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total greenhouse gas emissions (kt of CO2 equivalent)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves (includes gold, current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "Total reserves minus gold (current US$)",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "UHC service coverage index",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Urban population (% of total population)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
+          "Women Business and the Law Index Score (scale 1-100)",
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+            "gridcolor": "white",
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+           "caxis": {
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+           "automargin": true,
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+        }
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+    }
+   ],
+   "source": [
+    "display(HBox([path_1_dropdown, path_2_dropdown, path_3_dropdown,path_4_dropdown,ok_button]), out)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('base')",
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+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/WDI-Data normalization.ipynb b/WDI-Data normalization.ipynb
deleted file mode 100644
index 4ab91c5..0000000
--- a/WDI-Data normalization.ipynb	
+++ /dev/null
@@ -1,3639 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "BronzeDataFrame= pd.read_csv (os.getcwd()+'/Data/'+'BronzeDataFrame.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# NORMALIZATION"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Taking as reference both works of https://www.pluralsight.com/guides/cleaning-up-data-from-outliers and https://careerfoundry.com/en/blog/data-analytics/how-to-find-outliers/, for normalizing our data we need to start computing the outliers and removing them from our dataframe. As there is not a direct function of pandas that performs this step, it´s been step-by-step code, where we begin with the computation of the quartiles, then the IQR (Inter Quartile Range) and finally the upper and lower limit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### IQR explanation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The interquartile range (IQR) measures the spread of the middle half of your data. It is the range for the middle 50% of your sample. Use the IQR to assess the variability where most of your values lie. Larger values indicate that the central portion of your data spread out further. Conversely, smaller values show that the middle values cluster more tightly.\n",
-    "\n",
-    "To visualize the interquartile range, imagine dividing your data into quarters. Statisticians refer to these quarters as quartiles and label them from low to high as Q1, Q2, Q3, and Q4. The lowest quartile (Q1) covers the smallest quarter of values in your dataset. The upper quartile (Q4) comprises the highest quarter of values. The interquartile range is the middle half of the data that lies between the upper and lower quartiles. In other words, the interquartile range includes the 50% of data points that are above Q1 and below Q4.\n",
-    "\n",
-    "When measuring variability, statisticians prefer using the interquartile range instead of the full data range because extreme values and outliers affect it less. Typically, use the IQR with a measure of central tendency, such as the median, to understand your data’s center and spread. This combination creates a fuller picture of your data’s distribution.\n",
-    "\n",
-    "Therefore it is being utilized to get rid of all the outliers that may come from errors when creating the data or from unexpected years."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Firstly, what we have done is to change the name of our indicators, as their original denomination is not easy to handle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
-       "      <th>1960</th>\n",
-       "      <th>1961</th>\n",
-       "      <th>1962</th>\n",
-       "      <th>1963</th>\n",
-       "      <th>1964</th>\n",
-       "      <th>1965</th>\n",
-       "      <th>1966</th>\n",
-       "      <th>1967</th>\n",
-       "      <th>...</th>\n",
-       "      <th>2013</th>\n",
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-       "      <th>2015</th>\n",
-       "      <th>2016</th>\n",
-       "      <th>2017</th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "      <th>2021</th>\n",
-       "      <th>Unnamed: 66</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to clean fuels and technologies for coo...</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>16.936004</td>\n",
-       "      <td>17.337896</td>\n",
-       "      <td>17.687093</td>\n",
-       "      <td>18.140971</td>\n",
-       "      <td>18.491344</td>\n",
-       "      <td>18.825520</td>\n",
-       "      <td>19.272212</td>\n",
-       "      <td>19.628009</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to clean fuels and technologies for coo...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.499471</td>\n",
-       "      <td>6.680066</td>\n",
-       "      <td>6.859110</td>\n",
-       "      <td>7.016238</td>\n",
-       "      <td>7.180364</td>\n",
-       "      <td>7.322294</td>\n",
-       "      <td>7.517191</td>\n",
-       "      <td>7.651598</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to clean fuels and technologies for coo...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>37.855399</td>\n",
-       "      <td>38.046781</td>\n",
-       "      <td>38.326255</td>\n",
-       "      <td>38.468426</td>\n",
-       "      <td>38.670044</td>\n",
-       "      <td>38.722783</td>\n",
-       "      <td>38.927016</td>\n",
-       "      <td>39.042839</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to electricity (% of population)</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>31.794160</td>\n",
-       "      <td>32.001027</td>\n",
-       "      <td>33.871910</td>\n",
-       "      <td>38.880173</td>\n",
-       "      <td>40.261358</td>\n",
-       "      <td>43.061877</td>\n",
-       "      <td>44.270860</td>\n",
-       "      <td>45.803485</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>AFE</td>\n",
-       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>18.663502</td>\n",
-       "      <td>17.633986</td>\n",
-       "      <td>16.464681</td>\n",
-       "      <td>24.531436</td>\n",
-       "      <td>25.345111</td>\n",
-       "      <td>27.449908</td>\n",
-       "      <td>29.641760</td>\n",
-       "      <td>30.404935</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>384365</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Women who believe a husband is justified in be...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14.500000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>384366</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Women who were first married by age 15 (% of w...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.700000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.418352</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>384367</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Women who were first married by age 18 (% of w...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>33.500000</td>\n",
-       "      <td>32.400000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>33.658057</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>384368</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Women's share of population ages 15+ living wi...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>59.200000</td>\n",
-       "      <td>59.400000</td>\n",
-       "      <td>59.500000</td>\n",
-       "      <td>59.700000</td>\n",
-       "      <td>59.900000</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>60.200000</td>\n",
-       "      <td>60.400000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>384369</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>18000.000000</td>\n",
-       "      <td>17000.000000</td>\n",
-       "      <td>15000.000000</td>\n",
-       "      <td>14000.000000</td>\n",
-       "      <td>12000.000000</td>\n",
-       "      <td>9700.000000</td>\n",
-       "      <td>9600.000000</td>\n",
-       "      <td>7500.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>384370 rows × 65 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Country Code                                     Indicator Name  1960  \\\n",
-       "0               AFE  Access to clean fuels and technologies for coo...   NaN   \n",
-       "1               AFE  Access to clean fuels and technologies for coo...   NaN   \n",
-       "2               AFE  Access to clean fuels and technologies for coo...   NaN   \n",
-       "3               AFE            Access to electricity (% of population)   NaN   \n",
-       "4               AFE  Access to electricity, rural (% of rural popul...   NaN   \n",
-       "...             ...                                                ...   ...   \n",
-       "384365          ZWE  Women who believe a husband is justified in be...   NaN   \n",
-       "384366          ZWE  Women who were first married by age 15 (% of w...   NaN   \n",
-       "384367          ZWE  Women who were first married by age 18 (% of w...   NaN   \n",
-       "384368          ZWE  Women's share of population ages 15+ living wi...   NaN   \n",
-       "384369          ZWE  Young people (ages 15-24) newly infected with HIV   NaN   \n",
-       "\n",
-       "        1961  1962  1963  1964  1965  1966  1967  ...          2013  \\\n",
-       "0        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     16.936004   \n",
-       "1        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...      6.499471   \n",
-       "2        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     37.855399   \n",
-       "3        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     31.794160   \n",
-       "4        NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     18.663502   \n",
-       "...      ...   ...   ...   ...   ...   ...   ...  ...           ...   \n",
-       "384365   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN   \n",
-       "384366   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN   \n",
-       "384367   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN   \n",
-       "384368   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...     59.200000   \n",
-       "384369   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...  18000.000000   \n",
-       "\n",
-       "                2014          2015          2016          2017         2018  \\\n",
-       "0          17.337896     17.687093     18.140971     18.491344    18.825520   \n",
-       "1           6.680066      6.859110      7.016238      7.180364     7.322294   \n",
-       "2          38.046781     38.326255     38.468426     38.670044    38.722783   \n",
-       "3          32.001027     33.871910     38.880173     40.261358    43.061877   \n",
-       "4          17.633986     16.464681     24.531436     25.345111    27.449908   \n",
-       "...              ...           ...           ...           ...          ...   \n",
-       "384365           NaN     14.500000           NaN           NaN          NaN   \n",
-       "384366           NaN      3.700000           NaN           NaN          NaN   \n",
-       "384367     33.500000     32.400000           NaN           NaN          NaN   \n",
-       "384368     59.400000     59.500000     59.700000     59.900000    60.000000   \n",
-       "384369  17000.000000  15000.000000  14000.000000  12000.000000  9700.000000   \n",
-       "\n",
-       "               2019         2020  2021  Unnamed: 66  \n",
-       "0         19.272212    19.628009   NaN          NaN  \n",
-       "1          7.517191     7.651598   NaN          NaN  \n",
-       "2         38.927016    39.042839   NaN          NaN  \n",
-       "3         44.270860    45.803485   NaN          NaN  \n",
-       "4         29.641760    30.404935   NaN          NaN  \n",
-       "...             ...          ...   ...          ...  \n",
-       "384365          NaN          NaN   NaN          NaN  \n",
-       "384366     5.418352          NaN   NaN          NaN  \n",
-       "384367    33.658057          NaN   NaN          NaN  \n",
-       "384368    60.200000    60.400000   NaN          NaN  \n",
-       "384369  9600.000000  7500.000000   NaN          NaN  \n",
-       "\n",
-       "[384370 rows x 65 columns]"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "BronzeDataFrame['Indicator Name']=BronzeDataFrame['Indicator Name'].replace(['CPIA gender equality rating (1=low to 6=high)','Commercial service exports (current US$)','Electricity production from renewable sources, excluding hydroelectric (kWh)','Employment in agriculture (% of total employment) (modeled ILO estimate)','Employment in industry (% of total employment) (modeled ILO estimate)','Employment in services (% of total employment) (modeled ILO estimate)','Exports of goods and services (current US$)','Fertility rate, total (births per woman)','Foreign direct investment, net (BoP, current US$)','GDP (current US$)','Government expenditure on education, total (% of government expenditure)','Labor force with advanced education (% of total working-age population with advanced education)','Literacy rate, adult total (% of people ages 15 and above)','Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)','Net migration','Number of infant deaths','People using at least basic sanitation services (% of population)','Research and development expenditure (% of GDP)','Share of youth not in education, employment or training, total (% of youth population)','Suicide mortality rate (per 100,000 population)','Taxes on international trade (current LCU)','Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)'],['Gender equality','Exports-Commercial services','Renewable electricity','Employment-agriculture','Employment-industry','Employment-services','Exports-G&S','Fertility rate','Foreign investment','GDP','Education GExp','Workers high education','Literacy rate','Mortality-pollution','Net migration','Mortality-infants','Health services use','R&D GExp','Ninis','Suicide','International taxes','Alcohol per capita'])\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Secondly, we compute the first quartile (Q1=25%) and the third quartile (Q3=75%). For that, we have grouped the data by country code and indicator name, so we get the Q1 and Q3 values for each indicator in each geographical area. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001D1757ABB50>"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grouped=BronzeDataFrame.groupby(['Country Code','Indicator Name'])\n",
-    "grouped"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Value</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <td>10.0</td>\n",
-       "      <td>6.400000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Education GExp</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <td>14.0</td>\n",
-       "      <td>5.130000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <td>14.0</td>\n",
-       "      <td>1.449997e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-services</th>\n",
-       "      <td>14.0</td>\n",
-       "      <td>3.830002e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <td>12.5</td>\n",
-       "      <td>2.997499e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.011100e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <td>12.5</td>\n",
-       "      <td>2.292500e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Suicide</th>\n",
-       "      <td>9.5</td>\n",
-       "      <td>1.025000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Workers high education</th>\n",
-       "      <td>10.5</td>\n",
-       "      <td>1.712500e+00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>978 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     Date         Value\n",
-       "Country Code Indicator Name                            \n",
-       "ARE          Alcohol per capita      10.0  6.400000e-01\n",
-       "             Education GExp           0.0  0.000000e+00\n",
-       "             Employment-agriculture  14.0  5.130000e+00\n",
-       "             Employment-industry     14.0  1.449997e+00\n",
-       "             Employment-services     14.0  3.830002e+00\n",
-       "...                                   ...           ...\n",
-       "ZAF          Ninis                   12.5  2.997499e+00\n",
-       "             R&D GExp                 8.0  1.011100e-01\n",
-       "             Renewable electricity   12.5  2.292500e+08\n",
-       "             Suicide                  9.5  1.025000e+00\n",
-       "             Workers high education  10.5  1.712500e+00\n",
-       "\n",
-       "[978 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Q1=BronzeDataFrame.groupby(['Country Code','Indicator Name']).quantile(0.25)\n",
-    "Q3=BronzeDataFrame.groupby(['Country Code','Indicator Name']).quantile(0.75)\n",
-    "IQR=Q3-Q1\n",
-    "IQR"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once we got the quartiles, we compute the upper and lower limit, with a basic mathematical expression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Lower limit</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <td>2.190000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Education GExp</th>\n",
-       "      <td>1.026766e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <td>-4.905000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <td>3.131501e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-services</th>\n",
-       "      <td>5.269500e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <td>2.684375e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <td>5.828450e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <td>-2.623750e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Suicide</th>\n",
-       "      <td>2.203750e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Workers high education</th>\n",
-       "      <td>8.050875e+01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>978 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      Lower limit\n",
-       "Country Code Indicator Name                      \n",
-       "ARE          Alcohol per capita      2.190000e+00\n",
-       "             Education GExp          1.026766e+01\n",
-       "             Employment-agriculture -4.905000e+00\n",
-       "             Employment-industry     3.131501e+01\n",
-       "             Employment-services     5.269500e+01\n",
-       "...                                           ...\n",
-       "ZAF          Ninis                   2.684375e+01\n",
-       "             R&D GExp                5.828450e-01\n",
-       "             Renewable electricity  -2.623750e+08\n",
-       "             Suicide                 2.203750e+01\n",
-       "             Workers high education  8.050875e+01\n",
-       "\n",
-       "[978 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lower_limit=Q1 - 1.5 * IQR\n",
-    "lower=lower_limit.drop(['Date'],axis=1)\n",
-    "lower.rename(columns={\"Value\":\"Lower limit\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Upper limit</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <td>4.750000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Education GExp</th>\n",
-       "      <td>1.026766e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <td>1.561500e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <td>3.711499e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Employment-services</th>\n",
-       "      <td>6.801500e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <td>3.883375e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <td>9.872850e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <td>6.546250e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Suicide</th>\n",
-       "      <td>2.613750e+01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Workers high education</th>\n",
-       "      <td>8.735875e+01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>978 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      Upper limit\n",
-       "Country Code Indicator Name                      \n",
-       "ARE          Alcohol per capita      4.750000e+00\n",
-       "             Education GExp          1.026766e+01\n",
-       "             Employment-agriculture  1.561500e+01\n",
-       "             Employment-industry     3.711499e+01\n",
-       "             Employment-services     6.801500e+01\n",
-       "...                                           ...\n",
-       "ZAF          Ninis                   3.883375e+01\n",
-       "             R&D GExp                9.872850e-01\n",
-       "             Renewable electricity   6.546250e+08\n",
-       "             Suicide                 2.613750e+01\n",
-       "             Workers high education  8.735875e+01\n",
-       "\n",
-       "[978 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "upper_limit=Q3 + 1.5 * IQR\n",
-    "upper=upper_limit.drop(['Date'],axis=1)\n",
-    "upper.rename(columns={\"Value\":\"Upper limit\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Thirdly, we join the three tables we have (main dataframe, upper limit and lower limit) by matching country code and indicator name.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Value_x</th>\n",
-       "      <th>Value_y</th>\n",
-       "      <th>Value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>4.795977e+08</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>3.747657e+08</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20372</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>7.900000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20373</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20374</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20375</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20376</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>20377 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Country Code               Indicator Name  Date       Value_x  \\\n",
-       "0              DZA  Exports-Commercial services  1990  4.795977e+08   \n",
-       "1              DZA  Exports-Commercial services  1991  3.747657e+08   \n",
-       "2              DZA  Exports-Commercial services  2005  2.466000e+09   \n",
-       "3              DZA  Exports-Commercial services  2006  2.512000e+09   \n",
-       "4              DZA  Exports-Commercial services  2007  2.786733e+09   \n",
-       "...            ...                          ...   ...           ...   \n",
-       "20372          YEM           Alcohol per capita  2000  7.900000e-01   \n",
-       "20373          YEM           Alcohol per capita  2005  3.400000e-01   \n",
-       "20374          YEM           Alcohol per capita  2010  1.800000e-01   \n",
-       "20375          YEM           Alcohol per capita  2015  5.500000e-02   \n",
-       "20376          YEM           Alcohol per capita  2018  5.100000e-02   \n",
-       "\n",
-       "            Value_y         Value  \n",
-       "0      1.736231e+09  4.453536e+09  \n",
-       "1      1.736231e+09  4.453536e+09  \n",
-       "2      1.736231e+09  4.453536e+09  \n",
-       "3      1.736231e+09  4.453536e+09  \n",
-       "4      1.736231e+09  4.453536e+09  \n",
-       "...             ...           ...  \n",
-       "20372 -3.725000e-01  7.675000e-01  \n",
-       "20373 -3.725000e-01  7.675000e-01  \n",
-       "20374 -3.725000e-01  7.675000e-01  \n",
-       "20375 -3.725000e-01  7.675000e-01  \n",
-       "20376 -3.725000e-01  7.675000e-01  \n",
-       "\n",
-       "[20377 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfs = [BronzeDataFrame,lower,upper]\n",
-    "df_joined = ft.reduce(lambda left, right: pd.merge(left, right, on=['Country Code','Indicator Name']), dfs)\n",
-    "df_joined"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Country Code', 'Indicator Name', 'Date', 'Value_x', 'Value_y', 'Value']"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(df_joined)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We rename the columns of the new table, as the columns headers are not saved after the joining. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Real value</th>\n",
-       "      <th>Lower value</th>\n",
-       "      <th>Upper value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>4.795977e+08</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>3.747657e+08</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20372</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>7.900000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20373</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20374</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20375</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20376</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>20377 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Country                    Indicator  Year    Real value   Lower value  \\\n",
-       "0         DZA  Exports-Commercial services  1990  4.795977e+08  1.736231e+09   \n",
-       "1         DZA  Exports-Commercial services  1991  3.747657e+08  1.736231e+09   \n",
-       "2         DZA  Exports-Commercial services  2005  2.466000e+09  1.736231e+09   \n",
-       "3         DZA  Exports-Commercial services  2006  2.512000e+09  1.736231e+09   \n",
-       "4         DZA  Exports-Commercial services  2007  2.786733e+09  1.736231e+09   \n",
-       "...       ...                          ...   ...           ...           ...   \n",
-       "20372     YEM           Alcohol per capita  2000  7.900000e-01 -3.725000e-01   \n",
-       "20373     YEM           Alcohol per capita  2005  3.400000e-01 -3.725000e-01   \n",
-       "20374     YEM           Alcohol per capita  2010  1.800000e-01 -3.725000e-01   \n",
-       "20375     YEM           Alcohol per capita  2015  5.500000e-02 -3.725000e-01   \n",
-       "20376     YEM           Alcohol per capita  2018  5.100000e-02 -3.725000e-01   \n",
-       "\n",
-       "        Upper value  \n",
-       "0      4.453536e+09  \n",
-       "1      4.453536e+09  \n",
-       "2      4.453536e+09  \n",
-       "3      4.453536e+09  \n",
-       "4      4.453536e+09  \n",
-       "...             ...  \n",
-       "20372  7.675000e-01  \n",
-       "20373  7.675000e-01  \n",
-       "20374  7.675000e-01  \n",
-       "20375  7.675000e-01  \n",
-       "20376  7.675000e-01  \n",
-       "\n",
-       "[20377 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "renamed=df_joined.set_axis(['Country','Indicator','Year', 'Real value', 'Lower value', 'Upper value'], axis=1, inplace=False)\n",
-    "renamed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have the table correctly defined, we remove from our dataframe the values that are outside our range, as it means that they are outliers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Real value</th>\n",
-       "      <th>Lower value</th>\n",
-       "      <th>Upper value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>3.412421e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2009</td>\n",
-       "      <td>2.744716e+09</td>\n",
-       "      <td>1.736231e+09</td>\n",
-       "      <td>4.453536e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20371</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Suicide</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>5.800000e+00</td>\n",
-       "      <td>5.400000e+00</td>\n",
-       "      <td>6.200000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20373</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20374</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20375</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20376</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "      <td>-3.725000e-01</td>\n",
-       "      <td>7.675000e-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19847 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Country                    Indicator  Year    Real value   Lower value  \\\n",
-       "2         DZA  Exports-Commercial services  2005  2.466000e+09  1.736231e+09   \n",
-       "3         DZA  Exports-Commercial services  2006  2.512000e+09  1.736231e+09   \n",
-       "4         DZA  Exports-Commercial services  2007  2.786733e+09  1.736231e+09   \n",
-       "5         DZA  Exports-Commercial services  2008  3.412421e+09  1.736231e+09   \n",
-       "6         DZA  Exports-Commercial services  2009  2.744716e+09  1.736231e+09   \n",
-       "...       ...                          ...   ...           ...           ...   \n",
-       "20371     YEM                      Suicide  2019  5.800000e+00  5.400000e+00   \n",
-       "20373     YEM           Alcohol per capita  2005  3.400000e-01 -3.725000e-01   \n",
-       "20374     YEM           Alcohol per capita  2010  1.800000e-01 -3.725000e-01   \n",
-       "20375     YEM           Alcohol per capita  2015  5.500000e-02 -3.725000e-01   \n",
-       "20376     YEM           Alcohol per capita  2018  5.100000e-02 -3.725000e-01   \n",
-       "\n",
-       "        Upper value  \n",
-       "2      4.453536e+09  \n",
-       "3      4.453536e+09  \n",
-       "4      4.453536e+09  \n",
-       "5      4.453536e+09  \n",
-       "6      4.453536e+09  \n",
-       "...             ...  \n",
-       "20371  6.200000e+00  \n",
-       "20373  7.675000e-01  \n",
-       "20374  7.675000e-01  \n",
-       "20375  7.675000e-01  \n",
-       "20376  7.675000e-01  \n",
-       "\n",
-       "[19847 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sin_outliers=renamed.loc[~((renamed['Real value']<renamed['Lower value']) | (renamed['Real value']>renamed['Upper value']))]\n",
-    "sin_outliers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From the data above, we can perceive that our data comes down from 19944 rows to 19424, so 500 were outliers. The next steps are to order and display data better, removing those columns that we just do not need and pivoting the rows and columns. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Real value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>3.412421e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Exports-Commercial services</td>\n",
-       "      <td>2009</td>\n",
-       "      <td>2.744716e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20371</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Suicide</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>5.800000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20373</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20374</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20375</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20376</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Alcohol per capita</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19847 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Country                    Indicator  Year    Real value\n",
-       "2         DZA  Exports-Commercial services  2005  2.466000e+09\n",
-       "3         DZA  Exports-Commercial services  2006  2.512000e+09\n",
-       "4         DZA  Exports-Commercial services  2007  2.786733e+09\n",
-       "5         DZA  Exports-Commercial services  2008  3.412421e+09\n",
-       "6         DZA  Exports-Commercial services  2009  2.744716e+09\n",
-       "...       ...                          ...   ...           ...\n",
-       "20371     YEM                      Suicide  2019  5.800000e+00\n",
-       "20373     YEM           Alcohol per capita  2005  3.400000e-01\n",
-       "20374     YEM           Alcohol per capita  2010  1.800000e-01\n",
-       "20375     YEM           Alcohol per capita  2015  5.500000e-02\n",
-       "20376     YEM           Alcohol per capita  2018  5.100000e-02\n",
-       "\n",
-       "[19847 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_limpio=sin_outliers.drop(['Lower value','Upper value'],axis=1)\n",
-    "df_limpio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
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-       "      <th>Fertility rate</th>\n",
-       "      <th>...</th>\n",
-       "      <th>International taxes</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Mortality-pollution</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>ARE</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>4.454</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>672.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>8.46</td>\n",
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-       "      <td>58.200001</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>4.253</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>645.0</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>4.041</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>618.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>368126.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ARE</td>\n",
-       "      <td>1993</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.24</td>\n",
-       "      <td>33.470001</td>\n",
-       "      <td>58.290001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.827</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>592.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ARE</td>\n",
-       "      <td>1994</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.13</td>\n",
-       "      <td>33.490002</td>\n",
-       "      <td>58.380001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.618</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>568.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1504</th>\n",
-       "      <td>ZAF</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>18.719290</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>23.340000</td>\n",
-       "      <td>71.379997</td>\n",
-       "      <td>1.614806e+10</td>\n",
-       "      <td>1.042884e+11</td>\n",
-       "      <td>2.430</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.993941e+10</td>\n",
-       "      <td>87.046669</td>\n",
-       "      <td>32777.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>727026.0</td>\n",
-       "      <td>31.010000</td>\n",
-       "      <td>0.83215</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>25.2</td>\n",
-       "      <td>83.809998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1505</th>\n",
-       "      <td>ZAF</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>9.52</td>\n",
-       "      <td>18.901590</td>\n",
-       "      <td>5.16</td>\n",
-       "      <td>23.129999</td>\n",
-       "      <td>71.709999</td>\n",
-       "      <td>1.670823e+10</td>\n",
-       "      <td>1.112854e+11</td>\n",
-       "      <td>2.405</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.572291e+10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31810.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31.559999</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>24.1</td>\n",
-       "      <td>82.879997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1506</th>\n",
-       "      <td>ZAF</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>19.596230</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>22.309999</td>\n",
-       "      <td>72.410004</td>\n",
-       "      <td>1.554886e+10</td>\n",
-       "      <td>1.060698e+11</td>\n",
-       "      <td>2.381</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.522342e+10</td>\n",
-       "      <td>95.022972</td>\n",
-       "      <td>30937.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>32.459999</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>23.5</td>\n",
-       "      <td>82.019997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1507</th>\n",
-       "      <td>ZAF</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>19.527281</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.404204e+09</td>\n",
-       "      <td>9.317915e+10</td>\n",
-       "      <td>2.358</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30153.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>32.400002</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1508</th>\n",
-       "      <td>ZAF</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>18.417240</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1509 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Indicator Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "0             ARE  1990                 NaN             NaN   \n",
-       "1             ARE  1991                 NaN             NaN   \n",
-       "2             ARE  1992                 NaN             NaN   \n",
-       "3             ARE  1993                 NaN             NaN   \n",
-       "4             ARE  1994                 NaN             NaN   \n",
-       "...           ...   ...                 ...             ...   \n",
-       "1504          ZAF  2017                 NaN       18.719290   \n",
-       "1505          ZAF  2018                9.52       18.901590   \n",
-       "1506          ZAF  2019                 NaN       19.596230   \n",
-       "1507          ZAF  2020                 NaN       19.527281   \n",
-       "1508          ZAF  2021                 NaN       18.417240   \n",
-       "\n",
-       "Indicator  Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "0                             NaN                  NaN                  NaN   \n",
-       "1                            8.46            33.330002            58.200001   \n",
-       "2                            8.37            33.360001            58.279999   \n",
-       "3                            8.24            33.470001            58.290001   \n",
-       "4                            8.13            33.490002            58.380001   \n",
-       "...                           ...                  ...                  ...   \n",
-       "1504                         5.28            23.340000            71.379997   \n",
-       "1505                         5.16            23.129999            71.709999   \n",
-       "1506                         5.28            22.309999            72.410004   \n",
-       "1507                          NaN                  NaN                  NaN   \n",
-       "1508                          NaN                  NaN                  NaN   \n",
-       "\n",
-       "Indicator  Exports-Commercial services   Exports-G&S  Fertility rate  ...  \\\n",
-       "0                                  NaN           NaN           4.454  ...   \n",
-       "1                                  NaN           NaN           4.253  ...   \n",
-       "2                                  NaN           NaN           4.041  ...   \n",
-       "3                                  NaN           NaN           3.827  ...   \n",
-       "4                                  NaN           NaN           3.618  ...   \n",
-       "...                                ...           ...             ...  ...   \n",
-       "1504                      1.614806e+10  1.042884e+11           2.430  ...   \n",
-       "1505                      1.670823e+10  1.112854e+11           2.405  ...   \n",
-       "1506                      1.554886e+10  1.060698e+11           2.381  ...   \n",
-       "1507                      8.404204e+09  9.317915e+10           2.358  ...   \n",
-       "1508                               NaN           NaN             NaN  ...   \n",
-       "\n",
-       "Indicator  International taxes  Literacy rate  Mortality-infants  \\\n",
-       "0                          NaN            NaN              672.0   \n",
-       "1                          NaN            NaN              645.0   \n",
-       "2                          NaN            NaN              618.0   \n",
-       "3                          NaN            NaN              592.0   \n",
-       "4                          NaN            NaN              568.0   \n",
-       "...                        ...            ...                ...   \n",
-       "1504              4.993941e+10      87.046669            32777.0   \n",
-       "1505              5.572291e+10            NaN            31810.0   \n",
-       "1506              5.522342e+10      95.022972            30937.0   \n",
-       "1507                       NaN            NaN            30153.0   \n",
-       "1508                       NaN            NaN                NaN   \n",
-       "\n",
-       "Indicator  Mortality-pollution  Net migration      Ninis  R&D GExp  \\\n",
-       "0                          NaN            NaN        NaN       NaN   \n",
-       "1                          NaN            NaN        NaN       NaN   \n",
-       "2                          NaN       368126.0        NaN       NaN   \n",
-       "3                          NaN            NaN        NaN       NaN   \n",
-       "4                          NaN            NaN        NaN       NaN   \n",
-       "...                        ...            ...        ...       ...   \n",
-       "1504                       NaN       727026.0  31.010000   0.83215   \n",
-       "1505                       NaN            NaN  31.559999       NaN   \n",
-       "1506                       NaN            NaN  32.459999       NaN   \n",
-       "1507                       NaN            NaN  32.400002       NaN   \n",
-       "1508                       NaN            NaN        NaN       NaN   \n",
-       "\n",
-       "Indicator  Renewable electricity  Suicide  Workers high education  \n",
-       "0                            0.0      NaN                     NaN  \n",
-       "1                            0.0      NaN                     NaN  \n",
-       "2                            0.0      NaN                     NaN  \n",
-       "3                            0.0      NaN                     NaN  \n",
-       "4                            0.0      NaN                     NaN  \n",
-       "...                          ...      ...                     ...  \n",
-       "1504                         NaN     25.2               83.809998  \n",
-       "1505                         NaN     24.1               82.879997  \n",
-       "1506                         NaN     23.5               82.019997  \n",
-       "1507                         NaN      NaN                     NaN  \n",
-       "1508                         NaN      NaN                     NaN  \n",
-       "\n",
-       "[1509 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "SilverDataFrame=df_limpio.set_index([\"Country\", \"Year\"]).pivot(columns=\"Indicator\", values=\"Real value\").reset_index()\n",
-    "SilverDataFrame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "SilverDataFrame.to_csv(os.getcwd()+'/Data/SilverDataFrame.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "On the other hand, another big stone of normalizations is to nan/null values, which we have in all variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13351"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "SilverDataFrame.isna().sum().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can observe, we have lots of missing data, and as there is no optimal way to fullfill these values, thus, we will test some to arrive to the optimal method for our data set."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we need to create some lists so our loops work."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df=SilverDataFrame\n",
-    "europe_list=['DEU','FRA','SWE','GBR','ESP','HRV','POL','GRC','AUT','NLD']\n",
-    "persian_list=['IRQ','QAT','ARE','SAU','AZE','YEM','YDR','OMN']\n",
-    "naf_list=['DZA','EGY','LBY','ISR','TUR','MAR']\n",
-    "saf_list=['SEN','ZAF','LBR','MOZ','CMR','NGA','GHA']\n",
-    "asia_list=['BGD','IND','VNM','THA','IDN','PHL','KOR']\n",
-    "latam_list=['MEX','BRA','ARG','PER','VEN','COL','CHL','PAN','CRI']\n",
-    "two_list=['USA','CHN']\n",
-    "country_list=europe_list+persian_list+naf_list+saf_list+asia_list+latam_list+two_list\n",
-    "col_to_scale=['Gender equality','Exports-Commercial services','Renewable electricity','Employment-agriculture','Employment-industry','Employment-services','Exports-G&S','Fertility rate','Foreign investment','GDP','Education GExp','Workers high education','Literacy rate','Mortality-pollution','Net migration','Mortality-infants','Health services use','R&D GExp','Ninis','Suicide','International taxes','Alcohol per capita']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are attempting the linear interpolation, which is achieved by geometrically rendering a straight line between two adjacent points on a graph or plane."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8519"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
-    "datc=dat.interpolate(method=\"linear\")\n",
-    "data=datc\n",
-    "\n",
-    "for i in range(1,len(country_list)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
-    "    datc=dat.interpolate(method=\"linear\")\n",
-    "    data=pd.concat((data, datc), axis = 0)\n",
-    "data.isna().sum().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we attempt the backward filling. (Filling the previous cell with future values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4500"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
-    "datc=dat.fillna(method='bfill')\n",
-    "data=datc\n",
-    "\n",
-    "for i in range(1,len(country_list)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
-    "    datc=dat.fillna(method='bfill')\n",
-    "    data=pd.concat((data, datc), axis = 0)\n",
-    "data.isna().sum().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we will attempt the forward filling. (Filling the next cell with previous values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8519"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
-    "datc=dat.fillna(method='ffill')\n",
-    "data=datc\n",
-    "\n",
-    "for i in range(1,len(country_list)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
-    "    datc=dat.fillna(method='ffill')\n",
-    "    data=pd.concat((data, datc), axis = 0)\n",
-    "data.isna().sum().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And as none of the methods have worked out correctly, independently, we are going to mix them, to achieve a better result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8519"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
-    "datc=dat.interpolate(method=\"linear\")\n",
-    "datc=datc.fillna(method='ffill')\n",
-    "data=datc\n",
-    "\n",
-    "for i in range(1,len(country_list)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
-    "    datc=dat.interpolate(method=\"linear\")\n",
-    "    datc=datc.fillna(method='ffill')\n",
-    "    data=pd.concat((data, datc), axis = 0)\n",
-    "data.isna().sum().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2447"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
-    "datc=dat.interpolate(method=\"linear\")\n",
-    "datc=datc.fillna(method='bfill')\n",
-    "data=datc\n",
-    "\n",
-    "for i in range(1,len(country_list)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
-    "    datc=dat.interpolate(method=\"linear\")\n",
-    "    datc=datc.fillna(method='bfill')\n",
-    "    data=pd.concat((data, datc), axis = 0)\n",
-    "data.isna().sum().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And finally, mixing the three methods all together."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2447"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
-    "datc=dat.interpolate(method=\"linear\")\n",
-    "datf=datc.fillna(method='bfill')\n",
-    "datr=datf.fillna(method='ffill')\n",
-    "data=datr\n",
-    "\n",
-    "for i in range(1,len(country_list)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
-    "    datc=dat.interpolate(method=\"linear\")\n",
-    "    datc=datc.fillna(method='bfill')\n",
-    "    datc=datc.fillna(method='ffill')\n",
-    "    data=pd.concat((data, datc), axis = 0)\n",
-    "data.isna().sum().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Therefore, the preferred method for the Nan values´ treatment that we are going to develop is a mix, between the linear interpolation and backwards filling. The linear interpolation a form of interpolation, which involves the generation of new values based on an existing set of values. Linear interpolation is achieved by geometrically rendering a straight line between two adjacent points on a graph or plane. Whereas the backwards filling, will help us to arrive to those values which have not been fullfilled with the linear interpolation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Moreover, we are also going to scale all the values between the max and min of each country for each variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>Fertility rate</th>\n",
-       "      <th>...</th>\n",
-       "      <th>International taxes</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Mortality-pollution</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>347</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002386</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.636364</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>348</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.025535</td>\n",
-       "      <td>0.272727</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002525</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>349</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.964758</td>\n",
-       "      <td>0.966793</td>\n",
-       "      <td>0.034321</td>\n",
-       "      <td>0.018415</td>\n",
-       "      <td>0.046400</td>\n",
-       "      <td>0.151515</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.875324</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.004025</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>350</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1993</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.942731</td>\n",
-       "      <td>0.907021</td>\n",
-       "      <td>0.088143</td>\n",
-       "      <td>0.012182</td>\n",
-       "      <td>0.010957</td>\n",
-       "      <td>0.121212</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.765220</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.823187</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.005848</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>351</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1994</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.380220</td>\n",
-       "      <td>0.903084</td>\n",
-       "      <td>0.876660</td>\n",
-       "      <td>0.119345</td>\n",
-       "      <td>0.018513</td>\n",
-       "      <td>0.041261</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.670984</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.680250</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011012</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>247</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>0.99002</td>\n",
-       "      <td>0.246406</td>\n",
-       "      <td>0.068956</td>\n",
-       "      <td>0.831461</td>\n",
-       "      <td>0.867725</td>\n",
-       "      <td>0.849888</td>\n",
-       "      <td>0.804316</td>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.790308</td>\n",
-       "      <td>0.977482</td>\n",
-       "      <td>0.042573</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.264203</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.974482</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.016129</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>248</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>0.98004</td>\n",
-       "      <td>0.212024</td>\n",
-       "      <td>0.048007</td>\n",
-       "      <td>0.753933</td>\n",
-       "      <td>0.917460</td>\n",
-       "      <td>0.867490</td>\n",
-       "      <td>0.888169</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.988741</td>\n",
-       "      <td>0.030745</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.984436</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>249</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.021530</td>\n",
-       "      <td>0.777528</td>\n",
-       "      <td>0.942152</td>\n",
-       "      <td>0.953748</td>\n",
-       "      <td>0.974699</td>\n",
-       "      <td>0.698529</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.019650</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.964730</td>\n",
-       "      <td>0.742647</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.009383</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.957514</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.772059</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1509 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Indicator Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "347           DEU  1990             1.00000        0.443529   \n",
-       "348           DEU  1991             1.00000        0.443529   \n",
-       "349           DEU  1992             1.00000        0.443529   \n",
-       "350           DEU  1993             1.00000        0.443529   \n",
-       "351           DEU  1994             1.00000        0.380220   \n",
-       "..            ...   ...                 ...             ...   \n",
-       "247           CHN  2016             0.99002        0.246406   \n",
-       "248           CHN  2017             0.98004        0.212024   \n",
-       "249           CHN  2018             0.97006        0.000000   \n",
-       "250           CHN  2019             0.97006        0.000000   \n",
-       "251           CHN  2020             0.97006        0.000000   \n",
-       "\n",
-       "Indicator  Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "347                      1.000000             1.000000             0.000000   \n",
-       "348                      1.000000             1.000000             0.000000   \n",
-       "349                      0.964758             0.966793             0.034321   \n",
-       "350                      0.942731             0.907021             0.088143   \n",
-       "351                      0.903084             0.876660             0.119345   \n",
-       "..                            ...                  ...                  ...   \n",
-       "247                      0.068956             0.831461             0.867725   \n",
-       "248                      0.048007             0.753933             0.917460   \n",
-       "249                      0.021530             0.777528             0.942152   \n",
-       "250                      0.000000             0.676405             1.000000   \n",
-       "251                      0.000000             0.676405             1.000000   \n",
-       "\n",
-       "Indicator  Exports-Commercial services  Exports-G&S  Fertility rate  ...  \\\n",
-       "347                           0.002386     0.000000        0.636364  ...   \n",
-       "348                           0.000000     0.025535        0.272727  ...   \n",
-       "349                           0.018415     0.046400        0.151515  ...   \n",
-       "350                           0.012182     0.010957        0.121212  ...   \n",
-       "351                           0.018513     0.041261        0.000000  ...   \n",
-       "..                                 ...          ...             ...  ...   \n",
-       "247                           0.849888     0.804316        0.588235  ...   \n",
-       "248                           0.867490     0.888169        0.647059  ...   \n",
-       "249                           0.953748     0.974699        0.698529  ...   \n",
-       "250                           1.000000     0.964730        0.742647  ...   \n",
-       "251                           0.957514     1.000000        0.772059  ...   \n",
-       "\n",
-       "Indicator  International taxes  Literacy rate  Mortality-infants  \\\n",
-       "347                        NaN            NaN           1.000000   \n",
-       "348                        NaN            NaN           1.000000   \n",
-       "349                        NaN            NaN           0.875324   \n",
-       "350                        NaN            NaN           0.765220   \n",
-       "351                        NaN            NaN           0.670984   \n",
-       "..                         ...            ...                ...   \n",
-       "247                   0.790308       0.977482           0.042573   \n",
-       "248                   1.000000       0.988741           0.030745   \n",
-       "249                   0.920304       1.000000           0.019650   \n",
-       "250                   0.920304       1.000000           0.009383   \n",
-       "251                   0.920304       1.000000           0.000000   \n",
-       "\n",
-       "Indicator  Mortality-pollution  Net migration    Ninis  R&D GExp  \\\n",
-       "347                        NaN       0.966124  0.77758  0.000000   \n",
-       "348                        NaN       0.966124  0.77758  0.000000   \n",
-       "349                        NaN       0.966124  0.77758  0.000000   \n",
-       "350                        NaN       0.823187  0.77758  0.000000   \n",
-       "351                        NaN       0.680250  0.77758  0.000000   \n",
-       "..                         ...            ...      ...       ...   \n",
-       "247                        NaN       0.264203      NaN  0.974482   \n",
-       "248                        NaN       0.243061      NaN  0.984436   \n",
-       "249                        NaN       0.243061      NaN  1.000000   \n",
-       "250                        NaN       0.243061      NaN  1.000000   \n",
-       "251                        NaN       0.243061      NaN  1.000000   \n",
-       "\n",
-       "Indicator  Renewable electricity   Suicide  Workers high education  \n",
-       "347                     0.000000  0.956522                     1.0  \n",
-       "348                     0.002525  0.956522                     1.0  \n",
-       "349                     0.004025  0.956522                     1.0  \n",
-       "350                     0.005848  0.956522                     1.0  \n",
-       "351                     0.011012  0.956522                     1.0  \n",
-       "..                           ...       ...                     ...  \n",
-       "247                     1.000000  0.016129                     NaN  \n",
-       "248                     1.000000  0.000000                     NaN  \n",
-       "249                     1.000000  0.000000                     NaN  \n",
-       "250                     1.000000  0.000000                     NaN  \n",
-       "251                     1.000000  0.000000                     NaN  \n",
-       "\n",
-       "[1509 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
-    "datc=dat.interpolate(method=\"linear\")\n",
-    "datf=datc.fillna(method='bfill')\n",
-    "datr[col_to_scale]=(datr[col_to_scale]-datr[col_to_scale].min())/(datr[col_to_scale].max()-datr[col_to_scale].min())\n",
-    "data=datr\n",
-    "\n",
-    "for i in range(1,len(country_list)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
-    "    datc=dat.interpolate(method=\"linear\")\n",
-    "    datc=datc.fillna(method='bfill')\n",
-    "    datc[col_to_scale]=(datc[col_to_scale]-datc[col_to_scale].min())/(datc[col_to_scale].max()-datc[col_to_scale].min())\n",
-    "    data=pd.concat((data, datc), axis = 0)\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we will drop the columns which have over 1000 missing values, because the absence of data creates an unreliable source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gender equality\n",
-      "Mortality-pollution\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>Fertility rate</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Health services use</th>\n",
-       "      <th>International taxes</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>347</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002386</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.636364</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>348</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.025535</td>\n",
-       "      <td>0.272727</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002525</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>349</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.964758</td>\n",
-       "      <td>0.966793</td>\n",
-       "      <td>0.034321</td>\n",
-       "      <td>0.018415</td>\n",
-       "      <td>0.046400</td>\n",
-       "      <td>0.151515</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.875324</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.004025</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>350</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1993</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.942731</td>\n",
-       "      <td>0.907021</td>\n",
-       "      <td>0.088143</td>\n",
-       "      <td>0.012182</td>\n",
-       "      <td>0.010957</td>\n",
-       "      <td>0.121212</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.765220</td>\n",
-       "      <td>0.823187</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.005848</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>351</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1994</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.380220</td>\n",
-       "      <td>0.903084</td>\n",
-       "      <td>0.876660</td>\n",
-       "      <td>0.119345</td>\n",
-       "      <td>0.018513</td>\n",
-       "      <td>0.041261</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.670984</td>\n",
-       "      <td>0.680250</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011012</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>247</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>0.99002</td>\n",
-       "      <td>0.246406</td>\n",
-       "      <td>0.068956</td>\n",
-       "      <td>0.831461</td>\n",
-       "      <td>0.867725</td>\n",
-       "      <td>0.849888</td>\n",
-       "      <td>0.804316</td>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.822241</td>\n",
-       "      <td>0.790308</td>\n",
-       "      <td>0.977482</td>\n",
-       "      <td>0.042573</td>\n",
-       "      <td>0.264203</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.974482</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.016129</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>248</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>0.98004</td>\n",
-       "      <td>0.212024</td>\n",
-       "      <td>0.048007</td>\n",
-       "      <td>0.753933</td>\n",
-       "      <td>0.917460</td>\n",
-       "      <td>0.867490</td>\n",
-       "      <td>0.888169</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.868064</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.988741</td>\n",
-       "      <td>0.030745</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.984436</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>249</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.021530</td>\n",
-       "      <td>0.777528</td>\n",
-       "      <td>0.942152</td>\n",
-       "      <td>0.953748</td>\n",
-       "      <td>0.974699</td>\n",
-       "      <td>0.698529</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.912945</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.019650</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.964730</td>\n",
-       "      <td>0.742647</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.956910</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.009383</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.957514</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.772059</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1509 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Indicator Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "347           DEU  1990             1.00000        0.443529   \n",
-       "348           DEU  1991             1.00000        0.443529   \n",
-       "349           DEU  1992             1.00000        0.443529   \n",
-       "350           DEU  1993             1.00000        0.443529   \n",
-       "351           DEU  1994             1.00000        0.380220   \n",
-       "..            ...   ...                 ...             ...   \n",
-       "247           CHN  2016             0.99002        0.246406   \n",
-       "248           CHN  2017             0.98004        0.212024   \n",
-       "249           CHN  2018             0.97006        0.000000   \n",
-       "250           CHN  2019             0.97006        0.000000   \n",
-       "251           CHN  2020             0.97006        0.000000   \n",
-       "\n",
-       "Indicator  Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "347                      1.000000             1.000000             0.000000   \n",
-       "348                      1.000000             1.000000             0.000000   \n",
-       "349                      0.964758             0.966793             0.034321   \n",
-       "350                      0.942731             0.907021             0.088143   \n",
-       "351                      0.903084             0.876660             0.119345   \n",
-       "..                            ...                  ...                  ...   \n",
-       "247                      0.068956             0.831461             0.867725   \n",
-       "248                      0.048007             0.753933             0.917460   \n",
-       "249                      0.021530             0.777528             0.942152   \n",
-       "250                      0.000000             0.676405             1.000000   \n",
-       "251                      0.000000             0.676405             1.000000   \n",
-       "\n",
-       "Indicator  Exports-Commercial services  Exports-G&S  Fertility rate  ...  \\\n",
-       "347                           0.002386     0.000000        0.636364  ...   \n",
-       "348                           0.000000     0.025535        0.272727  ...   \n",
-       "349                           0.018415     0.046400        0.151515  ...   \n",
-       "350                           0.012182     0.010957        0.121212  ...   \n",
-       "351                           0.018513     0.041261        0.000000  ...   \n",
-       "..                                 ...          ...             ...  ...   \n",
-       "247                           0.849888     0.804316        0.588235  ...   \n",
-       "248                           0.867490     0.888169        0.647059  ...   \n",
-       "249                           0.953748     0.974699        0.698529  ...   \n",
-       "250                           1.000000     0.964730        0.742647  ...   \n",
-       "251                           0.957514     1.000000        0.772059  ...   \n",
-       "\n",
-       "Indicator  Health services use  International taxes  Literacy rate  \\\n",
-       "347                   0.000000                  NaN            NaN   \n",
-       "348                   0.000000                  NaN            NaN   \n",
-       "349                   0.000000                  NaN            NaN   \n",
-       "350                   0.000000                  NaN            NaN   \n",
-       "351                   0.000000                  NaN            NaN   \n",
-       "..                         ...                  ...            ...   \n",
-       "247                   0.822241             0.790308       0.977482   \n",
-       "248                   0.868064             1.000000       0.988741   \n",
-       "249                   0.912945             0.920304       1.000000   \n",
-       "250                   0.956910             0.920304       1.000000   \n",
-       "251                   1.000000             0.920304       1.000000   \n",
-       "\n",
-       "Indicator  Mortality-infants  Net migration    Ninis  R&D GExp  \\\n",
-       "347                 1.000000       0.966124  0.77758  0.000000   \n",
-       "348                 1.000000       0.966124  0.77758  0.000000   \n",
-       "349                 0.875324       0.966124  0.77758  0.000000   \n",
-       "350                 0.765220       0.823187  0.77758  0.000000   \n",
-       "351                 0.670984       0.680250  0.77758  0.000000   \n",
-       "..                       ...            ...      ...       ...   \n",
-       "247                 0.042573       0.264203      NaN  0.974482   \n",
-       "248                 0.030745       0.243061      NaN  0.984436   \n",
-       "249                 0.019650       0.243061      NaN  1.000000   \n",
-       "250                 0.009383       0.243061      NaN  1.000000   \n",
-       "251                 0.000000       0.243061      NaN  1.000000   \n",
-       "\n",
-       "Indicator  Renewable electricity   Suicide  Workers high education  \n",
-       "347                     0.000000  0.956522                     1.0  \n",
-       "348                     0.002525  0.956522                     1.0  \n",
-       "349                     0.004025  0.956522                     1.0  \n",
-       "350                     0.005848  0.956522                     1.0  \n",
-       "351                     0.011012  0.956522                     1.0  \n",
-       "..                           ...       ...                     ...  \n",
-       "247                     1.000000  0.016129                     NaN  \n",
-       "248                     1.000000  0.000000                     NaN  \n",
-       "249                     1.000000  0.000000                     NaN  \n",
-       "250                     1.000000  0.000000                     NaN  \n",
-       "251                     1.000000  0.000000                     NaN  \n",
-       "\n",
-       "[1509 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(0, len(col_to_scale)):\n",
-    "    if data[col_to_scale[i]].isna().sum()>1000:\n",
-    "        del(data[col_to_scale[i]])\n",
-    "        print(col_to_scale[i])\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a result we have dropped the *Gender equality* and *Mortality-pollution* variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the next part of analyzing this data, we think it is gonna be interesting to have it classify by the categories of the Country groups defined before, to which we call \"Continent\". This category is useful as it groups the nations with similar economies or geographical proximity, so we can extract common conclusions from them."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We create a dictionary with the regions and the countries included in each one. Where we will relate the countries and regions so then we can apply the .map function and arrive to the final dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'DEU': 'Europe', 'FRA': 'Europe', 'SWE': 'Europe', 'GBR': 'Europe', 'ESP': 'Europe', 'HRV': 'Europe', 'POL': 'Europe', 'GRC': 'Europe', 'AUT': 'Europe', 'NLD': 'Europe', 'IRQ': 'Persian Gulf', 'QAT': 'Persian Gulf', 'ARE': 'Persian Gulf', 'SAU': 'Persian Gulf', 'AZE': 'Persian Gulf', 'YEM': 'Persian Gulf', 'YDR': 'Persian Gulf', 'OMN': 'Persian Gulf', 'DZA': 'North Africa', 'EGY': 'North Africa', 'LBY': 'North Africa', 'ISR': 'North Africa', 'TUR': 'North Africa', 'MAR': 'North Africa', 'SEN': 'South Africa', 'ZAF': 'South Africa', 'LBR': 'South Africa', 'MOZ': 'South Africa', 'CMR': 'South Africa', 'NGA': 'South Africa', 'GHA': 'South Africa', 'BGD': 'Asia', 'IND': 'Asia', 'VNM': 'Asia', 'THA': 'Asia', 'IDN': 'Asia', 'PHL': 'Asia', 'KOR': 'Asia', 'MEX': 'Latam', 'BRA': 'Latam', 'ARG': 'Latam', 'PER': 'Latam', 'VEN': 'Latam', 'COL': 'Latam', 'CHL': 'Latam', 'PAN': 'Latam', 'CRI': 'Latam', 'USA': 'Pair', 'CHN': 'Pair'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "countries_by_region = {\n",
-    "    \"Europe\": ('DEU','FRA','SWE','GBR','ESP','HRV','POL','GRC','AUT','NLD'),\n",
-    "    'Persian Gulf': ('IRQ','QAT','ARE','SAU','AZE','YEM','YDR','OMN'),\n",
-    "    'North Africa':('DZA','EGY','LBY','ISR','TUR','MAR'),\n",
-    "    'South Africa':('SEN','ZAF','LBR','MOZ','CMR','NGA','GHA'),\n",
-    "    'Asia':('BGD','IND','VNM','THA','IDN','PHL','KOR'),\n",
-    "    'Latam':('MEX','BRA','ARG','PER','VEN','COL','CHL','PAN','CRI'),\n",
-    "    'Pair':('USA','CHN')\n",
-    "    }\n",
-    "\n",
-    "all_countries = {}\n",
-    "for region in countries_by_region.keys():\n",
-    "  for country in countries_by_region[region]:\n",
-    "    all_countries[country] = region\n",
-    "\n",
-    "print(all_countries)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Indicator</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>Fertility rate</th>\n",
-       "      <th>...</th>\n",
-       "      <th>International taxes</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
-       "      <th>Continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>347</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002386</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.636364</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>348</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.025535</td>\n",
-       "      <td>0.272727</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002525</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>349</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.964758</td>\n",
-       "      <td>0.966793</td>\n",
-       "      <td>0.034321</td>\n",
-       "      <td>0.018415</td>\n",
-       "      <td>0.046400</td>\n",
-       "      <td>0.151515</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.875324</td>\n",
-       "      <td>0.966124</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.004025</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>350</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1993</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.443529</td>\n",
-       "      <td>0.942731</td>\n",
-       "      <td>0.907021</td>\n",
-       "      <td>0.088143</td>\n",
-       "      <td>0.012182</td>\n",
-       "      <td>0.010957</td>\n",
-       "      <td>0.121212</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.765220</td>\n",
-       "      <td>0.823187</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.005848</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>351</th>\n",
-       "      <td>DEU</td>\n",
-       "      <td>1994</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.380220</td>\n",
-       "      <td>0.903084</td>\n",
-       "      <td>0.876660</td>\n",
-       "      <td>0.119345</td>\n",
-       "      <td>0.018513</td>\n",
-       "      <td>0.041261</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.670984</td>\n",
-       "      <td>0.680250</td>\n",
-       "      <td>0.77758</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011012</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>247</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>0.99002</td>\n",
-       "      <td>0.246406</td>\n",
-       "      <td>0.068956</td>\n",
-       "      <td>0.831461</td>\n",
-       "      <td>0.867725</td>\n",
-       "      <td>0.849888</td>\n",
-       "      <td>0.804316</td>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.790308</td>\n",
-       "      <td>0.977482</td>\n",
-       "      <td>0.042573</td>\n",
-       "      <td>0.264203</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.974482</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.016129</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>248</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>0.98004</td>\n",
-       "      <td>0.212024</td>\n",
-       "      <td>0.048007</td>\n",
-       "      <td>0.753933</td>\n",
-       "      <td>0.917460</td>\n",
-       "      <td>0.867490</td>\n",
-       "      <td>0.888169</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.988741</td>\n",
-       "      <td>0.030745</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.984436</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>249</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.021530</td>\n",
-       "      <td>0.777528</td>\n",
-       "      <td>0.942152</td>\n",
-       "      <td>0.953748</td>\n",
-       "      <td>0.974699</td>\n",
-       "      <td>0.698529</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.019650</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.964730</td>\n",
-       "      <td>0.742647</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.009383</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>0.97006</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.676405</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.957514</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.772059</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.920304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.243061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pair</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1509 rows × 23 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Indicator Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "347           DEU  1990             1.00000        0.443529   \n",
-       "348           DEU  1991             1.00000        0.443529   \n",
-       "349           DEU  1992             1.00000        0.443529   \n",
-       "350           DEU  1993             1.00000        0.443529   \n",
-       "351           DEU  1994             1.00000        0.380220   \n",
-       "..            ...   ...                 ...             ...   \n",
-       "247           CHN  2016             0.99002        0.246406   \n",
-       "248           CHN  2017             0.98004        0.212024   \n",
-       "249           CHN  2018             0.97006        0.000000   \n",
-       "250           CHN  2019             0.97006        0.000000   \n",
-       "251           CHN  2020             0.97006        0.000000   \n",
-       "\n",
-       "Indicator  Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "347                      1.000000             1.000000             0.000000   \n",
-       "348                      1.000000             1.000000             0.000000   \n",
-       "349                      0.964758             0.966793             0.034321   \n",
-       "350                      0.942731             0.907021             0.088143   \n",
-       "351                      0.903084             0.876660             0.119345   \n",
-       "..                            ...                  ...                  ...   \n",
-       "247                      0.068956             0.831461             0.867725   \n",
-       "248                      0.048007             0.753933             0.917460   \n",
-       "249                      0.021530             0.777528             0.942152   \n",
-       "250                      0.000000             0.676405             1.000000   \n",
-       "251                      0.000000             0.676405             1.000000   \n",
-       "\n",
-       "Indicator  Exports-Commercial services  Exports-G&S  Fertility rate  ...  \\\n",
-       "347                           0.002386     0.000000        0.636364  ...   \n",
-       "348                           0.000000     0.025535        0.272727  ...   \n",
-       "349                           0.018415     0.046400        0.151515  ...   \n",
-       "350                           0.012182     0.010957        0.121212  ...   \n",
-       "351                           0.018513     0.041261        0.000000  ...   \n",
-       "..                                 ...          ...             ...  ...   \n",
-       "247                           0.849888     0.804316        0.588235  ...   \n",
-       "248                           0.867490     0.888169        0.647059  ...   \n",
-       "249                           0.953748     0.974699        0.698529  ...   \n",
-       "250                           1.000000     0.964730        0.742647  ...   \n",
-       "251                           0.957514     1.000000        0.772059  ...   \n",
-       "\n",
-       "Indicator  International taxes  Literacy rate  Mortality-infants  \\\n",
-       "347                        NaN            NaN           1.000000   \n",
-       "348                        NaN            NaN           1.000000   \n",
-       "349                        NaN            NaN           0.875324   \n",
-       "350                        NaN            NaN           0.765220   \n",
-       "351                        NaN            NaN           0.670984   \n",
-       "..                         ...            ...                ...   \n",
-       "247                   0.790308       0.977482           0.042573   \n",
-       "248                   1.000000       0.988741           0.030745   \n",
-       "249                   0.920304       1.000000           0.019650   \n",
-       "250                   0.920304       1.000000           0.009383   \n",
-       "251                   0.920304       1.000000           0.000000   \n",
-       "\n",
-       "Indicator  Net migration    Ninis  R&D GExp  Renewable electricity   Suicide  \\\n",
-       "347             0.966124  0.77758  0.000000               0.000000  0.956522   \n",
-       "348             0.966124  0.77758  0.000000               0.002525  0.956522   \n",
-       "349             0.966124  0.77758  0.000000               0.004025  0.956522   \n",
-       "350             0.823187  0.77758  0.000000               0.005848  0.956522   \n",
-       "351             0.680250  0.77758  0.000000               0.011012  0.956522   \n",
-       "..                   ...      ...       ...                    ...       ...   \n",
-       "247             0.264203      NaN  0.974482               1.000000  0.016129   \n",
-       "248             0.243061      NaN  0.984436               1.000000  0.000000   \n",
-       "249             0.243061      NaN  1.000000               1.000000  0.000000   \n",
-       "250             0.243061      NaN  1.000000               1.000000  0.000000   \n",
-       "251             0.243061      NaN  1.000000               1.000000  0.000000   \n",
-       "\n",
-       "Indicator  Workers high education  Continent  \n",
-       "347                           1.0     Europe  \n",
-       "348                           1.0     Europe  \n",
-       "349                           1.0     Europe  \n",
-       "350                           1.0     Europe  \n",
-       "351                           1.0     Europe  \n",
-       "..                            ...        ...  \n",
-       "247                           NaN       Pair  \n",
-       "248                           NaN       Pair  \n",
-       "249                           NaN       Pair  \n",
-       "250                           NaN       Pair  \n",
-       "251                           NaN       Pair  \n",
-       "\n",
-       "[1509 rows x 23 columns]"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data['Continent']=data['Country'].map(all_countries)\n",
-    "Goldendataframe=data\n",
-    "Goldendataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With that all, we export our dataframe all-in-one and by the continent category."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Goldendataframe.to_csv(os.getcwd()+'/Data/GoldenDataFrame.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for region, data in Goldendataframe.groupby('Continent'):\n",
-    "   data.to_csv(os.getcwd()+'/Data/{}.csv'.format(region))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 ('.venv': poetry)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "292912297167048338b5d1136ec8c661b8a2f1d3fcecd6650d637809a967c218"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/WDI-Data visualization (Case).ipynb b/WDI-Data visualization (Case).ipynb
deleted file mode 100644
index f13019d..0000000
--- a/WDI-Data visualization (Case).ipynb	
+++ /dev/null
@@ -1,23619 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Correlation matrix and visualization of variables and GDP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### This is the master notebook, please copy and create a new one for each group."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy import stats\n",
-    "from scipy.stats import shapiro\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Alcohol per capita</th>\n",
-       "      <th>Education GExp</th>\n",
-       "      <th>Employment-agriculture</th>\n",
-       "      <th>Employment-industry</th>\n",
-       "      <th>Employment-services</th>\n",
-       "      <th>Exports-Commercial services</th>\n",
-       "      <th>Exports-G&amp;S</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Literacy rate</th>\n",
-       "      <th>Mortality-infants</th>\n",
-       "      <th>Mortality-pollution</th>\n",
-       "      <th>Net migration</th>\n",
-       "      <th>Ninis</th>\n",
-       "      <th>R&amp;D GExp</th>\n",
-       "      <th>Renewable electricity</th>\n",
-       "      <th>Suicide</th>\n",
-       "      <th>Workers high education</th>\n",
-       "      <th>Continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>880</td>\n",
-       "      <td>MEX</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>0.967949</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.468438</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.139214</td>\n",
-       "      <td>0.814433</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.497584</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>881</td>\n",
-       "      <td>MEX</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>0.967949</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.468438</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.086627</td>\n",
-       "      <td>0.005844</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.038082</td>\n",
-       "      <td>0.941673</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.139214</td>\n",
-       "      <td>0.814433</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.497584</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>882</td>\n",
-       "      <td>MEX</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>0.967949</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.980256</td>\n",
-       "      <td>0.428572</td>\n",
-       "      <td>0.033030</td>\n",
-       "      <td>0.159343</td>\n",
-       "      <td>0.014734</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.076165</td>\n",
-       "      <td>0.884159</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.139214</td>\n",
-       "      <td>0.814433</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.277981</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.497584</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>883</td>\n",
-       "      <td>MEX</td>\n",
-       "      <td>1993</td>\n",
-       "      <td>0.967949</td>\n",
-       "      <td>0.358272</td>\n",
-       "      <td>0.959350</td>\n",
-       "      <td>0.378738</td>\n",
-       "      <td>0.070615</td>\n",
-       "      <td>0.220628</td>\n",
-       "      <td>0.028455</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.114247</td>\n",
-       "      <td>0.828258</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.111371</td>\n",
-       "      <td>0.814433</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.363688</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.497584</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>884</td>\n",
-       "      <td>MEX</td>\n",
-       "      <td>1994</td>\n",
-       "      <td>0.967949</td>\n",
-       "      <td>0.716544</td>\n",
-       "      <td>0.930314</td>\n",
-       "      <td>0.355482</td>\n",
-       "      <td>0.107062</td>\n",
-       "      <td>0.383841</td>\n",
-       "      <td>0.049046</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.152329</td>\n",
-       "      <td>0.773986</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.083529</td>\n",
-       "      <td>0.814433</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.284184</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.497584</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>246</th>\n",
-       "      <td>342</td>\n",
-       "      <td>CRI</td>\n",
-       "      <td>2016</td>\n",
-       "      <td>0.046414</td>\n",
-       "      <td>0.714923</td>\n",
-       "      <td>0.288750</td>\n",
-       "      <td>0.215447</td>\n",
-       "      <td>0.751260</td>\n",
-       "      <td>0.813167</td>\n",
-       "      <td>0.828978</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.956392</td>\n",
-       "      <td>0.109116</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.015366</td>\n",
-       "      <td>0.779070</td>\n",
-       "      <td>0.621510</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.78125</td>\n",
-       "      <td>0.108000</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>247</th>\n",
-       "      <td>343</td>\n",
-       "      <td>CRI</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>0.092827</td>\n",
-       "      <td>0.857461</td>\n",
-       "      <td>0.335000</td>\n",
-       "      <td>0.199187</td>\n",
-       "      <td>0.739496</td>\n",
-       "      <td>0.819092</td>\n",
-       "      <td>0.901126</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.978196</td>\n",
-       "      <td>0.098066</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.019207</td>\n",
-       "      <td>0.646512</td>\n",
-       "      <td>0.569895</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.28125</td>\n",
-       "      <td>0.150666</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>248</th>\n",
-       "      <td>344</td>\n",
-       "      <td>CRI</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>0.139241</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.312500</td>\n",
-       "      <td>0.323171</td>\n",
-       "      <td>0.681232</td>\n",
-       "      <td>0.938219</td>\n",
-       "      <td>0.963286</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.078729</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.019207</td>\n",
-       "      <td>0.395349</td>\n",
-       "      <td>0.380715</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.62500</td>\n",
-       "      <td>0.117333</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>249</th>\n",
-       "      <td>345</td>\n",
-       "      <td>CRI</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>0.139241</td>\n",
-       "      <td>0.835635</td>\n",
-       "      <td>0.258750</td>\n",
-       "      <td>0.234756</td>\n",
-       "      <td>0.753501</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.046961</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.019207</td>\n",
-       "      <td>0.134884</td>\n",
-       "      <td>0.380715</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.90625</td>\n",
-       "      <td>0.653333</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250</th>\n",
-       "      <td>346</td>\n",
-       "      <td>CRI</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>0.139241</td>\n",
-       "      <td>0.513304</td>\n",
-       "      <td>0.258750</td>\n",
-       "      <td>0.234756</td>\n",
-       "      <td>0.753501</td>\n",
-       "      <td>0.758953</td>\n",
-       "      <td>0.884100</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.019207</td>\n",
-       "      <td>0.660465</td>\n",
-       "      <td>0.380715</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.90625</td>\n",
-       "      <td>0.417333</td>\n",
-       "      <td>Latam</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>251 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Unnamed: 0 Country  Year  Alcohol per capita  Education GExp  \\\n",
-       "0           880     MEX  1990            0.967949        0.000000   \n",
-       "1           881     MEX  1991            0.967949        0.000000   \n",
-       "2           882     MEX  1992            0.967949        0.000000   \n",
-       "3           883     MEX  1993            0.967949        0.358272   \n",
-       "4           884     MEX  1994            0.967949        0.716544   \n",
-       "..          ...     ...   ...                 ...             ...   \n",
-       "246         342     CRI  2016            0.046414        0.714923   \n",
-       "247         343     CRI  2017            0.092827        0.857461   \n",
-       "248         344     CRI  2018            0.139241        1.000000   \n",
-       "249         345     CRI  2019            0.139241        0.835635   \n",
-       "250         346     CRI  2020            0.139241        0.513304   \n",
-       "\n",
-       "     Employment-agriculture  Employment-industry  Employment-services  \\\n",
-       "0                  1.000000             0.468438             0.000000   \n",
-       "1                  1.000000             0.468438             0.000000   \n",
-       "2                  0.980256             0.428572             0.033030   \n",
-       "3                  0.959350             0.378738             0.070615   \n",
-       "4                  0.930314             0.355482             0.107062   \n",
-       "..                      ...                  ...                  ...   \n",
-       "246                0.288750             0.215447             0.751260   \n",
-       "247                0.335000             0.199187             0.739496   \n",
-       "248                0.312500             0.323171             0.681232   \n",
-       "249                0.258750             0.234756             0.753501   \n",
-       "250                0.258750             0.234756             0.753501   \n",
-       "\n",
-       "     Exports-Commercial services  Exports-G&S  ...  Literacy rate  \\\n",
-       "0                       0.000000     0.000000  ...       0.000000   \n",
-       "1                       0.086627     0.005844  ...       0.038082   \n",
-       "2                       0.159343     0.014734  ...       0.076165   \n",
-       "3                       0.220628     0.028455  ...       0.114247   \n",
-       "4                       0.383841     0.049046  ...       0.152329   \n",
-       "..                           ...          ...  ...            ...   \n",
-       "246                     0.813167     0.828978  ...       0.956392   \n",
-       "247                     0.819092     0.901126  ...       0.978196   \n",
-       "248                     0.938219     0.963286  ...       1.000000   \n",
-       "249                     1.000000     1.000000  ...       1.000000   \n",
-       "250                     0.758953     0.884100  ...       1.000000   \n",
-       "\n",
-       "     Mortality-infants  Mortality-pollution  Net migration     Ninis  \\\n",
-       "0             1.000000                  NaN       0.139214  0.814433   \n",
-       "1             0.941673                  NaN       0.139214  0.814433   \n",
-       "2             0.884159                  NaN       0.139214  0.814433   \n",
-       "3             0.828258                  NaN       0.111371  0.814433   \n",
-       "4             0.773986                  NaN       0.083529  0.814433   \n",
-       "..                 ...                  ...            ...       ...   \n",
-       "246           0.109116                  NaN       0.015366  0.779070   \n",
-       "247           0.098066                  NaN       0.019207  0.646512   \n",
-       "248           0.078729                  NaN       0.019207  0.395349   \n",
-       "249           0.046961                  NaN       0.019207  0.134884   \n",
-       "250           0.000000                  NaN       0.019207  0.660465   \n",
-       "\n",
-       "     R&D GExp  Renewable electricity  Suicide  Workers high education  \\\n",
-       "0    0.000000               0.000000  0.00000                0.497584   \n",
-       "1    0.000000               0.000000  0.00000                0.497584   \n",
-       "2    0.000000               0.277981  0.00000                0.497584   \n",
-       "3    0.000000               0.363688  0.00000                0.497584   \n",
-       "4    0.000000               0.284184  0.00000                0.497584   \n",
-       "..        ...                    ...      ...                     ...   \n",
-       "246  0.621510               1.000000  0.78125                0.108000   \n",
-       "247  0.569895               1.000000  0.28125                0.150666   \n",
-       "248  0.380715               1.000000  0.62500                0.117333   \n",
-       "249  0.380715               1.000000  0.90625                0.653333   \n",
-       "250  0.380715               1.000000  0.90625                0.417333   \n",
-       "\n",
-       "     Continent  \n",
-       "0        Latam  \n",
-       "1        Latam  \n",
-       "2        Latam  \n",
-       "3        Latam  \n",
-       "4        Latam  \n",
-       "..         ...  \n",
-       "246      Latam  \n",
-       "247      Latam  \n",
-       "248      Latam  \n",
-       "249      Latam  \n",
-       "250      Latam  \n",
-       "\n",
-       "[251 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df= pd.read_csv (os.getcwd()+'\\\\Data'+'\\\\Latam.csv')\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "STEP 0: Line graphs by country to anticipate which correlations are we looking for."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: plotly in c:\\users\\mferna38\\documents\\github\\python-data-driven-decisions\\.venv\\lib\\site-packages (5.9.0)\n",
-      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\mferna38\\documents\\github\\python-data-driven-decisions\\.venv\\lib\\site-packages (from plotly) (8.0.1)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: You are using pip version 22.0.3; however, version 22.1.2 is available.\n",
-      "You should consider upgrading via the 'c:\\Users\\mferna38\\Documents\\GitHub\\python-data-driven-decisions\\.venv\\Scripts\\python.exe -m pip install --upgrade pip' command.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pip install plotly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import plotly.express as px"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "myvariables=['Alcohol per capita',\n",
-    " 'Education GExp',\n",
-    " 'Employment-agriculture',\n",
-    " 'Employment-industry',\n",
-    " 'Employment-services',\n",
-    " 'Exports-Commercial services',\n",
-    " 'Exports-G&S',\n",
-    " 'Fertility rate',\n",
-    " 'Foreign investment',\n",
-    " 'GDP',\n",
-    " 'Health services use',\n",
-    " 'International taxes',\n",
-    " 'Literacy rate',\n",
-    " 'Mortality-infants',\n",
-    " 'Net migration',\n",
-    " 'Ninis',\n",
-    " 'R&D GExp',\n",
-    " 'Renewable electricity',\n",
-    " 'Suicide',\n",
-    " 'Workers high education']\n",
-    "latam_list=['MEX','BRA','ARG','PER','VEN','COL','CHL','PAN','CRI']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "hovertemplate": "variable=Alcohol per capita<br>Year=%{x}<br>value=%{y}<extra></extra>",
-         "legendgroup": "Alcohol per capita",
-         "line": {
-          "color": "#636efa",
-          "dash": "solid"
-         },
-         "marker": {
-          "symbol": "circle"
-         },
-         "mode": "lines",
-         "name": "Alcohol per capita",
-         "orientation": "v",
-         "showlegend": true,
-         "type": "scatter",
-         "x": [
-          1990,
-          1991,
-          1992,
-          1993,
-          1994,
-          1995,
-          1996,
-          1997,
-          1998,
-          1999,
-          2000,
-          2001,
-          2002,
-          2003,
-          2004,
-          2005,
-          2006,
-          2007,
-          2008,
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-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "yaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "zaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           }
-          },
-          "shapedefaults": {
-           "line": {
-            "color": "#2a3f5f"
-           }
-          },
-          "ternary": {
-           "aaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "baxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "CRI"
-        },
-        "width": 800,
-        "xaxis": {
-         "anchor": "y",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "Year"
-         }
-        },
-        "yaxis": {
-         "anchor": "x",
-         "domain": [
-          0,
-          1
-         ],
-         "title": {
-          "text": "value"
-         }
-        }
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(0,len(latam_list)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == latam_list[i]]\n",
-    "    a=px.line(dat, x=\"Year\", y=myvariables, width=800, height=600,title=latam_list[i])\n",
-    "    display(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "columns=['Country','Year','Gender equality','Exports-Commercial services','Renewable electricity','Employment-agriculture','Employment-industry','Employment-services','Exports-G&S','Fertility rate','Foreign investment','GDP','Education GExp','Workers high education','Literacy rate','Mortality-pollution','Net migration','Mortality-infants','Health services use','R&D GExp','Ninis','Suicide','International taxes','Alcohol per capita']\n",
-    "clist=df['Country'].unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following loop has been created to test if each variable for each country follows a normal distribution and also to present in an orderly manner all the correlations between the GDP and the variables so they can be understood on a lower scale."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def multcolumn(frame):\n",
-    "    for u in range(2, len(columns)):\n",
-    "        name2=columns[u]+'.^2'\n",
-    "        name3=columns[u]+'.^3'\n",
-    "        namelog=columns[u]+'.log'\n",
-    "        frame.loc[:,name2] = frame[columns[u]]**2\n",
-    "        frame.loc[:,name3] = frame[columns[u]]**3\n",
-    "        frame.loc[:,namelog] = np.log(frame[columns[u]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MEX-Gender equality\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Exports-Commercial services\n",
-      "Statistical=0.734, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Renewable electricity\n",
-      "Statistical=0.964, p=0.361\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Employment-agriculture\n",
-      "Statistical=0.852, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Employment-industry\n",
-      "Statistical=0.960, p=0.291\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Employment-services\n",
-      "Statistical=0.872, p=0.002\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Exports-G&S\n",
-      "Statistical=0.951, p=0.169\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Fertility rate\n",
-      "Statistical=0.927, p=0.036\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Foreign investment\n",
-      "Statistical=0.929, p=0.042\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-GDP\n",
-      "Statistical=0.967, p=0.445\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Education GExp\n",
-      "Statistical=0.928, p=0.039\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Workers high education\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-Literacy rate\n",
-      "Statistical=0.856, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Mortality-pollution\n",
-      "Statistical=0.926, p=0.035\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Net migration\n",
-      "Statistical=0.931, p=0.046\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Mortality-infants\n",
-      "Statistical=0.927, p=0.036\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Health services use\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "MEX-R&D GExp\n",
-      "Statistical=0.780, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Ninis\n",
-      "Statistical=0.751, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Suicide\n",
-      "Statistical=0.928, p=0.038\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-International taxes\n",
-      "Statistical=0.862, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX-Alcohol per capita\n",
-      "Statistical=0.846, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "MEX\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <th id=\"T_ac1f4_level0_col2\" class=\"col_heading level0 col2\" >GDP-R^2</th>\n",
-       "      <th id=\"T_ac1f4_level0_col3\" class=\"col_heading level0 col3\" >Behaviour</th>\n",
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-       "  <tbody>\n",
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-       "      <th id=\"T_ac1f4_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
-       "      <td id=\"T_ac1f4_row0_col0\" class=\"data row0 col0\" >Employment-agriculture</td>\n",
-       "      <td id=\"T_ac1f4_row0_col1\" class=\"data row0 col1\" >None</td>\n",
-       "      <td id=\"T_ac1f4_row0_col2\" class=\"data row0 col2\" >0.921578</td>\n",
-       "      <td id=\"T_ac1f4_row0_col3\" class=\"data row0 col3\" >Negative</td>\n",
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-       "    <tr>\n",
-       "      <th id=\"T_ac1f4_level0_row1\" class=\"row_heading level0 row1\" >4</th>\n",
-       "      <td id=\"T_ac1f4_row1_col0\" class=\"data row1 col0\" >Exports-G&S</td>\n",
-       "      <td id=\"T_ac1f4_row1_col1\" class=\"data row1 col1\" >None</td>\n",
-       "      <td id=\"T_ac1f4_row1_col2\" class=\"data row1 col2\" >0.920006</td>\n",
-       "      <td id=\"T_ac1f4_row1_col3\" class=\"data row1 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ac1f4_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_ac1f4_row2_col0\" class=\"data row2 col0\" >Employment-services</td>\n",
-       "      <td id=\"T_ac1f4_row2_col1\" class=\"data row2 col1\" >None</td>\n",
-       "      <td id=\"T_ac1f4_row2_col2\" class=\"data row2 col2\" >0.915810</td>\n",
-       "      <td id=\"T_ac1f4_row2_col3\" class=\"data row2 col3\" >Positive</td>\n",
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-       "    <tr>\n",
-       "      <th id=\"T_ac1f4_level0_row3\" class=\"row_heading level0 row3\" >9</th>\n",
-       "      <td id=\"T_ac1f4_row3_col0\" class=\"data row3 col0\" >Mortality-infants</td>\n",
-       "      <td id=\"T_ac1f4_row3_col1\" class=\"data row3 col1\" >None</td>\n",
-       "      <td id=\"T_ac1f4_row3_col2\" class=\"data row3 col2\" >0.912197</td>\n",
-       "      <td id=\"T_ac1f4_row3_col3\" class=\"data row3 col3\" >Negative</td>\n",
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-       "    <tr>\n",
-       "      <th id=\"T_ac1f4_level0_row4\" class=\"row_heading level0 row4\" >8</th>\n",
-       "      <td id=\"T_ac1f4_row4_col0\" class=\"data row4 col0\" >Literacy rate</td>\n",
-       "      <td id=\"T_ac1f4_row4_col1\" class=\"data row4 col1\" >None</td>\n",
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-       "      <td id=\"T_ac1f4_row5_col0\" class=\"data row5 col0\" >Fertility rate</td>\n",
-       "      <td id=\"T_ac1f4_row5_col1\" class=\"data row5 col1\" >None</td>\n",
-       "      <td id=\"T_ac1f4_row5_col2\" class=\"data row5 col2\" >0.896893</td>\n",
-       "      <td id=\"T_ac1f4_row5_col3\" class=\"data row5 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ac1f4_level0_row6\" class=\"row_heading level0 row6\" >11</th>\n",
-       "      <td id=\"T_ac1f4_row6_col0\" class=\"data row6 col0\" >Net migration</td>\n",
-       "      <td id=\"T_ac1f4_row6_col1\" class=\"data row6 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_ac1f4_row6_col2\" class=\"data row6 col2\" >0.828048</td>\n",
-       "      <td id=\"T_ac1f4_row6_col3\" class=\"data row6 col3\" >Positive</td>\n",
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-       "    <tr>\n",
-       "      <th id=\"T_ac1f4_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_ac1f4_row7_col0\" class=\"data row7 col0\" >Health services use</td>\n",
-       "      <td id=\"T_ac1f4_row7_col1\" class=\"data row7 col1\" >None</td>\n",
-       "      <td id=\"T_ac1f4_row7_col2\" class=\"data row7 col2\" >0.798128</td>\n",
-       "      <td id=\"T_ac1f4_row7_col3\" class=\"data row7 col3\" >Positive</td>\n",
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-       "      <td id=\"T_ac1f4_row8_col0\" class=\"data row8 col0\" >Suicide</td>\n",
-       "      <td id=\"T_ac1f4_row8_col1\" class=\"data row8 col1\" >None</td>\n",
-       "      <td id=\"T_ac1f4_row8_col2\" class=\"data row8 col2\" >0.785826</td>\n",
-       "      <td id=\"T_ac1f4_row8_col3\" class=\"data row8 col3\" >Positive</td>\n",
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-       "      <th id=\"T_ac1f4_level0_row9\" class=\"row_heading level0 row9\" >12</th>\n",
-       "      <td id=\"T_ac1f4_row9_col0\" class=\"data row9 col0\" >Alcohol per capita</td>\n",
-       "      <td id=\"T_ac1f4_row9_col1\" class=\"data row9 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_ac1f4_row9_col2\" class=\"data row9 col2\" >0.752071</td>\n",
-       "      <td id=\"T_ac1f4_row9_col3\" class=\"data row9 col3\" >Negative</td>\n",
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-     "text": [
-      "BRA-Gender equality\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BRA-Exports-Commercial services\n",
-      "Statistical=0.824, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Renewable electricity\n",
-      "Statistical=0.875, p=0.002\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Employment-agriculture\n",
-      "Statistical=0.904, p=0.008\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Employment-industry\n",
-      "Statistical=0.890, p=0.003\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Employment-services\n",
-      "Statistical=0.932, p=0.045\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Exports-G&S\n",
-      "Statistical=0.846, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Fertility rate\n",
-      "Statistical=0.866, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Foreign investment\n",
-      "Statistical=0.861, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-GDP\n",
-      "Statistical=0.937, p=0.062\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BRA-Education GExp\n",
-      "Statistical=0.907, p=0.009\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Workers high education\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BRA-Literacy rate\n",
-      "Statistical=0.856, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Mortality-pollution\n",
-      "Statistical=0.672, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Net migration\n",
-      "Statistical=0.785, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Mortality-infants\n",
-      "Statistical=0.861, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Health services use\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "BRA-R&D GExp\n",
-      "Statistical=0.679, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Ninis\n",
-      "Statistical=0.834, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Suicide\n",
-      "Statistical=0.906, p=0.009\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-International taxes\n",
-      "Statistical=0.797, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA-Alcohol per capita\n",
-      "Statistical=0.842, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "BRA\n"
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-       "      <th id=\"T_559f9_level0_row0\" class=\"row_heading level0 row0\" >5</th>\n",
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-       "    <tr>\n",
-       "      <th id=\"T_559f9_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
-       "      <td id=\"T_559f9_row1_col0\" class=\"data row1 col0\" >Exports-Commercial services</td>\n",
-       "      <td id=\"T_559f9_row1_col1\" class=\"data row1 col1\" >None</td>\n",
-       "      <td id=\"T_559f9_row1_col2\" class=\"data row1 col2\" >0.906864</td>\n",
-       "      <td id=\"T_559f9_row1_col3\" class=\"data row1 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_559f9_level0_row2\" class=\"row_heading level0 row2\" >1</th>\n",
-       "      <td id=\"T_559f9_row2_col0\" class=\"data row2 col0\" >Foreign investment</td>\n",
-       "      <td id=\"T_559f9_row2_col1\" class=\"data row2 col1\" >None</td>\n",
-       "      <td id=\"T_559f9_row2_col2\" class=\"data row2 col2\" >0.785408</td>\n",
-       "      <td id=\"T_559f9_row2_col3\" class=\"data row2 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_559f9_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
-       "      <td id=\"T_559f9_row3_col0\" class=\"data row3 col0\" >Fertility rate</td>\n",
-       "      <td id=\"T_559f9_row3_col1\" class=\"data row3 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_559f9_row3_col2\" class=\"data row3 col2\" >0.775957</td>\n",
-       "      <td id=\"T_559f9_row3_col3\" class=\"data row3 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_559f9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_559f9_row4_col0\" class=\"data row4 col0\" >Employment-agriculture</td>\n",
-       "      <td id=\"T_559f9_row4_col1\" class=\"data row4 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_559f9_row4_col2\" class=\"data row4 col2\" >0.770973</td>\n",
-       "      <td id=\"T_559f9_row4_col3\" class=\"data row4 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_559f9_level0_row5\" class=\"row_heading level0 row5\" >3</th>\n",
-       "      <td id=\"T_559f9_row5_col0\" class=\"data row5 col0\" >Renewable electricity</td>\n",
-       "      <td id=\"T_559f9_row5_col1\" class=\"data row5 col1\" >None</td>\n",
-       "      <td id=\"T_559f9_row5_col2\" class=\"data row5 col2\" >0.756093</td>\n",
-       "      <td id=\"T_559f9_row5_col3\" class=\"data row5 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1cfb8c2ecb0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ARG-Gender equality\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARG-Exports-Commercial services\n",
-      "Statistical=0.767, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Renewable electricity\n",
-      "Statistical=0.879, p=0.002\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Employment-agriculture\n",
-      "Statistical=0.956, p=0.227\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARG-Employment-industry\n",
-      "Statistical=0.915, p=0.017\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Employment-services\n",
-      "Statistical=0.932, p=0.048\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Exports-G&S\n",
-      "Statistical=0.864, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Fertility rate\n",
-      "Statistical=0.907, p=0.011\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Foreign investment\n",
-      "Statistical=0.880, p=0.002\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-GDP\n",
-      "Statistical=0.898, p=0.006\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Education GExp\n",
-      "Statistical=0.931, p=0.047\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Workers high education\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARG-Literacy rate\n",
-      "Statistical=0.827, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Mortality-pollution\n",
-      "Statistical=0.776, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Net migration\n",
-      "Statistical=0.745, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Mortality-infants\n",
-      "Statistical=0.954, p=0.204\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARG-Health services use\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "ARG-R&D GExp\n",
-      "Statistical=0.737, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Ninis\n",
-      "Statistical=0.772, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Suicide\n",
-      "Statistical=0.843, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-International taxes\n",
-      "Statistical=0.871, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG-Alcohol per capita\n",
-      "Statistical=0.909, p=0.012\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "ARG\n"
-     ]
-    },
-    {
-     "data": {
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-       "#T_5771c_row0_col2 {\n",
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-       "#T_5771c_row1_col2 {\n",
-       "  background-color: #045687;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_5771c_row2_col2 {\n",
-       "  background-color: #04588a;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_5771c_row3_col2 {\n",
-       "  background-color: #4496c3;\n",
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-       "#T_5771c_row4_col2 {\n",
-       "  background-color: #dfddec;\n",
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-       "  background-color: #fff7fb;\n",
-       "  color: #000000;\n",
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-       "</style>\n",
-       "<table id=\"T_5771c\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_5771c_level0_col0\" class=\"col_heading level0 col0\" >Indicator</th>\n",
-       "      <th id=\"T_5771c_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_5771c_level0_col2\" class=\"col_heading level0 col2\" >GDP-R^2</th>\n",
-       "      <th id=\"T_5771c_level0_col3\" class=\"col_heading level0 col3\" >Behaviour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5771c_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_5771c_row0_col0\" class=\"data row0 col0\" >Literacy rate</td>\n",
-       "      <td id=\"T_5771c_row0_col1\" class=\"data row0 col1\" >None</td>\n",
-       "      <td id=\"T_5771c_row0_col2\" class=\"data row0 col2\" >0.843882</td>\n",
-       "      <td id=\"T_5771c_row0_col3\" class=\"data row0 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5771c_level0_row1\" class=\"row_heading level0 row1\" >3</th>\n",
-       "      <td id=\"T_5771c_row1_col0\" class=\"data row1 col0\" >Exports-Commercial services</td>\n",
-       "      <td id=\"T_5771c_row1_col1\" class=\"data row1 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_5771c_row1_col2\" class=\"data row1 col2\" >0.835598</td>\n",
-       "      <td id=\"T_5771c_row1_col3\" class=\"data row1 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5771c_level0_row2\" class=\"row_heading level0 row2\" >6</th>\n",
-       "      <td id=\"T_5771c_row2_col0\" class=\"data row2 col0\" >Alcohol per capita</td>\n",
-       "      <td id=\"T_5771c_row2_col1\" class=\"data row2 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_5771c_row2_col2\" class=\"data row2 col2\" >0.835137</td>\n",
-       "      <td id=\"T_5771c_row2_col3\" class=\"data row2 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5771c_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
-       "      <td id=\"T_5771c_row3_col0\" class=\"data row3 col0\" >Net migration</td>\n",
-       "      <td id=\"T_5771c_row3_col1\" class=\"data row3 col1\" >None</td>\n",
-       "      <td id=\"T_5771c_row3_col2\" class=\"data row3 col2\" >0.814017</td>\n",
-       "      <td id=\"T_5771c_row3_col3\" class=\"data row3 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5771c_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_5771c_row4_col0\" class=\"data row4 col0\" >R&D GExp</td>\n",
-       "      <td id=\"T_5771c_row4_col1\" class=\"data row4 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_5771c_row4_col2\" class=\"data row4 col2\" >0.783710</td>\n",
-       "      <td id=\"T_5771c_row4_col3\" class=\"data row4 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5771c_level0_row5\" class=\"row_heading level0 row5\" >4</th>\n",
-       "      <td id=\"T_5771c_row5_col0\" class=\"data row5 col0\" >Renewable electricity</td>\n",
-       "      <td id=\"T_5771c_row5_col1\" class=\"data row5 col1\" >Cubic</td>\n",
-       "      <td id=\"T_5771c_row5_col2\" class=\"data row5 col2\" >0.769916</td>\n",
-       "      <td id=\"T_5771c_row5_col3\" class=\"data row5 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1cfb8c2fa00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PER-Gender equality\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PER-Exports-Commercial services\n",
-      "Statistical=0.918, p=0.018\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Renewable electricity\n",
-      "Statistical=0.866, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Employment-agriculture\n",
-      "Statistical=0.811, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Employment-industry\n",
-      "Statistical=0.951, p=0.154\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PER-Employment-services\n",
-      "Statistical=0.904, p=0.008\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Exports-G&S\n",
-      "Statistical=0.861, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Fertility rate\n",
-      "Statistical=0.848, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Foreign investment\n",
-      "Statistical=0.905, p=0.008\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-GDP\n",
-      "Statistical=0.915, p=0.015\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Education GExp\n",
-      "Statistical=0.845, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Workers high education\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PER-Literacy rate\n",
-      "Statistical=0.858, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Mortality-pollution\n",
-      "Statistical=0.936, p=0.058\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PER-Net migration\n",
-      "Statistical=0.772, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Mortality-infants\n",
-      "Statistical=0.865, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Health services use\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "PER-R&D GExp\n",
-      "Statistical=0.919, p=0.019\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Ninis\n",
-      "Statistical=0.854, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Suicide\n",
-      "Statistical=0.873, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-International taxes\n",
-      "Statistical=0.770, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER-Alcohol per capita\n",
-      "Statistical=0.924, p=0.026\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "PER\n"
-     ]
-    },
-    {
-     "data": {
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-       "#T_faa6c_row1_col2 {\n",
-       "  background-color: #045687;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_faa6c_row2_col2 {\n",
-       "  background-color: #0569a4;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_faa6c_row3_col2 {\n",
-       "  background-color: #0569a5;\n",
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-       "#T_faa6c_row4_col2 {\n",
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-       "#T_faa6c_row5_col2 {\n",
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-       "#T_faa6c_row6_col2 {\n",
-       "  background-color: #83afd3;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_faa6c_row7_col2 {\n",
-       "  background-color: #d9d8ea;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_faa6c_row8_col2 {\n",
-       "  background-color: #fff7fb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_faa6c\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_faa6c_level0_col0\" class=\"col_heading level0 col0\" >Indicator</th>\n",
-       "      <th id=\"T_faa6c_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_faa6c_level0_col2\" class=\"col_heading level0 col2\" >GDP-R^2</th>\n",
-       "      <th id=\"T_faa6c_level0_col3\" class=\"col_heading level0 col3\" >Behaviour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
-       "      <td id=\"T_faa6c_row0_col0\" class=\"data row0 col0\" >Literacy rate</td>\n",
-       "      <td id=\"T_faa6c_row0_col1\" class=\"data row0 col1\" >None</td>\n",
-       "      <td id=\"T_faa6c_row0_col2\" class=\"data row0 col2\" >0.981591</td>\n",
-       "      <td id=\"T_faa6c_row0_col3\" class=\"data row0 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
-       "      <td id=\"T_faa6c_row1_col0\" class=\"data row1 col0\" >Renewable electricity</td>\n",
-       "      <td id=\"T_faa6c_row1_col1\" class=\"data row1 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_faa6c_row1_col2\" class=\"data row1 col2\" >0.964792</td>\n",
-       "      <td id=\"T_faa6c_row1_col3\" class=\"data row1 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row2\" class=\"row_heading level0 row2\" >6</th>\n",
-       "      <td id=\"T_faa6c_row2_col0\" class=\"data row2 col0\" >Health services use</td>\n",
-       "      <td id=\"T_faa6c_row2_col1\" class=\"data row2 col1\" >None</td>\n",
-       "      <td id=\"T_faa6c_row2_col2\" class=\"data row2 col2\" >0.950532</td>\n",
-       "      <td id=\"T_faa6c_row2_col3\" class=\"data row2 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_faa6c_row3_col0\" class=\"data row3 col0\" >Exports-G&S</td>\n",
-       "      <td id=\"T_faa6c_row3_col1\" class=\"data row3 col1\" >None</td>\n",
-       "      <td id=\"T_faa6c_row3_col2\" class=\"data row3 col2\" >0.949986</td>\n",
-       "      <td id=\"T_faa6c_row3_col3\" class=\"data row3 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row4\" class=\"row_heading level0 row4\" >9</th>\n",
-       "      <td id=\"T_faa6c_row4_col0\" class=\"data row4 col0\" >Employment-services</td>\n",
-       "      <td id=\"T_faa6c_row4_col1\" class=\"data row4 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_faa6c_row4_col2\" class=\"data row4 col2\" >0.929864</td>\n",
-       "      <td id=\"T_faa6c_row4_col3\" class=\"data row4 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row5\" class=\"row_heading level0 row5\" >11</th>\n",
-       "      <td id=\"T_faa6c_row5_col0\" class=\"data row5 col0\" >Mortality-infants</td>\n",
-       "      <td id=\"T_faa6c_row5_col1\" class=\"data row5 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_faa6c_row5_col2\" class=\"data row5 col2\" >0.912324</td>\n",
-       "      <td id=\"T_faa6c_row5_col3\" class=\"data row5 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row6\" class=\"row_heading level0 row6\" >2</th>\n",
-       "      <td id=\"T_faa6c_row6_col0\" class=\"data row6 col0\" >Employment-agriculture</td>\n",
-       "      <td id=\"T_faa6c_row6_col1\" class=\"data row6 col1\" >None</td>\n",
-       "      <td id=\"T_faa6c_row6_col2\" class=\"data row6 col2\" >0.901629</td>\n",
-       "      <td id=\"T_faa6c_row6_col3\" class=\"data row6 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row7\" class=\"row_heading level0 row7\" >10</th>\n",
-       "      <td id=\"T_faa6c_row7_col0\" class=\"data row7 col0\" >Fertility rate</td>\n",
-       "      <td id=\"T_faa6c_row7_col1\" class=\"data row7 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_faa6c_row7_col2\" class=\"data row7 col2\" >0.863525</td>\n",
-       "      <td id=\"T_faa6c_row7_col3\" class=\"data row7 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_faa6c_level0_row8\" class=\"row_heading level0 row8\" >3</th>\n",
-       "      <td id=\"T_faa6c_row8_col0\" class=\"data row8 col0\" >Exports-Commercial services</td>\n",
-       "      <td id=\"T_faa6c_row8_col1\" class=\"data row8 col1\" >None</td>\n",
-       "      <td id=\"T_faa6c_row8_col2\" class=\"data row8 col2\" >0.832625</td>\n",
-       "      <td id=\"T_faa6c_row8_col3\" class=\"data row8 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1cfb8c2eb60>"
-      ]
-     },
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "VEN-Gender equality\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Exports-Commercial services\n",
-      "Statistical=0.683, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Renewable electricity\n",
-      "Statistical=0.407, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Employment-agriculture\n",
-      "Statistical=0.909, p=0.012\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Employment-industry\n",
-      "Statistical=0.959, p=0.272\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Employment-services\n",
-      "Statistical=0.958, p=0.263\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Exports-G&S\n",
-      "Statistical=0.946, p=0.119\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Fertility rate\n",
-      "Statistical=0.874, p=0.002\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Foreign investment\n",
-      "Statistical=0.936, p=0.066\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-GDP\n",
-      "Statistical=0.941, p=0.087\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Education GExp\n",
-      "Statistical=0.818, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Workers high education\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Literacy rate\n",
-      "Statistical=0.781, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Mortality-pollution\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Net migration\n",
-      "Statistical=0.834, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Mortality-infants\n",
-      "Statistical=0.914, p=0.016\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Health services use\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-R&D GExp\n",
-      "Statistical=0.775, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Ninis\n",
-      "Statistical=0.759, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-Suicide\n",
-      "Statistical=0.716, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN-International taxes\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "VEN-Alcohol per capita\n",
-      "Statistical=0.826, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "VEN\n"
-     ]
-    },
-    {
-     "data": {
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-       "#T_6c983_row8_col2 {\n",
-       "  background-color: #f1ebf4;\n",
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-       "#T_6c983_row9_col2 {\n",
-       "  background-color: #fdf5fa;\n",
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-       "  background-color: #fff7fb;\n",
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-       "</style>\n",
-       "<table id=\"T_6c983\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_6c983_level0_col0\" class=\"col_heading level0 col0\" >Indicator</th>\n",
-       "      <th id=\"T_6c983_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_6c983_level0_col2\" class=\"col_heading level0 col2\" >GDP-R^2</th>\n",
-       "      <th id=\"T_6c983_level0_col3\" class=\"col_heading level0 col3\" >Behaviour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
-       "      <td id=\"T_6c983_row0_col0\" class=\"data row0 col0\" >Net migration</td>\n",
-       "      <td id=\"T_6c983_row0_col1\" class=\"data row0 col1\" >None</td>\n",
-       "      <td id=\"T_6c983_row0_col2\" class=\"data row0 col2\" >0.956769</td>\n",
-       "      <td id=\"T_6c983_row0_col3\" class=\"data row0 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
-       "      <td id=\"T_6c983_row1_col0\" class=\"data row1 col0\" >Suicide</td>\n",
-       "      <td id=\"T_6c983_row1_col1\" class=\"data row1 col1\" >None</td>\n",
-       "      <td id=\"T_6c983_row1_col2\" class=\"data row1 col2\" >0.950687</td>\n",
-       "      <td id=\"T_6c983_row1_col3\" class=\"data row1 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
-       "      <td id=\"T_6c983_row2_col0\" class=\"data row2 col0\" >Health services use</td>\n",
-       "      <td id=\"T_6c983_row2_col1\" class=\"data row2 col1\" >None</td>\n",
-       "      <td id=\"T_6c983_row2_col2\" class=\"data row2 col2\" >0.941372</td>\n",
-       "      <td id=\"T_6c983_row2_col3\" class=\"data row2 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
-       "      <td id=\"T_6c983_row3_col0\" class=\"data row3 col0\" >Literacy rate</td>\n",
-       "      <td id=\"T_6c983_row3_col1\" class=\"data row3 col1\" >None</td>\n",
-       "      <td id=\"T_6c983_row3_col2\" class=\"data row3 col2\" >0.929998</td>\n",
-       "      <td id=\"T_6c983_row3_col3\" class=\"data row3 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row4\" class=\"row_heading level0 row4\" >11</th>\n",
-       "      <td id=\"T_6c983_row4_col0\" class=\"data row4 col0\" >Fertility rate</td>\n",
-       "      <td id=\"T_6c983_row4_col1\" class=\"data row4 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_6c983_row4_col2\" class=\"data row4 col2\" >0.860478</td>\n",
-       "      <td id=\"T_6c983_row4_col3\" class=\"data row4 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row5\" class=\"row_heading level0 row5\" >13</th>\n",
-       "      <td id=\"T_6c983_row5_col0\" class=\"data row5 col0\" >Alcohol per capita</td>\n",
-       "      <td id=\"T_6c983_row5_col1\" class=\"data row5 col1\" >Cubic</td>\n",
-       "      <td id=\"T_6c983_row5_col2\" class=\"data row5 col2\" >0.846460</td>\n",
-       "      <td id=\"T_6c983_row5_col3\" class=\"data row5 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row6\" class=\"row_heading level0 row6\" >10</th>\n",
-       "      <td id=\"T_6c983_row6_col0\" class=\"data row6 col0\" >Employment-agriculture</td>\n",
-       "      <td id=\"T_6c983_row6_col1\" class=\"data row6 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_6c983_row6_col2\" class=\"data row6 col2\" >0.846406</td>\n",
-       "      <td id=\"T_6c983_row6_col3\" class=\"data row6 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_6c983_row7_col0\" class=\"data row7 col0\" >Ninis</td>\n",
-       "      <td id=\"T_6c983_row7_col1\" class=\"data row7 col1\" >None</td>\n",
-       "      <td id=\"T_6c983_row7_col2\" class=\"data row7 col2\" >0.797741</td>\n",
-       "      <td id=\"T_6c983_row7_col3\" class=\"data row7 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row8\" class=\"row_heading level0 row8\" >12</th>\n",
-       "      <td id=\"T_6c983_row8_col0\" class=\"data row8 col0\" >R&D GExp</td>\n",
-       "      <td id=\"T_6c983_row8_col1\" class=\"data row8 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_6c983_row8_col2\" class=\"data row8 col2\" >0.791297</td>\n",
-       "      <td id=\"T_6c983_row8_col3\" class=\"data row8 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row9\" class=\"row_heading level0 row9\" >3</th>\n",
-       "      <td id=\"T_6c983_row9_col0\" class=\"data row9 col0\" >Exports-G&S</td>\n",
-       "      <td id=\"T_6c983_row9_col1\" class=\"data row9 col1\" >None</td>\n",
-       "      <td id=\"T_6c983_row9_col2\" class=\"data row9 col2\" >0.777485</td>\n",
-       "      <td id=\"T_6c983_row9_col3\" class=\"data row9 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6c983_level0_row10\" class=\"row_heading level0 row10\" >2</th>\n",
-       "      <td id=\"T_6c983_row10_col0\" class=\"data row10 col0\" >Employment-services</td>\n",
-       "      <td id=\"T_6c983_row10_col1\" class=\"data row10 col1\" >None</td>\n",
-       "      <td id=\"T_6c983_row10_col2\" class=\"data row10 col2\" >0.773967</td>\n",
-       "      <td id=\"T_6c983_row10_col3\" class=\"data row10 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
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-      "text/plain": [
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-     },
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-    {
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-     "output_type": "stream",
-     "text": [
-      "COL-Gender equality\n",
-      "Statistical=0.957, p=0.229\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "COL-Exports-Commercial services\n",
-      "Statistical=0.900, p=0.006\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Renewable electricity\n",
-      "Statistical=0.898, p=0.006\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Employment-agriculture\n",
-      "Statistical=0.883, p=0.002\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Employment-industry\n",
-      "Statistical=0.889, p=0.003\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Employment-services\n",
-      "Statistical=0.925, p=0.028\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Exports-G&S\n",
-      "Statistical=0.851, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Fertility rate\n",
-      "Statistical=0.886, p=0.003\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Foreign investment\n",
-      "Statistical=0.901, p=0.007\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-GDP\n",
-      "Statistical=0.907, p=0.010\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Education GExp\n",
-      "Statistical=0.880, p=0.002\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Workers high education\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "COL-Literacy rate\n",
-      "Statistical=0.855, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Mortality-pollution\n",
-      "Statistical=0.826, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Net migration\n",
-      "Statistical=0.927, p=0.033\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Mortality-infants\n",
-      "Statistical=0.921, p=0.023\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Health services use\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "COL-R&D GExp\n",
-      "Statistical=0.797, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Ninis\n",
-      "Statistical=0.827, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Suicide\n",
-      "Statistical=0.907, p=0.009\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-International taxes\n",
-      "Statistical=0.798, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL-Alcohol per capita\n",
-      "Statistical=0.832, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "COL\n"
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-       "<table id=\"T_c49c9\">\n",
-       "  <thead>\n",
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-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_c49c9_level0_col0\" class=\"col_heading level0 col0\" >Indicator</th>\n",
-       "      <th id=\"T_c49c9_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_c49c9_level0_col2\" class=\"col_heading level0 col2\" >GDP-R^2</th>\n",
-       "      <th id=\"T_c49c9_level0_col3\" class=\"col_heading level0 col3\" >Behaviour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row0\" class=\"row_heading level0 row0\" >5</th>\n",
-       "      <td id=\"T_c49c9_row0_col0\" class=\"data row0 col0\" >Exports-G&S</td>\n",
-       "      <td id=\"T_c49c9_row0_col1\" class=\"data row0 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row0_col2\" class=\"data row0 col2\" >0.964646</td>\n",
-       "      <td id=\"T_c49c9_row0_col3\" class=\"data row0 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row1\" class=\"row_heading level0 row1\" >11</th>\n",
-       "      <td id=\"T_c49c9_row1_col0\" class=\"data row1 col0\" >Ninis</td>\n",
-       "      <td id=\"T_c49c9_row1_col1\" class=\"data row1 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row1_col2\" class=\"data row1 col2\" >0.917990</td>\n",
-       "      <td id=\"T_c49c9_row1_col3\" class=\"data row1 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_c49c9_row2_col0\" class=\"data row2 col0\" >Employment-agriculture</td>\n",
-       "      <td id=\"T_c49c9_row2_col1\" class=\"data row2 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row2_col2\" class=\"data row2 col2\" >0.894700</td>\n",
-       "      <td id=\"T_c49c9_row2_col3\" class=\"data row2 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_c49c9_row3_col0\" class=\"data row3 col0\" >Exports-Commercial services</td>\n",
-       "      <td id=\"T_c49c9_row3_col1\" class=\"data row3 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row3_col2\" class=\"data row3 col2\" >0.879453</td>\n",
-       "      <td id=\"T_c49c9_row3_col3\" class=\"data row3 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row4\" class=\"row_heading level0 row4\" >13</th>\n",
-       "      <td id=\"T_c49c9_row4_col0\" class=\"data row4 col0\" >Renewable electricity</td>\n",
-       "      <td id=\"T_c49c9_row4_col1\" class=\"data row4 col1\" >Cubic</td>\n",
-       "      <td id=\"T_c49c9_row4_col2\" class=\"data row4 col2\" >0.869699</td>\n",
-       "      <td id=\"T_c49c9_row4_col3\" class=\"data row4 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row5\" class=\"row_heading level0 row5\" >15</th>\n",
-       "      <td id=\"T_c49c9_row5_col0\" class=\"data row5 col0\" >Net migration</td>\n",
-       "      <td id=\"T_c49c9_row5_col1\" class=\"data row5 col1\" >Cubic</td>\n",
-       "      <td id=\"T_c49c9_row5_col2\" class=\"data row5 col2\" >0.864357</td>\n",
-       "      <td id=\"T_c49c9_row5_col3\" class=\"data row5 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_c49c9_row6_col0\" class=\"data row6 col0\" >Fertility rate</td>\n",
-       "      <td id=\"T_c49c9_row6_col1\" class=\"data row6 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row6_col2\" class=\"data row6 col2\" >0.830613</td>\n",
-       "      <td id=\"T_c49c9_row6_col3\" class=\"data row6 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row7\" class=\"row_heading level0 row7\" >3</th>\n",
-       "      <td id=\"T_c49c9_row7_col0\" class=\"data row7 col0\" >Employment-services</td>\n",
-       "      <td id=\"T_c49c9_row7_col1\" class=\"data row7 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row7_col2\" class=\"data row7 col2\" >0.814804</td>\n",
-       "      <td id=\"T_c49c9_row7_col3\" class=\"data row7 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row8\" class=\"row_heading level0 row8\" >10</th>\n",
-       "      <td id=\"T_c49c9_row8_col0\" class=\"data row8 col0\" >Mortality-infants</td>\n",
-       "      <td id=\"T_c49c9_row8_col1\" class=\"data row8 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row8_col2\" class=\"data row8 col2\" >0.813618</td>\n",
-       "      <td id=\"T_c49c9_row8_col3\" class=\"data row8 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row9\" class=\"row_heading level0 row9\" >8</th>\n",
-       "      <td id=\"T_c49c9_row9_col0\" class=\"data row9 col0\" >Health services use</td>\n",
-       "      <td id=\"T_c49c9_row9_col1\" class=\"data row9 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row9_col2\" class=\"data row9 col2\" >0.806753</td>\n",
-       "      <td id=\"T_c49c9_row9_col3\" class=\"data row9 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row10\" class=\"row_heading level0 row10\" >12</th>\n",
-       "      <td id=\"T_c49c9_row10_col0\" class=\"data row10 col0\" >Suicide</td>\n",
-       "      <td id=\"T_c49c9_row10_col1\" class=\"data row10 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row10_col2\" class=\"data row10 col2\" >0.779785</td>\n",
-       "      <td id=\"T_c49c9_row10_col3\" class=\"data row10 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row11\" class=\"row_heading level0 row11\" >14</th>\n",
-       "      <td id=\"T_c49c9_row11_col0\" class=\"data row11 col0\" >Workers high education</td>\n",
-       "      <td id=\"T_c49c9_row11_col1\" class=\"data row11 col1\" >Cubic</td>\n",
-       "      <td id=\"T_c49c9_row11_col2\" class=\"data row11 col2\" >0.754164</td>\n",
-       "      <td id=\"T_c49c9_row11_col3\" class=\"data row11 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c49c9_level0_row12\" class=\"row_heading level0 row12\" >9</th>\n",
-       "      <td id=\"T_c49c9_row12_col0\" class=\"data row12 col0\" >Literacy rate</td>\n",
-       "      <td id=\"T_c49c9_row12_col1\" class=\"data row12 col1\" >None</td>\n",
-       "      <td id=\"T_c49c9_row12_col2\" class=\"data row12 col2\" >0.750022</td>\n",
-       "      <td id=\"T_c49c9_row12_col3\" class=\"data row12 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1cfb8c2ebc0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CHL-Gender equality\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Exports-Commercial services\n",
-      "Statistical=0.771, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Renewable electricity\n",
-      "Statistical=0.933, p=0.053\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Employment-agriculture\n",
-      "Statistical=0.925, p=0.033\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Employment-industry\n",
-      "Statistical=0.843, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Employment-services\n",
-      "Statistical=0.921, p=0.025\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Exports-G&S\n",
-      "Statistical=0.924, p=0.031\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Fertility rate\n",
-      "Statistical=0.847, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Foreign investment\n",
-      "Statistical=0.912, p=0.015\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-GDP\n",
-      "Statistical=0.963, p=0.348\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Education GExp\n",
-      "Statistical=0.865, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Workers high education\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Literacy rate\n",
-      "Statistical=0.835, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Mortality-pollution\n",
-      "Statistical=0.867, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Net migration\n",
-      "Statistical=0.851, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Mortality-infants\n",
-      "Statistical=0.848, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Health services use\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-R&D GExp\n",
-      "Statistical=0.833, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Ninis\n",
-      "Statistical=0.984, p=0.910\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CHL-Suicide\n",
-      "Statistical=0.728, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-International taxes\n",
-      "Statistical=0.903, p=0.009\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL-Alcohol per capita\n",
-      "Statistical=0.925, p=0.032\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CHL\n"
-     ]
-    },
-    {
-     "data": {
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-       "#T_42934_row8_col2 {\n",
-       "  background-color: #fff7fb;\n",
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-       "</style>\n",
-       "<table id=\"T_42934\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_42934_level0_col0\" class=\"col_heading level0 col0\" >Indicator</th>\n",
-       "      <th id=\"T_42934_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_42934_level0_col2\" class=\"col_heading level0 col2\" >GDP-R^2</th>\n",
-       "      <th id=\"T_42934_level0_col3\" class=\"col_heading level0 col3\" >Behaviour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
-       "      <td id=\"T_42934_row0_col0\" class=\"data row0 col0\" >Net migration</td>\n",
-       "      <td id=\"T_42934_row0_col1\" class=\"data row0 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_42934_row0_col2\" class=\"data row0 col2\" >0.970016</td>\n",
-       "      <td id=\"T_42934_row0_col3\" class=\"data row0 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
-       "      <td id=\"T_42934_row1_col0\" class=\"data row1 col0\" >Health services use</td>\n",
-       "      <td id=\"T_42934_row1_col1\" class=\"data row1 col1\" >None</td>\n",
-       "      <td id=\"T_42934_row1_col2\" class=\"data row1 col2\" >0.947701</td>\n",
-       "      <td id=\"T_42934_row1_col3\" class=\"data row1 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row2\" class=\"row_heading level0 row2\" >4</th>\n",
-       "      <td id=\"T_42934_row2_col0\" class=\"data row2 col0\" >Exports-G&S</td>\n",
-       "      <td id=\"T_42934_row2_col1\" class=\"data row2 col1\" >None</td>\n",
-       "      <td id=\"T_42934_row2_col2\" class=\"data row2 col2\" >0.918738</td>\n",
-       "      <td id=\"T_42934_row2_col3\" class=\"data row2 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row3\" class=\"row_heading level0 row3\" >7</th>\n",
-       "      <td id=\"T_42934_row3_col0\" class=\"data row3 col0\" >Employment-agriculture</td>\n",
-       "      <td id=\"T_42934_row3_col1\" class=\"data row3 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_42934_row3_col2\" class=\"data row3 col2\" >0.905977</td>\n",
-       "      <td id=\"T_42934_row3_col3\" class=\"data row3 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row4\" class=\"row_heading level0 row4\" >8</th>\n",
-       "      <td id=\"T_42934_row4_col0\" class=\"data row4 col0\" >Employment-services</td>\n",
-       "      <td id=\"T_42934_row4_col1\" class=\"data row4 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_42934_row4_col2\" class=\"data row4 col2\" >0.901296</td>\n",
-       "      <td id=\"T_42934_row4_col3\" class=\"data row4 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row5\" class=\"row_heading level0 row5\" >9</th>\n",
-       "      <td id=\"T_42934_row5_col0\" class=\"data row5 col0\" >Workers high education</td>\n",
-       "      <td id=\"T_42934_row5_col1\" class=\"data row5 col1\" >Cubic</td>\n",
-       "      <td id=\"T_42934_row5_col2\" class=\"data row5 col2\" >0.889408</td>\n",
-       "      <td id=\"T_42934_row5_col3\" class=\"data row5 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row6\" class=\"row_heading level0 row6\" >3</th>\n",
-       "      <td id=\"T_42934_row6_col0\" class=\"data row6 col0\" >Exports-Commercial services</td>\n",
-       "      <td id=\"T_42934_row6_col1\" class=\"data row6 col1\" >None</td>\n",
-       "      <td id=\"T_42934_row6_col2\" class=\"data row6 col2\" >0.828029</td>\n",
-       "      <td id=\"T_42934_row6_col3\" class=\"data row6 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row7\" class=\"row_heading level0 row7\" >2</th>\n",
-       "      <td id=\"T_42934_row7_col0\" class=\"data row7 col0\" >Education GExp</td>\n",
-       "      <td id=\"T_42934_row7_col1\" class=\"data row7 col1\" >None</td>\n",
-       "      <td id=\"T_42934_row7_col2\" class=\"data row7 col2\" >0.798479</td>\n",
-       "      <td id=\"T_42934_row7_col3\" class=\"data row7 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_42934_level0_row8\" class=\"row_heading level0 row8\" >11</th>\n",
-       "      <td id=\"T_42934_row8_col0\" class=\"data row8 col0\" >Mortality-infants</td>\n",
-       "      <td id=\"T_42934_row8_col1\" class=\"data row8 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_42934_row8_col2\" class=\"data row8 col2\" >0.764060</td>\n",
-       "      <td id=\"T_42934_row8_col3\" class=\"data row8 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
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-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1cf96ef9690>"
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-     },
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-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
-      "CRI-Gender equality\n",
-      "Statistical=0.957, p=0.247\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Exports-Commercial services\n",
-      "Statistical=0.764, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Renewable electricity\n",
-      "Statistical=0.941, p=0.086\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Employment-agriculture\n",
-      "Statistical=0.904, p=0.009\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Employment-industry\n",
-      "Statistical=0.951, p=0.163\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Employment-services\n",
-      "Statistical=0.938, p=0.071\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Exports-G&S\n",
-      "Statistical=0.913, p=0.016\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Fertility rate\n",
-      "Statistical=0.927, p=0.037\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Foreign investment\n",
-      "Statistical=0.856, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-GDP\n",
-      "Statistical=0.888, p=0.004\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Education GExp\n",
-      "Statistical=0.861, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Workers high education\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-Literacy rate\n",
-      "Statistical=0.852, p=0.001\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Mortality-pollution\n",
-      "Statistical=0.821, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Net migration\n",
-      "Statistical=0.816, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Mortality-infants\n",
-      "Statistical=0.915, p=0.017\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Health services use\n",
-      "Statistical=nan, p=1.000\n",
-      "Data is NORMAL ( H0 not denied )\n",
-      "CRI-R&D GExp\n",
-      "Statistical=0.838, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Ninis\n",
-      "Statistical=0.830, p=0.000\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Suicide\n",
-      "Statistical=0.929, p=0.042\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-International taxes\n",
-      "Statistical=0.927, p=0.037\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI-Alcohol per capita\n",
-      "Statistical=0.914, p=0.017\n",
-      "It does NOT seem normal( Denies H0 )\n",
-      "CRI\n"
-     ]
-    },
-    {
-     "data": {
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-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_621c2_row9_col2 {\n",
-       "  background-color: #71a8ce;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_621c2_row10_col2 {\n",
-       "  background-color: #a5bddb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_621c2_row11_col2 {\n",
-       "  background-color: #abbfdc;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_621c2_row12_col2 {\n",
-       "  background-color: #d5d5e8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_621c2_row13_col2 {\n",
-       "  background-color: #f3edf5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_621c2_row14_col2 {\n",
-       "  background-color: #f7f0f7;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_621c2_row15_col2 {\n",
-       "  background-color: #fff7fb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_621c2\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_621c2_level0_col0\" class=\"col_heading level0 col0\" >Indicator</th>\n",
-       "      <th id=\"T_621c2_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_621c2_level0_col2\" class=\"col_heading level0 col2\" >GDP-R^2</th>\n",
-       "      <th id=\"T_621c2_level0_col3\" class=\"col_heading level0 col3\" >Behaviour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row0\" class=\"row_heading level0 row0\" >16</th>\n",
-       "      <td id=\"T_621c2_row0_col0\" class=\"data row0 col0\" >Literacy rate</td>\n",
-       "      <td id=\"T_621c2_row0_col1\" class=\"data row0 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_621c2_row0_col2\" class=\"data row0 col2\" >0.982076</td>\n",
-       "      <td id=\"T_621c2_row0_col3\" class=\"data row0 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
-       "      <td id=\"T_621c2_row1_col0\" class=\"data row1 col0\" >Exports-G&S</td>\n",
-       "      <td id=\"T_621c2_row1_col1\" class=\"data row1 col1\" >None</td>\n",
-       "      <td id=\"T_621c2_row1_col2\" class=\"data row1 col2\" >0.979859</td>\n",
-       "      <td id=\"T_621c2_row1_col3\" class=\"data row1 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row2\" class=\"row_heading level0 row2\" >9</th>\n",
-       "      <td id=\"T_621c2_row2_col0\" class=\"data row2 col0\" >Health services use</td>\n",
-       "      <td id=\"T_621c2_row2_col1\" class=\"data row2 col1\" >None</td>\n",
-       "      <td id=\"T_621c2_row2_col2\" class=\"data row2 col2\" >0.977659</td>\n",
-       "      <td id=\"T_621c2_row2_col3\" class=\"data row2 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row3\" class=\"row_heading level0 row3\" >10</th>\n",
-       "      <td id=\"T_621c2_row3_col0\" class=\"data row3 col0\" >International taxes</td>\n",
-       "      <td id=\"T_621c2_row3_col1\" class=\"data row3 col1\" >None</td>\n",
-       "      <td id=\"T_621c2_row3_col2\" class=\"data row3 col2\" >0.971802</td>\n",
-       "      <td id=\"T_621c2_row3_col3\" class=\"data row3 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_621c2_row4_col0\" class=\"data row4 col0\" >Exports-Commercial services</td>\n",
-       "      <td id=\"T_621c2_row4_col1\" class=\"data row4 col1\" >None</td>\n",
-       "      <td id=\"T_621c2_row4_col2\" class=\"data row4 col2\" >0.970797</td>\n",
-       "      <td id=\"T_621c2_row4_col3\" class=\"data row4 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row5\" class=\"row_heading level0 row5\" >11</th>\n",
-       "      <td id=\"T_621c2_row5_col0\" class=\"data row5 col0\" >Renewable electricity</td>\n",
-       "      <td id=\"T_621c2_row5_col1\" class=\"data row5 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_621c2_row5_col2\" class=\"data row5 col2\" >0.959768</td>\n",
-       "      <td id=\"T_621c2_row5_col3\" class=\"data row5 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row6\" class=\"row_heading level0 row6\" >18</th>\n",
-       "      <td id=\"T_621c2_row6_col0\" class=\"data row6 col0\" >Mortality-infants</td>\n",
-       "      <td id=\"T_621c2_row6_col1\" class=\"data row6 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_621c2_row6_col2\" class=\"data row6 col2\" >0.924853</td>\n",
-       "      <td id=\"T_621c2_row6_col3\" class=\"data row6 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row7\" class=\"row_heading level0 row7\" >13</th>\n",
-       "      <td id=\"T_621c2_row7_col0\" class=\"data row7 col0\" >Fertility rate</td>\n",
-       "      <td id=\"T_621c2_row7_col1\" class=\"data row7 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_621c2_row7_col2\" class=\"data row7 col2\" >0.923296</td>\n",
-       "      <td id=\"T_621c2_row7_col3\" class=\"data row7 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row8\" class=\"row_heading level0 row8\" >17</th>\n",
-       "      <td id=\"T_621c2_row8_col0\" class=\"data row8 col0\" >Net migration</td>\n",
-       "      <td id=\"T_621c2_row8_col1\" class=\"data row8 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_621c2_row8_col2\" class=\"data row8 col2\" >0.900249</td>\n",
-       "      <td id=\"T_621c2_row8_col3\" class=\"data row8 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row9\" class=\"row_heading level0 row9\" >2</th>\n",
-       "      <td id=\"T_621c2_row9_col0\" class=\"data row9 col0\" >Alcohol per capita</td>\n",
-       "      <td id=\"T_621c2_row9_col1\" class=\"data row9 col1\" >None</td>\n",
-       "      <td id=\"T_621c2_row9_col2\" class=\"data row9 col2\" >0.871295</td>\n",
-       "      <td id=\"T_621c2_row9_col3\" class=\"data row9 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row10\" class=\"row_heading level0 row10\" >12</th>\n",
-       "      <td id=\"T_621c2_row10_col0\" class=\"data row10 col0\" >Employment-industry</td>\n",
-       "      <td id=\"T_621c2_row10_col1\" class=\"data row10 col1\" >Logarithmic</td>\n",
-       "      <td id=\"T_621c2_row10_col2\" class=\"data row10 col2\" >0.842319</td>\n",
-       "      <td id=\"T_621c2_row10_col3\" class=\"data row10 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row11\" class=\"row_heading level0 row11\" >7</th>\n",
-       "      <td id=\"T_621c2_row11_col0\" class=\"data row11 col0\" >Foreign investment</td>\n",
-       "      <td id=\"T_621c2_row11_col1\" class=\"data row11 col1\" >None</td>\n",
-       "      <td id=\"T_621c2_row11_col2\" class=\"data row11 col2\" >0.839332</td>\n",
-       "      <td id=\"T_621c2_row11_col3\" class=\"data row11 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row12\" class=\"row_heading level0 row12\" >4</th>\n",
-       "      <td id=\"T_621c2_row12_col0\" class=\"data row12 col0\" >Employment-services</td>\n",
-       "      <td id=\"T_621c2_row12_col1\" class=\"data row12 col1\" >None</td>\n",
-       "      <td id=\"T_621c2_row12_col2\" class=\"data row12 col2\" >0.809260</td>\n",
-       "      <td id=\"T_621c2_row12_col3\" class=\"data row12 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row13\" class=\"row_heading level0 row13\" >15</th>\n",
-       "      <td id=\"T_621c2_row13_col0\" class=\"data row13 col0\" >Workers high education</td>\n",
-       "      <td id=\"T_621c2_row13_col1\" class=\"data row13 col1\" >Cubic</td>\n",
-       "      <td id=\"T_621c2_row13_col2\" class=\"data row13 col2\" >0.775853</td>\n",
-       "      <td id=\"T_621c2_row13_col3\" class=\"data row13 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "      <td id=\"T_621c2_row14_col0\" class=\"data row14 col0\" >Education GExp</td>\n",
-       "      <td id=\"T_621c2_row14_col1\" class=\"data row14 col1\" >Cuadratic</td>\n",
-       "      <td id=\"T_621c2_row14_col2\" class=\"data row14 col2\" >0.771182</td>\n",
-       "      <td id=\"T_621c2_row14_col3\" class=\"data row14 col3\" >Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_621c2_level0_row15\" class=\"row_heading level0 row15\" >3</th>\n",
-       "      <td id=\"T_621c2_row15_col0\" class=\"data row15 col0\" >Employment-agriculture</td>\n",
-       "      <td id=\"T_621c2_row15_col1\" class=\"data row15 col1\" >None</td>\n",
-       "      <td id=\"T_621c2_row15_col2\" class=\"data row15 col2\" >0.758186</td>\n",
-       "      <td id=\"T_621c2_row15_col3\" class=\"data row15 col3\" >Negative</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1cfb8c2ed40>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multcolumn(df)\n",
-    "for i in range(0,len(clist)):\n",
-    "    dat=df.loc[df.loc[:, 'Country'] == clist[i]]\n",
-    "    for e in range(2,len(columns)):\n",
-    "        data=dat.iloc[:, e]\n",
-    "        stat, p = shapiro(data)\n",
-    "        print(clist[i] +\"-\"+ columns[e])\n",
-    "        print('Statistical=%.3f, p=%.3f' % (stat, p))\n",
-    "        alpha = 0.05\n",
-    "        if p > alpha:\n",
-    "            print('Data is NORMAL ( H0 not denied )')\n",
-    "        else:\n",
-    "            print('It does NOT seem normal( Denies H0 )')\n",
-    "    print(clist[i])\n",
-    "    cor=dat.corr()\n",
-    "    cor.loc[:,'GDP-R^2']=cor['GDP']**2\n",
-    "    cor.loc[:,'Indicator']=cor.index    \n",
-    "    cor[['Indicator','Type']]=cor.Indicator.str.split('.',expand=True)\n",
-    "    corcolumn=cor[['Indicator','Type', 'GDP-R^2','GDP']]\n",
-    "    corcolumn=corcolumn.loc[corcolumn.loc[:,'GDP-R^2']>=0.75]\n",
-    "    id=corcolumn.groupby('Indicator')['GDP-R^2'].transform(max)==corcolumn['GDP-R^2']\n",
-    "    corcolumn[id]\n",
-    "    max_df=pd.DataFrame(corcolumn[id])\n",
-    "    max_df['Behaviour']=np.where(max_df['GDP']>0, 'Positive', 'Negative')\n",
-    "    max_df['Type']=max_df['Type'].replace(['None','^2','^3','log'],['Linear','Cuadratic','Cubic','Logarithmic'])\n",
-    "    max_df.drop(\"GDP\",axis=1,inplace=True)\n",
-    "    max_df=max_df.reset_index(drop=True)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='Year'].index)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='GDP'].index)\n",
-    "    max_df = max_df.drop(max_df[max_df['Indicator']=='Unnamed: 0'].index)\n",
-    "    max_df=max_df.sort_values(by = 'GDP-R^2',ascending = False).style.background_gradient()\n",
-    "    display(max_df)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 ('.venv': poetry)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "292912297167048338b5d1136ec8c661b8a2f1d3fcecd6650d637809a967c218"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/WDI-Data extraction.ipynb b/WDI-Extraction.ipynb
similarity index 74%
rename from WDI-Data extraction.ipynb
rename to WDI-Extraction.ipynb
index c13b935..d31ec6b 100644
--- a/WDI-Data extraction.ipynb	
+++ b/WDI-Extraction.ipynb
@@ -2,36 +2,49 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "292a79ab",
    "metadata": {},
    "source": [
-    "# Data extraction"
+    "# Extraction"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fa3ee36c",
    "metadata": {},
    "source": [
-    "In the following cells we are going to import the data from the [World Bank website](https://www.worldbank.org/en/home), Data sections, and decompress it. So later on, we can process it in the integration, and subsequent parts."
+    "Import libraries and functions."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "760c86c6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import requests\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
     "import glob\n",
     "import os\n",
-    "from zipfile import ZipFile"
+    "from zipfile import ZipFile\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This step has been depreciated due to security features added to the World Bank website. However, the data is still obtainable by openinng the `url`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following cells we are going to import the data from the [World Bank website](https://www.worldbank.org/en/home), Data sections, and decompress it. So later on, we can process it in the integration, and subsequent parts."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4066cd3c",
    "metadata": {},
    "source": [
     "To download the data, we are going to use the `request` library as just to be sure that we do not run into any inconvinient we are going to use the `stream` and `verify` to have a simple and cleaner download. Therefore, at the end we will have the data downloaded in our working directory data by design. (It can be obtained with the following function `os.getcwd()`)"
@@ -40,7 +53,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fbae03c0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,16 +63,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef0affe5",
    "metadata": {},
    "source": [
-    "Then we extract all the files that are contained in *WDI_csv.zip* , into the default directory in a new folder called *Data*. "
+    "Then we extract all the files that are contained in WDI_csv.zip , into the default directory in a new folder called Data."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1aa3d76c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,34 +82,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d97ef77",
    "metadata": {},
    "source": [
-    "Finally we are going to delete the *WDI_csv.zip*."
+    "Finally we are going to delete the WDI_csv.zip."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "566bfbbc",
    "metadata": {},
    "outputs": [],
    "source": [
     "os.remove(os.getcwd()+'/Data/'+\"/**.zip\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61a307d6",
-   "metadata": {},
-   "source": [
-    "As this online extraction was giving us errors, we made it offline manually. We are working on a fix."
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.5 ('.venv': poetry)",
+   "display_name": "Python 3.9.12 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -113,14 +113,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.12"
   },
   "vscode": {
    "interpreter": {
-    "hash": "6fbc2df73014fb514f729a0213c4b355904e0c6715319af310302fb291c75f15"
+    "hash": "db2f77d7771d8a0072cf3f8e7f9965a22a9c65bf4f8ee5135b3d479c9967abaa"
    }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 5
+ "nbformat_minor": 2
 }
diff --git a/WDI-Data integration.ipynb b/WDI-Integration.ipynb
similarity index 51%
rename from WDI-Data integration.ipynb
rename to WDI-Integration.ipynb
index cee41f8..6c33e64 100644
--- a/WDI-Data integration.ipynb	
+++ b/WDI-Integration.ipynb
@@ -1,5 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Integration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import libraries and functions."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -10,24 +24,416 @@
     "import numpy as np\n",
     "import glob\n",
     "import os\n",
+    "from zipfile import ZipFile\n",
     "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")"
+    "warnings.filterwarnings(\"ignore\")\n",
+    "import functools as ft\n",
+    "from pandas.api.types import is_numeric_dtype"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# INTEGRATION"
+    "Firstly we load the database from World Data Bank that has been downloaded and extracted in the *Data extraction* notebook. We acquire it from the predetermined path that is on our computer."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country Name</th>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>Indicator Name</th>\n",
+       "      <th>Indicator Code</th>\n",
+       "      <th>1960</th>\n",
+       "      <th>1961</th>\n",
+       "      <th>1962</th>\n",
+       "      <th>1963</th>\n",
+       "      <th>1964</th>\n",
+       "      <th>1965</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2013</th>\n",
+       "      <th>2014</th>\n",
+       "      <th>2015</th>\n",
+       "      <th>2016</th>\n",
+       "      <th>2017</th>\n",
+       "      <th>2018</th>\n",
+       "      <th>2019</th>\n",
+       "      <th>2020</th>\n",
+       "      <th>2021</th>\n",
+       "      <th>Unnamed: 66</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Africa Eastern and Southern</td>\n",
+       "      <td>AFE</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>EG.CFT.ACCS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.936004</td>\n",
+       "      <td>17.337896</td>\n",
+       "      <td>17.687093</td>\n",
+       "      <td>18.140971</td>\n",
+       "      <td>18.491344</td>\n",
+       "      <td>18.825520</td>\n",
+       "      <td>19.272212</td>\n",
+       "      <td>19.628009</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Africa Eastern and Southern</td>\n",
+       "      <td>AFE</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>EG.CFT.ACCS.RU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.499471</td>\n",
+       "      <td>6.680066</td>\n",
+       "      <td>6.859110</td>\n",
+       "      <td>7.016238</td>\n",
+       "      <td>7.180364</td>\n",
+       "      <td>7.322294</td>\n",
+       "      <td>7.517191</td>\n",
+       "      <td>7.651598</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Africa Eastern and Southern</td>\n",
+       "      <td>AFE</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>EG.CFT.ACCS.UR.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>37.855399</td>\n",
+       "      <td>38.046781</td>\n",
+       "      <td>38.326255</td>\n",
+       "      <td>38.468426</td>\n",
+       "      <td>38.670044</td>\n",
+       "      <td>38.722783</td>\n",
+       "      <td>38.927016</td>\n",
+       "      <td>39.042839</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Africa Eastern and Southern</td>\n",
+       "      <td>AFE</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>EG.ELC.ACCS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.794160</td>\n",
+       "      <td>32.001027</td>\n",
+       "      <td>33.871910</td>\n",
+       "      <td>38.880173</td>\n",
+       "      <td>40.261358</td>\n",
+       "      <td>43.061877</td>\n",
+       "      <td>44.270860</td>\n",
+       "      <td>45.803485</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Africa Eastern and Southern</td>\n",
+       "      <td>AFE</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>EG.ELC.ACCS.RU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.663502</td>\n",
+       "      <td>17.633986</td>\n",
+       "      <td>16.464681</td>\n",
+       "      <td>24.531436</td>\n",
+       "      <td>25.345111</td>\n",
+       "      <td>27.449908</td>\n",
+       "      <td>29.641760</td>\n",
+       "      <td>30.404935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384365</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.REFU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384366</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who were first married by age 15 (% of w...</td>\n",
+       "      <td>SP.M15.2024.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.700000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.418352</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384367</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who were first married by age 18 (% of w...</td>\n",
+       "      <td>SP.M18.2024.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>33.500000</td>\n",
+       "      <td>32.400000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>33.658057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384368</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women's share of population ages 15+ living wi...</td>\n",
+       "      <td>SH.DYN.AIDS.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>59.200000</td>\n",
+       "      <td>59.400000</td>\n",
+       "      <td>59.500000</td>\n",
+       "      <td>59.700000</td>\n",
+       "      <td>59.900000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>60.200000</td>\n",
+       "      <td>60.400000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384369</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>SH.HIV.INCD.YG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18000.000000</td>\n",
+       "      <td>17000.000000</td>\n",
+       "      <td>15000.000000</td>\n",
+       "      <td>14000.000000</td>\n",
+       "      <td>12000.000000</td>\n",
+       "      <td>9700.000000</td>\n",
+       "      <td>9600.000000</td>\n",
+       "      <td>7500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>384370 rows × 67 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Country Name Country Code  \\\n",
+       "0       Africa Eastern and Southern          AFE   \n",
+       "1       Africa Eastern and Southern          AFE   \n",
+       "2       Africa Eastern and Southern          AFE   \n",
+       "3       Africa Eastern and Southern          AFE   \n",
+       "4       Africa Eastern and Southern          AFE   \n",
+       "...                             ...          ...   \n",
+       "384365                     Zimbabwe          ZWE   \n",
+       "384366                     Zimbabwe          ZWE   \n",
+       "384367                     Zimbabwe          ZWE   \n",
+       "384368                     Zimbabwe          ZWE   \n",
+       "384369                     Zimbabwe          ZWE   \n",
+       "\n",
+       "                                           Indicator Name     Indicator Code  \\\n",
+       "0       Access to clean fuels and technologies for coo...     EG.CFT.ACCS.ZS   \n",
+       "1       Access to clean fuels and technologies for coo...  EG.CFT.ACCS.RU.ZS   \n",
+       "2       Access to clean fuels and technologies for coo...  EG.CFT.ACCS.UR.ZS   \n",
+       "3                 Access to electricity (% of population)     EG.ELC.ACCS.ZS   \n",
+       "4       Access to electricity, rural (% of rural popul...  EG.ELC.ACCS.RU.ZS   \n",
+       "...                                                   ...                ...   \n",
+       "384365  Women who believe a husband is justified in be...     SG.VAW.REFU.ZS   \n",
+       "384366  Women who were first married by age 15 (% of w...  SP.M15.2024.FE.ZS   \n",
+       "384367  Women who were first married by age 18 (% of w...  SP.M18.2024.FE.ZS   \n",
+       "384368  Women's share of population ages 15+ living wi...  SH.DYN.AIDS.FE.ZS   \n",
+       "384369  Young people (ages 15-24) newly infected with HIV     SH.HIV.INCD.YG   \n",
+       "\n",
+       "        1960  1961  1962  1963  1964  1965  ...          2013          2014  \\\n",
+       "0        NaN   NaN   NaN   NaN   NaN   NaN  ...     16.936004     17.337896   \n",
+       "1        NaN   NaN   NaN   NaN   NaN   NaN  ...      6.499471      6.680066   \n",
+       "2        NaN   NaN   NaN   NaN   NaN   NaN  ...     37.855399     38.046781   \n",
+       "3        NaN   NaN   NaN   NaN   NaN   NaN  ...     31.794160     32.001027   \n",
+       "4        NaN   NaN   NaN   NaN   NaN   NaN  ...     18.663502     17.633986   \n",
+       "...      ...   ...   ...   ...   ...   ...  ...           ...           ...   \n",
+       "384365   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN           NaN   \n",
+       "384366   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN           NaN   \n",
+       "384367   NaN   NaN   NaN   NaN   NaN   NaN  ...           NaN     33.500000   \n",
+       "384368   NaN   NaN   NaN   NaN   NaN   NaN  ...     59.200000     59.400000   \n",
+       "384369   NaN   NaN   NaN   NaN   NaN   NaN  ...  18000.000000  17000.000000   \n",
+       "\n",
+       "                2015          2016          2017         2018         2019  \\\n",
+       "0          17.687093     18.140971     18.491344    18.825520    19.272212   \n",
+       "1           6.859110      7.016238      7.180364     7.322294     7.517191   \n",
+       "2          38.326255     38.468426     38.670044    38.722783    38.927016   \n",
+       "3          33.871910     38.880173     40.261358    43.061877    44.270860   \n",
+       "4          16.464681     24.531436     25.345111    27.449908    29.641760   \n",
+       "...              ...           ...           ...          ...          ...   \n",
+       "384365     14.500000           NaN           NaN          NaN          NaN   \n",
+       "384366      3.700000           NaN           NaN          NaN     5.418352   \n",
+       "384367     32.400000           NaN           NaN          NaN    33.658057   \n",
+       "384368     59.500000     59.700000     59.900000    60.000000    60.200000   \n",
+       "384369  15000.000000  14000.000000  12000.000000  9700.000000  9600.000000   \n",
+       "\n",
+       "               2020  2021  Unnamed: 66  \n",
+       "0         19.628009   NaN          NaN  \n",
+       "1          7.651598   NaN          NaN  \n",
+       "2         39.042839   NaN          NaN  \n",
+       "3         45.803485   NaN          NaN  \n",
+       "4         30.404935   NaN          NaN  \n",
+       "...             ...   ...          ...  \n",
+       "384365          NaN   NaN          NaN  \n",
+       "384366          NaN   NaN          NaN  \n",
+       "384367          NaN   NaN          NaN  \n",
+       "384368    60.400000   NaN          NaN  \n",
+       "384369  7500.000000   NaN          NaN  \n",
+       "\n",
+       "[384370 rows x 67 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df= pd.read_csv (os.getcwd()+'/Data/'+'WDIData.csv')"
+    "df= pd.read_csv (os.getcwd()+'/Data/'+'WDIData.csv')\n",
+    "df"
    ]
   },
   {
@@ -52,7 +458,7 @@
    "source": [
     "FILTER 1: BY COUNTRY\n",
     "\n",
-    "From the almost two hundred countries we have information about in the worldwide database, we have decided to study 50 of them, grouping them by geographical and economical similiarities. With this, we can keep in our dataframe the selected countries."
+    "From the almost two hundred countries we have information about in the worldwide database, we have decided to study 50 of them, making an initial grouping by geographical and economical similiarities. With this, we can keep in our dataframe the selected countries."
    ]
   },
   {
@@ -191,35 +597,35 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729300</th>\n",
+       "      <th>1769874</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2016</td>\n",
        "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729301</th>\n",
+       "      <th>1769875</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2017</td>\n",
        "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729302</th>\n",
+       "      <th>1769876</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2018</td>\n",
        "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729303</th>\n",
+       "      <th>1769877</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2019</td>\n",
        "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729304</th>\n",
+       "      <th>1769878</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2020</td>\n",
@@ -227,7 +633,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1729305 rows × 4 columns</p>\n",
+       "<p>1769879 rows × 4 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -238,11 +644,11 @@
        "3                DZA  Access to clean fuels and technologies for coo...  2003   \n",
        "4                DZA  Access to clean fuels and technologies for coo...  2004   \n",
        "...              ...                                                ...   ...   \n",
-       "1729300          YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
-       "1729301          YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
-       "1729302          YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
-       "1729303          YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
-       "1729304          YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "1769874          YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1769875          YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1769876          YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1769877          YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1769878          YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
        "\n",
        "         Value  \n",
        "0         97.1  \n",
@@ -251,13 +657,13 @@
        "3         98.0  \n",
        "4         98.2  \n",
        "...        ...  \n",
-       "1729300  200.0  \n",
-       "1729301  200.0  \n",
-       "1729302  200.0  \n",
-       "1729303  200.0  \n",
-       "1729304  200.0  \n",
+       "1769874  200.0  \n",
+       "1769875  200.0  \n",
+       "1769876  200.0  \n",
+       "1769877  200.0  \n",
+       "1769878  200.0  \n",
        "\n",
-       "[1729305 rows x 4 columns]"
+       "[1769879 rows x 4 columns]"
       ]
      },
      "execution_count": 6,
@@ -388,35 +794,35 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729300</th>\n",
+       "      <th>1769874</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2016</td>\n",
        "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729301</th>\n",
+       "      <th>1769875</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2017</td>\n",
        "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729302</th>\n",
+       "      <th>1769876</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2018</td>\n",
        "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729303</th>\n",
+       "      <th>1769877</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2019</td>\n",
        "      <td>200.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1729304</th>\n",
+       "      <th>1769878</th>\n",
        "      <td>YEM</td>\n",
        "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
        "      <td>2020</td>\n",
@@ -424,7 +830,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1198444 rows × 4 columns</p>\n",
+       "<p>1225418 rows × 4 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -435,11 +841,11 @@
        "3                DZA  Access to clean fuels and technologies for coo...  2003   \n",
        "4                DZA  Access to clean fuels and technologies for coo...  2004   \n",
        "...              ...                                                ...   ...   \n",
-       "1729300          YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
-       "1729301          YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
-       "1729302          YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
-       "1729303          YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
-       "1729304          YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "1769874          YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1769875          YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1769876          YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1769877          YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1769878          YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
        "\n",
        "         Value  \n",
        "0         97.1  \n",
@@ -448,13 +854,13 @@
        "3         98.0  \n",
        "4         98.2  \n",
        "...        ...  \n",
-       "1729300  200.0  \n",
-       "1729301  200.0  \n",
-       "1729302  200.0  \n",
-       "1729303  200.0  \n",
-       "1729304  200.0  \n",
+       "1769874  200.0  \n",
+       "1769875  200.0  \n",
+       "1769876  200.0  \n",
+       "1769877  200.0  \n",
+       "1769878  200.0  \n",
        "\n",
-       "[1198444 rows x 4 columns]"
+       "[1225418 rows x 4 columns]"
       ]
      },
      "execution_count": 9,
@@ -467,218 +873,19 @@
     "df3"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "FILTER 3: BY INDICATOR"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As there are lots of indicators that have very similar meaning we have decided to select some indicators to perform the study (**Indicator group** = *Name of the selected indicator*):\n",
-    "- **GDP** = *GDP (current US$)*\n",
-    "- **Literacy** = *Literacy rate, adult total (% of people ages 15 and above)', 'Government expenditure on education, total (% of government expenditure)*\n",
-    "- **Migration** = *Net migration*\n",
-    "- **Exports** = *Commercial service exports (current US$)* & *Exports of goods and services (current US$)*\n",
-    "- **International trading** = *Taxes on international trade (current LCU)*\n",
-    "- **Fertility** = *Fertility rate, total (births per woman)*\n",
-    "- **Healthcare** = *People using at least basic sanitation services (% of population)*\n",
-    "- **Employment** = *Employment in agriculture (% of total employment) (modeled ILO estimate)*, *Employment in services (% of total employment) (modeled ILO estimate)* & *Employment in industry (% of total employment) (modeled ILO estimate)*\n",
-    "- **Renewable energy** = *Electricity production from renewable sources, excluding hydroelectric (kWh)*\n",
-    "- **Mortality** = *Number of infant deaths*\n",
-    "- **Outside investment** = *Foreign direct investment, net (BoP, current US$)*\n",
-    "- **Pollution** = *Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)*\n",
-    "- **Alcoholism** = *Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)*\n",
-    "- **Tech adoption** = *Research and development expenditure (% of GDP)*\n",
-    "- **Workers high education** = *Labor force with advanced education (% of total working-age population with advanced education)*\n",
-    "- **Optimisim and pessimisim** = *Suicide mortality rate (per 100,000 population)*\n",
-    "- **Gender inequality** = *CPIA gender equality rating (**1=low to **6=high)*\n",
-    "- **Education** = *Share of youth not in education, employment or training, total (% of youth population)* & *Government expenditure on education, total (% of government expenditure)'*\n",
-    "\n",
-    "To acomplish this, we use the function `isin` that will allow us to only select the the indicators afromentioned, that have been compilied in the list called *indicators_list*"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "indicators_list=['GDP (current US$)','Literacy rate, adult total (% of people ages 15 and above)', 'Government expenditure on education, total (% of government expenditure)','Net migration','Commercial service exports (current US$)','Exports of goods and services (current US$)','Taxes on international trade (current LCU)','Fertility rate, total (births per woman)','People using at least basic sanitation services (% of population)','Employment in agriculture (% of total employment) (modeled ILO estimate)','Employment in services (% of total employment) (modeled ILO estimate)','Employment in industry (% of total employment) (modeled ILO estimate)','Electricity production from renewable sources, excluding hydroelectric (kWh)','Number of infant deaths','Number of infant deaths','Foreign direct investment, net (BoP, current US$)','Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)','Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)','Research and development expenditure (% of GDP)','Labor force with advanced education (% of total working-age population with advanced education)','Suicide mortality rate (per 100,000 population)','CPIA gender equality rating (1=low to 6=high)','Share of youth not in education, employment or training, total (% of youth population)','Government expenditure on education, total (% of government expenditure)']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country Code</th>\n",
-       "      <th>Indicator Name</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5334</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>4.795977e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5335</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>3.747657e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5336</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>2.466000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5337</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>2.512000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5338</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>Commercial service exports (current US$)</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>2.786733e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1727918</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>7.900000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1727919</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>3.400000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1727920</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>1.800000e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1727921</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>5.500000e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1727922</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>Total alcohol consumption per capita (liters o...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>5.100000e-02</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19944 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Country Code                                     Indicator Name  Date  \\\n",
-       "5334             DZA           Commercial service exports (current US$)  1990   \n",
-       "5335             DZA           Commercial service exports (current US$)  1991   \n",
-       "5336             DZA           Commercial service exports (current US$)  2005   \n",
-       "5337             DZA           Commercial service exports (current US$)  2006   \n",
-       "5338             DZA           Commercial service exports (current US$)  2007   \n",
-       "...              ...                                                ...   ...   \n",
-       "1727918          YEM  Total alcohol consumption per capita (liters o...  2000   \n",
-       "1727919          YEM  Total alcohol consumption per capita (liters o...  2005   \n",
-       "1727920          YEM  Total alcohol consumption per capita (liters o...  2010   \n",
-       "1727921          YEM  Total alcohol consumption per capita (liters o...  2015   \n",
-       "1727922          YEM  Total alcohol consumption per capita (liters o...  2018   \n",
-       "\n",
-       "                Value  \n",
-       "5334     4.795977e+08  \n",
-       "5335     3.747657e+08  \n",
-       "5336     2.466000e+09  \n",
-       "5337     2.512000e+09  \n",
-       "5338     2.786733e+09  \n",
-       "...               ...  \n",
-       "1727918  7.900000e-01  \n",
-       "1727919  3.400000e-01  \n",
-       "1727920  1.800000e-01  \n",
-       "1727921  5.500000e-02  \n",
-       "1727922  5.100000e-02  \n",
-       "\n",
-       "[19944 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "BronzeDataFrame=df3.loc[df3['Indicator Name'].isin(indicators_list)]\n",
-    "pd.set_option('display.max_rows', 10)\n",
-    "BronzeDataFrame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "BronzeDataFrame.to_csv(os.getcwd()+'/Data/BronzeDataFrame.csv')"
+    "BronzeDataFrame=df3"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4 ('.venv': poetry)",
+   "display_name": "Python 3.9.12 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -692,11 +899,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.12"
   },
   "vscode": {
    "interpreter": {
-    "hash": "ad72b07258a3fa24452fee21e868a537ead700a3a6ac2a1adaf5006160c747dc"
+    "hash": "db2f77d7771d8a0072cf3f8e7f9965a22a9c65bf4f8ee5135b3d479c9967abaa"
    }
   }
  },
diff --git a/WDI-Normalization&Categorization.ipynb b/WDI-Normalization&Categorization.ipynb
new file mode 100644
index 0000000..5881920
--- /dev/null
+++ b/WDI-Normalization&Categorization.ipynb
@@ -0,0 +1,8155 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normalization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import libraries and functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import glob\n",
+    "import os\n",
+    "from zipfile import ZipFile\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "import functools as ft\n",
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import Layout\n",
+    "from ipywidgets import interact, interact_manual\n",
+    "from pandas.api.types import is_numeric_dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Taking as reference both works of https://www.pluralsight.com/guides/cleaning-up-data-from-outliers and https://careerfoundry.com/en/blog/data-analytics/how-to-find-outliers/, for normalizing our data we need to start computing the outliers and removing them from our dataframe. As there is not a direct function of pandas that performs this step, it´s been step-by-step code, where we begin with the computation of the quartiles, then the IQR (Inter Quartile Range) and finally the upper and lower limit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### IQR explanation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The interquartile range (IQR) measures the spread of the middle half of your data. It is the range for the middle 50% of your sample. Use the IQR to assess the variability where most of your values lie. Larger values indicate that the central portion of your data spread out further. Conversely, smaller values show that the middle values cluster more tightly.\n",
+    "\n",
+    "To visualize the interquartile range, imagine dividing your data into quarters. Statisticians refer to these quarters as quartiles and label them from low to high as Q1, Q2, Q3, and Q4. The lowest quartile (Q1) covers the smallest quarter of values in your dataset. The upper quartile (Q4) comprises the highest quarter of values. The interquartile range is the middle half of the data that lies between the upper and lower quartiles. In other words, the interquartile range includes the 50% of data points that are above Q1 and below Q4. The IQR is the red area in the graph below, containing Q2 and Q3 (not labeled).\n",
+    "\n",
+    "https://camo.githubusercontent.com/a5f6cf8164048f8c28f9b00b94e1264480c8c3b20a1b3d0bdca47083f3a86a19/68747470733a2f2f69302e77702e636f6d2f7374617469737469637362796a696d2e636f6d2f77702d636f6e74656e742f75706c6f6164732f323031382f30332f696e7465727175617274696c655f72616e67652e706e673f773d3537362673736c3d31\n",
+    "\n",
+    "When measuring variability, statisticians prefer using the interquartile range instead of the full data range because extreme values and outliers affect it less. Typically, use the IQR with a measure of central tendency, such as the median, to understand your data’s center and spread. This combination creates a fuller picture of your data’s distribution.\n",
+    "\n",
+    "Therefore it is being utilized to get rid of all the outliers that may come from errors when creating the data or from unexpected years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Firstly, we compute the first quartile (Q1=25%) and the third quartile (Q3=75%). For that, we have grouped the data by country code and indicator name, so we get the Q1 and Q3 values for each indicator in each geographical area. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001B1CA01DBB0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped=BronzeDataFrame.groupby(['Country Code','Indicator Name'])\n",
+    "grouped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>Indicator Name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>4.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>85000.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>59239 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                 Date  \\\n",
+       "Country Code Indicator Name                                             \n",
+       "ARE          Access to clean fuels and technologies for cook...  10.0   \n",
+       "             Access to clean fuels and technologies for cook...  10.0   \n",
+       "             Access to clean fuels and technologies for cook...  10.0   \n",
+       "             Access to electricity (% of population)             15.0   \n",
+       "             Access to electricity, rural (% of rural popula...  15.0   \n",
+       "...                                                               ...   \n",
+       "ZAF          Women who believe a husband is justified in bea...   0.0   \n",
+       "             Women who were first married by age 15 (% of wo...   9.0   \n",
+       "             Women who were first married by age 18 (% of wo...   9.0   \n",
+       "             Women's share of population ages 15+ living wit...  15.0   \n",
+       "             Young people (ages 15-24) newly infected with HIV   15.0   \n",
+       "\n",
+       "                                                                    Value  \n",
+       "Country Code Indicator Name                                                \n",
+       "ARE          Access to clean fuels and technologies for cook...      0.00  \n",
+       "             Access to clean fuels and technologies for cook...      0.00  \n",
+       "             Access to clean fuels and technologies for cook...      0.00  \n",
+       "             Access to electricity (% of population)                 0.00  \n",
+       "             Access to electricity, rural (% of rural popula...      0.00  \n",
+       "...                                                                   ...  \n",
+       "ZAF          Women who believe a husband is justified in bea...      0.00  \n",
+       "             Women who were first married by age 15 (% of wo...      0.15  \n",
+       "             Women who were first married by age 18 (% of wo...      2.15  \n",
+       "             Women's share of population ages 15+ living wit...      4.90  \n",
+       "             Young people (ages 15-24) newly infected with HIV   85000.00  \n",
+       "\n",
+       "[59239 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Q1=BronzeDataFrame.groupby(['Country Code','Indicator Name']).quantile(0.25)\n",
+    "Q3=BronzeDataFrame.groupby(['Country Code','Indicator Name']).quantile(0.75)\n",
+    "IQR=Q3-Q1\n",
+    "IQR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we got the quartiles, we compute the upper and lower limit, with a basic mathematical expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Lower limit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>Indicator Name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <td>0.625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <td>1.375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <td>49.850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
+       "      <td>-22500.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>59239 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                 Lower limit\n",
+       "Country Code Indicator Name                                                 \n",
+       "ARE          Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to electricity (% of population)                 100.000\n",
+       "             Access to electricity, rural (% of rural popula...      100.000\n",
+       "...                                                                      ...\n",
+       "ZAF          Women who believe a husband is justified in bea...        1.000\n",
+       "             Women who were first married by age 15 (% of wo...        0.625\n",
+       "             Women who were first married by age 18 (% of wo...        1.375\n",
+       "             Women's share of population ages 15+ living wit...       49.850\n",
+       "             Young people (ages 15-24) newly infected with HIV    -22500.000\n",
+       "\n",
+       "[59239 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lower_limit=Q1 - 1.5 * IQR\n",
+    "lower=lower_limit.drop(['Date'],axis=1)\n",
+    "lower.rename(columns={\"Value\":\"Lower limit\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Upper limit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>Indicator Name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ARE</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <td>100.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">ZAF</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <td>1.225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <td>9.975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <td>69.450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
+       "      <td>317500.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>59239 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                 Upper limit\n",
+       "Country Code Indicator Name                                                 \n",
+       "ARE          Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to clean fuels and technologies for cook...      100.000\n",
+       "             Access to electricity (% of population)                 100.000\n",
+       "             Access to electricity, rural (% of rural popula...      100.000\n",
+       "...                                                                      ...\n",
+       "ZAF          Women who believe a husband is justified in bea...        1.000\n",
+       "             Women who were first married by age 15 (% of wo...        1.225\n",
+       "             Women who were first married by age 18 (% of wo...        9.975\n",
+       "             Women's share of population ages 15+ living wit...       69.450\n",
+       "             Young people (ages 15-24) newly infected with HIV    317500.000\n",
+       "\n",
+       "[59239 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "upper_limit=Q3 + 1.5 * IQR\n",
+    "upper=upper_limit.drop(['Date'],axis=1)\n",
+    "upper.rename(columns={\"Value\":\"Upper limit\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thirdly, we join the three tables we have (main dataframe, upper limit and lower limit) by matching country code and indicator name.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>Indicator Name</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Value_x</th>\n",
+       "      <th>Value_y</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>97.1</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>97.3</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>97.8</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>98.2</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225413</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225414</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225415</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225416</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225417</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1225418 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country Code                                     Indicator Name  Date  \\\n",
+       "0                DZA  Access to clean fuels and technologies for coo...  2000   \n",
+       "1                DZA  Access to clean fuels and technologies for coo...  2001   \n",
+       "2                DZA  Access to clean fuels and technologies for coo...  2002   \n",
+       "3                DZA  Access to clean fuels and technologies for coo...  2003   \n",
+       "4                DZA  Access to clean fuels and technologies for coo...  2004   \n",
+       "...              ...                                                ...   ...   \n",
+       "1225413          YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1225414          YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1225415          YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1225416          YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1225417          YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "\n",
+       "         Value_x  Value_y  Value  \n",
+       "0           97.1     97.0  101.0  \n",
+       "1           97.3     97.0  101.0  \n",
+       "2           97.8     97.0  101.0  \n",
+       "3           98.0     97.0  101.0  \n",
+       "4           98.2     97.0  101.0  \n",
+       "...          ...      ...    ...  \n",
+       "1225413    200.0    -50.0  350.0  \n",
+       "1225414    200.0    -50.0  350.0  \n",
+       "1225415    200.0    -50.0  350.0  \n",
+       "1225416    200.0    -50.0  350.0  \n",
+       "1225417    200.0    -50.0  350.0  \n",
+       "\n",
+       "[1225418 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfs = [BronzeDataFrame,lower,upper]\n",
+    "df_joined = ft.reduce(lambda left, right: pd.merge(left, right, on=['Country Code','Indicator Name']), dfs)\n",
+    "df_joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Country Code', 'Indicator Name', 'Date', 'Value_x', 'Value_y', 'Value']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(df_joined)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We rename the columns of the new table, as the columns headers are not saved after the joining. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Real value</th>\n",
+       "      <th>Lower value</th>\n",
+       "      <th>Upper value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>97.1</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>97.3</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>97.8</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>98.2</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225413</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225414</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225415</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225416</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225417</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1225418 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country                                          Indicator  Year  \\\n",
+       "0           DZA  Access to clean fuels and technologies for coo...  2000   \n",
+       "1           DZA  Access to clean fuels and technologies for coo...  2001   \n",
+       "2           DZA  Access to clean fuels and technologies for coo...  2002   \n",
+       "3           DZA  Access to clean fuels and technologies for coo...  2003   \n",
+       "4           DZA  Access to clean fuels and technologies for coo...  2004   \n",
+       "...         ...                                                ...   ...   \n",
+       "1225413     YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1225414     YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1225415     YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1225416     YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1225417     YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "\n",
+       "         Real value  Lower value  Upper value  \n",
+       "0              97.1         97.0        101.0  \n",
+       "1              97.3         97.0        101.0  \n",
+       "2              97.8         97.0        101.0  \n",
+       "3              98.0         97.0        101.0  \n",
+       "4              98.2         97.0        101.0  \n",
+       "...             ...          ...          ...  \n",
+       "1225413       200.0        -50.0        350.0  \n",
+       "1225414       200.0        -50.0        350.0  \n",
+       "1225415       200.0        -50.0        350.0  \n",
+       "1225416       200.0        -50.0        350.0  \n",
+       "1225417       200.0        -50.0        350.0  \n",
+       "\n",
+       "[1225418 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "renamed=df_joined.set_axis(['Country','Indicator','Year', 'Real value', 'Lower value', 'Upper value'], axis=1, inplace=False)\n",
+    "renamed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have the table correctly defined, we remove from our dataframe the values that are outside our range, as it means that they are outliers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Real value</th>\n",
+       "      <th>Lower value</th>\n",
+       "      <th>Upper value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>97.1</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>97.3</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>97.8</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>98.2</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225413</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225414</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225415</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225416</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225417</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1189068 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country                                          Indicator  Year  \\\n",
+       "0           DZA  Access to clean fuels and technologies for coo...  2000   \n",
+       "1           DZA  Access to clean fuels and technologies for coo...  2001   \n",
+       "2           DZA  Access to clean fuels and technologies for coo...  2002   \n",
+       "3           DZA  Access to clean fuels and technologies for coo...  2003   \n",
+       "4           DZA  Access to clean fuels and technologies for coo...  2004   \n",
+       "...         ...                                                ...   ...   \n",
+       "1225413     YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1225414     YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1225415     YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1225416     YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1225417     YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "\n",
+       "         Real value  Lower value  Upper value  \n",
+       "0              97.1         97.0        101.0  \n",
+       "1              97.3         97.0        101.0  \n",
+       "2              97.8         97.0        101.0  \n",
+       "3              98.0         97.0        101.0  \n",
+       "4              98.2         97.0        101.0  \n",
+       "...             ...          ...          ...  \n",
+       "1225413       200.0        -50.0        350.0  \n",
+       "1225414       200.0        -50.0        350.0  \n",
+       "1225415       200.0        -50.0        350.0  \n",
+       "1225416       200.0        -50.0        350.0  \n",
+       "1225417       200.0        -50.0        350.0  \n",
+       "\n",
+       "[1189068 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sin_outliers=renamed.loc[~((renamed['Real value']<renamed['Lower value']) | (renamed['Real value']>renamed['Upper value']))]\n",
+    "sin_outliers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the data above, we can perceive that our data comes down from 1225418  rows to 1189068, so 36.350  were outliers. The next steps are to order and display data better, removing those columns that we just do not need and pivoting the rows and columns. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Real value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>97.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>97.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>97.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>98.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>98.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225413</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225414</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225415</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225416</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1225417</th>\n",
+       "      <td>YEM</td>\n",
+       "      <td>Young people (ages 15-24) newly infected with HIV</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1189068 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country                                          Indicator  Year  \\\n",
+       "0           DZA  Access to clean fuels and technologies for coo...  2000   \n",
+       "1           DZA  Access to clean fuels and technologies for coo...  2001   \n",
+       "2           DZA  Access to clean fuels and technologies for coo...  2002   \n",
+       "3           DZA  Access to clean fuels and technologies for coo...  2003   \n",
+       "4           DZA  Access to clean fuels and technologies for coo...  2004   \n",
+       "...         ...                                                ...   ...   \n",
+       "1225413     YEM  Young people (ages 15-24) newly infected with HIV  2016   \n",
+       "1225414     YEM  Young people (ages 15-24) newly infected with HIV  2017   \n",
+       "1225415     YEM  Young people (ages 15-24) newly infected with HIV  2018   \n",
+       "1225416     YEM  Young people (ages 15-24) newly infected with HIV  2019   \n",
+       "1225417     YEM  Young people (ages 15-24) newly infected with HIV  2020   \n",
+       "\n",
+       "         Real value  \n",
+       "0              97.1  \n",
+       "1              97.3  \n",
+       "2              97.8  \n",
+       "3              98.0  \n",
+       "4              98.2  \n",
+       "...             ...  \n",
+       "1225413       200.0  \n",
+       "1225414       200.0  \n",
+       "1225415       200.0  \n",
+       "1225416       200.0  \n",
+       "1225417       200.0  \n",
+       "\n",
+       "[1189068 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_limpio=sin_outliers.drop(['Lower value','Upper value'],axis=1)\n",
+    "df_limpio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols=df_limpio['Indicator'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>ARI treatment (% of children under 5 taken to a health provider)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife (any of five reasons) (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she argues with him (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she burns the food (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she goes out without telling him (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she neglects the children (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ARE</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ARE</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ARE</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ARE</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ARE</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1531</th>\n",
+       "      <td>ZAF</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>85.2</td>\n",
+       "      <td>64.6</td>\n",
+       "      <td>94.20</td>\n",
+       "      <td>84.400002</td>\n",
+       "      <td>76.738983</td>\n",
+       "      <td>88.373024</td>\n",
+       "      <td>69.218491</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63.3</td>\n",
+       "      <td>100000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1532</th>\n",
+       "      <td>ZAF</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>85.7</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>94.65</td>\n",
+       "      <td>84.699997</td>\n",
+       "      <td>77.168495</td>\n",
+       "      <td>88.518814</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63.7</td>\n",
+       "      <td>92000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1533</th>\n",
+       "      <td>ZAF</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.3</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>94.90</td>\n",
+       "      <td>85.000000</td>\n",
+       "      <td>77.611824</td>\n",
+       "      <td>88.662704</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>85000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1534</th>\n",
+       "      <td>ZAF</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.8</td>\n",
+       "      <td>65.9</td>\n",
+       "      <td>95.20</td>\n",
+       "      <td>84.385536</td>\n",
+       "      <td>75.264854</td>\n",
+       "      <td>88.806267</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>64.4</td>\n",
+       "      <td>79000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1535</th>\n",
+       "      <td>ZAF</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1536 rows × 1438 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator Country  Year  \\\n",
+       "0             ARE  1990   \n",
+       "1             ARE  1991   \n",
+       "2             ARE  1992   \n",
+       "3             ARE  1993   \n",
+       "4             ARE  1994   \n",
+       "...           ...   ...   \n",
+       "1531          ZAF  2017   \n",
+       "1532          ZAF  2018   \n",
+       "1533          ZAF  2019   \n",
+       "1534          ZAF  2020   \n",
+       "1535          ZAF  2021   \n",
+       "\n",
+       "Indicator  ARI treatment (% of children under 5 taken to a health provider)  \\\n",
+       "0                                                        NaN                  \n",
+       "1                                                        NaN                  \n",
+       "2                                                        NaN                  \n",
+       "3                                                        NaN                  \n",
+       "4                                                        NaN                  \n",
+       "...                                                      ...                  \n",
+       "1531                                                     NaN                  \n",
+       "1532                                                     NaN                  \n",
+       "1533                                                     NaN                  \n",
+       "1534                                                     NaN                  \n",
+       "1535                                                     NaN                  \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "0                                                        NaN                      \n",
+       "1                                                        NaN                      \n",
+       "2                                                        NaN                      \n",
+       "3                                                        NaN                      \n",
+       "4                                                        NaN                      \n",
+       "...                                                      ...                      \n",
+       "1531                                                    85.2                      \n",
+       "1532                                                    85.7                      \n",
+       "1533                                                    86.3                      \n",
+       "1534                                                    86.8                      \n",
+       "1535                                                     NaN                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "0                                                        NaN                                   \n",
+       "1                                                        NaN                                   \n",
+       "2                                                        NaN                                   \n",
+       "3                                                        NaN                                   \n",
+       "4                                                        NaN                                   \n",
+       "...                                                      ...                                   \n",
+       "1531                                                    64.6                                   \n",
+       "1532                                                    65.5                                   \n",
+       "1533                                                    65.5                                   \n",
+       "1534                                                    65.9                                   \n",
+       "1535                                                     NaN                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "0                                                        NaN                                   \n",
+       "1                                                        NaN                                   \n",
+       "2                                                        NaN                                   \n",
+       "3                                                        NaN                                   \n",
+       "4                                                        NaN                                   \n",
+       "...                                                      ...                                   \n",
+       "1531                                                   94.20                                   \n",
+       "1532                                                   94.65                                   \n",
+       "1533                                                   94.90                                   \n",
+       "1534                                                   95.20                                   \n",
+       "1535                                                     NaN                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "0                                       100.000000   \n",
+       "1                                       100.000000   \n",
+       "2                                       100.000000   \n",
+       "3                                       100.000000   \n",
+       "4                                       100.000000   \n",
+       "...                                            ...   \n",
+       "1531                                     84.400002   \n",
+       "1532                                     84.699997   \n",
+       "1533                                     85.000000   \n",
+       "1534                                     84.385536   \n",
+       "1535                                           NaN   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "0                                                 100.000000      \n",
+       "1                                                 100.000000      \n",
+       "2                                                 100.000000      \n",
+       "3                                                 100.000000      \n",
+       "4                                                 100.000000      \n",
+       "...                                                      ...      \n",
+       "1531                                               76.738983      \n",
+       "1532                                               77.168495      \n",
+       "1533                                               77.611824      \n",
+       "1534                                               75.264854      \n",
+       "1535                                                     NaN      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "0                                                 100.000000      \n",
+       "1                                                 100.000000      \n",
+       "2                                                 100.000000      \n",
+       "3                                                 100.000000      \n",
+       "4                                                 100.000000      \n",
+       "...                                                      ...      \n",
+       "1531                                               88.373024      \n",
+       "1532                                               88.518814      \n",
+       "1533                                               88.662704      \n",
+       "1534                                               88.806267      \n",
+       "1535                                                     NaN      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "0                                                        NaN                                                                 \n",
+       "1                                                        NaN                                                                 \n",
+       "2                                                        NaN                                                                 \n",
+       "3                                                        NaN                                                                 \n",
+       "4                                                        NaN                                                                 \n",
+       "...                                                      ...                                                                 \n",
+       "1531                                               69.218491                                                                 \n",
+       "1532                                                     NaN                                                                 \n",
+       "1533                                                     NaN                                                                 \n",
+       "1534                                                     NaN                                                                 \n",
+       "1535                                                     NaN                                                                 \n",
+       "\n",
+       "Indicator  ...  \\\n",
+       "0          ...   \n",
+       "1          ...   \n",
+       "2          ...   \n",
+       "3          ...   \n",
+       "4          ...   \n",
+       "...        ...   \n",
+       "1531       ...   \n",
+       "1532       ...   \n",
+       "1533       ...   \n",
+       "1534       ...   \n",
+       "1535       ...   \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife (any of five reasons) (%)  \\\n",
+       "0                                                        NaN                                        \n",
+       "1                                                        NaN                                        \n",
+       "2                                                        NaN                                        \n",
+       "3                                                        NaN                                        \n",
+       "4                                                        NaN                                        \n",
+       "...                                                      ...                                        \n",
+       "1531                                                     NaN                                        \n",
+       "1532                                                     NaN                                        \n",
+       "1533                                                     NaN                                        \n",
+       "1534                                                     NaN                                        \n",
+       "1535                                                     NaN                                        \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she argues with him (%)  \\\n",
+       "0                                                        NaN                                           \n",
+       "1                                                        NaN                                           \n",
+       "2                                                        NaN                                           \n",
+       "3                                                        NaN                                           \n",
+       "4                                                        NaN                                           \n",
+       "...                                                      ...                                           \n",
+       "1531                                                     NaN                                           \n",
+       "1532                                                     NaN                                           \n",
+       "1533                                                     NaN                                           \n",
+       "1534                                                     NaN                                           \n",
+       "1535                                                     NaN                                           \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she burns the food (%)  \\\n",
+       "0                                                        NaN                                          \n",
+       "1                                                        NaN                                          \n",
+       "2                                                        NaN                                          \n",
+       "3                                                        NaN                                          \n",
+       "4                                                        NaN                                          \n",
+       "...                                                      ...                                          \n",
+       "1531                                                     NaN                                          \n",
+       "1532                                                     NaN                                          \n",
+       "1533                                                     NaN                                          \n",
+       "1534                                                     NaN                                          \n",
+       "1535                                                     NaN                                          \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she goes out without telling him (%)  \\\n",
+       "0                                                        NaN                                                        \n",
+       "1                                                        NaN                                                        \n",
+       "2                                                        NaN                                                        \n",
+       "3                                                        NaN                                                        \n",
+       "4                                                        NaN                                                        \n",
+       "...                                                      ...                                                        \n",
+       "1531                                                     NaN                                                        \n",
+       "1532                                                     NaN                                                        \n",
+       "1533                                                     NaN                                                        \n",
+       "1534                                                     NaN                                                        \n",
+       "1535                                                     NaN                                                        \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she neglects the children (%)  \\\n",
+       "0                                                        NaN                                                 \n",
+       "1                                                        NaN                                                 \n",
+       "2                                                        NaN                                                 \n",
+       "3                                                        NaN                                                 \n",
+       "4                                                        NaN                                                 \n",
+       "...                                                      ...                                                 \n",
+       "1531                                                     NaN                                                 \n",
+       "1532                                                     NaN                                                 \n",
+       "1533                                                     NaN                                                 \n",
+       "1534                                                     NaN                                                 \n",
+       "1535                                                     NaN                                                 \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she refuses sex with him (%)  \\\n",
+       "0                                                        NaN                                                \n",
+       "1                                                        NaN                                                \n",
+       "2                                                        NaN                                                \n",
+       "3                                                        NaN                                                \n",
+       "4                                                        NaN                                                \n",
+       "...                                                      ...                                                \n",
+       "1531                                                     NaN                                                \n",
+       "1532                                                     NaN                                                \n",
+       "1533                                                     NaN                                                \n",
+       "1534                                                     NaN                                                \n",
+       "1535                                                     NaN                                                \n",
+       "\n",
+       "Indicator  Women who were first married by age 15 (% of women ages 20-24)  \\\n",
+       "0                                                        NaN                \n",
+       "1                                                        NaN                \n",
+       "2                                                        NaN                \n",
+       "3                                                        NaN                \n",
+       "4                                                        NaN                \n",
+       "...                                                      ...                \n",
+       "1531                                                     NaN                \n",
+       "1532                                                     NaN                \n",
+       "1533                                                     NaN                \n",
+       "1534                                                     NaN                \n",
+       "1535                                                     NaN                \n",
+       "\n",
+       "Indicator  Women who were first married by age 18 (% of women ages 20-24)  \\\n",
+       "0                                                        NaN                \n",
+       "1                                                        NaN                \n",
+       "2                                                        NaN                \n",
+       "3                                                        NaN                \n",
+       "4                                                        NaN                \n",
+       "...                                                      ...                \n",
+       "1531                                                     NaN                \n",
+       "1532                                                     NaN                \n",
+       "1533                                                     NaN                \n",
+       "1534                                                     NaN                \n",
+       "1535                                                     NaN                \n",
+       "\n",
+       "Indicator  Women's share of population ages 15+ living with HIV (%)  \\\n",
+       "0                                                       18.8          \n",
+       "1                                                       18.2          \n",
+       "2                                                       19.4          \n",
+       "3                                                       20.0          \n",
+       "4                                                       20.0          \n",
+       "...                                                      ...          \n",
+       "1531                                                    63.3          \n",
+       "1532                                                    63.7          \n",
+       "1533                                                    64.1          \n",
+       "1534                                                    64.4          \n",
+       "1535                                                     NaN          \n",
+       "\n",
+       "Indicator  Young people (ages 15-24) newly infected with HIV  \n",
+       "0                                                      100.0  \n",
+       "1                                                      100.0  \n",
+       "2                                                      100.0  \n",
+       "3                                                      100.0  \n",
+       "4                                                      100.0  \n",
+       "...                                                      ...  \n",
+       "1531                                                100000.0  \n",
+       "1532                                                 92000.0  \n",
+       "1533                                                 85000.0  \n",
+       "1534                                                 79000.0  \n",
+       "1535                                                     NaN  \n",
+       "\n",
+       "[1536 rows x 1438 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SilverDataFrame=df_limpio.set_index([\"Country\", \"Year\"]).pivot(columns=\"Indicator\", values=\"Real value\").reset_index()\n",
+    "SilverDataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On the other hand, another big stone of normalizations is to nan/null values, which we have in all variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1016628"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SilverDataFrame.isna().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can observe, we have lots of missing data, and as there is no optimal way to fullfill these values, thus, we will test some to arrive to the optimal method for our data set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we need to create some lists so our loops work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=SilverDataFrame\n",
+    "europe_list=['DEU','FRA','SWE','GBR','ESP','HRV','POL','GRC','AUT','NLD']\n",
+    "persian_list=['IRQ','QAT','ARE','SAU','AZE','YEM','OMN']\n",
+    "naf_list=['DZA','EGY','LBY','ISR','TUR','MAR']\n",
+    "saf_list=['SEN','ZAF','LBR','MOZ','CMR','NGA','GHA']\n",
+    "asia_list=['BGD','IND','VNM','THA','IDN','PHL','KOR']\n",
+    "latam_list=['MEX','BRA','ARG','PER','VEN','COL','CHL','PAN','CRI']\n",
+    "two_list=['USA','CHN']\n",
+    "country_list=europe_list+persian_list+naf_list+saf_list+asia_list+latam_list+two_list\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are attempting the linear interpolation, which is achieved by geometrically rendering a straight line between two adjacent points on a graph or plane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "685787"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
+    "datc=dat.interpolate(method=\"linear\")\n",
+    "data=datc\n",
+    "\n",
+    "for i in range(1,len(country_list)):\n",
+    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
+    "    datc=dat.interpolate(method=\"linear\")\n",
+    "    data=pd.concat((data, datc), axis = 0)\n",
+    "data.isna().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we attempt the backward filling, filling the previous cell with future values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "498648"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
+    "datc=dat.fillna(method='bfill')\n",
+    "data=datc\n",
+    "\n",
+    "for i in range(1,len(country_list)):\n",
+    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
+    "    datc=dat.fillna(method='bfill')\n",
+    "    data=pd.concat((data, datc), axis = 0)\n",
+    "data.isna().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we will attempt the forward filling, which concists of filling the next cell with previous values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "685787"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
+    "datc=dat.fillna(method='ffill')\n",
+    "data=datc\n",
+    "\n",
+    "for i in range(1,len(country_list)):\n",
+    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
+    "    datc=dat.fillna(method='ffill')\n",
+    "    data=pd.concat((data, datc), axis = 0)\n",
+    "data.isna().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The linear interpolation a form of interpolation, which involves the generation of new values based on an existing set of values. Linear interpolation is achieved by geometrically rendering a straight line between two adjacent points on a graph or plane. Whereas the backwards filling, will help us to arrive to those values which have not been fullfilled with the linear interpolation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And as none of the methods have worked out correctly, independently, we are going to mix them, to achieve a better result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "685787"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
+    "datc=dat.interpolate(method=\"linear\")\n",
+    "datc=datc.fillna(method='ffill')\n",
+    "data=datc\n",
+    "\n",
+    "for i in range(1,len(country_list)):\n",
+    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
+    "    datc=dat.interpolate(method=\"linear\")\n",
+    "    datc=datc.fillna(method='ffill')\n",
+    "    data=pd.concat((data, datc), axis = 0)\n",
+    "data.isna().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "310048"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
+    "datc=dat.interpolate(method=\"linear\")\n",
+    "datc=datc.fillna(method='bfill')\n",
+    "data=datc\n",
+    "\n",
+    "for i in range(1,len(country_list)):\n",
+    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
+    "    datc=dat.interpolate(method=\"linear\")\n",
+    "    datc=datc.fillna(method='bfill')\n",
+    "    data=pd.concat((data, datc), axis = 0)\n",
+    "data.isna().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And finally, mixing the three methods all together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "310048"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
+    "datc=dat.interpolate(method=\"linear\")\n",
+    "datf=datc.fillna(method='bfill')\n",
+    "datr=datf.fillna(method='ffill')\n",
+    "data=datr\n",
+    "\n",
+    "for i in range(1,len(country_list)):\n",
+    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
+    "    datc=dat.interpolate(method=\"linear\")\n",
+    "    datc=datc.fillna(method='bfill')\n",
+    "    datc=datc.fillna(method='ffill')\n",
+    "    data=pd.concat((data, datc), axis = 0)\n",
+    "data.isna().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Therefore, the preferred method for the Nan values´ treatment that we are going to develop is a mix, between the linear interpolation and backwards filling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>ARI treatment (% of children under 5 taken to a health provider)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife (any of five reasons) (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she argues with him (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she burns the food (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she goes out without telling him (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she neglects the children (%)</th>\n",
+       "      <th>Women who believe a husband is justified in beating his wife when she refuses sex with him (%)</th>\n",
+       "      <th>Women who were first married by age 15 (% of women ages 20-24)</th>\n",
+       "      <th>Women who were first married by age 18 (% of women ages 20-24)</th>\n",
+       "      <th>Women's share of population ages 15+ living with HIV (%)</th>\n",
+       "      <th>Young people (ages 15-24) newly infected with HIV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.2</td>\n",
+       "      <td>55.2</td>\n",
+       "      <td>86.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>75.6</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>87.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>77.6</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79.4</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79.4</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1536 rows × 1438 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  ARI treatment (% of children under 5 taken to a health provider)  \\\n",
+       "352                                                      NaN                  \n",
+       "353                                                      NaN                  \n",
+       "354                                                      NaN                  \n",
+       "355                                                      NaN                  \n",
+       "356                                                      NaN                  \n",
+       "..                                                       ...                  \n",
+       "251                                                      NaN                  \n",
+       "252                                                      NaN                  \n",
+       "253                                                      NaN                  \n",
+       "254                                                      NaN                  \n",
+       "255                                                      NaN                  \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                    100.0                      \n",
+       "353                                                    100.0                      \n",
+       "354                                                    100.0                      \n",
+       "355                                                    100.0                      \n",
+       "356                                                    100.0                      \n",
+       "..                                                       ...                      \n",
+       "251                                                     73.2                      \n",
+       "252                                                     75.6                      \n",
+       "253                                                     77.6                      \n",
+       "254                                                     79.4                      \n",
+       "255                                                     79.4                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                    100.0                                   \n",
+       "353                                                    100.0                                   \n",
+       "354                                                    100.0                                   \n",
+       "355                                                    100.0                                   \n",
+       "356                                                    100.0                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                     55.2                                   \n",
+       "252                                                     59.0                                   \n",
+       "253                                                     61.9                                   \n",
+       "254                                                     65.2                                   \n",
+       "255                                                     65.2                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                    100.0                                   \n",
+       "353                                                    100.0                                   \n",
+       "354                                                    100.0                                   \n",
+       "355                                                    100.0                                   \n",
+       "356                                                    100.0                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                     86.2                                   \n",
+       "252                                                     87.4                                   \n",
+       "253                                                     88.4                                   \n",
+       "254                                                     89.4                                   \n",
+       "255                                                     89.4                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                          100.0   \n",
+       "353                                          100.0   \n",
+       "354                                          100.0   \n",
+       "355                                          100.0   \n",
+       "356                                          100.0   \n",
+       "..                                             ...   \n",
+       "251                                          100.0   \n",
+       "252                                          100.0   \n",
+       "253                                          100.0   \n",
+       "254                                          100.0   \n",
+       "255                                          100.0   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                    100.0      \n",
+       "353                                                    100.0      \n",
+       "354                                                    100.0      \n",
+       "355                                                    100.0      \n",
+       "356                                                    100.0      \n",
+       "..                                                       ...      \n",
+       "251                                                    100.0      \n",
+       "252                                                    100.0      \n",
+       "253                                                    100.0      \n",
+       "254                                                    100.0      \n",
+       "255                                                    100.0      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                    100.0      \n",
+       "353                                                    100.0      \n",
+       "354                                                    100.0      \n",
+       "355                                                    100.0      \n",
+       "356                                                    100.0      \n",
+       "..                                                       ...      \n",
+       "251                                                    100.0      \n",
+       "252                                                    100.0      \n",
+       "253                                                    100.0      \n",
+       "254                                                    100.0      \n",
+       "255                                                    100.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                98.133621                                                                 \n",
+       "353                                                98.133621                                                                 \n",
+       "354                                                98.133621                                                                 \n",
+       "355                                                98.133621                                                                 \n",
+       "356                                                98.133621                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                80.229118                                                                 \n",
+       "252                                                80.229118                                                                 \n",
+       "253                                                80.229118                                                                 \n",
+       "254                                                80.229118                                                                 \n",
+       "255                                                80.229118                                                                 \n",
+       "\n",
+       "Indicator  ...  \\\n",
+       "352        ...   \n",
+       "353        ...   \n",
+       "354        ...   \n",
+       "355        ...   \n",
+       "356        ...   \n",
+       "..         ...   \n",
+       "251        ...   \n",
+       "252        ...   \n",
+       "253        ...   \n",
+       "254        ...   \n",
+       "255        ...   \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife (any of five reasons) (%)  \\\n",
+       "352                                                      NaN                                        \n",
+       "353                                                      NaN                                        \n",
+       "354                                                      NaN                                        \n",
+       "355                                                      NaN                                        \n",
+       "356                                                      NaN                                        \n",
+       "..                                                       ...                                        \n",
+       "251                                                      NaN                                        \n",
+       "252                                                      NaN                                        \n",
+       "253                                                      NaN                                        \n",
+       "254                                                      NaN                                        \n",
+       "255                                                      NaN                                        \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she argues with him (%)  \\\n",
+       "352                                                      NaN                                           \n",
+       "353                                                      NaN                                           \n",
+       "354                                                      NaN                                           \n",
+       "355                                                      NaN                                           \n",
+       "356                                                      NaN                                           \n",
+       "..                                                       ...                                           \n",
+       "251                                                      NaN                                           \n",
+       "252                                                      NaN                                           \n",
+       "253                                                      NaN                                           \n",
+       "254                                                      NaN                                           \n",
+       "255                                                      NaN                                           \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she burns the food (%)  \\\n",
+       "352                                                      NaN                                          \n",
+       "353                                                      NaN                                          \n",
+       "354                                                      NaN                                          \n",
+       "355                                                      NaN                                          \n",
+       "356                                                      NaN                                          \n",
+       "..                                                       ...                                          \n",
+       "251                                                      NaN                                          \n",
+       "252                                                      NaN                                          \n",
+       "253                                                      NaN                                          \n",
+       "254                                                      NaN                                          \n",
+       "255                                                      NaN                                          \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she goes out without telling him (%)  \\\n",
+       "352                                                      NaN                                                        \n",
+       "353                                                      NaN                                                        \n",
+       "354                                                      NaN                                                        \n",
+       "355                                                      NaN                                                        \n",
+       "356                                                      NaN                                                        \n",
+       "..                                                       ...                                                        \n",
+       "251                                                      NaN                                                        \n",
+       "252                                                      NaN                                                        \n",
+       "253                                                      NaN                                                        \n",
+       "254                                                      NaN                                                        \n",
+       "255                                                      NaN                                                        \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she neglects the children (%)  \\\n",
+       "352                                                      NaN                                                 \n",
+       "353                                                      NaN                                                 \n",
+       "354                                                      NaN                                                 \n",
+       "355                                                      NaN                                                 \n",
+       "356                                                      NaN                                                 \n",
+       "..                                                       ...                                                 \n",
+       "251                                                      NaN                                                 \n",
+       "252                                                      NaN                                                 \n",
+       "253                                                      NaN                                                 \n",
+       "254                                                      NaN                                                 \n",
+       "255                                                      NaN                                                 \n",
+       "\n",
+       "Indicator  Women who believe a husband is justified in beating his wife when she refuses sex with him (%)  \\\n",
+       "352                                                      NaN                                                \n",
+       "353                                                      NaN                                                \n",
+       "354                                                      NaN                                                \n",
+       "355                                                      NaN                                                \n",
+       "356                                                      NaN                                                \n",
+       "..                                                       ...                                                \n",
+       "251                                                      NaN                                                \n",
+       "252                                                      NaN                                                \n",
+       "253                                                      NaN                                                \n",
+       "254                                                      NaN                                                \n",
+       "255                                                      NaN                                                \n",
+       "\n",
+       "Indicator  Women who were first married by age 15 (% of women ages 20-24)  \\\n",
+       "352                                                      NaN                \n",
+       "353                                                      NaN                \n",
+       "354                                                      NaN                \n",
+       "355                                                      NaN                \n",
+       "356                                                      NaN                \n",
+       "..                                                       ...                \n",
+       "251                                                      NaN                \n",
+       "252                                                      NaN                \n",
+       "253                                                      NaN                \n",
+       "254                                                      NaN                \n",
+       "255                                                      NaN                \n",
+       "\n",
+       "Indicator  Women who were first married by age 18 (% of women ages 20-24)  \\\n",
+       "352                                                      NaN                \n",
+       "353                                                      NaN                \n",
+       "354                                                      NaN                \n",
+       "355                                                      NaN                \n",
+       "356                                                      NaN                \n",
+       "..                                                       ...                \n",
+       "251                                                      NaN                \n",
+       "252                                                      NaN                \n",
+       "253                                                      NaN                \n",
+       "254                                                      NaN                \n",
+       "255                                                      NaN                \n",
+       "\n",
+       "Indicator  Women's share of population ages 15+ living with HIV (%)  \\\n",
+       "352                                                     19.1          \n",
+       "353                                                     19.1          \n",
+       "354                                                     19.1          \n",
+       "355                                                     19.1          \n",
+       "356                                                     19.1          \n",
+       "..                                                       ...          \n",
+       "251                                                      NaN          \n",
+       "252                                                      NaN          \n",
+       "253                                                      NaN          \n",
+       "254                                                      NaN          \n",
+       "255                                                      NaN          \n",
+       "\n",
+       "Indicator  Young people (ages 15-24) newly infected with HIV  \n",
+       "352                                                    500.0  \n",
+       "353                                                    500.0  \n",
+       "354                                                    500.0  \n",
+       "355                                                    500.0  \n",
+       "356                                                    500.0  \n",
+       "..                                                       ...  \n",
+       "251                                                      NaN  \n",
+       "252                                                      NaN  \n",
+       "253                                                      NaN  \n",
+       "254                                                      NaN  \n",
+       "255                                                      NaN  \n",
+       "\n",
+       "[1536 rows x 1438 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat=df.loc[df.loc[:, 'Country'] == country_list[0]]\n",
+    "datc=dat.interpolate(method=\"linear\")\n",
+    "datf=datc.fillna(method='bfill')\n",
+    "datr=datf.fillna(method='ffill')\n",
+    "data=datr\n",
+    "\n",
+    "for i in range(1,len(country_list)):\n",
+    "    dat=df.loc[df.loc[:, 'Country'] == country_list[i]]\n",
+    "    datc=dat.interpolate(method=\"linear\")\n",
+    "    datc=datc.fillna(method='bfill')\n",
+    "    datc=datc.fillna(method='ffill')\n",
+    "    data=pd.concat((data, datc), axis = 0)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we will drop the columns which have over X% missing values because the absence of data creates an unreliable source. This % can be adjusted in the following slider. We have predetermined that 20% is a great starting point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dda106d850e463580b809ff362ca4f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.2, continuous_update=False, description='% that creates unreliable source:', max=1.0, read…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Slider1=widgets.FloatSlider(\n",
+    "    value=0.2,\n",
+    "    min=0,\n",
+    "    max=1.0,\n",
+    "    step=0.05,\n",
+    "    description='% that creates unreliable source:',\n",
+    "    disabled=False,\n",
+    "    continuous_update=False,\n",
+    "    orientation='horizontal',\n",
+    "    readout=True,\n",
+    "    readout_format='.1f',\n",
+    ")\n",
+    "Slider1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adults (ages 15+) and children (ages 0-14) newly infected with HIV\n",
+      "Adults (ages 15-49) newly infected with HIV\n",
+      "Antiretroviral therapy coverage (% of people living with HIV)\n",
+      "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)\n",
+      "ARI treatment (% of children under 5 taken to a health provider)\n",
+      "Average transaction cost of sending remittances to a specific country (%)\n",
+      "Average working hours of children, study and work, ages 7-14 (hours per week)\n",
+      "Average working hours of children, study and work, female, ages 7-14 (hours per week)\n",
+      "Average working hours of children, study and work, male, ages 7-14 (hours per week)\n",
+      "Average working hours of children, working only, ages 7-14 (hours per week)\n",
+      "Average working hours of children, working only, female, ages 7-14 (hours per week)\n",
+      "Average working hours of children, working only, male, ages 7-14 (hours per week)\n",
+      "Bank capital to assets ratio (%)\n",
+      "Bank liquid reserves to bank assets ratio (%)\n",
+      "Bank nonperforming loans to total gross loans (%)\n",
+      "Battle-related deaths (number of people)\n",
+      "Borrowers from commercial banks (per 1,000 adults)\n",
+      "Bribery incidence (% of firms experiencing at least one bribe payment request)\n",
+      "Changes in inventories (constant LCU)\n",
+      "Children (0-14) living with HIV\n",
+      "Children (ages 0-14) newly infected with HIV\n",
+      "Children in employment, female (% of female children ages 7-14)\n",
+      "Children in employment, male (% of male children ages 7-14)\n",
+      "Children in employment, study and work (% of children in employment, ages 7-14)\n",
+      "Children in employment, study and work, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, study and work, male (% of male children in employment, ages 7-14)\n",
+      "Children in employment, total (% of children ages 7-14)\n",
+      "Children in employment, unpaid family workers (% of children in employment, ages 7-14)\n",
+      "Children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)\n",
+      "Children in employment, wage workers (% of children in employment, ages 7-14)\n",
+      "Children in employment, wage workers, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, wage workers, male (% of male children in employment, ages 7-14)\n",
+      "Children in employment, work only (% of children in employment, ages 7-14)\n",
+      "Children in employment, work only, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, work only, male (% of male children in employment, ages 7-14)\n",
+      "Claims on other sectors of the domestic economy (annual growth as % of broad money)\n",
+      "Commercial banks and other lending (PPG + PNG) (NFL, current US$)\n",
+      "Completeness of birth registration, rural (%)\n",
+      "Completeness of birth registration, urban (%)\n",
+      "Consumption of iodized salt (% of households)\n",
+      "Debt service (PPG and IMF only, % of exports of goods, services and primary income)\n",
+      "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)\n",
+      "Debt service on external debt, total (TDS, current US$)\n",
+      "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)\n",
+      "Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)\n",
+      "Diarrhea treatment (% of children under 5 who received ORS packet)\n",
+      "Disaster risk reduction progress score (1-5 scale; 5=best)\n",
+      "Exclusive breastfeeding (% of children under 6 months)\n",
+      "External debt stocks (% of GNI)\n",
+      "External debt stocks, long-term (DOD, current US$)\n",
+      "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)\n",
+      "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)\n",
+      "External debt stocks, short-term (DOD, current US$)\n",
+      "External debt stocks, total (DOD, current US$)\n",
+      "Financial intermediary services indirectly Measured (FISIM) (constant LCU)\n",
+      "Financial intermediary services indirectly Measured (FISIM) (current LCU)\n",
+      "Firms competing against unregistered firms (% of firms)\n",
+      "Firms formally registered when operations started (% of firms)\n",
+      "Firms that do not report all sales for tax purposes (% of firms)\n",
+      "Gross fixed capital formation, private sector (% of GDP)\n",
+      "Gross fixed capital formation, private sector (current LCU)\n",
+      "IBRD loans and IDA credits (DOD, current US$)\n",
+      "IFC, private nonguaranteed (NFL, current US$)\n",
+      "IMF repurchases and charges (TDS, current US$)\n",
+      "Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)\n",
+      "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)\n",
+      "Incidence of HIV, all (per 1,000 uninfected population)\n",
+      "Incidence of malaria (per 1,000 population at risk)\n",
+      "Informal payments to public officials (% of firms)\n",
+      "Intentional homicides, female (per 100,000 female)\n",
+      "Intentional homicides, male (per 100,000 male)\n",
+      "Interest rate spread (lending rate minus deposit rate, %)\n",
+      "Internally displaced persons, new displacement associated with conflict and violence (number of cases)\n",
+      "Internally displaced persons, total displaced by conflict and violence (number of people)\n",
+      "International tourism, number of departures\n",
+      "Investment in energy with private participation (current US$)\n",
+      "Investment in ICT with private participation (current US$)\n",
+      "Investment in transport with private participation (current US$)\n",
+      "Investment in water and sanitation with private participation (current US$)\n",
+      "Lending interest rate (%)\n",
+      "Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)\n",
+      "Merchandise imports from low- and middle-income economies within region (% of total merchandise imports)\n",
+      "Methodology assessment of statistical capacity (scale 0 - 100)\n",
+      "Multilateral debt service (% of public and publicly guaranteed debt service)\n",
+      "Multilateral debt service (TDS, current US$)\n",
+      "Net bilateral aid flows from DAC donors, Australia (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Belgium (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Canada (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Czech Republic (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Denmark (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Finland (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Greece (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Hungary (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Ireland (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Italy (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Korea, Rep. (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Luxembourg (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Netherlands (current US$)\n",
+      "Net bilateral aid flows from DAC donors, New Zealand (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Norway (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Poland (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Portugal (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Slovenia (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Spain (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Sweden (current US$)\n",
+      "Net bilateral aid flows from DAC donors, Switzerland (current US$)\n",
+      "Net financial flows, bilateral (NFL, current US$)\n",
+      "Net financial flows, IBRD (NFL, current US$)\n",
+      "Net financial flows, IMF nonconcessional (NFL, current US$)\n",
+      "Net financial flows, multilateral (NFL, current US$)\n",
+      "Net financial flows, others (NFL, current US$)\n",
+      "Net financial flows, RDB concessional (NFL, current US$)\n",
+      "Net financial flows, RDB nonconcessional (NFL, current US$)\n",
+      "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)\n",
+      "Net intake rate in grade 1 (% of official school-age population)\n",
+      "Net intake rate in grade 1, female (% of official school-age population)\n",
+      "Net intake rate in grade 1, male (% of official school-age population)\n",
+      "Net ODA received (% of GNI)\n",
+      "Net ODA received (% of gross capital formation)\n",
+      "Net ODA received (% of imports of goods, services and primary income)\n",
+      "Net ODA received per capita (current US$)\n",
+      "Net official development assistance received (constant 2018 US$)\n",
+      "Net official development assistance received (current US$)\n",
+      "Net official flows from UN agencies, FAO (current US$)\n",
+      "Net official flows from UN agencies, IAEA (current US$)\n",
+      "Net official flows from UN agencies, IFAD (current US$)\n",
+      "Net official flows from UN agencies, ILO (current US$)\n",
+      "Net official flows from UN agencies, UNAIDS (current US$)\n",
+      "Net official flows from UN agencies, UNDP (current US$)\n",
+      "Net official flows from UN agencies, UNFPA (current US$)\n",
+      "Net official flows from UN agencies, UNHCR (current US$)\n",
+      "Net official flows from UN agencies, UNICEF (current US$)\n",
+      "Net official flows from UN agencies, WFP (current US$)\n",
+      "Net official flows from UN agencies, WHO (current US$)\n",
+      "Net secondary income (Net current transfers from abroad) (constant LCU)\n",
+      "Newborns protected against tetanus (%)\n",
+      "People using safely managed drinking water services (% of population)\n",
+      "People using safely managed drinking water services, rural (% of rural population)\n",
+      "People using safely managed drinking water services, urban (% of urban population)\n",
+      "People using safely managed sanitation services, rural (% of rural population)\n",
+      "People using safely managed sanitation services, urban (% of urban population)\n",
+      "People with basic handwashing facilities including soap and water (% of population)\n",
+      "People with basic handwashing facilities including soap and water, rural (% of rural population)\n",
+      "People with basic handwashing facilities including soap and water, urban (% of urban population)\n",
+      "Periodicity and timeliness assessment of statistical capacity (scale 0 - 100)\n",
+      "PNG, commercial banks and other creditors (NFL, current US$)\n",
+      "Portfolio investment, bonds (PPG + PNG) (NFL, current US$)\n",
+      "Power outages in firms in a typical month (number)\n",
+      "PPG, bonds (NFL, current US$)\n",
+      "PPG, commercial banks (NFL, current US$)\n",
+      "PPG, IBRD (DOD, current US$)\n",
+      "PPG, official creditors (NFL, current US$)\n",
+      "PPG, other private creditors (NFL, current US$)\n",
+      "PPG, private creditors (NFL, current US$)\n",
+      "Present value of external debt (% of exports of goods, services and primary income)\n",
+      "Present value of external debt (% of GNI)\n",
+      "Present value of external debt (current US$)\n",
+      "Prevalence of HIV, female (% ages 15-24)\n",
+      "Prevalence of HIV, male (% ages 15-24)\n",
+      "Prevalence of HIV, total (% of population ages 15-49)\n",
+      "Prevalence of moderate or severe food insecurity in the population (%)\n",
+      "Prevalence of overweight, weight for height, female (% of children under 5)\n",
+      "Prevalence of overweight, weight for height, male (% of children under 5)\n",
+      "Prevalence of severe food insecurity in the population (%)\n",
+      "Prevalence of severe wasting, weight for height (% of children under 5)\n",
+      "Prevalence of severe wasting, weight for height, female (% of children under 5)\n",
+      "Prevalence of severe wasting, weight for height, male (% of children under 5)\n",
+      "Prevalence of stunting, height for age, female (% of children under 5)\n",
+      "Prevalence of stunting, height for age, male (% of children under 5)\n",
+      "Prevalence of underweight, weight for age, female (% of children under 5)\n",
+      "Prevalence of underweight, weight for age, male (% of children under 5)\n",
+      "Prevalence of wasting, weight for height, female (% of children under 5)\n",
+      "Prevalence of wasting, weight for height, male (% of children under 5)\n",
+      "Primary government expenditures as a proportion of original approved budget (%)\n",
+      "Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)\n",
+      "Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)\n",
+      "Public and publicly guaranteed debt service (% of exports of goods, services and primary income)\n",
+      "Public and publicly guaranteed debt service (% of GNI)\n",
+      "Public private partnerships investment in energy (current US$)\n",
+      "Public private partnerships investment in transport (current US$)\n",
+      "Public private partnerships investment in water and sanitation (current US$)\n",
+      "Real effective exchange rate index (2010 = 100)\n",
+      "Real interest rate (%)\n",
+      "Risk premium on lending (lending rate minus treasury bill rate, %)\n",
+      "Short-term debt (% of exports of goods, services and primary income)\n",
+      "Short-term debt (% of total external debt)\n",
+      "Short-term debt (% of total reserves)\n",
+      "Source data assessment of statistical capacity (scale 0 - 100)\n",
+      "Statistical Capacity Score (Overall Average) (scale 0 - 100)\n",
+      "Technicians in R&D (per million people)\n",
+      "Time required to obtain an operating license (days)\n",
+      "Total debt service (% of exports of goods, services and primary income)\n",
+      "Total debt service (% of GNI)\n",
+      "Total reserves (% of total external debt)\n",
+      "Trained teachers in primary education (% of total teachers)\n",
+      "Trained teachers in primary education, female (% of female teachers)\n",
+      "Trained teachers in primary education, male (% of male teachers)\n",
+      "Unmet need for contraception (% of married women ages 15-49)\n",
+      "Use of IMF credit (DOD, current US$)\n",
+      "Women who believe a husband is justified in beating his wife (any of five reasons) (%)\n",
+      "Women who were first married by age 15 (% of women ages 20-24)\n",
+      "Women who were first married by age 18 (% of women ages 20-24)\n",
+      "Women's share of population ages 15+ living with HIV (%)\n",
+      "Young people (ages 15-24) newly infected with HIV\n",
+      "Adequacy of social insurance programs (% of total welfare of beneficiary households)\n",
+      "Adequacy of social protection and labor programs (% of total welfare of beneficiary households)\n",
+      "Adequacy of social safety net programs (% of total welfare of beneficiary households)\n",
+      "Adequacy of unemployment benefits and ALMP (% of total welfare of beneficiary households)\n",
+      "Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)\n",
+      "Annualized average growth rate in per capita real survey mean consumption or income, total population (%)\n",
+      "Benefit incidence of social insurance programs to poorest quintile (% of total social insurance benefits)\n",
+      "Benefit incidence of social protection and labor programs to poorest quintile (% of total SPL benefits)\n",
+      "Benefit incidence of social safety net programs to poorest quintile (% of total safety net benefits)\n",
+      "Benefit incidence of unemployment benefits and ALMP to poorest quintile (% of total U/ALMP benefits)\n",
+      "Children in employment, self-employed (% of children in employment, ages 7-14)\n",
+      "Children in employment, self-employed, female (% of female children in employment, ages 7-14)\n",
+      "Children in employment, self-employed, male (% of male children in employment, ages 7-14)\n",
+      "Compensation of employees (% of expense)\n",
+      "Compensation of employees (current LCU)\n",
+      "Completeness of death registration with cause-of-death information (%)\n",
+      "Coverage of social insurance programs (% of population)\n",
+      "Coverage of social insurance programs in 2nd quintile (% of population)\n",
+      "Coverage of social insurance programs in 3rd quintile (% of population)\n",
+      "Coverage of social insurance programs in 4th quintile (% of population)\n",
+      "Coverage of social insurance programs in poorest quintile (% of population)\n",
+      "Coverage of social insurance programs in richest quintile (% of population)\n",
+      "Coverage of social protection and labor programs (% of population)\n",
+      "Coverage of social safety net programs (% of population)\n",
+      "Coverage of social safety net programs in 2nd quintile (% of population)\n",
+      "Coverage of social safety net programs in 3rd quintile (% of population)\n",
+      "Coverage of social safety net programs in 4th quintile (% of population)\n",
+      "Coverage of social safety net programs in poorest quintile (% of population)\n",
+      "Coverage of social safety net programs in richest quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in 2nd quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in 3rd quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in 4th quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in poorest quintile (% of population)\n",
+      "Coverage of unemployment benefits and ALMP in richest quintile (% of population)\n",
+      "Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)\n",
+      "Depositors with commercial banks (per 1,000 adults)\n",
+      "Domestic credit provided by financial sector (% of GDP)\n",
+      "Expenditure on primary education (% of government expenditure on education)\n",
+      "Expenditure on secondary education (% of government expenditure on education)\n",
+      "Expense (% of GDP)\n",
+      "Expense (current LCU)\n",
+      "Female share of employment in senior and middle management (%)\n",
+      "Firms experiencing electrical outages (% of firms)\n",
+      "Firms experiencing losses due to theft and vandalism (% of firms)\n",
+      "Firms that spend on R&D (% of firms)\n",
+      "Firms visited or required meetings with tax officials (% of firms)\n",
+      "Firms with female top manager (% of firms)\n",
+      "Goods and services expense (% of expense)\n",
+      "Goods and services expense (current LCU)\n",
+      "Interest payments (% of expense)\n",
+      "Losses due to theft and vandalism (% of annual sales of affected firms)\n",
+      "Net acquisition of financial assets (% of GDP)\n",
+      "Net acquisition of financial assets (current LCU)\n",
+      "Net bilateral aid flows from DAC donors, Slovak Republic (current US$)\n",
+      "Net incurrence of liabilities, total (% of GDP)\n",
+      "Net incurrence of liabilities, total (current LCU)\n",
+      "Net investment in nonfinancial assets (% of GDP)\n",
+      "Net investment in nonfinancial assets (current LCU)\n",
+      "Net lending (+) / net borrowing (-) (% of GDP)\n",
+      "Net lending (+) / net borrowing (-) (current LCU)\n",
+      "Net ODA received (% of central government expense)\n",
+      "Number of visits or required meetings with tax officials (average for affected firms)\n",
+      "Other expense (% of expense)\n",
+      "Other expense (current LCU)\n",
+      "PNG, bonds (NFL, current US$)\n",
+      "Population living in slums (% of urban population)\n",
+      "Public private partnerships investment in ICT (current US$)\n",
+      "S&P Global Equity Indices (annual % change)\n",
+      "Subsidies and other transfers (% of expense)\n",
+      "Subsidies and other transfers (current LCU)\n",
+      "Survey mean consumption or income per capita, bottom 40% of population (2011 PPP $ per day)\n",
+      "Survey mean consumption or income per capita, total population (2011 PPP $ per day)\n",
+      "Value lost due to electrical outages (% of sales for affected firms)\n",
+      "Average transaction cost of sending remittances from a specific country (%)\n",
+      "Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)\n",
+      "Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)\n",
+      "Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)\n",
+      "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)\n",
+      "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)\n",
+      "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)\n",
+      "Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)\n",
+      "Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)\n",
+      "Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)\n",
+      "Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)\n",
+      "Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)\n",
+      "Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)\n",
+      "Multidimensional poverty headcount ratio (% of total population)\n",
+      "Multidimensional poverty headcount ratio, children (% of population ages 0-17)\n",
+      "Multidimensional poverty headcount ratio, female (% of female population)\n",
+      "Multidimensional poverty headcount ratio, male (% of male population)\n",
+      "Net ODA provided to the least developed countries (% of GNI)\n",
+      "Net ODA provided, to the least developed countries (current US$)\n",
+      "Net ODA provided, total (% of GNI)\n",
+      "Net ODA provided, total (constant 2015 US$)\n",
+      "Net ODA provided, total (current US$)\n",
+      "Net primary income (Net income from abroad) (constant LCU)\n",
+      "Number of surgical procedures (per 100,000 population)\n",
+      "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)\n",
+      "Wholesale price index (2010 = 100)\n",
+      "Central government debt, total (% of GDP)\n",
+      "Central government debt, total (current LCU)\n",
+      "Child employment in agriculture (% of economically active children ages 7-14)\n",
+      "Child employment in agriculture, female (% of female economically active children ages 7-14)\n",
+      "Child employment in agriculture, male (% of male economically active children ages 7-14)\n",
+      "Child employment in manufacturing (% of economically active children ages 7-14)\n",
+      "Child employment in manufacturing, female (% of female economically active children ages 7-14)\n",
+      "Child employment in manufacturing, male (% of male economically active children ages 7-14)\n",
+      "Child employment in services (% of economically active children ages 7-14)\n",
+      "Child employment in services, female (% of female economically active children ages 7-14)\n",
+      "Child employment in services, male (% of male economically active children ages 7-14)\n",
+      "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)\n",
+      "Claims on other sectors of the domestic economy (% of GDP)\n",
+      "Condom use, population ages 15-24, female (% of females ages 15-24)\n",
+      "Condom use, population ages 15-24, male (% of males ages 15-24)\n",
+      "CPIA building human resources rating (1=low to 6=high)\n",
+      "CPIA business regulatory environment rating (1=low to 6=high)\n",
+      "CPIA debt policy rating (1=low to 6=high)\n",
+      "CPIA economic management cluster average (1=low to 6=high)\n",
+      "CPIA efficiency of revenue mobilization rating (1=low to 6=high)\n",
+      "CPIA equity of public resource use rating (1=low to 6=high)\n",
+      "CPIA financial sector rating (1=low to 6=high)\n",
+      "CPIA fiscal policy rating (1=low to 6=high)\n",
+      "CPIA gender equality rating (1=low to 6=high)\n",
+      "CPIA macroeconomic management rating (1=low to 6=high)\n",
+      "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)\n",
+      "CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)\n",
+      "CPIA property rights and rule-based governance rating (1=low to 6=high)\n",
+      "CPIA public sector management and institutions cluster average (1=low to 6=high)\n",
+      "CPIA quality of budgetary and financial management rating (1=low to 6=high)\n",
+      "CPIA quality of public administration rating (1=low to 6=high)\n",
+      "CPIA social protection rating (1=low to 6=high)\n",
+      "CPIA structural policies cluster average (1=low to 6=high)\n",
+      "CPIA trade rating (1=low to 6=high)\n",
+      "CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)\n",
+      "Female headed households (% of households with a female head)\n",
+      "IDA resource allocation index (1=low to 6=high)\n",
+      "Net financial flows, IDA (NFL, current US$)\n",
+      "PPG, IDA (DOD, current US$)\n",
+      "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)\n",
+      "Trained teachers in lower secondary education (% of total teachers)\n",
+      "Trained teachers in lower secondary education, female (% of female teachers)\n",
+      "Trained teachers in lower secondary education, male (% of male teachers)\n",
+      "Trained teachers in preprimary education (% of total teachers)\n",
+      "Trained teachers in preprimary education, female (% of female teachers)\n",
+      "Trained teachers in preprimary education, male (% of male teachers)\n",
+      "Trained teachers in upper secondary education (% of total teachers)\n",
+      "Trained teachers in upper secondary education, female (% of female teachers)\n",
+      "Trained teachers in upper secondary education, male (% of male teachers)\n",
+      "Use of insecticide-treated bed nets (% of under-5 population)\n",
+      "Vitamin A supplementation coverage rate (% of children ages 6-59 months)\n",
+      "Wanted fertility rate (births per woman)\n",
+      "Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)\n",
+      "Women who believe a husband is justified in beating his wife when she argues with him (%)\n",
+      "Women who believe a husband is justified in beating his wife when she burns the food (%)\n",
+      "Women who believe a husband is justified in beating his wife when she goes out without telling him (%)\n",
+      "Women who believe a husband is justified in beating his wife when she neglects the children (%)\n",
+      "Women who believe a husband is justified in beating his wife when she refuses sex with him (%)\n",
+      "Community health workers (per 1,000 people)\n",
+      "Net bilateral aid flows from DAC donors, Iceland (current US$)\n",
+      "Trained teachers in secondary education (% of total teachers)\n",
+      "Trained teachers in secondary education, female (% of female teachers)\n",
+      "Trained teachers in secondary education, male (% of male teachers)\n",
+      "Women making their own informed decisions regarding sexual relations, contraceptive use and reproductive health care  (% of women age 15-49)\n",
+      "Female genital mutilation prevalence (%)\n",
+      "Net official flows from UN agencies, UNPBF (current US$)\n",
+      "Multidimensional poverty headcount ratio, household (% of total households)\n",
+      "Multidimensional poverty index (scale 0-1)\n",
+      "Multidimensional poverty intensity (average share of deprivations experienced by the poor)\n",
+      "Net financial flows, IMF concessional (NFL, current US$)\n",
+      "Net official aid received (constant 2018 US$)\n",
+      "Net official aid received (current US$)\n",
+      "Multidimensional poverty index, children (population ages 0-17) (scale 0-1)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Urban population growth (annual %)</th>\n",
+       "      <th>Urban population living in areas where elevation is below 5 meters (% of total population)</th>\n",
+       "      <th>Vulnerable employment, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)</th>\n",
+       "      <th>Women Business and the Law Index Score (scale 1-100)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.056365</td>\n",
+       "      <td>3.031776</td>\n",
+       "      <td>5.700000</td>\n",
+       "      <td>4.800000</td>\n",
+       "      <td>5.170000</td>\n",
+       "      <td>92.050003</td>\n",
+       "      <td>89.110001</td>\n",
+       "      <td>90.330002</td>\n",
+       "      <td>54.519497</td>\n",
+       "      <td>71.250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.934908</td>\n",
+       "      <td>3.029378</td>\n",
+       "      <td>5.700000</td>\n",
+       "      <td>4.800000</td>\n",
+       "      <td>5.170000</td>\n",
+       "      <td>92.050003</td>\n",
+       "      <td>89.110001</td>\n",
+       "      <td>90.330002</td>\n",
+       "      <td>54.519497</td>\n",
+       "      <td>71.250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.884470</td>\n",
+       "      <td>3.026980</td>\n",
+       "      <td>5.740000</td>\n",
+       "      <td>4.930000</td>\n",
+       "      <td>5.270000</td>\n",
+       "      <td>91.910004</td>\n",
+       "      <td>88.589996</td>\n",
+       "      <td>89.970001</td>\n",
+       "      <td>54.519497</td>\n",
+       "      <td>71.250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.843967</td>\n",
+       "      <td>3.024581</td>\n",
+       "      <td>5.850000</td>\n",
+       "      <td>5.040000</td>\n",
+       "      <td>5.380000</td>\n",
+       "      <td>91.669998</td>\n",
+       "      <td>88.250000</td>\n",
+       "      <td>89.669998</td>\n",
+       "      <td>56.039631</td>\n",
+       "      <td>71.250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>98.133621</td>\n",
+       "      <td>98.704536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.636245</td>\n",
+       "      <td>3.022183</td>\n",
+       "      <td>5.610000</td>\n",
+       "      <td>5.200000</td>\n",
+       "      <td>5.370000</td>\n",
+       "      <td>91.629997</td>\n",
+       "      <td>87.739998</td>\n",
+       "      <td>89.370003</td>\n",
+       "      <td>57.559764</td>\n",
+       "      <td>71.250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>73.2</td>\n",
+       "      <td>55.2</td>\n",
+       "      <td>86.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.739664</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>46.530001</td>\n",
+       "      <td>42.949999</td>\n",
+       "      <td>44.530002</td>\n",
+       "      <td>52.430000</td>\n",
+       "      <td>54.169998</td>\n",
+       "      <td>53.400002</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>75.6</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>87.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.503401</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>45.720001</td>\n",
+       "      <td>41.940001</td>\n",
+       "      <td>43.609999</td>\n",
+       "      <td>53.209999</td>\n",
+       "      <td>55.139999</td>\n",
+       "      <td>54.290001</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>77.6</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.290177</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>44.760000</td>\n",
+       "      <td>40.819999</td>\n",
+       "      <td>42.540000</td>\n",
+       "      <td>54.150002</td>\n",
+       "      <td>56.279999</td>\n",
+       "      <td>55.340000</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>79.4</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.066047</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>44.760000</td>\n",
+       "      <td>40.819999</td>\n",
+       "      <td>42.540000</td>\n",
+       "      <td>54.150002</td>\n",
+       "      <td>56.279999</td>\n",
+       "      <td>55.340000</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>79.4</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>80.229118</td>\n",
+       "      <td>76.364731</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.066047</td>\n",
+       "      <td>4.203002</td>\n",
+       "      <td>44.760000</td>\n",
+       "      <td>40.819999</td>\n",
+       "      <td>42.540000</td>\n",
+       "      <td>54.150002</td>\n",
+       "      <td>56.279999</td>\n",
+       "      <td>55.340000</td>\n",
+       "      <td>21.358958</td>\n",
+       "      <td>75.625</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1536 rows × 1060 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                    100.0                      \n",
+       "353                                                    100.0                      \n",
+       "354                                                    100.0                      \n",
+       "355                                                    100.0                      \n",
+       "356                                                    100.0                      \n",
+       "..                                                       ...                      \n",
+       "251                                                     73.2                      \n",
+       "252                                                     75.6                      \n",
+       "253                                                     77.6                      \n",
+       "254                                                     79.4                      \n",
+       "255                                                     79.4                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                    100.0                                   \n",
+       "353                                                    100.0                                   \n",
+       "354                                                    100.0                                   \n",
+       "355                                                    100.0                                   \n",
+       "356                                                    100.0                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                     55.2                                   \n",
+       "252                                                     59.0                                   \n",
+       "253                                                     61.9                                   \n",
+       "254                                                     65.2                                   \n",
+       "255                                                     65.2                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                    100.0                                   \n",
+       "353                                                    100.0                                   \n",
+       "354                                                    100.0                                   \n",
+       "355                                                    100.0                                   \n",
+       "356                                                    100.0                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                     86.2                                   \n",
+       "252                                                     87.4                                   \n",
+       "253                                                     88.4                                   \n",
+       "254                                                     89.4                                   \n",
+       "255                                                     89.4                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                          100.0   \n",
+       "353                                          100.0   \n",
+       "354                                          100.0   \n",
+       "355                                          100.0   \n",
+       "356                                          100.0   \n",
+       "..                                             ...   \n",
+       "251                                          100.0   \n",
+       "252                                          100.0   \n",
+       "253                                          100.0   \n",
+       "254                                          100.0   \n",
+       "255                                          100.0   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                    100.0      \n",
+       "353                                                    100.0      \n",
+       "354                                                    100.0      \n",
+       "355                                                    100.0      \n",
+       "356                                                    100.0      \n",
+       "..                                                       ...      \n",
+       "251                                                    100.0      \n",
+       "252                                                    100.0      \n",
+       "253                                                    100.0      \n",
+       "254                                                    100.0      \n",
+       "255                                                    100.0      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                    100.0      \n",
+       "353                                                    100.0      \n",
+       "354                                                    100.0      \n",
+       "355                                                    100.0      \n",
+       "356                                                    100.0      \n",
+       "..                                                       ...      \n",
+       "251                                                    100.0      \n",
+       "252                                                    100.0      \n",
+       "253                                                    100.0      \n",
+       "254                                                    100.0      \n",
+       "255                                                    100.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                98.133621                                                                 \n",
+       "353                                                98.133621                                                                 \n",
+       "354                                                98.133621                                                                 \n",
+       "355                                                98.133621                                                                 \n",
+       "356                                                98.133621                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                80.229118                                                                 \n",
+       "252                                                80.229118                                                                 \n",
+       "253                                                80.229118                                                                 \n",
+       "254                                                80.229118                                                                 \n",
+       "255                                                80.229118                                                                 \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
+       "352                                                98.704536                                                                         \n",
+       "353                                                98.704536                                                                         \n",
+       "354                                                98.704536                                                                         \n",
+       "355                                                98.704536                                                                         \n",
+       "356                                                98.704536                                                                         \n",
+       "..                                                       ...                                                                         \n",
+       "251                                                76.364731                                                                         \n",
+       "252                                                76.364731                                                                         \n",
+       "253                                                76.364731                                                                         \n",
+       "254                                                76.364731                                                                         \n",
+       "255                                                76.364731                                                                         \n",
+       "\n",
+       "Indicator  ...  Urban population growth (annual %)  \\\n",
+       "352        ...                            1.056365   \n",
+       "353        ...                            0.934908   \n",
+       "354        ...                            0.884470   \n",
+       "355        ...                            0.843967   \n",
+       "356        ...                            0.636245   \n",
+       "..         ...                                 ...   \n",
+       "251        ...                            2.739664   \n",
+       "252        ...                            2.503401   \n",
+       "253        ...                            2.290177   \n",
+       "254        ...                            2.066047   \n",
+       "255        ...                            2.066047   \n",
+       "\n",
+       "Indicator  Urban population living in areas where elevation is below 5 meters (% of total population)  \\\n",
+       "352                                                 3.031776                                            \n",
+       "353                                                 3.029378                                            \n",
+       "354                                                 3.026980                                            \n",
+       "355                                                 3.024581                                            \n",
+       "356                                                 3.022183                                            \n",
+       "..                                                       ...                                            \n",
+       "251                                                 4.203002                                            \n",
+       "252                                                 4.203002                                            \n",
+       "253                                                 4.203002                                            \n",
+       "254                                                 4.203002                                            \n",
+       "255                                                 4.203002                                            \n",
+       "\n",
+       "Indicator  Vulnerable employment, female (% of female employment) (modeled ILO estimate)  \\\n",
+       "352                                                 5.700000                               \n",
+       "353                                                 5.700000                               \n",
+       "354                                                 5.740000                               \n",
+       "355                                                 5.850000                               \n",
+       "356                                                 5.610000                               \n",
+       "..                                                       ...                               \n",
+       "251                                                46.530001                               \n",
+       "252                                                45.720001                               \n",
+       "253                                                44.760000                               \n",
+       "254                                                44.760000                               \n",
+       "255                                                44.760000                               \n",
+       "\n",
+       "Indicator  Vulnerable employment, male (% of male employment) (modeled ILO estimate)  \\\n",
+       "352                                                 4.800000                           \n",
+       "353                                                 4.800000                           \n",
+       "354                                                 4.930000                           \n",
+       "355                                                 5.040000                           \n",
+       "356                                                 5.200000                           \n",
+       "..                                                       ...                           \n",
+       "251                                                42.949999                           \n",
+       "252                                                41.940001                           \n",
+       "253                                                40.819999                           \n",
+       "254                                                40.819999                           \n",
+       "255                                                40.819999                           \n",
+       "\n",
+       "Indicator  Vulnerable employment, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                 5.170000                             \n",
+       "353                                                 5.170000                             \n",
+       "354                                                 5.270000                             \n",
+       "355                                                 5.380000                             \n",
+       "356                                                 5.370000                             \n",
+       "..                                                       ...                             \n",
+       "251                                                44.530002                             \n",
+       "252                                                43.609999                             \n",
+       "253                                                42.540000                             \n",
+       "254                                                42.540000                             \n",
+       "255                                                42.540000                             \n",
+       "\n",
+       "Indicator  Wage and salaried workers, female (% of female employment) (modeled ILO estimate)  \\\n",
+       "352                                                92.050003                                   \n",
+       "353                                                92.050003                                   \n",
+       "354                                                91.910004                                   \n",
+       "355                                                91.669998                                   \n",
+       "356                                                91.629997                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                52.430000                                   \n",
+       "252                                                53.209999                                   \n",
+       "253                                                54.150002                                   \n",
+       "254                                                54.150002                                   \n",
+       "255                                                54.150002                                   \n",
+       "\n",
+       "Indicator  Wage and salaried workers, male (% of male employment) (modeled ILO estimate)  \\\n",
+       "352                                                89.110001                               \n",
+       "353                                                89.110001                               \n",
+       "354                                                88.589996                               \n",
+       "355                                                88.250000                               \n",
+       "356                                                87.739998                               \n",
+       "..                                                       ...                               \n",
+       "251                                                54.169998                               \n",
+       "252                                                55.139999                               \n",
+       "253                                                56.279999                               \n",
+       "254                                                56.279999                               \n",
+       "255                                                56.279999                               \n",
+       "\n",
+       "Indicator  Wage and salaried workers, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                90.330002                                 \n",
+       "353                                                90.330002                                 \n",
+       "354                                                89.970001                                 \n",
+       "355                                                89.669998                                 \n",
+       "356                                                89.370003                                 \n",
+       "..                                                       ...                                 \n",
+       "251                                                53.400002                                 \n",
+       "252                                                54.290001                                 \n",
+       "253                                                55.340000                                 \n",
+       "254                                                55.340000                                 \n",
+       "255                                                55.340000                                 \n",
+       "\n",
+       "Indicator  Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)  \\\n",
+       "352                                                54.519497                                                  \n",
+       "353                                                54.519497                                                  \n",
+       "354                                                54.519497                                                  \n",
+       "355                                                56.039631                                                  \n",
+       "356                                                57.559764                                                  \n",
+       "..                                                       ...                                                  \n",
+       "251                                                21.358958                                                  \n",
+       "252                                                21.358958                                                  \n",
+       "253                                                21.358958                                                  \n",
+       "254                                                21.358958                                                  \n",
+       "255                                                21.358958                                                  \n",
+       "\n",
+       "Indicator  Women Business and the Law Index Score (scale 1-100)  \n",
+       "352                                                   71.250     \n",
+       "353                                                   71.250     \n",
+       "354                                                   71.250     \n",
+       "355                                                   71.250     \n",
+       "356                                                   71.250     \n",
+       "..                                                       ...     \n",
+       "251                                                   75.625     \n",
+       "252                                                   75.625     \n",
+       "253                                                   75.625     \n",
+       "254                                                   75.625     \n",
+       "255                                                   75.625     \n",
+       "\n",
+       "[1536 rows x 1060 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "number_1=len(data.index)*Slider1.value\n",
+    "for i in range(0, len(cols)):\n",
+    "    if data[cols[i]].isna().sum()>number_1:\n",
+    "        del(data[cols[i]])\n",
+    "        print(cols[i])\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Afterwards, we have scaled the values. The escalation process has been done dividing each value by the initial one of an indicator (value in 1990). Considering the start point as 1 (initial value divided by itself), each result will show the growth respect to the initial data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns=data.columns.values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datae=data.loc[data.loc[:, 'Country'] == country_list[0]]\n",
+    "for i in range(2,len(columns)):\n",
+    "    a=columns[i]\n",
+    "    datae[a]=datae[a]/datae.iloc[0,i]\n",
+    "datau=datae"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Urban population growth (annual %)</th>\n",
+       "      <th>Urban population living in areas where elevation is below 5 meters (% of total population)</th>\n",
+       "      <th>Vulnerable employment, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)</th>\n",
+       "      <th>Women Business and the Law Index Score (scale 1-100)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.885023</td>\n",
+       "      <td>0.999209</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.837277</td>\n",
+       "      <td>0.998418</td>\n",
+       "      <td>1.007018</td>\n",
+       "      <td>1.027083</td>\n",
+       "      <td>1.019342</td>\n",
+       "      <td>0.998479</td>\n",
+       "      <td>0.994164</td>\n",
+       "      <td>0.996015</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.798935</td>\n",
+       "      <td>0.997627</td>\n",
+       "      <td>1.026316</td>\n",
+       "      <td>1.050000</td>\n",
+       "      <td>1.040619</td>\n",
+       "      <td>0.995872</td>\n",
+       "      <td>0.990349</td>\n",
+       "      <td>0.992693</td>\n",
+       "      <td>1.027882</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.602296</td>\n",
+       "      <td>0.996836</td>\n",
+       "      <td>0.984210</td>\n",
+       "      <td>1.083333</td>\n",
+       "      <td>1.038685</td>\n",
+       "      <td>0.995437</td>\n",
+       "      <td>0.984626</td>\n",
+       "      <td>0.989372</td>\n",
+       "      <td>1.055765</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1.742857</td>\n",
+       "      <td>2.348936</td>\n",
+       "      <td>1.261156</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.635700</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.707250</td>\n",
+       "      <td>0.617275</td>\n",
+       "      <td>0.656204</td>\n",
+       "      <td>1.555786</td>\n",
+       "      <td>1.984976</td>\n",
+       "      <td>1.768798</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2.510638</td>\n",
+       "      <td>1.278713</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.580879</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.694938</td>\n",
+       "      <td>0.602759</td>\n",
+       "      <td>0.642647</td>\n",
+       "      <td>1.578932</td>\n",
+       "      <td>2.020520</td>\n",
+       "      <td>1.798278</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1.847619</td>\n",
+       "      <td>2.634043</td>\n",
+       "      <td>1.293343</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.531403</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.680347</td>\n",
+       "      <td>0.586663</td>\n",
+       "      <td>0.626879</td>\n",
+       "      <td>1.606825</td>\n",
+       "      <td>2.062294</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.479397</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.680347</td>\n",
+       "      <td>0.586663</td>\n",
+       "      <td>0.626879</td>\n",
+       "      <td>1.606825</td>\n",
+       "      <td>2.062294</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.479397</td>\n",
+       "      <td>1.362819</td>\n",
+       "      <td>0.680347</td>\n",
+       "      <td>0.586663</td>\n",
+       "      <td>0.626879</td>\n",
+       "      <td>1.606825</td>\n",
+       "      <td>2.062294</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1536 rows × 1060 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                 1.000000                      \n",
+       "353                                                 1.000000                      \n",
+       "354                                                 1.000000                      \n",
+       "355                                                 1.000000                      \n",
+       "356                                                 1.000000                      \n",
+       "..                                                       ...                      \n",
+       "251                                                 1.742857                      \n",
+       "252                                                 1.800000                      \n",
+       "253                                                 1.847619                      \n",
+       "254                                                 1.890476                      \n",
+       "255                                                 1.890476                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 2.348936                                   \n",
+       "252                                                 2.510638                                   \n",
+       "253                                                 2.634043                                   \n",
+       "254                                                 2.774468                                   \n",
+       "255                                                 2.774468                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 1.261156                                   \n",
+       "252                                                 1.278713                                   \n",
+       "253                                                 1.293343                                   \n",
+       "254                                                 1.307974                                   \n",
+       "255                                                 1.307974                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                       1.000000   \n",
+       "353                                       1.000000   \n",
+       "354                                       1.000000   \n",
+       "355                                       1.000000   \n",
+       "356                                       1.000000   \n",
+       "..                                             ...   \n",
+       "251                                       1.030696   \n",
+       "252                                       1.030696   \n",
+       "253                                       1.030696   \n",
+       "254                                       1.030696   \n",
+       "255                                       1.030696   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.048707      \n",
+       "252                                                 1.048707      \n",
+       "253                                                 1.048707      \n",
+       "254                                                 1.048707      \n",
+       "255                                                 1.048707      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                      1.0      \n",
+       "353                                                      1.0      \n",
+       "354                                                      1.0      \n",
+       "355                                                      1.0      \n",
+       "356                                                      1.0      \n",
+       "..                                                       ...      \n",
+       "251                                                      1.0      \n",
+       "252                                                      1.0      \n",
+       "253                                                      1.0      \n",
+       "254                                                      1.0      \n",
+       "255                                                      1.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                 \n",
+       "353                                                 1.000000                                                                 \n",
+       "354                                                 1.000000                                                                 \n",
+       "355                                                 1.000000                                                                 \n",
+       "356                                                 1.000000                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                 1.257169                                                                 \n",
+       "252                                                 1.257169                                                                 \n",
+       "253                                                 1.257169                                                                 \n",
+       "254                                                 1.257169                                                                 \n",
+       "255                                                 1.257169                                                                 \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                         \n",
+       "353                                                 1.000000                                                                         \n",
+       "354                                                 1.000000                                                                         \n",
+       "355                                                 1.000000                                                                         \n",
+       "356                                                 1.000000                                                                         \n",
+       "..                                                       ...                                                                         \n",
+       "251                                                 1.272562                                                                         \n",
+       "252                                                 1.272562                                                                         \n",
+       "253                                                 1.272562                                                                         \n",
+       "254                                                 1.272562                                                                         \n",
+       "255                                                 1.272562                                                                         \n",
+       "\n",
+       "Indicator  ...  Urban population growth (annual %)  \\\n",
+       "352        ...                            1.000000   \n",
+       "353        ...                            0.885023   \n",
+       "354        ...                            0.837277   \n",
+       "355        ...                            0.798935   \n",
+       "356        ...                            0.602296   \n",
+       "..         ...                                 ...   \n",
+       "251        ...                            0.635700   \n",
+       "252        ...                            0.580879   \n",
+       "253        ...                            0.531403   \n",
+       "254        ...                            0.479397   \n",
+       "255        ...                            0.479397   \n",
+       "\n",
+       "Indicator  Urban population living in areas where elevation is below 5 meters (% of total population)  \\\n",
+       "352                                                 1.000000                                            \n",
+       "353                                                 0.999209                                            \n",
+       "354                                                 0.998418                                            \n",
+       "355                                                 0.997627                                            \n",
+       "356                                                 0.996836                                            \n",
+       "..                                                       ...                                            \n",
+       "251                                                 1.362819                                            \n",
+       "252                                                 1.362819                                            \n",
+       "253                                                 1.362819                                            \n",
+       "254                                                 1.362819                                            \n",
+       "255                                                 1.362819                                            \n",
+       "\n",
+       "Indicator  Vulnerable employment, female (% of female employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                               \n",
+       "353                                                 1.000000                               \n",
+       "354                                                 1.007018                               \n",
+       "355                                                 1.026316                               \n",
+       "356                                                 0.984210                               \n",
+       "..                                                       ...                               \n",
+       "251                                                 0.707250                               \n",
+       "252                                                 0.694938                               \n",
+       "253                                                 0.680347                               \n",
+       "254                                                 0.680347                               \n",
+       "255                                                 0.680347                               \n",
+       "\n",
+       "Indicator  Vulnerable employment, male (% of male employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                           \n",
+       "353                                                 1.000000                           \n",
+       "354                                                 1.027083                           \n",
+       "355                                                 1.050000                           \n",
+       "356                                                 1.083333                           \n",
+       "..                                                       ...                           \n",
+       "251                                                 0.617275                           \n",
+       "252                                                 0.602759                           \n",
+       "253                                                 0.586663                           \n",
+       "254                                                 0.586663                           \n",
+       "255                                                 0.586663                           \n",
+       "\n",
+       "Indicator  Vulnerable employment, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                             \n",
+       "353                                                 1.000000                             \n",
+       "354                                                 1.019342                             \n",
+       "355                                                 1.040619                             \n",
+       "356                                                 1.038685                             \n",
+       "..                                                       ...                             \n",
+       "251                                                 0.656204                             \n",
+       "252                                                 0.642647                             \n",
+       "253                                                 0.626879                             \n",
+       "254                                                 0.626879                             \n",
+       "255                                                 0.626879                             \n",
+       "\n",
+       "Indicator  Wage and salaried workers, female (% of female employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 0.998479                                   \n",
+       "355                                                 0.995872                                   \n",
+       "356                                                 0.995437                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 1.555786                                   \n",
+       "252                                                 1.578932                                   \n",
+       "253                                                 1.606825                                   \n",
+       "254                                                 1.606825                                   \n",
+       "255                                                 1.606825                                   \n",
+       "\n",
+       "Indicator  Wage and salaried workers, male (% of male employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                               \n",
+       "353                                                 1.000000                               \n",
+       "354                                                 0.994164                               \n",
+       "355                                                 0.990349                               \n",
+       "356                                                 0.984626                               \n",
+       "..                                                       ...                               \n",
+       "251                                                 1.984976                               \n",
+       "252                                                 2.020520                               \n",
+       "253                                                 2.062294                               \n",
+       "254                                                 2.062294                               \n",
+       "255                                                 2.062294                               \n",
+       "\n",
+       "Indicator  Wage and salaried workers, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                                 \n",
+       "353                                                 1.000000                                 \n",
+       "354                                                 0.996015                                 \n",
+       "355                                                 0.992693                                 \n",
+       "356                                                 0.989372                                 \n",
+       "..                                                       ...                                 \n",
+       "251                                                 1.768798                                 \n",
+       "252                                                 1.798278                                 \n",
+       "253                                                 1.833057                                 \n",
+       "254                                                 1.833057                                 \n",
+       "255                                                 1.833057                                 \n",
+       "\n",
+       "Indicator  Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)  \\\n",
+       "352                                                 1.000000                                                  \n",
+       "353                                                 1.000000                                                  \n",
+       "354                                                 1.000000                                                  \n",
+       "355                                                 1.027882                                                  \n",
+       "356                                                 1.055765                                                  \n",
+       "..                                                       ...                                                  \n",
+       "251                                                 8.610987                                                  \n",
+       "252                                                 8.610987                                                  \n",
+       "253                                                 8.610987                                                  \n",
+       "254                                                 8.610987                                                  \n",
+       "255                                                 8.610987                                                  \n",
+       "\n",
+       "Indicator  Women Business and the Law Index Score (scale 1-100)  \n",
+       "352                                                 1.000000     \n",
+       "353                                                 1.000000     \n",
+       "354                                                 1.000000     \n",
+       "355                                                 1.000000     \n",
+       "356                                                 1.000000     \n",
+       "..                                                       ...     \n",
+       "251                                                 1.273684     \n",
+       "252                                                 1.273684     \n",
+       "253                                                 1.273684     \n",
+       "254                                                 1.273684     \n",
+       "255                                                 1.273684     \n",
+       "\n",
+       "[1536 rows x 1060 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for u in range(1,len(country_list)):\n",
+    "    datae=data.loc[data.loc[:, 'Country'] == country_list[u]]   \n",
+    "    for i in range(2,len(columns)):\n",
+    "        a=columns[i]\n",
+    "        datae[a]=datae[a]/datae.iloc[0,i]\n",
+    "    datau=pd.concat((datau, datae), axis = 0)\n",
+    "datau"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As later on we want to study the correlations with time moved, we need to create new columns for it. The reason why is because, maybe the effect of a variable does not happen until a couple of years later. The time movements that have been considered are those of the Fibonacci serie within our time period. \n",
+    "\n",
+    "The following pictures helps to realize the behaviour that we were explaining. \n",
+    "\n",
+    "![](https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/Time%20moved.JPG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Wage and salaried workers, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)</th>\n",
+       "      <th>Women Business and the Law Index Score (scale 1-100)</th>\n",
+       "      <th>GDP (current US$)+1</th>\n",
+       "      <th>GDP (current US$)+2</th>\n",
+       "      <th>GDP (current US$)+3</th>\n",
+       "      <th>GDP (current US$)+5</th>\n",
+       "      <th>GDP (current US$)+8</th>\n",
+       "      <th>GDP (current US$)+13</th>\n",
+       "      <th>GDP (current US$)+21</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.996015</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.992693</td>\n",
+       "      <td>1.027882</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.203142</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.989372</td>\n",
+       "      <td>1.055765</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.169136</td>\n",
+       "      <td>1.203142</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1.742857</td>\n",
+       "      <td>2.348936</td>\n",
+       "      <td>1.261156</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.768798</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>29.029938</td>\n",
+       "      <td>23.644292</td>\n",
+       "      <td>14.137706</td>\n",
+       "      <td>5.418606</td>\n",
+       "      <td>2.393592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2.510638</td>\n",
+       "      <td>1.278713</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.798278</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>16.868589</td>\n",
+       "      <td>6.334809</td>\n",
+       "      <td>2.664772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1.847619</td>\n",
+       "      <td>2.634043</td>\n",
+       "      <td>1.293343</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>29.029938</td>\n",
+       "      <td>20.926519</td>\n",
+       "      <td>7.626636</td>\n",
+       "      <td>2.851657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>39.572189</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>23.644292</td>\n",
+       "      <td>9.838617</td>\n",
+       "      <td>3.031657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.833057</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>40.799246</td>\n",
+       "      <td>39.572189</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>3.356853</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1536 rows × 1067 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                 1.000000                      \n",
+       "353                                                 1.000000                      \n",
+       "354                                                 1.000000                      \n",
+       "355                                                 1.000000                      \n",
+       "356                                                 1.000000                      \n",
+       "..                                                       ...                      \n",
+       "251                                                 1.742857                      \n",
+       "252                                                 1.800000                      \n",
+       "253                                                 1.847619                      \n",
+       "254                                                 1.890476                      \n",
+       "255                                                 1.890476                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 2.348936                                   \n",
+       "252                                                 2.510638                                   \n",
+       "253                                                 2.634043                                   \n",
+       "254                                                 2.774468                                   \n",
+       "255                                                 2.774468                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 1.261156                                   \n",
+       "252                                                 1.278713                                   \n",
+       "253                                                 1.293343                                   \n",
+       "254                                                 1.307974                                   \n",
+       "255                                                 1.307974                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                       1.000000   \n",
+       "353                                       1.000000   \n",
+       "354                                       1.000000   \n",
+       "355                                       1.000000   \n",
+       "356                                       1.000000   \n",
+       "..                                             ...   \n",
+       "251                                       1.030696   \n",
+       "252                                       1.030696   \n",
+       "253                                       1.030696   \n",
+       "254                                       1.030696   \n",
+       "255                                       1.030696   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.048707      \n",
+       "252                                                 1.048707      \n",
+       "253                                                 1.048707      \n",
+       "254                                                 1.048707      \n",
+       "255                                                 1.048707      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                      1.0      \n",
+       "353                                                      1.0      \n",
+       "354                                                      1.0      \n",
+       "355                                                      1.0      \n",
+       "356                                                      1.0      \n",
+       "..                                                       ...      \n",
+       "251                                                      1.0      \n",
+       "252                                                      1.0      \n",
+       "253                                                      1.0      \n",
+       "254                                                      1.0      \n",
+       "255                                                      1.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                 \n",
+       "353                                                 1.000000                                                                 \n",
+       "354                                                 1.000000                                                                 \n",
+       "355                                                 1.000000                                                                 \n",
+       "356                                                 1.000000                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                 1.257169                                                                 \n",
+       "252                                                 1.257169                                                                 \n",
+       "253                                                 1.257169                                                                 \n",
+       "254                                                 1.257169                                                                 \n",
+       "255                                                 1.257169                                                                 \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                         \n",
+       "353                                                 1.000000                                                                         \n",
+       "354                                                 1.000000                                                                         \n",
+       "355                                                 1.000000                                                                         \n",
+       "356                                                 1.000000                                                                         \n",
+       "..                                                       ...                                                                         \n",
+       "251                                                 1.272562                                                                         \n",
+       "252                                                 1.272562                                                                         \n",
+       "253                                                 1.272562                                                                         \n",
+       "254                                                 1.272562                                                                         \n",
+       "255                                                 1.272562                                                                         \n",
+       "\n",
+       "Indicator  ...  \\\n",
+       "352        ...   \n",
+       "353        ...   \n",
+       "354        ...   \n",
+       "355        ...   \n",
+       "356        ...   \n",
+       "..         ...   \n",
+       "251        ...   \n",
+       "252        ...   \n",
+       "253        ...   \n",
+       "254        ...   \n",
+       "255        ...   \n",
+       "\n",
+       "Indicator  Wage and salaried workers, total (% of total employment) (modeled ILO estimate)  \\\n",
+       "352                                                 1.000000                                 \n",
+       "353                                                 1.000000                                 \n",
+       "354                                                 0.996015                                 \n",
+       "355                                                 0.992693                                 \n",
+       "356                                                 0.989372                                 \n",
+       "..                                                       ...                                 \n",
+       "251                                                 1.768798                                 \n",
+       "252                                                 1.798278                                 \n",
+       "253                                                 1.833057                                 \n",
+       "254                                                 1.833057                                 \n",
+       "255                                                 1.833057                                 \n",
+       "\n",
+       "Indicator  Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)  \\\n",
+       "352                                                 1.000000                                                  \n",
+       "353                                                 1.000000                                                  \n",
+       "354                                                 1.000000                                                  \n",
+       "355                                                 1.027882                                                  \n",
+       "356                                                 1.055765                                                  \n",
+       "..                                                       ...                                                  \n",
+       "251                                                 8.610987                                                  \n",
+       "252                                                 8.610987                                                  \n",
+       "253                                                 8.610987                                                  \n",
+       "254                                                 8.610987                                                  \n",
+       "255                                                 8.610987                                                  \n",
+       "\n",
+       "Indicator  Women Business and the Law Index Score (scale 1-100)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.273684      \n",
+       "252                                                 1.273684      \n",
+       "253                                                 1.273684      \n",
+       "254                                                 1.273684      \n",
+       "255                                                 1.273684      \n",
+       "\n",
+       "Indicator  GDP (current US$)+1  GDP (current US$)+2  GDP (current US$)+3  \\\n",
+       "352                        NaN                  NaN                  NaN   \n",
+       "353                   1.000000                  NaN                  NaN   \n",
+       "354                   1.054905             1.000000                  NaN   \n",
+       "355                   1.203142             1.054905             1.000000   \n",
+       "356                   1.169136             1.203142             1.054905   \n",
+       "..                         ...                  ...                  ...   \n",
+       "251                  31.129362            30.653486            29.029938   \n",
+       "252                  34.114284            31.129362            30.653486   \n",
+       "253                  38.504955            34.114284            31.129362   \n",
+       "254                  39.572189            38.504955            34.114284   \n",
+       "255                  40.799246            39.572189            38.504955   \n",
+       "\n",
+       "Indicator  GDP (current US$)+5  GDP (current US$)+8  GDP (current US$)+13  \\\n",
+       "352                        NaN                  NaN                   NaN   \n",
+       "353                        NaN                  NaN                   NaN   \n",
+       "354                        NaN                  NaN                   NaN   \n",
+       "355                        NaN                  NaN                   NaN   \n",
+       "356                        NaN                  NaN                   NaN   \n",
+       "..                         ...                  ...                   ...   \n",
+       "251                  23.644292            14.137706              5.418606   \n",
+       "252                  26.521259            16.868589              6.334809   \n",
+       "253                  29.029938            20.926519              7.626636   \n",
+       "254                  30.653486            23.644292              9.838617   \n",
+       "255                  31.129362            26.521259             12.731623   \n",
+       "\n",
+       "Indicator  GDP (current US$)+21  \n",
+       "352                         NaN  \n",
+       "353                         NaN  \n",
+       "354                         NaN  \n",
+       "355                         NaN  \n",
+       "356                         NaN  \n",
+       "..                          ...  \n",
+       "251                    2.393592  \n",
+       "252                    2.664772  \n",
+       "253                    2.851657  \n",
+       "254                    3.031657  \n",
+       "255                    3.356853  \n",
+       "\n",
+       "[1536 rows x 1067 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shifted=pd.DataFrame()\n",
+    "for i in range(0,len(country_list)):\n",
+    "    dat=datau.loc[datau.loc[:, 'Country'] == country_list[i]]\n",
+    "    dat['GDP (current US$)+1']=dat['GDP (current US$)'].shift(periods=1)\n",
+    "    dat['GDP (current US$)+2']=dat['GDP (current US$)'].shift(periods=2)\n",
+    "    dat['GDP (current US$)+3']=dat['GDP (current US$)'].shift(periods=3)\n",
+    "    dat['GDP (current US$)+5']=dat['GDP (current US$)'].shift(periods=5)\n",
+    "    dat['GDP (current US$)+8']=dat['GDP (current US$)'].shift(periods=8)\n",
+    "    dat['GDP (current US$)+13']=dat['GDP (current US$)'].shift(periods=13)\n",
+    "    dat['GDP (current US$)+21']=dat['GDP (current US$)'].shift(periods=21)\n",
+    "    shifted=pd.concat((shifted, dat), axis = 0)\n",
+    "shifted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=shifted"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the next part of analyzing this data, we think it is gonna be interesting to have it classify by the categories of the Country groups defined before, to which we call \"Continent\". This category is useful as it groups the nations with similar economies or geographical proximity, so we can extract common conclusions from them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We create a dictionary with the regions and the countries included in each one. Where we will relate the countries and regions so then we can apply the .map function and arrive to the final dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'DEU': 'Europe', 'FRA': 'Europe', 'SWE': 'Europe', 'GBR': 'Europe', 'ESP': 'Europe', 'HRV': 'Europe', 'POL': 'Europe', 'GRC': 'Europe', 'AUT': 'Europe', 'NLD': 'Europe', 'IRQ': 'Persian Gulf', 'QAT': 'Persian Gulf', 'ARE': 'Persian Gulf', 'SAU': 'Persian Gulf', 'AZE': 'Persian Gulf', 'YEM': 'Persian Gulf', 'YDR': 'Persian Gulf', 'OMN': 'Persian Gulf', 'DZA': 'North Africa', 'EGY': 'North Africa', 'LBY': 'North Africa', 'ISR': 'North Africa', 'TUR': 'North Africa', 'MAR': 'North Africa', 'SEN': 'South Africa', 'ZAF': 'South Africa', 'LBR': 'South Africa', 'MOZ': 'South Africa', 'CMR': 'South Africa', 'NGA': 'South Africa', 'GHA': 'South Africa', 'BGD': 'Asia', 'IND': 'Asia', 'VNM': 'Asia', 'THA': 'Asia', 'IDN': 'Asia', 'PHL': 'Asia', 'KOR': 'Asia', 'MEX': 'Latam', 'BRA': 'Latam', 'ARG': 'Latam', 'PER': 'Latam', 'VEN': 'Latam', 'COL': 'Latam', 'CHL': 'Latam', 'PAN': 'Latam', 'CRI': 'Latam', 'USA': 'Pair', 'CHN': 'Pair'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "countries_by_region = {\n",
+    "    \"Europe\": ('DEU','FRA','SWE','GBR','ESP','HRV','POL','GRC','AUT','NLD'),\n",
+    "    'Persian Gulf': ('IRQ','QAT','ARE','SAU','AZE','YEM','YDR','OMN'),\n",
+    "    'North Africa':('DZA','EGY','LBY','ISR','TUR','MAR'),\n",
+    "    'South Africa':('SEN','ZAF','LBR','MOZ','CMR','NGA','GHA'),\n",
+    "    'Asia':('BGD','IND','VNM','THA','IDN','PHL','KOR'),\n",
+    "    'Latam':('MEX','BRA','ARG','PER','VEN','COL','CHL','PAN','CRI'),\n",
+    "    'Pair':('USA','CHN')\n",
+    "    }\n",
+    "\n",
+    "all_countries = {}\n",
+    "for region in countries_by_region.keys():\n",
+    "  for country in countries_by_region[region]:\n",
+    "    all_countries[country] = region\n",
+    "\n",
+    "print(all_countries)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking (% of population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, rural (% of rural population)</th>\n",
+       "      <th>Access to clean fuels and technologies for cooking, urban (% of urban population)</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)</th>\n",
+       "      <th>Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)</th>\n",
+       "      <th>Women Business and the Law Index Score (scale 1-100)</th>\n",
+       "      <th>GDP (current US$)+1</th>\n",
+       "      <th>GDP (current US$)+2</th>\n",
+       "      <th>GDP (current US$)+3</th>\n",
+       "      <th>GDP (current US$)+5</th>\n",
+       "      <th>GDP (current US$)+8</th>\n",
+       "      <th>GDP (current US$)+13</th>\n",
+       "      <th>GDP (current US$)+21</th>\n",
+       "      <th>Continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.027882</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.203142</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.055765</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.169136</td>\n",
+       "      <td>1.203142</td>\n",
+       "      <td>1.054905</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1.742857</td>\n",
+       "      <td>2.348936</td>\n",
+       "      <td>1.261156</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>29.029938</td>\n",
+       "      <td>23.644292</td>\n",
+       "      <td>14.137706</td>\n",
+       "      <td>5.418606</td>\n",
+       "      <td>2.393592</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2.510638</td>\n",
+       "      <td>1.278713</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>16.868589</td>\n",
+       "      <td>6.334809</td>\n",
+       "      <td>2.664772</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1.847619</td>\n",
+       "      <td>2.634043</td>\n",
+       "      <td>1.293343</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>29.029938</td>\n",
+       "      <td>20.926519</td>\n",
+       "      <td>7.626636</td>\n",
+       "      <td>2.851657</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>39.572189</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>34.114284</td>\n",
+       "      <td>30.653486</td>\n",
+       "      <td>23.644292</td>\n",
+       "      <td>9.838617</td>\n",
+       "      <td>3.031657</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>1.890476</td>\n",
+       "      <td>2.774468</td>\n",
+       "      <td>1.307974</td>\n",
+       "      <td>1.030696</td>\n",
+       "      <td>1.048707</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.257169</td>\n",
+       "      <td>1.272562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.610987</td>\n",
+       "      <td>1.273684</td>\n",
+       "      <td>40.799246</td>\n",
+       "      <td>39.572189</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>Pair</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1536 rows × 1068 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator Country  Year  \\\n",
+       "352           DEU  1990   \n",
+       "353           DEU  1991   \n",
+       "354           DEU  1992   \n",
+       "355           DEU  1993   \n",
+       "356           DEU  1994   \n",
+       "..            ...   ...   \n",
+       "251           CHN  2017   \n",
+       "252           CHN  2018   \n",
+       "253           CHN  2019   \n",
+       "254           CHN  2020   \n",
+       "255           CHN  2021   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking (% of population)  \\\n",
+       "352                                                 1.000000                      \n",
+       "353                                                 1.000000                      \n",
+       "354                                                 1.000000                      \n",
+       "355                                                 1.000000                      \n",
+       "356                                                 1.000000                      \n",
+       "..                                                       ...                      \n",
+       "251                                                 1.742857                      \n",
+       "252                                                 1.800000                      \n",
+       "253                                                 1.847619                      \n",
+       "254                                                 1.890476                      \n",
+       "255                                                 1.890476                      \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, rural (% of rural population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 2.348936                                   \n",
+       "252                                                 2.510638                                   \n",
+       "253                                                 2.634043                                   \n",
+       "254                                                 2.774468                                   \n",
+       "255                                                 2.774468                                   \n",
+       "\n",
+       "Indicator  Access to clean fuels and technologies for cooking, urban (% of urban population)  \\\n",
+       "352                                                 1.000000                                   \n",
+       "353                                                 1.000000                                   \n",
+       "354                                                 1.000000                                   \n",
+       "355                                                 1.000000                                   \n",
+       "356                                                 1.000000                                   \n",
+       "..                                                       ...                                   \n",
+       "251                                                 1.261156                                   \n",
+       "252                                                 1.278713                                   \n",
+       "253                                                 1.293343                                   \n",
+       "254                                                 1.307974                                   \n",
+       "255                                                 1.307974                                   \n",
+       "\n",
+       "Indicator  Access to electricity (% of population)  \\\n",
+       "352                                       1.000000   \n",
+       "353                                       1.000000   \n",
+       "354                                       1.000000   \n",
+       "355                                       1.000000   \n",
+       "356                                       1.000000   \n",
+       "..                                             ...   \n",
+       "251                                       1.030696   \n",
+       "252                                       1.030696   \n",
+       "253                                       1.030696   \n",
+       "254                                       1.030696   \n",
+       "255                                       1.030696   \n",
+       "\n",
+       "Indicator  Access to electricity, rural (% of rural population)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.048707      \n",
+       "252                                                 1.048707      \n",
+       "253                                                 1.048707      \n",
+       "254                                                 1.048707      \n",
+       "255                                                 1.048707      \n",
+       "\n",
+       "Indicator  Access to electricity, urban (% of urban population)  \\\n",
+       "352                                                      1.0      \n",
+       "353                                                      1.0      \n",
+       "354                                                      1.0      \n",
+       "355                                                      1.0      \n",
+       "356                                                      1.0      \n",
+       "..                                                       ...      \n",
+       "251                                                      1.0      \n",
+       "252                                                      1.0      \n",
+       "253                                                      1.0      \n",
+       "254                                                      1.0      \n",
+       "255                                                      1.0      \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                 \n",
+       "353                                                 1.000000                                                                 \n",
+       "354                                                 1.000000                                                                 \n",
+       "355                                                 1.000000                                                                 \n",
+       "356                                                 1.000000                                                                 \n",
+       "..                                                       ...                                                                 \n",
+       "251                                                 1.257169                                                                 \n",
+       "252                                                 1.257169                                                                 \n",
+       "253                                                 1.257169                                                                 \n",
+       "254                                                 1.257169                                                                 \n",
+       "255                                                 1.257169                                                                 \n",
+       "\n",
+       "Indicator  Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)  \\\n",
+       "352                                                 1.000000                                                                         \n",
+       "353                                                 1.000000                                                                         \n",
+       "354                                                 1.000000                                                                         \n",
+       "355                                                 1.000000                                                                         \n",
+       "356                                                 1.000000                                                                         \n",
+       "..                                                       ...                                                                         \n",
+       "251                                                 1.272562                                                                         \n",
+       "252                                                 1.272562                                                                         \n",
+       "253                                                 1.272562                                                                         \n",
+       "254                                                 1.272562                                                                         \n",
+       "255                                                 1.272562                                                                         \n",
+       "\n",
+       "Indicator  ...  \\\n",
+       "352        ...   \n",
+       "353        ...   \n",
+       "354        ...   \n",
+       "355        ...   \n",
+       "356        ...   \n",
+       "..         ...   \n",
+       "251        ...   \n",
+       "252        ...   \n",
+       "253        ...   \n",
+       "254        ...   \n",
+       "255        ...   \n",
+       "\n",
+       "Indicator  Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)  \\\n",
+       "352                                                 1.000000                                                  \n",
+       "353                                                 1.000000                                                  \n",
+       "354                                                 1.000000                                                  \n",
+       "355                                                 1.027882                                                  \n",
+       "356                                                 1.055765                                                  \n",
+       "..                                                       ...                                                  \n",
+       "251                                                 8.610987                                                  \n",
+       "252                                                 8.610987                                                  \n",
+       "253                                                 8.610987                                                  \n",
+       "254                                                 8.610987                                                  \n",
+       "255                                                 8.610987                                                  \n",
+       "\n",
+       "Indicator  Women Business and the Law Index Score (scale 1-100)  \\\n",
+       "352                                                 1.000000      \n",
+       "353                                                 1.000000      \n",
+       "354                                                 1.000000      \n",
+       "355                                                 1.000000      \n",
+       "356                                                 1.000000      \n",
+       "..                                                       ...      \n",
+       "251                                                 1.273684      \n",
+       "252                                                 1.273684      \n",
+       "253                                                 1.273684      \n",
+       "254                                                 1.273684      \n",
+       "255                                                 1.273684      \n",
+       "\n",
+       "Indicator  GDP (current US$)+1  GDP (current US$)+2  GDP (current US$)+3  \\\n",
+       "352                        NaN                  NaN                  NaN   \n",
+       "353                   1.000000                  NaN                  NaN   \n",
+       "354                   1.054905             1.000000                  NaN   \n",
+       "355                   1.203142             1.054905             1.000000   \n",
+       "356                   1.169136             1.203142             1.054905   \n",
+       "..                         ...                  ...                  ...   \n",
+       "251                  31.129362            30.653486            29.029938   \n",
+       "252                  34.114284            31.129362            30.653486   \n",
+       "253                  38.504955            34.114284            31.129362   \n",
+       "254                  39.572189            38.504955            34.114284   \n",
+       "255                  40.799246            39.572189            38.504955   \n",
+       "\n",
+       "Indicator  GDP (current US$)+5  GDP (current US$)+8  GDP (current US$)+13  \\\n",
+       "352                        NaN                  NaN                   NaN   \n",
+       "353                        NaN                  NaN                   NaN   \n",
+       "354                        NaN                  NaN                   NaN   \n",
+       "355                        NaN                  NaN                   NaN   \n",
+       "356                        NaN                  NaN                   NaN   \n",
+       "..                         ...                  ...                   ...   \n",
+       "251                  23.644292            14.137706              5.418606   \n",
+       "252                  26.521259            16.868589              6.334809   \n",
+       "253                  29.029938            20.926519              7.626636   \n",
+       "254                  30.653486            23.644292              9.838617   \n",
+       "255                  31.129362            26.521259             12.731623   \n",
+       "\n",
+       "Indicator  GDP (current US$)+21  Continent  \n",
+       "352                         NaN     Europe  \n",
+       "353                         NaN     Europe  \n",
+       "354                         NaN     Europe  \n",
+       "355                         NaN     Europe  \n",
+       "356                         NaN     Europe  \n",
+       "..                          ...        ...  \n",
+       "251                    2.393592       Pair  \n",
+       "252                    2.664772       Pair  \n",
+       "253                    2.851657       Pair  \n",
+       "254                    3.031657       Pair  \n",
+       "255                    3.356853       Pair  \n",
+       "\n",
+       "[1536 rows x 1068 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Continent']=data['Country'].map(all_countries)\n",
+    "Goldendataframe=data\n",
+    "Goldendataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With that all, we export our dataframe all-in-one and by the continent category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Goldendataframe.to_csv(os.getcwd()+'/Data/GoldenDataFrame.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for region, data in Goldendataframe.groupby('Continent'):\n",
+    "   data.to_csv(os.getcwd()+'/Data/{}.csv'.format(region))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Categorization of variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section, we are going to attempt a categorization of the whole of the variables, which most of them come the same sources and just differ in the units that are measured, or the total that they are refering, between others. For a simpler treatment of the data, the variables have been pivoted into the same column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_golden=list(Goldendataframe.columns)\n",
+    "del columns_golden[0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1540363 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \n",
+       "0        Access to clean fuels and technologies for coo...  \n",
+       "1        Access to clean fuels and technologies for coo...  \n",
+       "2        Access to clean fuels and technologies for coo...  \n",
+       "3                  Access to electricity (% of population)  \n",
+       "4        Access to electricity, rural (% of rural popul...  \n",
+       "...                                                    ...  \n",
+       "1540358                                GDP (current US$)+3  \n",
+       "1540359                                GDP (current US$)+5  \n",
+       "1540360                                GDP (current US$)+8  \n",
+       "1540361                               GDP (current US$)+13  \n",
+       "1540362                               GDP (current US$)+21  \n",
+       "\n",
+       "[1540363 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization=Goldendataframe.set_index(['Country','Year', 'Continent']).stack().reset_index()\n",
+    "Categorization['Short indicator']=Categorization['Indicator']\n",
+    "Categorization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " There are some indicators which represent exactly the same through different units, so, we are going to select only one type. For example, in monetary cases, indicators which are expressed with current US $ has been selected. Then, which are showed with the percentage and the total value, we have programmed to selct which ones which show a greater value.\n",
+    "\n",
+    "The links used to learn about these functions have been:\n",
+    "\n",
+    "https://www.geeksforgeeks.org/how-to-drop-rows-that-contain-a-specific-string-in-pandas/ \n",
+    "\n",
+    "https://www.statology.org/pandas-drop-rows-that-contain-string/ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \n",
+       "0        Access to clean fuels and technologies for coo...  \n",
+       "1        Access to clean fuels and technologies for coo...  \n",
+       "2        Access to clean fuels and technologies for coo...  \n",
+       "3                  Access to electricity (% of population)  \n",
+       "4        Access to electricity, rural (% of rural popul...  \n",
+       "...                                                    ...  \n",
+       "1540358                                GDP (current US$)+3  \n",
+       "1540359                                GDP (current US$)+5  \n",
+       "1540360                                GDP (current US$)+8  \n",
+       "1540361                               GDP (current US$)+13  \n",
+       "1540362                               GDP (current US$)+21  \n",
+       "\n",
+       "[1372098 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "discard=[\"annual % growth\",\"constant 2015 US[$]\",\"% of GNI\",\"constant LCU\",\"current LCU\"]\n",
+    "Categorization2=Categorization[~Categorization['Short indicator'].str.contains('|'.join(discard))]\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To check previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Categorization2.apply(lambda row: row.astype(str).str.contains('US').any(), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are going to structure the indicators in a same way to work better. The first step consist of making a new column that shows the units of each variable. Units are showed inside the parenthesis of the indicator name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>(% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                  Access to electricity (% of population)   \n",
+       "4        Access to electricity, rural (% of rural popul...   \n",
+       "...                                                    ...   \n",
+       "1540358                                GDP (current US$)+3   \n",
+       "1540359                                GDP (current US$)+5   \n",
+       "1540360                                GDP (current US$)+8   \n",
+       "1540361                               GDP (current US$)+13   \n",
+       "1540362                               GDP (current US$)+21   \n",
+       "\n",
+       "                           Units  \n",
+       "0              (% of population)  \n",
+       "1        (% of rural population)  \n",
+       "2        (% of urban population)  \n",
+       "3              (% of population)  \n",
+       "4        (% of rural population)  \n",
+       "...                          ...  \n",
+       "1540358            (current US$)  \n",
+       "1540359            (current US$)  \n",
+       "1540360            (current US$)  \n",
+       "1540361            (current US$)  \n",
+       "1540362            (current US$)  \n",
+       "\n",
+       "[1372098 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2['Units']=Categorization2['Short indicator'].str.extract(' (\\(.*\\))')\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, short indicator refers to the original indicator name without the units. The extracted information from the origin column has been deleted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                             Access to electricity, rural   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units  \n",
+       "0              (% of population)  \n",
+       "1        (% of rural population)  \n",
+       "2        (% of urban population)  \n",
+       "3              (% of population)  \n",
+       "4        (% of rural population)  \n",
+       "...                          ...  \n",
+       "1540358            (current US$)  \n",
+       "1540359            (current US$)  \n",
+       "1540360            (current US$)  \n",
+       "1540361            (current US$)  \n",
+       "1540362            (current US$)  \n",
+       "\n",
+       "[1372098 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2['Short indicator']=Categorization2['Short indicator'].str.replace(r\" (\\(.*\\))\",\"\")\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In some cases there are extra information in indicators name. The information of the second parenthesis is extracted as a new column too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, female (% of fema...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, female</td>\n",
+       "      <td>(% of female employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, male (% of male e...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, male</td>\n",
+       "      <td>(% of male employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, total (% of total...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, total</td>\n",
+       "      <td>(% of total employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1151</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, female (% of fema...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, female</td>\n",
+       "      <td>(% of female employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1152</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Contributing family workers, male (% of male e...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Contributing family workers, male</td>\n",
+       "      <td>(% of male employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538535</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, male (% of male e...</td>\n",
+       "      <td>0.252894</td>\n",
+       "      <td>Contributing family workers, male</td>\n",
+       "      <td>(% of male employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538536</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, total (% of total...</td>\n",
+       "      <td>0.329385</td>\n",
+       "      <td>Contributing family workers, total</td>\n",
+       "      <td>(% of total employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1539524</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, female (% of fema...</td>\n",
+       "      <td>0.386565</td>\n",
+       "      <td>Contributing family workers, female</td>\n",
+       "      <td>(% of female employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1539525</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, male (% of male e...</td>\n",
+       "      <td>0.252894</td>\n",
+       "      <td>Contributing family workers, male</td>\n",
+       "      <td>(% of male employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1539526</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>Contributing family workers, total (% of total...</td>\n",
+       "      <td>0.329385</td>\n",
+       "      <td>Contributing family workers, total</td>\n",
+       "      <td>(% of total employment) (modeled ILO estimate)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4545 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "151         DEU  1990    Europe   \n",
+       "152         DEU  1990    Europe   \n",
+       "153         DEU  1990    Europe   \n",
+       "1151        DEU  1991    Europe   \n",
+       "1152        DEU  1991    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1538535     CHN  2020      Pair   \n",
+       "1538536     CHN  2020      Pair   \n",
+       "1539524     CHN  2021      Pair   \n",
+       "1539525     CHN  2021      Pair   \n",
+       "1539526     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator         0  \\\n",
+       "151      Contributing family workers, female (% of fema...  1.000000   \n",
+       "152      Contributing family workers, male (% of male e...  1.000000   \n",
+       "153      Contributing family workers, total (% of total...  1.000000   \n",
+       "1151     Contributing family workers, female (% of fema...  1.000000   \n",
+       "1152     Contributing family workers, male (% of male e...  1.000000   \n",
+       "...                                                    ...       ...   \n",
+       "1538535  Contributing family workers, male (% of male e...  0.252894   \n",
+       "1538536  Contributing family workers, total (% of total...  0.329385   \n",
+       "1539524  Contributing family workers, female (% of fema...  0.386565   \n",
+       "1539525  Contributing family workers, male (% of male e...  0.252894   \n",
+       "1539526  Contributing family workers, total (% of total...  0.329385   \n",
+       "\n",
+       "                             Short indicator  \\\n",
+       "151      Contributing family workers, female   \n",
+       "152        Contributing family workers, male   \n",
+       "153       Contributing family workers, total   \n",
+       "1151     Contributing family workers, female   \n",
+       "1152       Contributing family workers, male   \n",
+       "...                                      ...   \n",
+       "1538535    Contributing family workers, male   \n",
+       "1538536   Contributing family workers, total   \n",
+       "1539524  Contributing family workers, female   \n",
+       "1539525    Contributing family workers, male   \n",
+       "1539526   Contributing family workers, total   \n",
+       "\n",
+       "                                                   Units  \n",
+       "151      (% of female employment) (modeled ILO estimate)  \n",
+       "152        (% of male employment) (modeled ILO estimate)  \n",
+       "153       (% of total employment) (modeled ILO estimate)  \n",
+       "1151     (% of female employment) (modeled ILO estimate)  \n",
+       "1152       (% of male employment) (modeled ILO estimate)  \n",
+       "...                                                  ...  \n",
+       "1538535    (% of male employment) (modeled ILO estimate)  \n",
+       "1538536   (% of total employment) (modeled ILO estimate)  \n",
+       "1539524  (% of female employment) (modeled ILO estimate)  \n",
+       "1539525    (% of male employment) (modeled ILO estimate)  \n",
+       "1539526   (% of total employment) (modeled ILO estimate)  \n",
+       "\n",
+       "[4545 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_parent=Categorization2[Categorization2['Short indicator'].str.contains('Contributing family workers')]\n",
+    "two_parent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Moreover, there are some inidcators with an extra parenthesis adding some  more information. As this information isn't related with units, another column named as 'other specification' has been created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                             Access to electricity, rural   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units Other specification  \n",
+       "0              (% of population)                None  \n",
+       "1        (% of rural population)                None  \n",
+       "2        (% of urban population)                None  \n",
+       "3              (% of population)                None  \n",
+       "4        (% of rural population)                None  \n",
+       "...                          ...                 ...  \n",
+       "1540358            (current US$)                None  \n",
+       "1540359            (current US$)                None  \n",
+       "1540360            (current US$)                None  \n",
+       "1540361            (current US$)                None  \n",
+       "1540362            (current US$)                None  \n",
+       "\n",
+       "[1372098 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2[['Units','Other specification']]=Categorization2['Units'].str.split(\"\\) \", n=1,expand=True)\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the end of the variable name, separated by the last \",\" it is informing us about to which subgroup makes reference the variable. Thus, there are some indicators that have information divided for small groups. This information is shown as a new column named 'Subgroup'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "      <th>Subgroup</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>urban</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity, rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                             Access to electricity, rural   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units Other specification Subgroup  \n",
+       "0              (% of population)                None      NaN  \n",
+       "1        (% of rural population)                None    rural  \n",
+       "2        (% of urban population)                None    urban  \n",
+       "3              (% of population)                None      NaN  \n",
+       "4        (% of rural population)                None    rural  \n",
+       "...                          ...                 ...      ...  \n",
+       "1540358            (current US$)                None      NaN  \n",
+       "1540359            (current US$)                None      NaN  \n",
+       "1540360            (current US$)                None      NaN  \n",
+       "1540361            (current US$)                None      NaN  \n",
+       "1540362            (current US$)                None      NaN  \n",
+       "\n",
+       "[1372098 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2[['Subgroup']]=Categorization2['Short indicator'].str.extract(',(?P<field>[^,]*?)$')\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, information which is shown as a new column is deleted from the origin one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "      <th>Subgroup</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>urban</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                                    Access to electricity   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units Other specification Subgroup  \n",
+       "0              (% of population)                None      NaN  \n",
+       "1        (% of rural population)                None    rural  \n",
+       "2        (% of urban population)                None    urban  \n",
+       "3              (% of population)                None      NaN  \n",
+       "4        (% of rural population)                None    rural  \n",
+       "...                          ...                 ...      ...  \n",
+       "1540358            (current US$)                None      NaN  \n",
+       "1540359            (current US$)                None      NaN  \n",
+       "1540360            (current US$)                None      NaN  \n",
+       "1540361            (current US$)                None      NaN  \n",
+       "1540362            (current US$)                None      NaN  \n",
+       "\n",
+       "[1372098 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2['Short indicator']=Categorization2['Short indicator'].str.replace(',(?P<field>[^,]*?)$',\"\")\n",
+    "Categorization2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the indicators don't have these elements. So, a checking point is needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Categorization2['Subgroup']=Categorization2['Subgroup'].replace(['None'],['total'])\n",
+    "Categorization2['Subgroup']=Categorization2['Subgroup'].fillna('total')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some duplicate variables which should be removed too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>0</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "      <th>Subgroup</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>urban</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>rural</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>total</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1157276 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator          0  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                  Access to electricity (% of population)   1.000000   \n",
+       "4        Access to electricity, rural (% of rural popul...   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                GDP (current US$)+3  38.504955   \n",
+       "1540359                                GDP (current US$)+5  31.129362   \n",
+       "1540360                                GDP (current US$)+8  26.521259   \n",
+       "1540361                               GDP (current US$)+13  12.731623   \n",
+       "1540362                               GDP (current US$)+21   3.356853   \n",
+       "\n",
+       "                                           Short indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                                    Access to electricity   \n",
+       "4                                    Access to electricity   \n",
+       "...                                                    ...   \n",
+       "1540358                                              GDP+3   \n",
+       "1540359                                              GDP+5   \n",
+       "1540360                                              GDP+8   \n",
+       "1540361                                             GDP+13   \n",
+       "1540362                                             GDP+21   \n",
+       "\n",
+       "                           Units Other specification Subgroup  \n",
+       "0              (% of population)                None    total  \n",
+       "1        (% of rural population)                None    rural  \n",
+       "2        (% of urban population)                None    urban  \n",
+       "3              (% of population)                None    total  \n",
+       "4        (% of rural population)                None    rural  \n",
+       "...                          ...                 ...      ...  \n",
+       "1540358            (current US$)                None    total  \n",
+       "1540359            (current US$)                None    total  \n",
+       "1540360            (current US$)                None    total  \n",
+       "1540361            (current US$)                None    total  \n",
+       "1540362            (current US$)                None    total  \n",
+       "\n",
+       "[1157276 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2.drop_duplicates(subset=['Country','Year','Short indicator','Continent','Subgroup'], keep='first')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reordering columns, categorization3 is our df after all these division in categories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Subgroup</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>urban</td>\n",
+       "      <td>(% of urban population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>rural</td>\n",
+       "      <td>(% of rural population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1372098 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "1           DEU  1990    Europe   \n",
+       "2           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "4           DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "1        Access to clean fuels and technologies for coo...   \n",
+       "2        Access to clean fuels and technologies for coo...   \n",
+       "3                  Access to electricity (% of population)   \n",
+       "4        Access to electricity, rural (% of rural popul...   \n",
+       "...                                                    ...   \n",
+       "1540358                                GDP (current US$)+3   \n",
+       "1540359                                GDP (current US$)+5   \n",
+       "1540360                                GDP (current US$)+8   \n",
+       "1540361                               GDP (current US$)+13   \n",
+       "1540362                               GDP (current US$)+21   \n",
+       "\n",
+       "                                           Short indicator      Value  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "1        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "2        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                                    Access to electricity   1.000000   \n",
+       "4                                    Access to electricity   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                              GDP+3  38.504955   \n",
+       "1540359                                              GDP+5  31.129362   \n",
+       "1540360                                              GDP+8  26.521259   \n",
+       "1540361                                             GDP+13  12.731623   \n",
+       "1540362                                             GDP+21   3.356853   \n",
+       "\n",
+       "        Subgroup                    Units Other specification  \n",
+       "0          total        (% of population)                None  \n",
+       "1          rural  (% of rural population)                None  \n",
+       "2          urban  (% of urban population)                None  \n",
+       "3          total        (% of population)                None  \n",
+       "4          rural  (% of rural population)                None  \n",
+       "...          ...                      ...                 ...  \n",
+       "1540358    total            (current US$)                None  \n",
+       "1540359    total            (current US$)                None  \n",
+       "1540360    total            (current US$)                None  \n",
+       "1540361    total            (current US$)                None  \n",
+       "1540362    total            (current US$)                None  \n",
+       "\n",
+       "[1372098 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization2.rename(columns={Categorization2.columns[4]:'Value'},inplace=True)\n",
+    "Categorization3=Categorization2[['Country','Year','Continent','Indicator','Short indicator','Value','Subgroup','Units','Other specification']]\n",
+    "Categorization3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Continent</th>\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Short indicator</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Subgroup</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Other specification</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>Access to clean fuels and technologies for coo...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>Access to electricity</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Account ownership at a financial institution o...</td>\n",
+       "      <td>Account ownership at a financial institution o...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(% of population ages 15+)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Adjusted net national income (current US$)</td>\n",
+       "      <td>Adjusted net national income</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>DEU</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>Adjusted net national income per capita (curre...</td>\n",
+       "      <td>Adjusted net national income per capita</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540358</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+3</td>\n",
+       "      <td>GDP+3</td>\n",
+       "      <td>38.504955</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540359</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+5</td>\n",
+       "      <td>GDP+5</td>\n",
+       "      <td>31.129362</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540360</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+8</td>\n",
+       "      <td>GDP+8</td>\n",
+       "      <td>26.521259</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540361</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+13</td>\n",
+       "      <td>GDP+13</td>\n",
+       "      <td>12.731623</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540362</th>\n",
+       "      <td>CHN</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Pair</td>\n",
+       "      <td>GDP (current US$)+21</td>\n",
+       "      <td>GDP+21</td>\n",
+       "      <td>3.356853</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(current US$)</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>687830 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Country  Year Continent  \\\n",
+       "0           DEU  1990    Europe   \n",
+       "3           DEU  1990    Europe   \n",
+       "6           DEU  1990    Europe   \n",
+       "20          DEU  1990    Europe   \n",
+       "23          DEU  1990    Europe   \n",
+       "...         ...   ...       ...   \n",
+       "1540358     CHN  2021      Pair   \n",
+       "1540359     CHN  2021      Pair   \n",
+       "1540360     CHN  2021      Pair   \n",
+       "1540361     CHN  2021      Pair   \n",
+       "1540362     CHN  2021      Pair   \n",
+       "\n",
+       "                                                 Indicator  \\\n",
+       "0        Access to clean fuels and technologies for coo...   \n",
+       "3                  Access to electricity (% of population)   \n",
+       "6        Account ownership at a financial institution o...   \n",
+       "20              Adjusted net national income (current US$)   \n",
+       "23       Adjusted net national income per capita (curre...   \n",
+       "...                                                    ...   \n",
+       "1540358                                GDP (current US$)+3   \n",
+       "1540359                                GDP (current US$)+5   \n",
+       "1540360                                GDP (current US$)+8   \n",
+       "1540361                               GDP (current US$)+13   \n",
+       "1540362                               GDP (current US$)+21   \n",
+       "\n",
+       "                                           Short indicator      Value  \\\n",
+       "0        Access to clean fuels and technologies for coo...   1.000000   \n",
+       "3                                    Access to electricity   1.000000   \n",
+       "6        Account ownership at a financial institution o...   1.000000   \n",
+       "20                            Adjusted net national income   1.000000   \n",
+       "23                 Adjusted net national income per capita   1.000000   \n",
+       "...                                                    ...        ...   \n",
+       "1540358                                              GDP+3  38.504955   \n",
+       "1540359                                              GDP+5  31.129362   \n",
+       "1540360                                              GDP+8  26.521259   \n",
+       "1540361                                             GDP+13  12.731623   \n",
+       "1540362                                             GDP+21   3.356853   \n",
+       "\n",
+       "        Subgroup                       Units Other specification  \n",
+       "0          total           (% of population)                None  \n",
+       "3          total           (% of population)                None  \n",
+       "6          total  (% of population ages 15+)                None  \n",
+       "20         total               (current US$)                None  \n",
+       "23         total               (current US$)                None  \n",
+       "...          ...                         ...                 ...  \n",
+       "1540358    total               (current US$)                None  \n",
+       "1540359    total               (current US$)                None  \n",
+       "1540360    total               (current US$)                None  \n",
+       "1540361    total               (current US$)                None  \n",
+       "1540362    total               (current US$)                None  \n",
+       "\n",
+       "[687830 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Categorization4=Categorization3.loc[Categorization3['Subgroup']=='total']\n",
+    "Categorization4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Categorization4.to_csv(os.getcwd()+'/Data/Categorization.csv')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e21cf16a31979fe9d2d6e9786ebc932e404e707404f15260e38504afc6c53159"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/WDI-Spurious.ipynb b/WDI-Spurious.ipynb
new file mode 100644
index 0000000..58c3716
--- /dev/null
+++ b/WDI-Spurious.ipynb
@@ -0,0 +1,958 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spurious correlations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import libraries and functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import glob\n",
+    "import os\n",
+    "from zipfile import ZipFile\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "import functools as ft\n",
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import Layout\n",
+    "from ipywidgets import interact, interact_manual\n",
+    "import plotly.express as px\n",
+    "from scipy import stats\n",
+    "from scipy.stats import shapiro\n",
+    "from scipy.stats import pearsonr\n",
+    "from scipy.stats import spearmanr\n",
+    "from pandas.api.types import is_numeric_dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, there is an interesting phenomenon, in some cases there are correlations that have a high coefficient and also an adequate graphics, but they do not make sense in the analysis, these are called **spurious correlations**. Here are some examples:\n",
+    "\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/chart%20(2).jpeg\" width=\"800\" height=\"400\" />\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/devonfw-forge/python-data-driven-decisions/main-the-big-three/Logos/chart%20(1).jpeg\" width=\"800\" height=\"400\" />\n",
+    "\n",
+    "\n",
+    "Therefore, we have to be carefull with our results because correlation does not imply causation, it may have happened by chance that both variables are really similar.\n",
+    "So, after some thought and experimenting, we have developed a method that we think, it will allow us to find out if the correlation has happened by chance or if there is really a correlation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This method, consists of the following:\n",
+    "\n",
+    "\n",
+    "Firstly we have classified the indicators by a group, which can be one of the following: *A&D*, *Agriculture*, *Demography*, *Economy*, *Employment*, *Environment*, *Equality*, *Exports*, *Health*, *Mortality* or *Principal*. Moreover inside each group we have also assigned each varible a level, *primary* or *secondary*, depending on their level of relevance. For example we have consider more relevant the *Population in the largest city* over the *Rural population*, thus the first will be *primary* and the latter *secondary*, while both are part of the *Demography* group. \n",
+    "\n",
+    "With this set, we can expose our hypothesis:\n",
+    "\n",
+    "\"It is assumed that the correlation in the primary indicators can be caused by randomness, however if this correlation also appears in the secondary indicators for at least X% of the countries that appears in the primaries (Pareto's rule), we can suppose that there is no randomness affecting each group. Furthermore, the first assumption has to happen in Y% of the secondary indicators to avoid any fortuity.\" \n",
+    "\n",
+    "This hypothesis can be used in a global level, all the countries, or in the different regions. \n",
+    "\n",
+    "For example if, X and Y =80% a primary indicator is repeated 20 times the secondary indicators must have repeated 18 times. And if there are 10 secondary indicators, it has to happen for, at least, 8 indicators.\n",
+    "\n",
+    "Finally, we will finish with two possible errors of 20%, which combined (20%*20%), leaves us with a 4% of margin of error, which is lower than the wildly spread of 5%.\n",
+    "\n",
+    "**This will only work if the data has been collected by independent sources and uses different methods to collect it. Therefore, this step has only been developed for this data (WDI), which we have checked that comes from different sources and is gathered differently.**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e154ff7a4c684bebb8a1f62906fac3fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.8, continuous_update=False, description='% of primary indicators:', max=1.0, step=0.05)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1436ccab37804d3e82fbc5b3bd07c8f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=0.8, continuous_update=False, description='% of secondary indicators:', max=1.0, step=0.05)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "limita=widgets.FloatSlider( value=0.8, min=0, max=1.0, step=0.05, description='% of primary indicators:', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "limitb=widgets.FloatSlider( value=0.8, min=0, max=1.0, step=0.05, description='% of secondary indicators:', disabled=False, continuous_update=False, orientation='horizontal', readout=True)\n",
+    "display(limita,limitb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The margin of error in this combination is: 3.9999999999999982\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=(1-limita.value)*(1-limitb.value)*100\n",
+    "print('The margin of error in this combination is:',a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have selected both percentages and we agree with the margin of error, we can proceed to put into action our method. Firstly, we filter the primary indicators and get the minimun of times thatthe secondary have to be repeated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Min</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A&amp;D</th>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Agriculture</th>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Demography</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Economy</th>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Employment</th>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Environment</th>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equality</th>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exports</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Health</th>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Internet</th>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mortality</th>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Principal</th>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Min\n",
+       "Group            \n",
+       "A&D          13.0\n",
+       "Agriculture  10.0\n",
+       "Demography   24.0\n",
+       "Economy      26.0\n",
+       "Employment   11.0\n",
+       "Environment  13.0\n",
+       "Equality     14.0\n",
+       "Exports      28.0\n",
+       "Health        9.0\n",
+       "Internet     27.0\n",
+       "Mortality    14.0\n",
+       "Principal    23.0"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_p=categories.loc[categories['Level']=='primary']\n",
+    "minprimary=selected_p.groupby('Group').min()\n",
+    "minprimary['Min']=round(minprimary['Number of times repeated']*limita.value)\n",
+    "minprimary.drop(columns=['Indicator','Number of times repeated','Level'], inplace=True)\n",
+    "minprimary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouplist=minprimary.index.to_list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we test if the repetition are accomplished. \n",
+    "\n",
+    "- H_0 data has correlation buy has not happened by randomness.\n",
+    "- H_1 data has correlation due to randomness \n",
+    "\n",
+    "**If Number of times repeated the secondary indicator < Minimun per group, then H_0 denied and H_1 accepted**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Does it have some global casuallity implied?</th>\n",
+       "      <th>% of count (Global)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agriculture</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Demography</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Economy</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>71.039604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Employment</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Environment</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equality</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exports</th>\n",
+       "      <td>No</td>\n",
+       "      <td>82.379863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Health</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Internet</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.190083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mortality</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Principal</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Does it have some global casuallity implied?  % of count (Global)\n",
+       "Group                                                                        \n",
+       "Agriculture                                           No           100.000000\n",
+       "Demography                                            No           100.000000\n",
+       "Economy                                              Yes            71.039604\n",
+       "Employment                                            No           100.000000\n",
+       "Environment                                           No           100.000000\n",
+       "Equality                                              No           100.000000\n",
+       "Exports                                               No            82.379863\n",
+       "Health                                                No           100.000000\n",
+       "Internet                                             Yes            37.190083\n",
+       "Mortality                                             No           100.000000\n",
+       "Principal                                             No           100.000000"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "secondary=final.loc[final['Level']=='secondary']\n",
+    "secondary=pd.merge(secondary,minprimary, left_on='Group',right_on='Group')\n",
+    "secondaryp=secondary.loc[:,['Group','Min']]\n",
+    "Global_Count=secondaryp.groupby('Group').count()\n",
+    "Global_Count.rename(columns={'Min':'Global Count'},inplace=True)\n",
+    "secondary['H_0']=np.where(secondary['Number of times repeated']-secondary['Min']>0,'Not Discarded', 'Denied')\n",
+    "seco=secondary.groupby(['H_0','Group']).count()\n",
+    "sec=seco.loc['Not Discarded']\n",
+    "secondarycount=sec.drop(columns=['Indicator','R^2 Spearman','Behaviour','Country','Moved','Type','Continent','Number of times repeated','Level'])\n",
+    "secondarycount.rename(columns={'Min':'Secondary Count'},inplace=True)\n",
+    "continentlist=final['Continent'].unique()\n",
+    "namescontinents=['European', 'North African', 'Asian', 'Pair', 'Persian', 'South African', 'Latino-American']\n",
+    "finalcount=pd.merge(Global_Count,secondarycount, left_on='Group',right_on='Group')\n",
+    "finalcount['Does it have some global casuallity implied?']=np.where(finalcount['Secondary Count']/finalcount['Global Count']>limitb.value,'No', 'Yes')\n",
+    "finalcount['% of count (Global)']=finalcount['Secondary Count']/finalcount['Global Count']*100\n",
+    "finalcount.drop(columns=['Global Count','Secondary Count'],inplace=True)\n",
+    "finalcount"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, in a Global situation for the groups that have a **NO**, we do not need to worry about casualities, however for the rest of the groups correlation can still be a great indicator as a basis for decision making, if we carefully analyze the variables and found some sort of real relationship between them.   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Does it have some global casuallity implied?</th>\n",
+       "      <th>% of count (Global)</th>\n",
+       "      <th>Does it have some European casuallity implied?</th>\n",
+       "      <th>% of count (European)</th>\n",
+       "      <th>Does it have some North African casuallity implied?</th>\n",
+       "      <th>% of count (North African)</th>\n",
+       "      <th>Does it have some Asian casuallity implied?</th>\n",
+       "      <th>% of count (Asian)</th>\n",
+       "      <th>Does it have some Pair casuallity implied?</th>\n",
+       "      <th>% of count (Pair)</th>\n",
+       "      <th>Does it have some Persian casuallity implied?</th>\n",
+       "      <th>% of count (Persian)</th>\n",
+       "      <th>Does it have some South African casuallity implied?</th>\n",
+       "      <th>% of count (South African)</th>\n",
+       "      <th>Does it have some Latino-American casuallity implied?</th>\n",
+       "      <th>% of count (Latino-American)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agriculture</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Demography</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Economy</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>71.039604</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>75.799087</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>71.812081</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>72.794118</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>70.348837</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>66.968326</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>70.542636</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>66.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Employment</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Environment</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equality</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exports</th>\n",
+       "      <td>No</td>\n",
+       "      <td>82.379863</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>77.142857</td>\n",
+       "      <td>No</td>\n",
+       "      <td>80.701754</td>\n",
+       "      <td>No</td>\n",
+       "      <td>86.842105</td>\n",
+       "      <td>No</td>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>86.075949</td>\n",
+       "      <td>No</td>\n",
+       "      <td>83.333333</td>\n",
+       "      <td>No</td>\n",
+       "      <td>83.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Health</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Internet</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.190083</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>47.619048</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>35.294118</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>26.315789</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>42.857143</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>36.842105</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>37.500000</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>28.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mortality</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Principal</th>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>No</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Does it have some global casuallity implied?  % of count (Global)  \\\n",
+       "Group                                                                           \n",
+       "Agriculture                                           No           100.000000   \n",
+       "Demography                                            No           100.000000   \n",
+       "Economy                                              Yes            71.039604   \n",
+       "Employment                                            No           100.000000   \n",
+       "Environment                                           No           100.000000   \n",
+       "Equality                                              No           100.000000   \n",
+       "Exports                                               No            82.379863   \n",
+       "Health                                                No           100.000000   \n",
+       "Internet                                             Yes            37.190083   \n",
+       "Mortality                                             No           100.000000   \n",
+       "Principal                                             No           100.000000   \n",
+       "\n",
+       "            Does it have some European casuallity implied?  \\\n",
+       "Group                                                        \n",
+       "Agriculture                                             No   \n",
+       "Demography                                              No   \n",
+       "Economy                                                Yes   \n",
+       "Employment                                              No   \n",
+       "Environment                                             No   \n",
+       "Equality                                                No   \n",
+       "Exports                                                Yes   \n",
+       "Health                                                  No   \n",
+       "Internet                                               Yes   \n",
+       "Mortality                                               No   \n",
+       "Principal                                               No   \n",
+       "\n",
+       "             % of count (European)  \\\n",
+       "Group                                \n",
+       "Agriculture             100.000000   \n",
+       "Demography              100.000000   \n",
+       "Economy                  75.799087   \n",
+       "Employment              100.000000   \n",
+       "Environment             100.000000   \n",
+       "Equality                100.000000   \n",
+       "Exports                  77.142857   \n",
+       "Health                  100.000000   \n",
+       "Internet                 47.619048   \n",
+       "Mortality               100.000000   \n",
+       "Principal               100.000000   \n",
+       "\n",
+       "            Does it have some North African casuallity implied?  \\\n",
+       "Group                                                             \n",
+       "Agriculture                                                 No    \n",
+       "Demography                                                  No    \n",
+       "Economy                                                    Yes    \n",
+       "Employment                                                  No    \n",
+       "Environment                                                 No    \n",
+       "Equality                                                    No    \n",
+       "Exports                                                     No    \n",
+       "Health                                                      No    \n",
+       "Internet                                                   Yes    \n",
+       "Mortality                                                   No    \n",
+       "Principal                                                   No    \n",
+       "\n",
+       "             % of count (North African)  \\\n",
+       "Group                                     \n",
+       "Agriculture                  100.000000   \n",
+       "Demography                   100.000000   \n",
+       "Economy                       71.812081   \n",
+       "Employment                   100.000000   \n",
+       "Environment                  100.000000   \n",
+       "Equality                     100.000000   \n",
+       "Exports                       80.701754   \n",
+       "Health                       100.000000   \n",
+       "Internet                      35.294118   \n",
+       "Mortality                    100.000000   \n",
+       "Principal                    100.000000   \n",
+       "\n",
+       "            Does it have some Asian casuallity implied?  % of count (Asian)  \\\n",
+       "Group                                                                         \n",
+       "Agriculture                                          No          100.000000   \n",
+       "Demography                                           No          100.000000   \n",
+       "Economy                                             Yes           72.794118   \n",
+       "Employment                                           No          100.000000   \n",
+       "Environment                                          No          100.000000   \n",
+       "Equality                                             No          100.000000   \n",
+       "Exports                                              No           86.842105   \n",
+       "Health                                               No          100.000000   \n",
+       "Internet                                            Yes           26.315789   \n",
+       "Mortality                                            No          100.000000   \n",
+       "Principal                                            No          100.000000   \n",
+       "\n",
+       "            Does it have some Pair casuallity implied?  % of count (Pair)  \\\n",
+       "Group                                                                       \n",
+       "Agriculture                                         No         100.000000   \n",
+       "Demography                                          No         100.000000   \n",
+       "Economy                                            Yes          70.348837   \n",
+       "Employment                                          No         100.000000   \n",
+       "Environment                                         No         100.000000   \n",
+       "Equality                                            No         100.000000   \n",
+       "Exports                                             No          84.000000   \n",
+       "Health                                              No         100.000000   \n",
+       "Internet                                           Yes          42.857143   \n",
+       "Mortality                                           No         100.000000   \n",
+       "Principal                                           No         100.000000   \n",
+       "\n",
+       "            Does it have some Persian casuallity implied?  \\\n",
+       "Group                                                       \n",
+       "Agriculture                                            No   \n",
+       "Demography                                             No   \n",
+       "Economy                                               Yes   \n",
+       "Employment                                             No   \n",
+       "Environment                                            No   \n",
+       "Equality                                               No   \n",
+       "Exports                                                No   \n",
+       "Health                                                 No   \n",
+       "Internet                                              Yes   \n",
+       "Mortality                                              No   \n",
+       "Principal                                              No   \n",
+       "\n",
+       "             % of count (Persian)  \\\n",
+       "Group                               \n",
+       "Agriculture            100.000000   \n",
+       "Demography             100.000000   \n",
+       "Economy                 66.968326   \n",
+       "Employment             100.000000   \n",
+       "Environment            100.000000   \n",
+       "Equality               100.000000   \n",
+       "Exports                 86.075949   \n",
+       "Health                 100.000000   \n",
+       "Internet                36.842105   \n",
+       "Mortality              100.000000   \n",
+       "Principal              100.000000   \n",
+       "\n",
+       "            Does it have some South African casuallity implied?  \\\n",
+       "Group                                                             \n",
+       "Agriculture                                                 No    \n",
+       "Demography                                                  No    \n",
+       "Economy                                                    Yes    \n",
+       "Employment                                                  No    \n",
+       "Environment                                                 No    \n",
+       "Equality                                                    No    \n",
+       "Exports                                                     No    \n",
+       "Health                                                      No    \n",
+       "Internet                                                   Yes    \n",
+       "Mortality                                                   No    \n",
+       "Principal                                                   No    \n",
+       "\n",
+       "             % of count (South African)  \\\n",
+       "Group                                     \n",
+       "Agriculture                  100.000000   \n",
+       "Demography                   100.000000   \n",
+       "Economy                       70.542636   \n",
+       "Employment                   100.000000   \n",
+       "Environment                  100.000000   \n",
+       "Equality                     100.000000   \n",
+       "Exports                       83.333333   \n",
+       "Health                       100.000000   \n",
+       "Internet                      37.500000   \n",
+       "Mortality                    100.000000   \n",
+       "Principal                    100.000000   \n",
+       "\n",
+       "            Does it have some Latino-American casuallity implied?  \\\n",
+       "Group                                                               \n",
+       "Agriculture                                                 No      \n",
+       "Demography                                                  No      \n",
+       "Economy                                                    Yes      \n",
+       "Employment                                                  No      \n",
+       "Environment                                                 No      \n",
+       "Equality                                                    No      \n",
+       "Exports                                                     No      \n",
+       "Health                                                      No      \n",
+       "Internet                                                   Yes      \n",
+       "Mortality                                                   No      \n",
+       "Principal                                                   No      \n",
+       "\n",
+       "             % of count (Latino-American)  \n",
+       "Group                                      \n",
+       "Agriculture                    100.000000  \n",
+       "Demography                     100.000000  \n",
+       "Economy                         66.666667  \n",
+       "Employment                     100.000000  \n",
+       "Environment                    100.000000  \n",
+       "Equality                       100.000000  \n",
+       "Exports                         83.333333  \n",
+       "Health                         100.000000  \n",
+       "Internet                        28.571429  \n",
+       "Mortality                      100.000000  \n",
+       "Principal                      100.000000  "
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for i in range(0,len(continentlist)):\n",
+    "    apfinal=final.loc[final['Continent']==continentlist[i]]\n",
+    "    \n",
+    "    selected_p=categories.loc[categories['Level']=='primary']\n",
+    "    minprimary=selected_p.groupby('Group').min()\n",
+    "    minprimary['Min']=round(minprimary['Number of times repeated']*limita.value)\n",
+    "    minprimary.drop(columns=['Indicator','Number of times repeated','Level'], inplace=True)\n",
+    "\n",
+    "    grouplist=minprimary.index.to_list()\n",
+    "\n",
+    "    secondary=apfinal.loc[apfinal['Level']=='secondary']\n",
+    "    secondary=pd.merge(secondary,minprimary, left_on='Group',right_on='Group')\n",
+    "\n",
+    "    secondaryp=secondary.loc[:,['Group','Min']]\n",
+    "    Global_Count=secondaryp.groupby('Group').count()\n",
+    "    Global_Count.rename(columns={'Min':'Global Count'},inplace=True)\n",
+    "\n",
+    "    secondary['H_0']=np.where(secondary['Number of times repeated']-secondary['Min']>0,'Not Discarded', 'Denied')\n",
+    "    seco=secondary.groupby(['H_0','Group']).count()\n",
+    "    sec=seco.loc['Not Discarded']\n",
+    "    secondarycount=sec.drop(columns=['Indicator','R^2 Spearman','Behaviour','Country','Moved','Type','Continent','Number of times repeated','Level'])\n",
+    "    secondarycount.rename(columns={'Min':'Secondary Count'},inplace=True)\n",
+    "\n",
+    "    apfinalcount=pd.merge(Global_Count,secondarycount, left_on='Group',right_on='Group')\n",
+    "    apfinalcount['Does it have some '+namescontinents[i]+' casuallity implied?']=np.where(apfinalcount['Secondary Count']/apfinalcount['Global Count']>limitb.value,'No', 'Yes')\n",
+    "    apfinalcount['% of count ('+namescontinents[i]+')']=apfinalcount['Secondary Count']/apfinalcount['Global Count']*100\n",
+    "    apfinalcount.drop(columns=['Global Count','Secondary Count'],inplace=True)\n",
+    "    finalcount=pd.merge(finalcount,apfinalcount, left_on='Group',right_on='Group')\n",
+    "\n",
+    "finalcount"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can observe into much more detail the differnt regions that we defined before, and for groups that have a **NO**, we do not need to worry about casualities. Meanwhile, for the rest of the groups correlation can still be a great indicator as a basis for decision making, if we carefully analyze the variables and found some sort of real relationship between them.   "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e21cf16a31979fe9d2d6e9786ebc932e404e707404f15260e38504afc6c53159"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dynamic_path_treemap.ipynb b/dynamic_path_treemap.ipynb
new file mode 100644
index 0000000..9f00cd5
--- /dev/null
+++ b/dynamic_path_treemap.ipynb
@@ -0,0 +1,2590 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8559121d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>year</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "      <th>iso_alpha</th>\n",
+       "      <th>iso_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>43.828</td>\n",
+       "      <td>31889923</td>\n",
+       "      <td>974.580338</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>76.423</td>\n",
+       "      <td>3600523</td>\n",
+       "      <td>5937.029526</td>\n",
+       "      <td>ALB</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>72.301</td>\n",
+       "      <td>33333216</td>\n",
+       "      <td>6223.367465</td>\n",
+       "      <td>DZA</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>42.731</td>\n",
+       "      <td>12420476</td>\n",
+       "      <td>4797.231267</td>\n",
+       "      <td>AGO</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>75.320</td>\n",
+       "      <td>40301927</td>\n",
+       "      <td>12779.379640</td>\n",
+       "      <td>ARG</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1655</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>74.249</td>\n",
+       "      <td>85262356</td>\n",
+       "      <td>2441.576404</td>\n",
+       "      <td>VNM</td>\n",
+       "      <td>704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1667</th>\n",
+       "      <td>West Bank and Gaza</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>73.422</td>\n",
+       "      <td>4018332</td>\n",
+       "      <td>3025.349798</td>\n",
+       "      <td>PSE</td>\n",
+       "      <td>275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1679</th>\n",
+       "      <td>Yemen, Rep.</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>62.698</td>\n",
+       "      <td>22211743</td>\n",
+       "      <td>2280.769906</td>\n",
+       "      <td>YEM</td>\n",
+       "      <td>887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1691</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>42.384</td>\n",
+       "      <td>11746035</td>\n",
+       "      <td>1271.211593</td>\n",
+       "      <td>ZMB</td>\n",
+       "      <td>894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1703</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>43.487</td>\n",
+       "      <td>12311143</td>\n",
+       "      <td>469.709298</td>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>716</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>142 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 country continent  year  lifeExp       pop     gdpPercap  \\\n",
+       "11           Afghanistan      Asia  2007   43.828  31889923    974.580338   \n",
+       "23               Albania    Europe  2007   76.423   3600523   5937.029526   \n",
+       "35               Algeria    Africa  2007   72.301  33333216   6223.367465   \n",
+       "47                Angola    Africa  2007   42.731  12420476   4797.231267   \n",
+       "59             Argentina  Americas  2007   75.320  40301927  12779.379640   \n",
+       "...                  ...       ...   ...      ...       ...           ...   \n",
+       "1655             Vietnam      Asia  2007   74.249  85262356   2441.576404   \n",
+       "1667  West Bank and Gaza      Asia  2007   73.422   4018332   3025.349798   \n",
+       "1679         Yemen, Rep.      Asia  2007   62.698  22211743   2280.769906   \n",
+       "1691              Zambia    Africa  2007   42.384  11746035   1271.211593   \n",
+       "1703            Zimbabwe    Africa  2007   43.487  12311143    469.709298   \n",
+       "\n",
+       "     iso_alpha  iso_num  \n",
+       "11         AFG        4  \n",
+       "23         ALB        8  \n",
+       "35         DZA       12  \n",
+       "47         AGO       24  \n",
+       "59         ARG       32  \n",
+       "...        ...      ...  \n",
+       "1655       VNM      704  \n",
+       "1667       PSE      275  \n",
+       "1679       YEM      887  \n",
+       "1691       ZMB      894  \n",
+       "1703       ZWE      716  \n",
+       "\n",
+       "[142 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "import numpy as np\n",
+    "from ipywidgets import widgets, HBox\n",
+    "df = px.data.gapminder().query(\"year == 2007\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2a1f4b90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out = widgets.Output()\n",
+    "\n",
+    "def output_treemap(path):\n",
+    "    fig = px.treemap(df, path=path, values='pop',\n",
+    "                  color='lifeExp', hover_data=['iso_alpha'],\n",
+    "                  color_continuous_scale='RdBu',\n",
+    "                  color_continuous_midpoint=np.average(df['lifeExp'], weights=df['pop']))\n",
+    "    fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
+    "    out.clear_output(wait=True)\n",
+    "    with out:\n",
+    "        fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "81d13d00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_1_dropdown = widgets.Dropdown(\n",
+    "    options=['continent', 'country'],\n",
+    "    value='continent',\n",
+    "    description='Path 1',\n",
+    "    disabled=False,\n",
+    ")\n",
+    "path_2_dropdown = widgets.Dropdown(\n",
+    "    options=['continent', 'country'],\n",
+    "    value='country',\n",
+    "    description='Path 2',\n",
+    "    disabled=False,\n",
+    ")\n",
+    "\n",
+    "ok_button = widgets.Button(\n",
+    "    description='Ready to go',\n",
+    "    disabled=False,\n",
+    "    button_style='info', # 'success', 'info', 'warning', 'danger' or ''\n",
+    "    icon='check' # (FontAwesome names without the `fa-` prefix)\n",
+    ")\n",
+    "ok_button.on_click(lambda _: output_treemap([px.Constant(\"world\"), path_1_dropdown.value, path_2_dropdown.value]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9d413d86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b83f06cf8e8541ffae99fd6d7c5cb87a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Dropdown(description='Path 1', options=('continent', 'country'), value='continent'), Dropdown(d…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75bed787f5bb476cab05f62e6c1ac563",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "branchvalues": "total",
+         "customdata": [
+          [
+           "AFG",
+           43.828
+          ],
+          [
+           "ALB",
+           76.423
+          ],
+          [
+           "DZA",
+           72.301
+          ],
+          [
+           "AGO",
+           42.731
+          ],
+          [
+           "ARG",
+           75.32
+          ],
+          [
+           "AUS",
+           81.235
+          ],
+          [
+           "AUT",
+           79.829
+          ],
+          [
+           "BHR",
+           75.635
+          ],
+          [
+           "BGD",
+           64.062
+          ],
+          [
+           "BEL",
+           79.441
+          ],
+          [
+           "BEN",
+           56.728
+          ],
+          [
+           "BOL",
+           65.554
+          ],
+          [
+           "BIH",
+           74.852
+          ],
+          [
+           "BWA",
+           50.728
+          ],
+          [
+           "BRA",
+           72.39
+          ],
+          [
+           "BGR",
+           73.005
+          ],
+          [
+           "BFA",
+           52.295
+          ],
+          [
+           "BDI",
+           49.58
+          ],
+          [
+           "KHM",
+           59.723
+          ],
+          [
+           "CMR",
+           50.43
+          ],
+          [
+           "CAN",
+           80.65300000000002
+          ],
+          [
+           "CAF",
+           44.74100000000001
+          ],
+          [
+           "TCD",
+           50.651
+          ],
+          [
+           "CHL",
+           78.553
+          ],
+          [
+           "CHN",
+           72.961
+          ],
+          [
+           "COL",
+           72.889
+          ],
+          [
+           "COM",
+           65.152
+          ],
+          [
+           "COD",
+           46.462
+          ],
+          [
+           "COG",
+           55.322
+          ],
+          [
+           "CRI",
+           78.782
+          ],
+          [
+           "CIV",
+           48.328
+          ],
+          [
+           "HRV",
+           75.748
+          ],
+          [
+           "CUB",
+           78.273
+          ],
+          [
+           "CZE",
+           76.486
+          ],
+          [
+           "DNK",
+           78.332
+          ],
+          [
+           "DJI",
+           54.791
+          ],
+          [
+           "DOM",
+           72.235
+          ],
+          [
+           "ECU",
+           74.994
+          ],
+          [
+           "EGY",
+           71.33800000000002
+          ],
+          [
+           "SLV",
+           71.878
+          ],
+          [
+           "GNQ",
+           51.57899999999999
+          ],
+          [
+           "ERI",
+           58.03999999999999
+          ],
+          [
+           "ETH",
+           52.947
+          ],
+          [
+           "FIN",
+           79.313
+          ],
+          [
+           "FRA",
+           80.65699999999998
+          ],
+          [
+           "GAB",
+           56.73500000000001
+          ],
+          [
+           "GMB",
+           59.448
+          ],
+          [
+           "DEU",
+           79.406
+          ],
+          [
+           "GHA",
+           60.02199999999999
+          ],
+          [
+           "GRC",
+           79.483
+          ],
+          [
+           "GTM",
+           70.259
+          ],
+          [
+           "GIN",
+           56.007
+          ],
+          [
+           "GNB",
+           46.38800000000001
+          ],
+          [
+           "HTI",
+           60.916
+          ],
+          [
+           "HND",
+           70.19800000000001
+          ],
+          [
+           "HKG",
+           82.208
+          ],
+          [
+           "HUN",
+           73.33800000000002
+          ],
+          [
+           "ISL",
+           81.757
+          ],
+          [
+           "IND",
+           64.69800000000001
+          ],
+          [
+           "IDN",
+           70.65
+          ],
+          [
+           "IRN",
+           70.964
+          ],
+          [
+           "IRQ",
+           59.545
+          ],
+          [
+           "IRL",
+           78.885
+          ],
+          [
+           "ISR",
+           80.745
+          ],
+          [
+           "ITA",
+           80.546
+          ],
+          [
+           "JAM",
+           72.567
+          ],
+          [
+           "JPN",
+           82.603
+          ],
+          [
+           "JOR",
+           72.535
+          ],
+          [
+           "KEN",
+           54.11
+          ],
+          [
+           "KOR",
+           67.297
+          ],
+          [
+           "KOR",
+           78.623
+          ],
+          [
+           "KWT",
+           77.58800000000002
+          ],
+          [
+           "LBN",
+           71.993
+          ],
+          [
+           "LSO",
+           42.592
+          ],
+          [
+           "LBR",
+           45.678
+          ],
+          [
+           "LBY",
+           73.952
+          ],
+          [
+           "MDG",
+           59.443000000000005
+          ],
+          [
+           "MWI",
+           48.303
+          ],
+          [
+           "MYS",
+           74.241
+          ],
+          [
+           "MLI",
+           54.467
+          ],
+          [
+           "MRT",
+           64.164
+          ],
+          [
+           "MUS",
+           72.801
+          ],
+          [
+           "MEX",
+           76.195
+          ],
+          [
+           "MNG",
+           66.803
+          ],
+          [
+           "MNE",
+           74.543
+          ],
+          [
+           "MAR",
+           71.164
+          ],
+          [
+           "MOZ",
+           42.082
+          ],
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+   "id": "f14d0703",
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+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "292912297167048338b5d1136ec8c661b8a2f1d3fcecd6650d637809a967c218"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/images.png b/images.png
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